At one point in 1997, Valve had nearly completed Half-Life. The monsters were cool, and the technology was groundbreaking, but the game wasn't fun.

Ken Birdwell’s account of what happened next (from 1999!) is an excellent, occasionally uncomfortable read. He describes how a small group assembled the promising bits into one playable level. Everyone played this level and then immediately understood what needed to be done.

Then they had a lot of meetings: six hours a day of them, four days a week, for five months. In each meeting room were engineers, a writer, an animator, a level designer: the people responsible for making the actual thing, designing it together.

Longtime readers will know that I have often advocated for the ritual clearing of calendars, but it's worth remembering that in a lot of cases, the meetings are the work and the meetings can indeed be good. This story shows exactly what that looks like: make decisions about game, watch people play game, resist urge to explain anything to playtesters. Basically do demos and eliminate all distance between you and the product. (Imagine applying that rule to a strategy presentation.)

There's a bit in here about how someone might contribute nothing for two or three meetings, then become the main contributor for the rest of the week; a pretty good argument against measuring the usefulness of a person one meeting at a time.

2. You could add balconies

These three apartment buildings in Bordeaux contain 530 homes. Lacaton & Vassal, working with Frédéric Druot and Christophe Hutin, transformed them by adding winter gardens and balconies. They kept the principal structure, stairs and floors. The savings helped pay for more space, light and possibilities for the people living there. Balconies like this are also better for the climate emergency we're in today for the rest of time.

We're broadly taught that transformation should prefer reduction to addition, but this project shows us that important stuff is sometimes missing.

A team might have good relationships and useful expertise but spend half its week waiting for another department. Giving it access to a missing capability could change its working life considerably. You might not need to dissolve it, retitle it, or make everybody apply for their jobs. Let's see the balcony proposal before the demolition proposal.

3. Some of those saved hours never existed

METR has a note on AI productivity that distinguishes doing your old work faster from doing a different mix of work now that certain things are cheap. Imagine you generate an elaborate interactive dashboard in an afternoon. Building it manually would have taken three weeks. Have you saved three weeks?

Well. Previously, you might have just made a spreadsheet.

The dashboard could be much better! Perhaps people can finally understand something they couldn't see before! But the three weeks were never in the budget, and the value of the improvement needs its own accounting. METR calls the extreme version of this a "Cadillac Task": work undertaken because AI has made it cheap, with a much bigger apparent speedup than gain in value.

Which connects over to recent thoughts about project intake. Once we can make almost anything, prioritization has to accommodate a new category: things that are worth doing, but would never have been worth doing at their old price. I expect to enjoy quite a few of these, and we should still be careful adding up the hours.

4. The spreadsheet person gets a bigger workshop

Ink & Switch's essay on malleable software starts with an ordinary freedom: people rearrange their kitchens and workshops to suit themselves. Software rarely offers the same latitude.

Back to Ink & Switch: their ambition is for people to adapt tools where they use them, with a gradual path from using something to changing it. They also take communal creation seriously, which is nice.

Think about the person at work who knows Excel. Someone shows up with a problem, they make a thing, colleagues try it, and it improves. Over time, the spreadsheet contains a detailed understanding of how that particular group operates, including the exceptions nobody remembered to mention in the requirements. Give that person more powerful tools and the person who understands the work can improve the equipment.

That person will also, someday, go on vacation, and others will need to understand what they built. I’d love to see companies develop this as a supported practice, with time and help attached. Suddenly everyone has to become good (or teach their agents to become good) at documentation...

5. The restaurants with no menus

The New York Public Library has a wonderful collection of old menus. Much of it exists because Miss Frank E. Buttolph spent years asking restaurants for them. She wrote letters, placed advertisements, and pursued a collection that grew to roughly 25,000 menus during her involvement. The library's account of her is worth reading too; apparently she sometimes sent submissions back for failing to meet her standards.

Cartoonist Ben Katchor's research into dairy restaurants offers another angle into the collection, pointing out that many contemporary working-class Jewish restaurants were missing. Why? Some didn’t have printed menus. The waiter recited the specials, and the regulars knew what they wanted. You can have thousands of beautifully preserved records and still miss a whole way of doing things because it didn't produce the kind of object you collect. Perhaps there's a "the map is not the territory" lesson in there for those of us building world models.

Anyway, enjoy. The typography alone is worth it!

The best things you’ve missed since the last Five Things: Eskom made room for maintenance and got dramatically more reliable. UPS reorganized around the physical network. Meta tried shrinking conventional teams into 3–5-person AI-native pods. Joshua Gans has a neat model for how AI can redesign a job without automating any of it. Unilever’s end-to-end divisions turned out to be companies in waiting. And Target cut capacity from the center and bought more of it at the edge (some of the numbers moved in the right direction).

Send the designers to the dump

The nine-month Design Skills for Embedding Circularity pilot published its results September 4. Fifteen designers spent months inside UK waste infrastructure—materials recovery facilities, an electronics recycler, paper mill, energy-from-waste plant, landfills and a remanufacturing operation—before taking on live Biffa and Decathlon briefs covering discarded vapes, lithium batteries, festival tents, tennis balls and reusable packaging. The program was funded by the Chartered Institution of Wastes Management and delivered with the Design Council, WRAP, Circular Economy Institute and URGE Collective.

Pippa Bridges worked on small electronics that make batteries easier to detect and remove before processing. Jonathan Irawan proposed a platform connecting product designers with waste operators. Abby Brazier (product designer at Bibado, among other places) redesigned a toothbrush with a wider base specifically so it would not fall through the holes in industrial sorting trommels (TIL!).

Product designers usually work near users, engineers, suppliers and manufacturing, while waste operators encounter products at the end of their useful life. I have literally had a conversation with a toothbrush product manager who had come to the conclusion that recycling was mostly greenwashing precisely because he knew that all of their toothbrushes passed straight through to the dump. The pilot creates a feedback loop between those groups.

While we're here: I love Dyson as much as the next person, but a $400 toothbrush that needs its own toothpaste?

Introducing Camerajet

Dyson today announces the launch of the Camerajet, toothbrush with a camera powered by Gap Optical Targeting™ to identify gaps between teeth and precision-floss while you brush

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A minute for the minute

Pennsylvania furniture manufacturer Emeco has sued Zara Home in Hamburg District Court, alleging that Zara’s Stainless Steel Structure Chair ($320) and Bar Stool infringe the appearance and “protected design language” of Emeco’s 20-06 chair (retailing for >$1,000 before retailer discounts) and stool, designed with Norman Foster and Foster + Partners and launched in 2006. Emeco is seeking damages and an end to production and sale; Zara Home has reportedly declined public comment.

Emeco chairman Gregg Buchbinder’s argument is that specialist manufacturers pay for design development, tooling, testing and the risk of discovering whether anyone wants the thing; much larger retailers and dupers can more easily than ever steal share after the design has product-market fit. He calls firms like his “unpaid research and development laboratories.” I'm writing a bit more about this elsewhere, but there's a bit of an AirSpace-for-everything argument in here about slop and homogenization (I don't think anyone wants a world where everything is just the best-next token) that feels even more salient than figuring out whether or not Zara's chair plagiarizes Emeco's.

The other interesting thing to me is where this pushes tastes over time. Foster described the 20-06 as deliberately neutral, and ornament gives you obvious proprietary features. A minimalist design's distinction is in the minute detail: exactly how wide the back is, where a radius begins, how the seat rolls into the leg, whether the frame looks continuous, the visual weight of the tubing.

Does (waves around toward data-centers) all of this mean that we're going to want more ornamental, maximal forms because that's what's more protectable for designers?

Skyline envelopes

London has spent decades reviewing skyscrapers one at a time. Its new City Plan creates an invisible three-dimensional envelope defining the shape of the future skyline. Some parties, including Historic England, have specific objections. Basically they want the purple shape on the left of the image here, showing where future buildings may be built, to have some restrictions beyond what is proposed.

This feels very much like the way that F1 delivers regulations to teams as a prescribed volume within which bodywork is allowed. I don't have much to say beyond a) I think that's neat and b) I love to see how different standards get out into the world.

The company reduced the number of employees sitting seven or more layers below the CEO by 20%. It cut nearly half of its “micro-teams,” where a manager had only one or two reports. Roles primarily focused on coordination were reduced too. Uber’s internal Pulse surveys had been telling management that too much work required cross-team coordination, debates took too long and decision rights were unclear.

Let's talk about the micro-teams. How do they form? Mostly around good intentions, and usually not from a mechanical approach to spans-and-layers work.

1. A strong individual contributor became a manager (because of career aspirations, status, retention priorities, genuine need?), but there was not enough additional work or enough people to create a real team around them.
2. A team split before either half was large enough to operate independently, leaving two small groups where there had previously been one viable team.
3. A specialist needed an organizational home, so we created one around them. Instead of placing a specialist inside a larger team and letting them operate with less oversight, we create a small reporting unit to give their work clearer ownership.
4. A temporary project became permanent. A small project team was created for a specific initiative, the initiative stayed important, and nobody later revisited whether the team still needed to exist as a separate unit.
5. We optimized for clear ownership and created too many separate teams. Giving every problem, product area or capability a single owner can improve accountability, but taken too far it creates many very small teams with narrow scopes.
6. Instead of asking an existing team to absorb, trade off or stop work, we create a new team for the new priority. Over time the organization fragments.
7. A team may lose scope, automate part of its work or hand responsibilities elsewhere while keeping the same manager and reporting lines.
8. Management capacity is often added ahead of expected hiring. If that hiring never arrives, the manager can be left with only one or two direct reports.
9. The team shrank through normal attrition. A healthy team of six can become a team of two after departures, transfers or hiring freezes. Unless someone deliberately redesigns the structure, the manager and team often remain.
10. Two people really do have different work, but not enough work to justify a separate team.
11. When work crosses several teams, adding a manager can feel easier than redesigning ownership, interfaces or decision rights. This can produce managers whose main job is coordinating a very small group.
12. Nobody has a strong incentive to remove a tiny team. The structure is usually workable enough, the manager understandably wants to keep their role, and the costs are spread across the wider organization. So the micro-team persists.

This is mostly bad for everyone involved.

But Clay! Are you not a staunch proponent of small teams!? Yes, but not this small. IMO the lower boundary of a team is five, and if you have two micro-teams assembling to get one important thing done, now we have two manager-opinions to resolve. Separate teams need separate priorities, roadmaps, staffing decisions. When their work overlaps, somebody has to negotiate between them. Eventually the company starts employing people whose job is mostly to coordinate across boundaries the company itself created (and cause 11, above, becomes self-replicating).

For example, at Uber, Restaurants, Retail and Direct previously had three separate Delivery Ops organizations. Uber is combining them into single teams at the global, regional and country levels and putting their P&Ls under single owners. Core Services Engineering and Science are being combined too. Khosrowshahi says the separate Delivery structures made sense when the businesses were younger and no longer do.

The P&L moves are things that leaders are reticent to do unless there's substantial evidence for it. But when three related businesses have separate economics, shared resources become negotiations. Putting them under one owner allows one non-CEO person (and usefully, not a committee) to make the tradeoff instead.

Uber is also changing four things at once: headcount, layers, team boundaries and geography. Global teams will concentrate in New York and San Francisco, regional and local teams in designated hubs, and fully remote roles will fall to roughly 1%. So margins six months from now won’t tell us much about whether the redesign worked.

As ever, YMMV!

1. Nando Cesarone runs the global air network and gateways, surface transportation, buildings and engineering, automotive, the Intelligent Network of the Future and sustainability.
2. Matt Guffey runs the U.S. businesses.
3. Wilfredo Ramos runs International, Healthcare and Supply Chain Solutions.
4. A Chief Global Commercial Strategy Officer—still to be hired—will run strategy, marketing and communications, product management and pricing.

UPS’s 2022 structure put U.S. small package, transportation, UPS Airlines, automotive and global buildings and systems engineering under Cesarone. Kate Gutmann ran International, Healthcare and Supply Chain Solutions, including their operations and sales. Guffey later held strategy, U.S. sales, marketing, communications and the On Demand Network. A global airline and global engineering operation therefore reported through the U.S. business. International operations reported elsewhere.

With this new move, the aircraft, the gateways, the buildings and the feeder network sit under one executive. The businesses filling that network are elsewhere, and the announcement doesn't say where the package cars will go. My curiosity is whether this network ends up feeling like a platform, or a matrix.

UPS chose this moment after reducing Amazon volume by more than half from 2024 levels and closing daily operations at 45 buildings in the first half of 2026; 44 closed permanently. (Gutmann is also retiring after 37 years, which probably factors in somewhat.) The August announcement says the Amazon draw-down and network reconfiguration finished in June. UPS’s second-quarter filing says the broader Network Reconfiguration and Efficiency Reimagined programs will run into 2027.

The announcement gives Cesarone operational oversight, but is ambiguous about capital allocation, service-level authority or sales. These are all interactive, dependent pieces: pricing changes volume; volume changes routes, staffing and asset use; service exceptions for one account can challenge capacity elsewhere. None of those decisions belongs wholly to one of the new jobs. Does Healthcare pay for dedicated network capacity? Can a market reject a global standard? Can the commercial chief sell a service level that Operations finds inefficient? These tie-breaks are what makes a system such as this work well, and there are potentially thousands of them to negotiate. Without clarity in these spaces, or a culture that allows mid-level leaders to resolve conflict effectively, those calls will end up on CEO Carol Tomé's desk.

What about their competitors? One FedEx combined Express, Ground, Services and other operating companies inside Federal Express Corporation, with a unified air-ground network. They still have separate operating chiefs for U.S./Canada and Airline/International. On June 1, FedEx Freight became an independent public company. Air and ground parcel became one network; Less-than a Truck Load (or LTL) left the company. DHL kept its divisions. Its September 1 reorganization put Express, Global Forwarding, Supply Chain, eCommerce and Post & Parcel Germany into separate legal entities. Each division has its own headquarters. DHL AG, Global Business Services and Customer Solutions & Innovation provide group management, shared internal services, key-account management and innovation.

Coming back to UPS: at some point, a healthcare customer is going to need an exception that makes the global network less efficient. The new top leadership team (yes, with one role vacant) will need to figure out which side of the matrix wins out. This is a lot easier when these teams have a lot of history with each other, and in the case of the operating leaders, it's around three decades, with Cesarone starting as a part-time pre-loader in 1990. I'll be tracking how well this works.

Eskom’s board approved a Generation Recovery Plan on December 10, 2022. A month later, its chair said power stations needed "space and headroom" to do proper maintenance without constant firefighting, and offered two ways to get it: add capacity, or declare a permanent stage 2 or 3 for the next two years so the outages would at least be scheduled and the country could plan around them.

Six stations causing more than half of unplanned losses got first claim on experienced people, capital and equipment manufacturers. Each station got a recovery plan, central tracking and direct board attention.

Eskom raised planned maintenance from 10% of capacity in FY2023 to 12% in FY2024 and increased repair spending 30%. The numbers got worse in the short term, with availability falling to 55%, load shedding rising to 329 days and emergency diesel generation costing R34 billion. Planned maintenance peaked at nearly 13% in FY2025, and that is when the problem started to turn: unplanned losses fell six points and availability rose six. In FY2026 planned maintenance came back down to 11.5% while availability kept climbing to 65%; a fleet that breaks less needs less time to catch up. Through August 27 of this year, availability is averaging 68% for the financial year to date, diesel spending is down 82% against the same period last year, and South Africa has gone 469 days without load shedding.

Queueing theory predicts this: waiting times explode as utilization approaches 100%. Jay Galbraith treated slack resources as a basic response to uncertainty. Tom DeMarco called slack the degree of freedom that lets a company change. This ought to be a pattern in a future version of the book, called Protected Capacity. Teams and entire companies should intentionally reserve part of the system for maintenance, learning and the unexpected, and force themselves to not let ordinary demand claim it.

These four figures come from internal posts reviewed by Reuters. They capture the organizational gamble behind Meta’s Project OT: the company treated prospective agent capacity as actual operating capacity.

Reuters reconstructed the proposed design from scores of internal documents, posts and recordings and more than 20 sources. The plan was that conventional product teams of 10–20 people...

Would give way to 3–5-person pods: three or four fluid-role “builders” plus a Pod Lead.

Designers, researchers, data scientists, data engineers and ML specialists would be shared across pods. By June, at least 11 engineering and research units had adopted variants of this design.

Meta confirmed Project OT to Reuters and acknowledged that the most extreme scenarios contemplated shrinking some teams by up to 60%. It stressed that several major units were outside the exercise, the scenarios included redeployments, and 60% was never a company-wide layoff target. The project explicitly combined cost cutting with organization design.

The pod architecture economizes on the scarcity of local coordination, which is a real problem both at first glance and in my own data. After all, four people can decide faster than 15. Beyond that, generalist roles reduce internal handoffs, and pooling specialists removes duplicated capacity. And yet! A builder who needs design, research, data, ML and security now competes across shared service queues, because small teams in large organizations not optimized for their needs will always sit inside a larger, slower coordination system.

What we are observing is that AI does radically increase software development throughput, which is bounded by review, integration, release and general operating capacity. Pooling that integration capacity creates a bottleneck. Reuters’ metrics do not necessarily prove that Project OT caused the incident spike, but they are consistent with this failure mode.

Meta made some ways of working changes, too. Direction or Pod Leads steered daily work without formal people authority. Org Leads overseeing roughly 30–50 people owned ratings and promotions, supported by HR and unspecified AI systems; Meta says people made the decisions (important for regulators). This generally would give the person closest to the work more "free speed" without a full manager load (good). It also offers Pod Leads responsibility without reward authority, and Org Leads career authority without daily observation (not as good). This can work, but only with explicit conflict rules and unusually good information. Reuters found at least one new Pod Lead confused about lacking manager training and ratings tools.

The same pod model should perform very differently as those capabilities spread and become more ~industrialized. Meta’s own engineering posts show credible gains from specialized agents with bounded codebases, explicit tools, context, guardrails and human approval.

So what can future designers and managers learn from this? Changing team size, role boundaries, management spans and career authority before shaping the bottlenecks is a recipe for thrash. Designing for sustainable production changes is another thing entirely.

Reporting note: Reuters’ special report relies on internal Meta documents, posts, recordings and more than 20 sources. The internal percentages are not a controlled experiment, use different denominators, and are not identified to Project OT pod adopters. Meta declined to comment on the disruption data. Project OT also mixed AI-native design (some with layoffs, redeployments and cost reduction.

That is the question in Joshua Gans’s NBER working paper, originally issued in May and revised in August 2026. The paper offers a mechanism for understanding where one role ends and another begins.

Gans arranges all the tasks needed to produce a service from lowest to highest required expertise. Workers can perform tasks at or below their own level. Jobs become adjacent sections of that ladder. The hardest task in each section determines the expertise of the worker you need and therefore the wage you pay. That wage applies to the whole bundle; every hour a partner spends on associate-level work is still bought at the partner’s price.

So why do companies split the work? Because a lower-expertise worker is cheaper. Why might they keep work together? Because every boundary creates a handoff, and handoffs are costly. In Gans’ model, the person receiving the file spends \(h\) units of time reading it, reconstructing intent and checking what happened upstream. That time is also bought at the receiving worker’s wage. Expensive expert time pushes tasks into narrower roles, and expensive context reconstruction pushes them back together. The equilibrium boundary sits where the saving from moving one more task to the cheaper worker equals the cost of raising that worker’s expertise across their entire workload. Neat.

This gives AI a route into job design that task-exposure scores miss. Suppose an AI summarizes an issue's history for the next person. It performs none of the service’s tasks. It simply reduces \(h\).

Holding the number of jobs fixed at three or more, boundaries between those jobs would then shift: "lower" roles take on more of the expertise ladder, and the top role narrows. A larger reduction can make another job worthwhile, when a new boundary then appears all at once. As handoffs approach zero in the benchmark, jobs keep narrowing toward one market per task.

Interfaces also change what automation does. A tool that needs its operator to remember the surrounding context, with high \(h_m\), are likely to stay embedded within that job (top). And under specific local conditions, the containing job actually widens even as its human task time falls. A machine with that brings context with it, dropping \(h_m\), creates a free place to split the work (bottom). In this way, identical task removal can produce opposite job designs.

The model leaves out apprenticeship, accountability, incentives, regulation, identity and social ties, all of which can keep tasks together; we should remember these factors during times when jobs are being redesigned. Context-transfer cost is one reason jobs have their current shape. If you make that easier or harder, jobs can change before the machine has anything to do with it.

This story started in 2022, with an org chart.

Unilever moved away from a heavy matrix and created five end-to-end Business Groups. Each got responsibility for its own strategy, growth, results, and P&L. Most functional teams moved into the groups. Unilever Business Operations supplied technology, systems, and processes. A lean Corporate Centre held global strategy, functional expertise, and standards.

I liked the design then: make the businesses real businesses; make the center a platform. In January, I wrote that divisions were becoming more differentiated: strategy-specific leaders, clear P&Ls, thinner centers.

An autonomous division can be a stepping stone between a conglomerate and an independent company. Give a business its own strategy, leadership, resources, capabilities, and economics, and it becomes easier to see as a company. It also becomes easier to separate.

The operating models already contained some obvious breakpoints: frozen distribution (this is a Whole Thing™, btw) for Ice Cream; different customers and a large foodservice channel for Foods. The 2022 design turned those differences into management boundaries, with leadership, resources, decisions, and P&Ls gathered inside each one.

The sequence goes something like this:

• Break the matrix. Give categories end-to-end accountability.
• Thin the center. Turn shared infrastructure into services the businesses consume.
• Price the center's activities. Compare the value created by sharing against coordination and separation costs.
• Profit?

Unilever’s own language is direct on this topic. It expects “no revenue or operational dis-synergies” from separating Foods because Foods already has operational and go-to-market independence from the remaining groups. It also expects €400–500 million in stranded costs and another €500 million of restructuring through 2029. Cleaner ≠ free.

Modular operating models create optionality. This is Dissolvability at company scale: a unit can leave without rebuilding the whole machine. Once the center becomes a platform, you can see which capabilities are shared, what they cost, and what common ownership contributes.

The market has opinions, with Unilever trading at 11.5 times enterprise value to core earnings, behind P&G at 14.8 and L’Oréal at 17.5. Magnum’s shares are up roughly a third since listing, mostly because they jumped as much as 18% when Reuters reported that Blackstone and CD&R were circling. Its later 4.7% first-half organic growth helped.

Foods/McCormick received the opposite reaction: Unilever fell 7% and McCormick roughly 5% as investors balked at the long timeline, the separation costs, and a structure that leaves Unilever and its shareholders owning 65% of the combined food company.

The old conglomerate hid those choices inside coordination, and the modular, platform-pilled firm shines a light on them. Divisions are companies in waiting when common ownership fails to deliver value to the divisions or to shareholders, and good organization design changes which options are available to al.

Inside of this is a lurking question about how performance systems work.^[1] Most cannot directly observe the things they are supposed to assess: judgment, competence, effort, attention, understanding, originality. So they infer them from artifacts.

Historically that worked reasonably well:

good memo → probably understood the problem → probably exercised judgment → probably capable person

AI inserts itself between the person and the artifact:

person → AI → good memo

This new article, based on an open data set from Anthropic, indicates that people seem to draw a distinction between identity and labor. They are uncomfortable with AI pretending to be them (writing in their voice, taking authorship, falsely implying a belief). But they are much more comfortable letting AI hide how much work they personally did: how long they spent, how much uncertainty they had, whether they struggled, whether they even paid much attention.

A funny example of the OK categories is the recent prompt that solved an unsolvable math problem:

Construct a counterexample to general (non-planar) case of Dinitz Garg Goemans conjecture. You should do a breakthrough and find a structured counterexample.

In general this research seems to support the "future world will be more small teams and small companies" hypothesis, where individuals are likely to care a lot less about how their colleagues use AI, and be much more open and honest about what sort of labor or intelligence was involved in the production of their art.

Just like remote work and even r/overemployed, we're treating the democratization of long-standard allowances the working world gives to its seniormost people as nothing less than a moral panic.

• Executives for national global have worked "away from HQ" since that was possible and important (think of this like "management by walking around" but at industrial scale.
• It seems commonplace now for tech CEOs to lead multiple firms with no public penalty (even while most companies expressly disallow this practice for everyone else).
• Almost every VP, Director, and Manager is expected to deliver work of unclear voice, provenance, vulnerability, attention, and investment.^[2]

The difference, perhaps, is that in the third example, the folks who are performing the work of others have (hopefully) long since demonstrated their capability and are assumed to be able to apply that capability as a filter for the output of their direct reports/broader organization. Still, I worry that we're seeing more K-shaped outcomes more of the time.

Target eliminated 100 roles from the layer between stores and headquarters, including district senior directors, HR partners and food-and-beverage coordinators. Another 400 were removed from supply-chain sites. Target consolidated its store districts and said the change would help better empower store directors.^[1]

At the same time, Target made an “immediate addition” to store payrolls. It added labor hours and a companywide guest-experience course. In March, the company put hundreds of millions of dollars behind store payroll and training specifically, inside an incremental $2 billion for the year.

By May, more than 300,000 Target employees and leaders had completed the new training. It trained them against four measures: interaction with guests and coworkers, execution, teamwork and reliability. The stated aim was to make each Target trip easy, inspiring and friendly. Both Q1 and Q2 expenses included higher compensation from additional field hours. Q2 SG&A rose 7 percent, despite the earlier cuts. The restructuring reduced payroll in one part of the company while payroll costs increased closer to the sales floor.

In Q1, guest ratings for wait times, cleanliness, product availability and interactions with employees reached three-year highs. Target said stores receiving added support produced early gains in guest satisfaction. In Q2, satisfaction rose again. Inventory reliability reached multi-year highs, with record availability for Target’s most frequently purchased items. Same-store comps increased 3.8 percent and traffic rose 3.6 percent. Those are outcomes that store staffing can affect: shorter waits, stocked shelves, cleaner stores and employees available to help.

There are some confounding factors here, though. Target also cut prices on more than 10,000 items, changed nearly half its grocery presentation, refreshed major product lines, upgraded its inventory technology and began more than 100 store remodels. Ignore the headline doubling of earnings too: a $994 million tariff refund did much of that work. Two quarters cannot isolate the effect of the labor transfer.

Regardless, Target removed capacity from coordination and support, then added capacity where employees meet customers. Management linked some early service gains to that added support. More store labor and training also gave Target a way to execute everything else it was changing.

A new revision of Stanford’s “Canaries in the Coal Mine?” tracks this pattern in employment data. Between November 2022 and June 2026, employment among workers aged 22 to 25 fell about 11% in highly AI-exposed occupations. It grew about 10% in less-exposed work.^[1]

The gap came from hiring: exposed young workers were not leaving or being fired at higher rates, but fewer of them were joining firms.

Stanford defines a hire as a new worker–firm match, and it cannot see open requisitions or replacement decisions. The study may also be missing some important factors at play. A generally terrible market for new entrants, pandemic overhiring, education mix and sector effects remain possible explanations.

Related clues can be found in a study of ChatGPT Enterprise covering 1,764 organizations and 17.4 million messages. Active early-career workers and trainees sent about eight to nine more messages per week than the average active user in the same firm. The study measures usage, though—not productivity, output or headcount.

Juniors use AI heavily, AI increases their capacity, and firms need fewer juniors.

California may begin collecting the missing evidence. SB 951 defines a “technological cessation in hiring” as permanently ending hiring for a role because of AI or automation. If passed, covered employers would report positions they have decided not to fill, along with the work and technology involved. That would capture organizational capacity that disappears before anyone occupies it.

The career-system problem is that its ladder is made from work. First drafts teach framing. Basic analysis teaches judgment. Research teaches source evaluation. Debugging teaches how systems fail. Coordination teaches when to escalate.

Removing those tasks, annoying as they may be to senior people,^[2] weakens the system that creates expertise, and the productivity everyone assumes in exchange may not be there. A small randomized study of working developers—most with seven or more years of experience—found that AI users learned less from the task, losing conceptual understanding, code reading, and debugging ability, without finishing meaningfully faster.

Organizations may keep today’s experts while quietly reducing the number of people who can become tomorrow’s.

A paper [PDF] published on August 13, 2026 indicates that open-source development is associated with higher gross margins. This sample here covers 977 firms: 231 open-source adopters and 746 non-adopters.

This basically means what we had suspected all along is true: Openness and Community Orientation lead to more success, not less, and organizations are able to capture value from production that crosses the employment boundary.

Researchers considered firms to be active if they had pushes, pull requests, review comments, or issue activity within a given year, and then used the log of the number of active projects as an indicator of development intensity. Now, the research does not necessarily capture the firm’s full our development system, and it excludes contributions to projects that the firm does not itself host on GitHub. The study's authors describe the mechanism behind this margin improvement as something like distributed knowledge integration.^[1] Having a popular open-source repo leads to the discovery of valuable edge cases, deployment contexts that would otherwise go unseen internally, and other unmet needs.

Open-source projects don't magically just generate value from nothing, though, as they require additional internal work focused on evaluation, integration, architecture, commercialization...all things that you would expect from a normal software project, but now with a much larger contribution base.^[2]

The effect became discernible when outside contributions passed 35% of total contributions, with a clearer effect above 51%, but there is more to this than “be more open”. The authors found five high-margin configurations, each combining high open-source activity and high R&D:

1. A large, labor-intensive firm with low outside participation, capital intensity and market share.
2. A large, labor-intensive firm with high outside participation but low capital intensity and market share.
3. A smaller, labor-intensive firm with low outside participation, capital intensity and market share.
4. A large, high-market-share firm with high outside participation but low capital and labor intensity.
5. A large, high-market-share firm with high capital and labor intensity but low outside participation.

Haters will probably flag that this is borderline uncompensated labor, but open-source projects (even if they're corporate) are genuine contributions to the field: take VSCode (Microsoft) or Chromium (Google), or Posthog.^[3] And there are plenty of businesses built on other businesses' open source: Microsoft Edge extends Chromium, Vercel's Next.js extends Meta's React, PostHog itself builds upon Yandex's Clickhouse. For more on this topic, I strongly recommend Yochai Benkler's "Coase's Penguin". It's a thinker, but it's worth it.

In a 2013 McKinsey survey of 2,063 executives, 1,534 had experienced a redesign at their current organization. Among respondents personally involved in one, 21% said the new structure had been fully implemented, met its initial objectives very or extremely well, and improved performance.^[1]

That does not mean that the remaining 79% are a dumpster fire. Still: only 21% cleared all three bars. That is bad! Especially for something that absorbs months of senior attention, a lot of consulting fees, and an otherworldly quantity of PowerPoint slides.

The challenge is that the market for organization design has split one problem set into separate practices. One group gets hired to design the formal organization: reporting lines, roles, decision rights, resources, governance. Another gets hired to work on the lived organization: trust, habits, informal networks, conflict, participation, and how work actually gets done. The first usually has a clear executive sponsor and a transformation budget. The second often follows the first one's design work, and helps it actually work, but at a lower rate with lighter formal endorsement.

Two questions:

1. What is being designed? Formal architecture or lived behavior and informal organization?
2. Who gets to design it? A concentrated group with positional authority or a broader group of people who do the work?

A conventional reorg combines formal architecture with concentrated authorship. A centrally sponsored culture program works on behavior with the same concentrated authorship. Sociocracy pays serious attention to participation and distributes a great deal of operating authority. Holacracy is an extremely formal architecture built to distribute authority. Buurtzorg uses deliberate formal constraints to support local autonomy.

So “structuralist” and “mutualist,” the terms I'll use here, are bundles rather than settled academic schools. Structuralists tend to favor formal architecture and concentrated authorship. Mutualists tend to favor lived behavior and distributed authorship. Tend to.

• Every structural intervention has behavioral consequences.
• Every participatory process operates inside a formal distribution of power.

You cannot redraw reporting lines without changing status, identity, attention, and who talks to whom. You also cannot invite everyone into a circle and pretend away the CEO's authority to set budgets, hire and fire, appoint leaders, or end the exercise. Participation can influence power. It is not automatically power.

I've spent my career in the gap between these two traditions.^[2] I co-founded August after coming up through Undercurrent, where we practiced participation-first organization design on ourselves and with clients (I wrote about how Undercurrent worked in “Open Undercurrent”). More recently, at R/GA, Black Glass, and Airbnb, I have led structure-first work with executive sponsors, design criteria, and the kinds of artifacts that get real budgets. I've watched the two traditions work on the same organization without realizing they were working on the same thing. I have also been part of the problem. Sometimes the budget ran out. Other times we could not sell the other half of the work.

The Structuralist Tradition

Anyone who has watched an org chart get unveiled in a town hall, sat through a deck on spans and layers, or endured a reorg announcement promising clarity and accountability has experienced structuralism in practice. It is the part of organization design that buyers already know how to buy. It produces a scope, a steering committee, a model, and eventually some boxes.

Someone has to draw the boxes! In commercial practice, though, formal design usually comes with concentrated authorship, while the social organization gets pushed into implementation.

Strategy → Structure

Alfred Chandler's 1962 book Strategy and Structure examined DuPont, General Motors, Standard Oil of New Jersey, and Sears. Its famous argument was that the administrative structures of large firms changed in response to strategic expansion and diversification.^[1] In practice, “structure follows strategy” became a pleasing (and powerful!) sequence: executives choose a direction, then design an organization to execute it.

That sequence made structure into a lever for strategy. It also put authorship at the top. At that point—and in many organizations today—the strategy belonged to senior leaders; the structure followed; everybody else heard the story during implementation.

Structure as information processing

Jay Galbraith made Chandler's research more useful. His information-processing view starts with uncertainty: the more uncertainty a task contains, the more information an organization must process while doing it. Organizations can reduce the need for information through excess/slack resources or more self-contained tasks, or increase their capacity to process information through lateral relationships and better information systems.^[2]

An org chart is a theory about information. Put two teams under one leader and you are betting that coordination through that leader will help, that the new relationships created will yield better decisions. Create a cross-functional unit and you are betting that colocating interdependent work will reduce the cost of passing information across boundaries. Add a matrix and you are betting that the value of another channel exceeds the meetings it will spawn. (Sometimes a terrible bet.)

Galbraith's later Star Model connected strategy, structure, processes, rewards, and people, making clear that structure ≠ boxes and lines.^[3] In commercial practice, though, the boxes and lines remain seductively legible. The formal artifacts win partly because buyers can see them and consultants can produce them. Structural diagnosis can be presented to an executive committee, but informal trust, workarounds, and the person everybody calls when something breaks are harder to fit on a slide and frequently cut because the Partners rightfully know their clients don't have time or attention for it.

The contemporary practice

In Designing Your Organization, co-authored with Galbraith, Amy Kates details a process that begins with strategy, develops design criteria, compares options, and carries choices into detailed design and implementation. Kates co-founded Kates Kesler, which Accenture acquired in 2020.

I actively use a version of this process, and it works very well. Clear design criteria beat executive whim. Explicit options expose tradeoffs. Decision rights, accountabilities, and resource flows need to be designed, often precisely; large organizations cannot indefinitely facilitate their way toward these answers.

The top team is usually the client. The organization is the thing being designed. People with detailed knowledge of the work may be consulted, interviewed, or placed on a design team, but senior leaders in general retain authorship and approval.^[4]

The bridge to nowhere

In 1951, Eric Trist and Ken Bamforth published their study of work in Britain's newly nationalized coal industry, then run by the National Coal Board.^[5] Mechanized longwall mining had improved the technical system while breaking apart the cohesive work groups that had organized the older method. Trist and Bamforth contrasted that arrangement with a “composite” group that rotated tasks and took collective responsibility for a full production cycle.^[6]

That became sociotechnical systems theory and, later, the language of joint optimization. Technology changes interdependence, and interdependence changes the information people need, the relationships they form, and the authority required to handle exceptions. Optimizing the machine while treating the crew as interchangeable labor can make the whole system worse.

As I started writing this essay, I thought I had found a genuinely novel synthesis. NOPE. Trist and Bamforth were already there in 1951. (As I tell my team often, being right is rarely a requirement in this field.)

The work did not instantly change British mining. Enid Mumford's later history describes a mixed reception: composite group work conflicted with wage bargaining organized around traditional jobs, while the Coal Board was reluctant to provoke a union dispute.^[7] Sociotechnical thinking spread elsewhere. The commercial market I know still tends to split the problem back apart: structure first; relationships, informal authority, and behavior later, as “change impacts” or implementation.

The Mutualist Tradition

Between 1997 and 2001, economist Robert Jensen studied the staggered arrival of mobile-phone coverage in fishing regions of Kerala, India. Before coverage, boats sometimes returned to a crowded market and dumped unsold sardines while buyers elsewhere faced shortages. Jensen found that price dispersion and waste fell as coverage grew, and he attributed the pattern to fishermen comparing prices while still at sea; his estimates also showed higher fishermen's profits and lower consumer prices.^[1] In his account, the technology moved information and choice toward the people deciding where to sell fish.

I call the alternative tradition mutualist. Sociocracy, self-management, participatory facilitation, culture work, and informal-network analysis have different histories and sometimes incompatible commitments. The thing that connects these practices is a shared orientation to start with relationships and local knowledge, involve the people affected in authoring the organization, treat structure as something that can evolve through use...and leave the people with the tools necessary to make it better on purpose.

Classic Organization Development fits in here, too. Lewin's action research tied inquiry to action. Schein's process consultation made the client part of diagnosis and remedy instead of treating a consultant as an outside expert who could simply install an answer.^[2]

Participation is more than what happens when the “real” designers leave and somebody orders Post-its.

Sociocracy: Ward → Boeke → Endenburg

Auguste Comte coined the term "sociocracy" in the nineteenth century; Lester Frank Ward later used it for a scientifically guided social order. The participatory meaning came later through Dutch educator Kees Boeke and his wife, Beatrice “Betty” Cadbury (yes, that Cadbury family). They founded the Werkplaats school community in Bilthoven in 1926 and used a form of consent governance. Boeke opposed majority rule because a durable minority could be repeatedly overruled; his alternative was to proceed when no participant had a reasoned objection they regarded as paramount.^[3]

Gerard Endenburg, who had attended Boeke's school, adapted those ideas for an electrical-engineering company. The Sociocratic Circle Organization Method added nested circles, consent-based policy decisions, elected roles, and double-linking between levels.^[4] Holacracy is a weirder idea, with connections to Ken Wilber, integral theory, and libertarianism. Its constitution specifies roles, domains, accountabilities, governance meetings, and rules for changing the rules.^[5] You may find the language cultish—I often do—but you cannot call it informal.

At a Holacracy training in 2014, Jordan, my then-colleague at Undercurrent, and I met a participant who had been to EIGHT not-very-inexpensive trainings and had never really implemented any of it at work. The ridiculous formalism that makes it so hard to learn gave us a way to bring a conversation about self-management into boardrooms where it would not otherwise have reached. It made it legible.

The practice I came from

I learned this orientation at Undercurrent, a kinda-sorta management consultancy in the early 2010s, and then co-founded August with four friends from UC. In both companies, we experimented on ourselves: distributed authority, transparent information, explicit decision practices, new meeting forms. We also helped clients work differently. Some of it was excellent. Some of it was very earnest. We were not always great at telling the difference.

Undercurrent closed suddenly on July 31, 2015, a few months after Quirky acquired it. Mike wrote that “What would you do if you could start all over with a blank slate?” went from philosophical to very, very real overnight. August was one answer. Undercurrent's people went on to help build The Ready, NOBL, Kindred, and other firms. The Responsive.org manifesto, authored by UCers and friends, put out a call to action that still resonates today: Purpose over profit; Networks over hierarchies; Empowering over controlling; Experimentation over planning; Transparency over privacy.

But participation has its own bullshit.

First, consent can become theater. If the CEO can set the budget, remove the facilitator, hire and fire the team, appoint senior leaders, or end the experiment, everybody in the circle knows it. Distributed operating authority can be real and valuable while ownership and governance remain completely conventional. Say which power is moving.

Frederic Laloux gets pretty bleak about this at the end of Reinventing Organizations. In his account, BSO/Origin's self-managing system reverted after the company was sold to Philips and the new board restored conventional management. His two necessary conditions for a real transformation: the CEO has to want it, and the board has to agree.^[6]

Second, participation can conceal power. Jo Freeman's “The Tyranny of Structurelessness” made the point bluntly: refusing formal structure does not remove elites; it makes them less accountable.^[7]

Third, emergence does not settle enterprise tradeoffs by magic. Local teams can optimize their own work while shifting costs onto another team, duplicating capability, or starving shared infrastructure. “Let the team decide” is not an answer until we know which team, about what, within which constraints, and who is in trouble when something goes wrong.

Anyway. I have walked into rooms with executive teams and focused on facilitation when what they needed was someone to draw the boxes. I have prioritized buy-in over clarity and watched the ambiguity I preserved become its own kind of dysfunction. I have treated emergence as morally cleaner than design, even when the ambiguity protected existing power.

Synthesis: The Integrated Practice

In practice, these traditions usually meet in sequence. Structural designers produce the target operating model. Change and culture people arrive afterward to communicate it, gather feedback, and help it land. Sometimes the handoff is reversed: participatory practitioners facilitate discovery for months, then call a structural specialist when the client finally asks who reports to whom.

An integrated engagement keeps both forms of evidence in the same diagnosis and at the same decision table: reporting lines, roles, resource flows, decision rights, incentives, governance, informal networks, practical knowledge, trust, conflict, identity, and how the work already gets done. One person can hold all of that. A tightly coupled team can, too. Everybody at the table needs enough fluency to challenge everybody else.

We have some examples that refuse the mapping of structure to concentrated authorship, of autonomy to informality.

Buurtzorg is usually told as a story about removing managers from home care. Their Dutch nursing teams are small—generally no more than twelve nurses—and take responsibility for a defined neighborhood and patient group. Teams plan care, divide roles, manage schedules, and coordinate with other providers. Regional coaches advise multiple teams but do not act as line managers. A small central organization handles functions such as payroll and contracting. BuurtzorgWeb gives teams a common information and communication system.^[1] The autonomy here is heavily designed. Team size, geography, information infrastructure, coaching, shared services, and the scope of local authority are all choices someone made. The nurses then have substantial freedom inside them.

And in spite of it flying in the face of management orthodoxy—even while making complete sense to every nurse I've ever discussed it with—it works very, very well. A KPMG case-mix-adjusted analysis cited by the Commonwealth Fund found fewer home-care hours and lower home-care costs in 2013. When physician, hospital, and nursing-home costs were included, total cost per client was about average.^[2]

Under RenDanHeYi, Haier describes the enterprise as a network of customer-facing microenterprises connected to internal platforms and shared services. Its company materials describe microenterprises as the basic unit in a flat or grid organization and assign them rights over management, people, and distribution, along with responsibility for results.^[3] A microenterprise may control product decisions, hiring, and compensation within its domain while operating inside a listed corporation with a conventional board, shareholders, and senior appointments. Haier Smart Home, the group's publicly listed appliance business, reported 2025 revenue of RMB 302.33 billion and 134,995 employees at year end.^[4]

Again, distributed authority sits inside a lot of architecture: defined microenterprises, internal markets and platforms, rights over people and compensation, and conventional corporate governance around the whole thing. So yes, this combination works at enormous scale.

A practical standard

Here is a test. An integrated engagement should make four things explicit:

1. The formal system being changed. Roles, reporting relationships, decision rights, resources, incentives, governance, ownership constraints, and the technical interdependencies of the work.
2. The lived system already doing the work. Informal networks, practical routines, workarounds, trust, conflict, status, and where decisions actually happen.
3. The conflicts between them. Places where the formal process blocks necessary behavior, where the informal system defeats legitimate controls, or where each side is compensating for a failure in the other.
4. The evidence that will show whether the design works. Outcomes, decision quality and speed, coordination load, employee and customer experience, and unintended shifts in power or risk.

Start with the work and its interdependencies, not the current chart or a blank canvas. Map how information reaches decisions and where exceptions travel. Compare formal authority with authority-in-use. Bring people with local knowledge into diagnosis and option testing, while naming which decisions are consultative, delegated, consent-based, or reserved. Test structural options against behavioral evidence; test participatory proposals against budget, governance, and accountability. Repeat before anybody unveils anything in a town hall.

The space between drawing the boxes and bottom-upping the answer is a negotiation. People inside the organization often cannot conduct it cleanly; they are living inside the authority, incentives, and risk preferences being negotiated. That is one reason outsiders are useful. Participation cannot solve a mismatch between the risk the organization must carry and the risk its people are willing to carry. Sometimes the constraint is real, and sometimes the answer should come from the top. — Shoutout to Jordan for prompting this quick aside

Concentrated authority can be legitimate. A board may reserve the right to appoint a CEO. A leader may set a budget. A regulator may impose a control. Integrated design asks that we consider these facts rather than hide them behind the word “stakeholder.”

Where do the consequences go?

Who bears the downside if a local team makes a bad call?

Who receives the surplus if it makes a good one?

Who can reverse the decision?

Who can remove the decision-maker?

Why the Split Persists

Marvin Weisbord's 1976 Six-Box Model asked practitioners to examine purposes, structure, relationships, rewards, leadership, and helpful mechanisms, including the fit between the formal system and what people actually do. David Nadler and Michael Tushman's 1980 Congruence Model treated work, people, formal organization, and informal organization as interdependent components.^[1]

And...training follows professional identity. Structural designers learn strategy, work decomposition, operating models, and governance. Organization Development practitioners learn groups, participation, conflict, and change. Budgets follow procurement categories. A formal redesign produces legible artifacts quickly; a serious informal diagnosis traditionally requires interviews, observation, workshops, and time.

What computation changes

Spans, layers, reporting lines, role inventories, and policy documents have long been relatively cheap to analyze because the data are already structured. Informal organization has been expensive to measure and design because the useful evidence is dispersed across interviews, observation, stories, communication patterns, and exceptions. Modern language models and network-analysis tools can help code interviews, compare role descriptions, trace decision paths, find contradictory policies, and generate hypotheses from a larger body of qualitative material than one team could read unaided.

This will change the price and speed of assembling evidence, and it will tempt the impatient to use an LLM as an organization designer. This will miss the judgment to test patterns against context, politics, and lived experience.

In my experience so far, the most valuable computational output is usually a question, like:

• This team appears central to three workflows but owns no corresponding decision right. Why?
• The org chart says one function approves the work; interviews describe four different approval paths. Which one operates under pressure?
• Two groups have nearly identical role descriptions and very different reputations. What explains the difference?
• A redesign moved authority on paper, but escalation patterns did not change. Is the new authority understood, trusted, or real?

The surveillance issue

The same tools can become workplace surveillance with impressive speed.

Slack, email, calendar, meeting, and badge data reveal selected, digitally visible traces of the informal organization. They miss phone calls, private chats, friendship, avoidance, shop-floor interaction, and all the work done outside the chosen systems. They can overstate the importance of people whose jobs generate messages, understate expertise, and confuse frequent contact with trust or influence.

Analysis also changes behavior when employees reasonably believe it may be used to rank them, evaluate performance, or identify dissent. Consultants can extract, misclassify, and expose people in interviews and workshops, too. Computation can now do that across much more of the organization, much faster.

The formal organization has always been easier for management to see. But a technically clever network map can destroy the trust it claims to measure.

Onward. Where?

Trist and Bamforth had the basic answer in 1951. The technical system and the social system are one design problem. We spent the next 75 years unbundling the practice, to the detriment of organizations, teams, and people everywhere: structure over here, behavior over there; executives working on one, everybody else living in the other.

Earlier this year, Nicole Abi-Esber, Lindred Greer and Annebel De Hoogh have evidence for the next bit.^[2] They call it hierarchical adaptability: a team's ability to repeatedly change the shape of its influence hierarchy as the work changes, becoming flatter or more hierarchical while the formal hierarchy stays put. In their experiment, adaptable teams coordinated and performed better than teams that stayed hierarchical or stayed flat.

The challenge for our field: how do we give teams the capacity to keep how they organize aligned with changing conditions. The adaptation happens in the team itself.

Further reading

To understand formal architecture and information processing:

• Jay Galbraith, Designing Complex Organizations (1973).
• Amy Kates and Jay Galbraith, Designing Your Organization (2007).
• David Nadler and Michael Tushman, “A Model for Diagnosing Organizational Behavior” (1980).

To understand participation, informal power, and distributed authority:

• Jo Freeman, “The Tyranny of Structurelessness” (1972).
• Aaron Dignan, Brave New Work (2019).
• Sociocracy 3.0 patterns (continuously updated).

To hold both systems together:

• Eric Trist, “The Evolution of Socio-Technical Systems” (1981).
• Marvin Weisbord, “Organizational Diagnosis: Six Places to Look for Trouble with or Without a Theory” (1976).

Code, Craft, and the Making of Nested Folders | Figma Blog

From treating code as a proposal to turning handoffs into conversations, here’s how we shaped a new way of working to ship nested folders.

FigmaEthan Adams and Cai Charniga and Sachi Shah

Figma’s writeup about building nested folders is nominally about...nested folders. But like everything (let me tell you someday about my belief that all movies are kinda-sorta org design movies. Jason Bourne? Guy struggling with role clarity and bosses with different POVs, incentives), this is an OD/ways-of-working story.

About a year ago, they started the project normally: Product worked through requirements, Design mapped the experience, Engineering started building when the thinking felt resolved. Then code got cheap and accessible enough that they stopped waiting for the thinking to resolve.

Copy not perfect? No worries. Functionality lacking? That was alright. Logical gaps? We could fix those later!

Engineers put up PRs instead of continuing debates in Slack. Engineers made design calls. Their designer pushed code. The PM spent less time directing execution and more time with customers and on the broader product question. As someone who remembers when static marketing sites would cost companies millions of dollars and several external vendors to build, I'm still amazed that this is where we are.

Remember when a prototype—an embodied question—was a representation of a product, because building the actual product was expensive? The whole thing is a prototype now.

As in many stories I've heard directly from people working on these kinds of projects, the role stuff is still fairly normal. Cai Charniga, the designer, is pretty explicit that they did not suddenly become an engineer, and that knowing when to hand something back was part of the work. The boundaries just became much more permeable, and everyone got a bunch more permission. This is a nicer description of what may happen to role clarity than “everyone becomes a generalist.” People retain craft while being a lot less precious about jurisdiction.

Gusto went further

No Figma. No Jira. No docs. How Gusto built a new product line with Claude Code | Eddie Kim (CTO)

Watch now | 🎙️ Eddie Kim, the CTO of Gusto, shows how a 5-person team shipped a full AI product line in 10 weeks using Claude Code, a perma-Zoom, and zero documentation

Lenny's NewsletterClaire Vo

Around the same time, Gusto CTO Eddie Kim described how a team of four engineers and one designer built Gusto Cofounder in about ten weeks.

No dedicated PM. No Jira. No Figma. No specs. No standups or retros. They worked from a permanent Zoom room and built things directly, including things they expected to throw away. Gusto designer Katie Kovalcin has a good account of the same project from the design side.

I'm not sure that this is The New Product Development Model™. Five exceptional people working on a new product with support from the CTO is an unusual setup for a company with circa 4,000 employees and $1b in revenue.

Figma is doing this inside a fairly recognizable cross-functional team. Gusto basically took the walls off.

Cool!

More output, more mess

An Almanac for the Age of Chaos

Marketing’s three-body problem: how to navigate when customers, channels and craft are all changing at once

AlephicTom Critchlow

Tom Critchlow’s Almanac for the Age of Chaos is mostly about marketing (and many of you came here when this blog was about marketing, so, here's one for the old heads), but there’s a very good organization-design section in the middle.

AI increases individual throughput but so far it has not increased the organization’s capacity to coordinate at the same rate. The advent of terms like slop cannon, and Clay's (no relation) recent buzzy policy on AI writing would indicate that the opposite is happening. More drafts. More variants. More experiments. More work arriving for review. More things that look finished even though nobody has decided whether they are finished.

Tom's answer is context and traces. Slack threads, issue states, CRM fields, transcripts, approvals, customer notes, source trails. In a slower organization these can look like administrative residue. Once humans and agents can produce work much faster, they become the machinery by which everyone knows what is current, dead, approved, speculative, superseded, etc.

This is basically the problem a Logbook is trying to solve.

There’s an appealing fantasy where AI removes the coordination tax from organizations. Could also be that it removes the production constraint and we discover how much coordination tax we're paying. (NB: a lot.)

An argument for more people talking to each other

The Social Edge of Intelligence

Simons challenges the dominant story of AI-driven efficiency, arguing that the intelligence powering today’s systems is built from accumulated human interaction. As organizations automate work and offload thinking, they risk…

MyWebSiteBright Simons

Bright Simons has an essay in The Ideas Letter called The Social Edge of Intelligence, and inside of it, he points to a study where researchers gave roughly 300 writers a short-fiction task. AI assistance improved the judged creativity of individual stories, but the AI-assisted stories became more similar to one another. Individual improvement, collective convergence.

The intelligence inside AI, such as it is, is partly an inheritance from the social systems that produced its training material: disagreement, institutions, different disciplines, apprenticeship, argument, accumulated tacit knowledge. If organizations automate away too much of that substrate, they can get locally more efficient while slowly impoverishing the collective wisdom on which they depend.

There are already some funny cross-currents in the evidence. Anthropic found people using Claude Code reporting fewer mentorship interactions and team touchpoints. A separate INSEAD field experiment found that giving workers AI actually increased human collaboration and knowledge-sharing ties.

I figure a year-ish from now we'll start seeing a lot more companies with customized or ground-up harnesses, reliance on open-weights or open-source models, etc. I also think that companies doing this well will focus much more on the social systems and even the safety nets/ladders they provide to each other.

Systems thinking has a family tree

Lineages of Systems Practices

Saskia Rysenbry and Laura Winn visually explore their understanding of how different currents of systems change thinking had come about, the lineages they have formed, and where the tools and frameworks of this field situate themselves in wider origins of practice.

School of System Change

Laura Winn and Saskia Rysenbry at the School of System Change made a beautiful map of the lineages of systems practices: systems dynamics, complexity, cybernetics, indigenous practice, living systems, participatory traditions and a bunch of tributaries running between them.

This reminds me of a thing that I drew to help on board folks to August. A method detached from the philosophy and history that produced it is weaker, and can even reproduce the thing it was designed to change.

(This is also where Two Camps—a new essay coming soon to an inbox near you! click that preview link!—is headed. There’s a structuralist tradition in org design, which says that structure shapes behavior. And a mutualist tradition, which says that people interacting together produce structure. We have a nasty habit of grabbing one and forgetting the other.)

There’s a lovely example in this old Marvin Weisbord paper.

After WWII, Ken Bamforth returned to a South Yorkshire coal mine and found miners reorganizing themselves around new mining technology. Instead of fragmented shifts with narrow jobs, they worked as multi-skilled teams that coordinated their own work. Waste fell, productivity rose and safety improved. Eric Trist visited, and the observation became foundational to socio-technical systems design. Then consultants got ahold of it.

Decades later Weisbord encountered companies installing a standardized “multi-skilled work team model.” People resisted it. The participative practice had become a thing done to them.

This is an extremely funny thing to read in 2026.

Figma’s engineers making design calls, Gusto’s designer shipping production code, teams figuring out how to work with agents...this is all about how the technology changes, and how people reorganize the work around it. Then somebody notices, calls it a model, makes a framework, and rolls it out to 40,000 people.

Maybe don’t do that this time.

This goes back to a couple pattern-shaped ideas in Rework: Everybody Works argued that managers should still make things; Hire Managers of One pushed readers to hire people capable of directing themselves.

As the company grew, it tried something more conventional. Full-time professional managers appeared, including in engineering. Then, in 2024, DHH got rid of them again. Senior practitioners would manage one or two people while continuing to do their own work.

In a 2025 REWORK episode on managing versus mentoring, principal programmer Jeffrey Hardy described senior people mentoring junior ones, coordinating work and conducting performance evaluations without becoming career managers. Later that year, DHH argued that managers still need to understand someone’s work well enough to judge its quality and pace. This is basically what I write about in Distributed Management: management is too important to be trapped in a single role.

In their new handbook, 37signals asks managers to hold 1:1s, inspect work, coach judgment and run performance reviews, which involve looking at actual projects and PRs rather than assembling a broad 360. Managers regularly assess skills, engagement and coachability, document problems and escalate when necessary.

But! Much of the normal manager bundle has been removed. Managers don’t set compensation or approve PTO. Salaries are standardized by role and level. Serious complaints go to People Ops. Status reporting happens asynchronously. Profit sharing is formulaic. If an underperforming employee starts consuming enough managerial attention to interfere with the manager’s actual work, People Ops and senior leadership step in.

That last bit was the weakness in the model. DHH acknowledged as much in 2024: strong employees often require very little management, while a struggling employee can suddenly require a lot of it.

Most companies create managers by accumulation: give someone direct reports and gradually add performance, development, approvals, staffing, compensation, coordination, administration and status collection. Eventually that becomes a full-time job. 37signals has spent years pulling those responsibilities apart, deciding where each should live, and keeping the remaining manager role small enough for a practitioner to do alongside real work.

When the block shifts over from normal car right-of-way to insta-plaza on Friday afternoons, traffic backs up. Giant trucks trying to sneak through downtown Brooklyn lay on their horns, solo commuters driving down State to avoid traffic on Atlantic do the same, and neighborhood folks enjoying a cocktail or a Cubano shake their heads.

Two blocks south, the city has put up laminated signs asking for community input on a plan to convert Dean St. and Bergen St. into bike boulevards. Both Dean and Bergen host the B65 bus, and each has a bike lane along at least a portion of their run. As someone who uses Citi eBikes as their primary mode of transportation (less so recently as I've been mostly walking my 3-month-old son around in a carrier, and we do own a car that lives in a garage near BAM), I was thrilled to see that the city was planning to make these streets safer for everyone.

A street that is designed around slower, less massive forms of transit (walking, rolling, being pushed in a stroller, cycling) is safer for everyone. Folks with limited mobility can cross at any point in the block, and generally cross a smaller distance. People who need to park close to their home should find that easier, even if there is less parking overall, because the block will see less car usage. Over time as other transit options become more attractive, perhaps some in the neighborhood who do not truly need a car will give up theirs. When emergency vehicles need to get somewhere they need to go, a street with less traffic will help them get there faster. Heck, anyone with a car will be less likely to injure or kill someone else—ruining many lives in the process—because speeds are limited by infrastructure, rather than signage.

In addition to being safer, it could also just be...nicer.

Perhaps some blocks of Dean or Bergen could turn into something that feels more like a permanent mixed-use plaza, as we see in Manhattan on Broadway, or in Copenhagen's Kartoffelrækkerne (“Potato Rows”). Emily and I happened upon this 11-block sorta-neighborhood, where the middle of each block is a living/play space: some have play sets, some have hopscotch, some have picnic tables where families can eat dinner outside. The blocks definitely still have parking, but it's not the main feature. And there's no need for a bike lane, let alone a protected one, because nothing about the surrounding area suggests that this is a place for cars to go fast.

And just to call out two examples from the same two cities, let's not forget that Washington Square Park was once used almost entirely for parking, and Copenhagen's magical Strøget was once similarly choked with cars.

I think some of the pushback to this, and similar plans, is due to a failure of imagination. We can only imagine how much more challenging it would be to not be able to park exactly where we park today, or imagine the frustration that would come from trying to drive the length of Dean Street behind a bunch of cyclists, or imagine navigating temporary or permanent gateways on a block-by-block basis, as we once had in the neighborhood during COVID.

But what if we imagined a street that kept our homes and bodies cooler in the summertime? What if we imagined a street where our children could play without fear? What if we imagined not just having a block party once a year, but having it every weekend, every evening, every morning? Not that this is the point, but what if we imagined a street that made all of our homes, more desirable, more valuable?

I made a tool to help you imagine it, at streets.cpj.fyi.

It uses real NYC data: street and intersection geometry from the NYC Planimetric Database and CSCL street centerlines; building footprints and MapPLUTO; live NYC Parks tree data; DOT speed limits, parking regulations, speed humps and bike routes; and NYPD crash records. I've tried to be sure to cite all the calculations, and if you want to really interrogate it or make your own version, the source is available here.

It's not perfect yet (for example, how do we render parking on Hoyt, given that the signage prevents parking at certain times? still working on that), but it allows you to see what that future might look like.

Comments open!

Employees at a multinational industrial company were assigned to work with or without a tool that used GPT-4o mini and retrieval from the company’s database. They had to find facts in internal documents, compress a product description into four sentences, and propose new products and research priorities.

With AI, they finished the research task 29 percent faster, the summary 52 percent faster, and the idea-generation task 39 percent faster. The summaries were more faithful, representative, concise and conventionally written. The ideas were more complete, though no more novel.

AI also changed the spread of performance. It narrowed quality gaps on summaries and ideas by improving weaker outputs. But they found a wider gap on research. About a quarter of the AI-assisted responses to the quantitative research question misreported or invented numbers, even though the correct figures were in the database. This means that the same colleague may need close review on one step and none on the next, which is hard to observe and mitigate.

The authors call this “task-technology fit.” More plainly: language models are good at turning source material into smooth prose. They are unreliable at exact retrieval, where a single wrong number can invalidate the answer; participants appear to have accepted the AI’s research rather than spending time checking it.

Research becomes a summary, recommendation, slide or plan. A fabricated number can pass through each stage while the language around it becomes clearer and more persuasive. The experiment cannot tell us how often that happens because the three tasks were evaluated independently. The authors call for studies of continuous workflows in which early errors create rework downstream.

False claims also gain credibility through repetition. The illusory truth effect makes repeated claims feel more credible even when people know better. A number repeated in an AI answer, summary, deck and meeting notes can harden into organizational fact before anyone checks it.

For what it's worth, when I need an exact answer from a body of data, I ask a model to write a script that retrieves and checks the answer, and then refine the script. This is what I meant in When It Starts Feeling Like a Video Game: software is its easy mode. A script can be wrong, but it leaves an inspectable method that can be tested, rerun and corrected.

Scale AI says nearly half of its new data-training projects now involve reinforcement-learning environments. These simulated workplaces contain incomplete records, multiple tools and recoverable errors. An agent starts from a known system state. Its actions and intermediate results are recorded, and verifiers check what changed in the system, which is more or less exactly the same idea as described above, played out at a much larger scale.

Training this way forces an explicit description of how the organization operates: starting conditions, permissions, handoffs, exceptions, decision rights and definitions of done. It resembles Company as Code applied to production. A network of teams needs interfaces an agent can traverse. A logbook supplies decisions and context that system state cannot explain.

Legibility has a cost. Shannon Mattern’s A City Is Not a Computer, which I wrote about here, warns against confusing the map for the territory. Software records clicks, approvals and changed fields. It misses the unofficial expert, the short conversation that prevents a foolish decision. Clean reward signals make work easier to automate and easier to overvalue.

What then, instead? Measure the full workflow: Time to a verified outcome; Errors caught before reuse; Rework created downstream.

Six months later, we have some numbers: Unsurprisingly, Block's adjusted operating margin reached 27%, up from 22%. Revenue and adjusted earnings beat expectations, and the company raised its full-year gross-profit forecast.

Square also shipped 130 features in the first half of 2026, compared with 40 in the same period last year. There were substantial releases: a new Register, drive-through support for restaurants, AI-assisted inventory management and Managerbot, which monitors sales, labor, stock and schedules. There were also smaller changes: adjustable receipt fonts, a sound for declined payments, negative-priced modifiers and an option to print a second receipt.

Much of business software consists of fixing minor irritations for particular kinds of customers. (I was recently in an Uber in SF and spent the entire ride hearing about how it's impossible to turn down audio instructions and safety guidance. Sometimes an issue for Trust and Safety becomes a new irritation for someone else.) However, counting each change as one "feature" makes unlike things look alike. The 130 new features also cover a period from January through June, so some predate the February cuts. Block has not disclosed Square's before-and-after staffing, whether its counting method changed or how many customers adopted the releases.

Block has offered better evidence elsewhere. In April, it said production code changes per engineer had risen more than 2.5 times since January. Builderbot and other AI tools were reviewing more than 90% of production change requests, and Builderbot was making 15% of production code changes with people intervening mainly for the final decision to deploy. Incidents following production changes were down more than 70% from a year earlier.

On the topic of Builderbot: It checks proposed changes against Block's architectural rules, security requirements and operating constraints. Different agents review different parts of a change in parallel. The system also examines incidents, internal announcements and messages, then proposes new checks for human approval. So Builderbot has a view across the codebase and changes its behavior as Block's people add knowledge and rules. As engineers use the tool, they are teaching it what Block should notice and protect.

That is still narrower than what Dorsey described in From Hierarchy to Intelligence. In March, he and Sequoia's Roelof Botha sketched a "world model" containing a continuously updated account of Block's work and customers. Eventually, they argued, an AI system could use that model to coordinate teams, identify what customers need and assemble Block's financial capabilities into new products.

Buzz, announced in July, is designed to collect more of the raw material. The open-source workspace combines chat, software repositories, workflows and AI agents. People and agents have identities and audit trails; messages, patches, approvals and automated actions go into the same searchable record. An agent can retrieve the history of an incident, review a patch or draft a release from the work recorded in project channels. Buzz could help Block build the company model Dorsey describes. For now, it is an early product,^[1] not proof that such a model is deciding what Square builds. And to me, Square's feature log still looks like a conventional roadmap moving unusually quickly.

So has Block moved from Hierarchy to Intelligence? Not quite yet. It has built a serious system for producing and reviewing software, and it may be building the record from which a broader intelligence could learn.

His alternative is executive search applied to every role. Great exec teams define what exceptional performance requires, map the people who appear capable of it—Ward suggests assuming roughly 50 exist for any given job—and pursue them for weeks, months, sometimes years. He warns against using prestigious employers as a proxy: the question is not who the best product engineer someone knows is, but who they've worked with who is exceptional at collaborating with designers, or translating frameworks into products.

Cursor has organized itself around this. Employees drop impressive people into a #hiring-ideas Slack channel; the company works out who knows a promising name and may open a dedicated channel to coordinate the pursuit of one candidate. Brie Wolfson, who spent two months embedded at Cursor last year, reported spending about a quarter of her time recruiting. The company had grown from under 20 employees to nearly 250 in roughly a year, leadership still approved every hire, and the atomic unit of recruiting, in her phrase, was the person rather than the job spec.

Large-company folks will argue that this works because Cursor is small. New research suggests companies like Cursor may stay small. A working paper from INSEAD's Hyunjin Kim and Harvard's Rembrand Koning compared AI-native startups with similar venture-backed firms and found that among Y Combinator companies, the AI-native ones employed about 25% fewer people—more engineers, roughly 15% fewer managers and entry-level workers, flatter hierarchies—while posting comparable valuations and higher valuation per employee. Similar, smaller effects appear in a broader sample of US venture-backed startups.

If a company reaches scale with 500 people where a predecessor needed 2,000, it can spend four times the organizational attention per hire at the same total cost. This should still work at larger sizes than you might expect: a 1,000-person company replacing 15% of its workforce annually makes 150 hires, so mapping 50 candidates per vacancy means 7,500 targets a year—research AI can increasingly do, leaving humans to define excellence and build relationships. At 5,000 people the system would more likely differentiate than disappear, with executive-search treatment for roles where the gap between adequate and exceptional is large, rather than exclusively for levels above an arbitrary seniority level. Everything else gets a more standard throughput-oriented process.

Regardless of the scale at which it’s applied, the model's weak point is discovery. Selection research starts from nearly the opposite premise: humans are unreliable judges of future performance, so broaden access and improve assessment. Structured interviews rank among the strongest general predictors of job performance in recent meta-analytic work, with lower adverse racial impact than several alternatives—structure exists partly to keep confidence, reputation, and familiarity from doing the selecting. Ward does not reject assessment; Cursor runs hard interviews and work trials. But any system that identifies the 50 exceptional people before formally assessing them depends on who is visible to the people drawing the map.

Cursor illustrates both sides. Wolfson describes candidates surfaced through unusual signals—someone running Cursor workshops in Stockholm, a user who appeared as an extreme outlier in product telemetry. Wolfson also reported that nearly 40% of employees had attended one of eight highly selective universities, and that women in product and engineering were a known recruiting weakness.

There is a second-order problem if the model spreads. The same research finding smaller AI-native firms also finds fewer entry-level roles. Pair that with recruiting effort concentrated on already-proven performers and the result is a labor market that reallocates demonstrated talent efficiently while producing less of it. Broad funnels are wasteful, but they also discover people—large organizations have historically absorbed inexperienced workers and given people without elite networks a route to become legible through performance.

On this reading, funnel hiring was partly a technology for staffing very large organizations. If AI-native firms can grow without adding people at the old rate, the economics of hiring attention move with them. The unresolved question is who gets onto the map.

A beloved Danish-American design company is back, and here to stay

After rescuing Dansk from bankruptcy, Form Portfolios is returning the Danish-American brand to its roots

Wallpaper*Ali Morris

After Food52’s bankruptcy, Form Portfolios acquired Dansk and is relaunching it by restoring the way Dansk originally made design decisions. Dansk was an American company whose creative center sat in Copenhagen with Jens Quistgaard. Its new owner believes that arrangement was part of what made the products distinctive, and is rebuilding a dedicated Copenhagen design team alongside its manufacturing relationships.

I like this way of thinking about a heritage brand. Companies usually preserve the outputs of good design, like drawings, colors, molds, recognizable forms, and then try to reproduce the style. Dansk is treating the conditions that produced those objects as part of the archive.

Quality is sometimes encoded in a design system. Sometimes it comes from things that are sadly harder to document, like who has authority, whose judgment matters, and how close those people are to the work.

Tate gives most of the restaurant to the people making dinner

Vicky Lau’s new Tate Dining Room in Hong Kong is 5,500 square feet and seats just 20 guests. More than 60% of that space goes to kitchens, preparation, wine storage, laundry, offices, and places for staff to eat and rest. There are lovely customer-facing details too: a brass ring at the entrance, places for phones built into the chairs, custom tables for finishing dishes.

But the best design decision is that space allocation. Restaurants normally maximize the part customers occupy and squeeze the operation into what remains. Tate reverses this. If exceptional service depends on preparation, coordination, storage and craft, then those activities get exceptional space too.

The ratio obviously is harder to replicate at an ordinary restaurant, with ordinary restaurant margins. But the principle applies everywhere. An old client running a large fast-food chain once told me and my team, "If the employees aren't happy, the guests won't be happy, either." Most organizations spend enormous energy designing what customers see, then ask employees to produce it from whatever workspace is left over. Workplace is part of the product.

Berlin Museum standardizes inconsistency

The new Berlin Museum identity by Stan Hema has 81 possible wordmarks, created by combining nine typefaces drawn from different pieces of Berlin’s visual history. Most identities create recognition through repetition: one logo, one typeface, tightly controlled applications. Berlin Museum puts the consistency in the rules for producing variation.

That makes sense for a museum spread across multiple sites and representing a city as layered as Berlin. The system gives later designers genuine choices while keeping them inside a recognizable vocabulary. Which...also makes it harder to operate. A rigid brand can survive encounters with mediocre judgment because the template does most of the work. An identity with 81 legitimate answers asks people to decide which answer is right, when an unusual pairing works, and when something technically permissible is simply ugly.

The organizational question: where does design judgment live once everyone is allowed to make choices?

Demis Hassabis will become Alphabet’s chief scientist and chairman of DeepMind, concentrating on long-term research, artificial general intelligence and its consequences. Koray Kavukcuoglu will run DeepMind as a senior vice president while retaining his role as Alphabet’s chief AI architect and reporting to Sundar Pichai.

The change comes as Jeff Dean, Sanjay Ghemawat, Oriol Vinyals and Quoc Le leave to establish Google-backed Discovery Loop, a public-benefit company focused on AI-assisted scientific work. Google's next flagship Gemini model has also been delayed, and Alphabet shares fell about 4% on the announcement.

The Financial Times interprets the reshuffle as a further absorption of DeepMind into Google’s broader business, and as an outsider, I'd tend to agree. Rather than becoming chief executive of an autonomous laboratory, Kavukcuoglu is becoming the Google executive responsible for connecting research, models, infrastructure and products across the company.

The model

The first frontier AI labs were organized around a powerful idea: put exceptional researchers together, give them abundant computing resources and allow scientific judgment to govern the institution.

That model produced extraordinary results but is unsuited for the current state of the work, which spans multiple subject matters:

• Searching for new capabilities;
• Turning discoveries into reliable systems;
• Securing the infrastructure, including chips, energy and data centers;
• Turning models into things people can use;
• Selling those things to companies, governments, NGOs, people;
• Anticipating and guarding against potential consequences.

These activities run at different speeds and respond to different incentives. Research may benefit from patience and uncertainty. Products often benefit from deadlines. Infrastructure requires decisions years in advance. Commercial deployment rewards speed, reliability and customer demand.

The labs’ own reorganizations suggest a shared conclusion: science should remain prestigious and influential, but it can no longer remain organizationally ~sovereign.

OpenAI describes itself as a research, infrastructure and applications company. Fidji Simo runs applications; other leaders oversee research, compute and business operations; Sam Altman remains responsible for integrating the whole. Anthropic has separated exploratory product work from the scaling of established products, while adding more conventional commercial leadership. Meta has placed research, model development, infrastructure and products inside a centrally controlled superintelligence organization.

Google is making the same transition more visibly because DeepMind began with greater independence and a more distinctive scientific identity. Google acquired it as a laboratory with a mission, later merged it with Google Brain, and it is now directing more of its people and research toward Gemini, coding systems, and the company’s competitive priorities.

So why has separating science from operations become the consensus? Because the work now requires distinct forms of authority without allowing clean boundaries to form between them. Scientific leaders decide which questions are worth pursuing. Operators turn those choices into infrastructure, products and release decisions. The parent company decides what to fund and what risks to take. And each depends on the others. The frontier lab is therefore becoming a federation of separately led systems, held together by concentrated authority at the top.

Google’s new structure fits that pattern. Hassabis defines the scientific horizon and increasingly acts as an AI statesman; he is probably more valuable thinking about frontier science and its consequences than supervising release calendars. Kavukcuoglu runs the machine and integrates it with Google. Pichai controls the company, its capital and its products.

As frontier labs separate scientific authority from operating responsibility, we'll soon see if science plays against type and becomes the authority that drives the movement forward. When science loses sovereignty inside the operating company, it reconstitutes outside it, as it has with the aforementioned Discovery Loop, and Hassabis' Isomorphic Labs, the drug-discovery company Alphabet spun out of DeepMind.

We'll see.

The set

Each program can be defended on its own. Together they may be liquidating the management market on which all of the companies depend.

• BMW: admin. The buyout offers go to about 40,000 desk-based employees, nearly half of BMW's permanent German staff, targeting administration, development, and planning. Production is explicitly excluded. Expected savings: about €1 billion a year from 2028.
• VW: two waves. An initial program cuts about 50,000 jobs in Germany by 2030 across the VW brand, Audi, and the software unit Cariad, in both manufacturing and back-office roles. The next wave targets another 50,000 in "indirect" areas—administration, product development, sales—after an internal analysis found VW's administrative costs run 30% above other manufacturers', "largely driven by the complexity of the group structure across all levels," in chief financial officer Arno Antlitz's words. The FT calls the full 100,000 unlikely, given union opposition.
• Porsche: the top of the house. The newest tranche, 5,000 jobs that are primarily back-office, comes on top of 3,900 already agreed. Chief executive Michael Leiters says Porsche "experienced disproportionate growth in indirect areas, and also...where management positions were created," and is now "intervening and making disproportionate reductions": eliminating departments, merging business areas, and cutting its own management board from eight seats to seven.
• Mercedes and the suppliers match. About 5,500 Mercedes employees have taken voluntary packages in a program aimed at office roles rather than plants. Bosch is cutting 13,000 positions in its mobility division, ZF 7,600 in powertrain, and Continental 3,000 in R&D.

Context

Codetermination makes plant closures and production cuts politically difficult, as works councils hold meaningful negotiating power, and existing agreements protect many manufacturing jobs. Some senior executives sit outside ordinary works-council representation, while many other managers are employed above the collectively bargained pay scale. That can make white-collar and management reductions a less resistant path, though not an entirely resistance-free one.

One side effect of that governance: German corporates are unusually public about how they're designed. BMW documents its structure and operating governance in a level of detail most American companies would treat as confidential. Codetermination may be one reason German companies make their operating structures unusually explicit; when structure must be negotiated, it must first be documented.

The backdrop is a shrinking industry. German vehicle production has fallen from six million a decade ago to around 4.2 million, the sector has lost about 51,500 jobs in two years, and unemployment among qualified engineers is up nearly 50% since 2022. The entry side of the pipeline is frozen too. Anja Robert, who has run the careers service at RWTH Aachen for twenty years, told AFP that even some of the best students now report writing 30 applications and hearing almost nothing back. "It's not the case anymore that you just get your application in with BMW and you get a job."

Each of these programs is probably justifiable delayering, reducing spans, layers, and coordination overhead. Collectively, they are something else. German automotive management behaves like a relatively closed labor market. Tenures are long and careers are homegrown; Oliver Zipse joined BMW as a trainee in 1991 and ran it for seven years, and when he stepped down this May, the job went to Milan Nedeljković, the production chief, another lifer, who has told workers that BMW will "reorganise management and merge organisational units." Managers circulate among the same five companies and their suppliers. When one company delayers, the ecosystem absorbs the people and keeps the knowledge in the sector. When every company delayers at once, the market those managers would flood into does not exist, which is why Volkswagen is asking headhunters to place its own people. The knowledge in those layers, the seven-year product cycles, the negotiation skill that codetermination itself demands, is sector-specific and likely hard to replace.

The visible costs will be shouldered to the employees and the local economies around these companies, which can be helped along by social plans and severance budgets. And the financial argument is solid: an industry that builds two-thirds of what it used to should have fewer managers, so each program is defensible to some extent. The FT notes that even after cutting the full 100,000, VW would still employ about 580,000 people; Toyota employs 390,000, Hyundai 335,000. The cost on nobody's spreadsheet is the shared bench, the pool of experienced people the whole industry hires from, which every company is drawing down at the same time.

Whether that's right-sizing or capability liquidation depends on what was in those layers: Coordination overhead, which deserved to go? or Capability that takes a career to build? Nobody is positioned to answer that at the sector level—each works council defends its plants, each board defends its margins, and the commons has no steward. The companies will find out which it was the next time they need experienced leaders in numbers. By then the people will be wherever the headhunters placed them.

What happened

WestJet's 4,400 flight attendants walked out on August 2 at the peak of Canadian summer travel. By the time the airline and CUPE, the flight attendants' union, reached a tentative deal a day later, 615 flights were cancelled and roughly 250,000 travelers were scrambling. The strike lasted about thirty hours. The issue at the center of it has been running for about ninety years: flight attendants are mostly paid only while the plane is moving.

How flight attendants actually get paid

• The clock starts at pushback. Under the credit-hour systems standard across North American aviation, cabin crew earn their hourly rate from roughly the moment the aircraft pushes back to the moment it parks at the destination gate. Boarding, deplaning, pre-flight safety checks, and gate delays have historically been unpaid or folded into small allowances.
• The unpaid part is not small. CUPE estimates its members perform up to 35 hours of unpaid work per month. Junior crew on short domestic routes get hit hardest, with more boardings per credited flight hour.
• The responsibility starts much earlier than the pay. Transport Canada requires cabin crew aboard for evacuation readiness, safety checks, and briefings, which begin the moment they step on the aircraft. The airline's legal need for a flight attendant and its financial recognition of one appear to be two separate concepts.
• The two sides weren't far apart on principle. WestJet says it offered a new hourly model covering all hours worked plus a double-digit first-year raise; the union said the money didn't match the work. The tentative deal "improves the flight-credit system by recognizing more of the work". Details are undisclosed, and a ratification vote is pending.

Context

This is the fifth major repricing of the same boundary in four years. Delta added boarding pay in 2022 at 50% of the hourly rate—notably, at a non-union carrier in the middle of an organizing drive. American's flight attendants won 50% boarding pay in their 2024 contract. United's nearly 30,000 attendants ratified a deal this May with a 31% average raise, boarding pay worth another 7-8%, and $741 million in retroactive pay. And last August, Air Canada's 10,517 flight attendants struck, defied a federal back-to-work order—CUPE national president Mark Hancock said, "If it means folks like me going to jail, then so be it"—and won ground pay, with 50% of the hourly rate for 60-70 minutes of ground time per flight, escalating to 70% by year four. (Nobody has defended, out loud, the idea that safety work is worth less at the gate than at altitude. The percentages belie the talking points here.) Air Canada's members kept the ground pay but rejected the wage offer by 99.1%, sending wages to arbitration.

The fly-time pay clock descends from Decision 83, a 1934 National Labor Board ruling that pegged pilot pay to hours flown and aircraft speed, because pilots feared faster planes would shrink their paychecks. It was codified into the Civil Aeronautics Act of 1938, and cabin crew inherited it wholesale. Meanwhile the ground portion of flight attendants' jobs kept growing as airlines squeezed more out of every departure, with fuller planes, checked-bag fees pushing everything into the cabin, boarding stretched into forty minutes of bag Tetris and de-escalation.

The org design angle

Every role has three definitions running simultaneously: the work as performed, the work as required (here, by a federal regulator), and the work as paid. The last one is what the organization actually cares about. When the paid definition freezes, while the performed and required definitions keep expanding, the gap becomes invisible work, real labor without a clear assignment. Tanya Reilly's Being Glue describes this well for software and knowledge work. Flight attendants' glue work has higher stakes—passengers' safety relies on it.

This is a boundary problem, and boundaries that aren't explicitly and regularly renegotiated drift until someone forces a reconciliation. (That's the argument of Boundary Management: make the implicit explicit, then revisit it on a cadence.) When the only renegotiation mechanism is a contract cycle measured in years, reconciliation turns into a rupture: two national strikes in twelve months, and a C$430 million revenue hit at Air Canada alone.

So: look at what your compensation, staffing, or review model doesn't see. Onboarding buddies, incident on-call, meeting prep, the pre-work before the paid work. Especially as AI (and/or the hangover from a few years of overhiring) drives layoffs, plenty of organizations are about to find out what those roles actually contained. The discovery usually happens after the people doing the invisible parts are gone.

Over millennia, many different plants arrived independently at the same basic form: a persistent trunk, a canopy of branches, and internal structures providing strength to hold the whole thing upright. Height provides access to light. Long-lasting structures create more opportunities to reproduce. Raised canopies help pollen and seeds travel. The advantages to this form drove plants toward tree-shaped-ness.

Is hierarchy the same?

It’s tempting to think of hierarchy as an historical artifact, a leftover from the military, church, railroads, or Frederick Winslow Taylor. Remove the industrial-era assumptions, the argument goes, and the pyramid should disappear with them.

It does not.

Cooperatives develop governing bodies. Startups acquire management layers. Self-managing organizations invent nested circles, lead links, delegates and constitutions. Movements suspicious of authority eventually discover spokespeople, committees and people who control the mailing list.

We do this because branching authority solves recurring problems. It lets an organization divide a large purpose into smaller ones, allocate resources across distance and maintain some coherence as the number of participants grows.

This does not make every hierarchy good. Evolution produces workable forms, not morally admirable ones. It does suggest to me that abolishing tree-shaped structures is unlikely to be a complete theory of organization.

The point of this article, though, is not to big-up the hierarchy. The story of the evolution of trees points to something even more useful: where growth is allowed to begin.

In some early trees, the body plan was directed from a growing point at the apex of the trunk. That point determined where branches appeared and how the organism developed. The arrangement could produce an impressively tall plant. It was also vulnerable. Damage the apex and the organism’s main source of new growth disappeared.

Later, Archaeopteris developed another capability. New branches could emerge from the surface of the existing trunk rather than only from the top. Those branches could become large and long-lived; some could even form a new trunk. The tree retained its vertical structure, but its ability to adapt was no longer concentrated in a single place. It became harder to kill and survived as a genus for roughly 25m years.

Most corporate hierarchies still behave more like the earlier tree.

They centralize authority. They also centralize opportunities for new growth. A new team, product, market or way of working generally requires sponsorship from somewhere above it. Local units may detect a change in the environment first, but sensing it and acting on it are separate rights. The branch can notice that the light has moved. Only the apex may reposition the tree.

This is where networks become useful. They create more places from which information, initiative and new growth can begin.

We should replace the idea of “hierarchy vs. network” with "one center of growth vs. many."

Platforms

See the full presentation on cpj.fyi →

In 2016 I flew to Dusseldorf to give a talk called Regarding Platforms at B4B DayOne. I just reread the talk track and clicked through the deck, and I want to do a quick lookback because I never posted about it here. Two reasons for this: one, to debut my new homegrown slideshare alternative; two, because I'm on parental leave and as I'm told, you should sleep when the baby sleeps and write when the baby writes.

So. What was I right about? What I was wrong about? What lines would I reuse from 35-year-old Clay?

The argument, in shorthand:

1. Bill Gates defined a platform as a thing where the economic value of everyone using it exceeds the value of the company that built it.
2. Cities are platforms in that sense – they grow superlinearly because they're made by everybody, not designed by experts.
3. Companies, per Geoffrey West's research, scale more like biological systems: maintenance eventually eats growth, and...they die.
4. The fix: make companies more city-like by building platforms inside them, lowering the maintenance burden so growth can compound.

What I got right

My favorite Clay Shirky quote is still a keeper. "Communications tools don't get socially interesting until they get technologically boring." I used it to argue management systems were still in their hobbyist phase – too interesting to be useful. Watching AI become boring infrastructure (to some) 2025 and 2026 is exactly this dynamic playing out. The genuinely weird stuff is happening now precisely because the technology stopped being a magic trick.

The diagnosis that maintenance eats growth. Everything I'm doing now at work – the company-as-code database, the org-spine work, the durable workflows architecture – is downstream of this idea. If you can't make maintenance cheap and legible, growth never compounds. Baby-me identified the right problem via Geoffrey West.

I still love that Airbus A380 story. Two centimeters of wiring, two different versions of the same CAD software, two years late, six billion over budget. It's a perfect story about what happens when the substrate underneath a company is poorly maintained.

What I got wrong

As ever! I was (am?) too optimistic about self-organization. The whole talk leaned mutualist – give people autonomy, let networks form, watch emergence happen. A thing I'm working on now called "Two Camps" (coming soon to an inbox near you) says that's half the story. The structuralist tradition would point out that platforms also need governance, standards, and accountability, or they get captured. Which leads to...

Discounting platforms' extractive tendencies. In 2016, my talk track on platforms was pretty utopian. By 2018 or so it was clear that Bill Gates' definition leaves out an important case: platforms that extract more value from users than they return. Uber, Amazon's marketplace, the whole surveillance-capital stack. I didn't reckon with the extractive side, and the talk feels a little naïve as a result.

Platforms not fixing engagement. I trotted out the usual 70% disengaged number and proposed platforms as the fix. Ten years later the number is still the same. Either the prescription was wrong, platforms-as-I-defined-them didn't address the actual cause, or companies didn't really apply them internally. I think I'm still mostly right; engagement is downstream of meaning and agency, but perfect internal platforms don't necessarily touch either of those things (see the point above)...and the primitives described in Jack's Block essay still feel like completely foreign objects to most organizations.

And (of course) It wasn't about computing power, but about attention. The Moore's Law story was correct, but the thing that actually mattered for the next decade was, yes, transistor density and the scaling laws for foundation models, which weren't being talked about much in 2016. I was pointing at the right kind of curve but on the wrong hardware.

Lines I'd still use

• Platforms are only made of people.
• The everything store, supplied by everyone.
• Cities » Ease. Boring » Easy. Platforms » Ease.
• Bill Gates: A platform is when the economic value of everybody that uses it exceeds the value of the company that creates it.
• Jane Jacobs: Cities have the capability of providing something for everybody, only because, and only when, they are created by everybody.

The Jacobs and Gates pairing is the one I'd keep above all the others. They're saying the same thing across sixty years and two industries – value accrues where everyone gets to build, and only then.

The rest of the deck is mostly me being thirty-five and excited about my own micro version of a TED talk. Which, fair.

(Not worth mentioning but nice: new sidenotes; KaTeX for math in posts.)

The new feature images

Stock photography is bad. AI illustration looks like everyone else's AI illustration. Much as I would like to commission art for a personal blog, that would be unhinged. So with Claude's help I built a small Cloudflare Worker that takes a post slug^[1], hashes it, and renders a deterministic pixel-art SVG from the bits.

The system has roughly the shape of a tiny pattern language. Each image is two to four non-overlapping panels, laid out by binary space partitioning^[2] with a 24px outer margin and 16px gaps between them. Each panel runs one of ten strategies — grid, quilt, checker, strata, columns, field, plus the more characterful gravity, chaotic, scatter, clusters — and draws from a vocabulary of seven atomic marks:

About forty percent of panels run a secondary pass with a different mark on a palette subset—producing a sense of two systems negotiating inside one rectangle.

The math underneath is just bit reading. SHA-256^[3] of the slug gives 256 bits of pseudo-random output, and the renderer doesn't really generate from that seed so much as address into it. Every design decision—strategy, mark, palette pick, density, cell size, which cells get filled—pulls a few bits off the stream, treats them as a number, and uses that number to look up an answer from a fixed list of options. Read three bits, get a number from 0 to 7, pick one of eight strategies. Read five bits, get a number from 0 to 31, pick a palette entry. This is what makes it deterministic.

For example, when a post has a particular tag, the math weights the tag color eight-to-one, the two color neighbors five each, with other brand colors and a muted accent once each — total weight twenty. So picking a color is "read five bits, take mod twenty^[4], see which bucket the integer lands in": $n \in [0, 32)$, index by $n \bmod 20$ into a length-20 array. The probabilities fall out as $P_\text{section} = 8/20$, $P_\text{neighbor} = 5/20$ for each of the two neighbors, and $1/20$ each for the distant arcs and the muted accent. Over a few hundred draws across an image, the section color will usually be dominant. Per-cell decisions like "is this grid square filled" or "does this column get a stripe" are a coin flip against a density threshold that itself came from earlier reads. Read a byte^[5] $b_c \in [0, 256)$, fill cell $c$ if $b_c < \tau$ where $\tau$ is the threshold, then move on.

$$\text{slug} \xrightarrow{\text{SHA-256}} \text{seed} \xrightarrow{\text{read}} \text{choices} \xrightarrow{\text{render}} \text{SVG}$$

Same slug → same bits → same sequence of reads → byte-identical^[6] SVG. I don't want my post art to depend on which image model was state-of-the-art the month I published, and I don't want it to drift if I regenerate it next year. The inspiration is Anna Lucia, whose generative^[7] work I saw last week and felt immediately drawn to. She even does fiber art with the generative output (my Mom is a fiber artist), so I had to borrow the general feel of her work for this, while applying the general vibe of the work here.

Code is at github.com/cpj-fyi/art, MIT licensed. One Cloudflare Worker, ~1500 lines of TypeScript, no model calls, no manual design step.

The new search

Most Ghost blogs have their standard search tool built in, but it only searches titles. I wanted the full-text to be included so that folks can even find citations that are relevant to their work. So again, with Claude's help, I built full-text site search running on Pagefind. Pagefind crawls the rendered HTML of a site, builds a search index, and ships that index as a folder of static files. Searches run entirely in the browser via WebAssembly^[8]. The index is just files that live in GitHub; the query runs on the reader's machine.

The architecture: a GitHub Action runs every night, builds the Pagefind index against cpj.fyi, and pushes the result to a gh-pages^[9] branch where it gets served as static assets. The theme's JavaScript loads the Pagefind library from that index folder when someone opens the search box. When you type, the engine reads only the index chunks relevant to your query and returns ranked results.

Pagefind splits the index into small chunks keyed by the words that index them, so the browser fetches only the chunks matching what you typed. For a site my size the savings are academic, but it's why this approach scales to documentation sites with thousands of pages, and I think that's why the search feels especially snappy. Try it!

Building nightly means search is up to twenty-four hours stale, but this is fine, because I almost never post daily like I say I will, and new posts almost never get searched in their first day—readers are reading them, not looking for them. If it ever started to bother me I could trigger the workflow on publish via webhook^[10]; the cron^[11] is enough.

Pagefind is by CloudCannon, MIT licensed. The site theme is at cpj-fyi/cpj-theme — one workflow file, a small JavaScript hook, and the search box markup itself.

Permission To Move

See the full presentation on cpj.fyi →

I want to tell you about three of the most beautiful moments I've ever had at work.

The first was in a cramped, greige conference room outside London. The kind with a window that looks out into a car park and a thermostat that lies to you.

Four junior employees, a Google spreadsheet they weren't supposed to be using, and me.

One of them looked up and said, "We did it." They had just cracked a pricing strategy that had stumped the entire company for a year.

Senior leadership had thrown consultants at it, task forces at it, a very expensive offsite at it. These four people solved it in two weeks. What did we give them? Nothing. We cleared their calendars. That's it.

The second was a Zoom grid in 2021. A strategist, an art director, a media planner, all of them ground down by nine-month campaign cycles.

Nine months to launch a single campaign. The same amount of time it takes to grow a human being.

One Tuesday the art director stopped mid-sentence and said, "Why are we doing it this way?" Nobody had an answer. Not because the answer was complicated, but because the answer was nobody knows. It had just always been nine months.

We asked them: what if you threw out the plan? Within two weeks they were testing new creative ideas directly with Twitch, Meta, and Roblox, and within six months they’d written a playbook for marketing in a new era. When applied across the entire portfolio, that new playbook grew their share of wallet by $400 million. With the same people, the same budget, and the same tools.

The third was with a major luxury retailer's US marketing team in 2024: hundreds of people, campaigns across every channel, a brand that could not tolerate mediocrity.

They went all the way in: a full network-of-teams model with real autonomy and clear standards.

About six months later, one of the senior leaders (someone who had been diplomatically skeptical the entire time) called me and said, "I have to be honest with you. I didn't quite believe this was going to work. But it did. The teams are moving faster than they ever have, and I'm not losing sleep over quality."

Now let me take you somewhere less beautiful. A boardroom. A Chief Digital Officer with great hair and great slides.

A three-horizon AI transformation roadmap. A center of excellence. A governance framework, AKA a PDF that nobody will read. And a plan to get it all done by Q3 of this year. Spectacular. And none of it is going to work.

Why do the first stories work (all of which are true, btw) and this one never does? The same pattern was present in every story: we removed an invisible barrier, and people moved.

By the way: the thing about $400 million dollars with the same people and the same budget. I think about this example every time someone tells me the path to performance is cutting costs. There is free money sitting inside your organization right now, but it's trapped under process and approvals and timelines that nobody ever questioned. The answer isn't to cut costs or even to invest more. It's to remove the stuff that's in the way.

For fifteen years I've been doing what I can only describe as organizational surgery, for every kind of company you can imagine, but always for a particular kind of leader who believes that something is broken in our systems, and that a better thing is possible.

And the pattern was always the same. The thing that's blocking people is never visible in the org chart, the strategy deck, or the performance review.

It's in the meeting that exists so a manager can feel informed. It's in the approval chain where nobody actually approves anything...they just slow it down. It's in the strategy role that's really just glue holding two teams together that should just talk to each other. It's in the meeting before the meeting, where the real decisions get made so the actual meeting is just comfortable theater.

It's the accumulated weight of how an organization decided, without ever actually deciding, that people's time and judgment weren't their own.

You know this. You've felt it. You've sat in a meeting and thought, I could be doing the work right now, but instead I'm explaining the work to someone who's going to ask me to explain it again next week.

The lights come on

AI is removing grunt work at enormous speed. Translation, first drafts, data wrangling, summarization, the forty-five minutes you used to spend turning a meeting transcript into a status update: disappearing. In theory, this should free up millions of hours for the work that actually matters: the thinking, the building, the creative leaps.

But what's going to happen to those hours? Will organizations let people use them? Or will they fill them with more meetings, more dashboards, more status updates about the status updates?

I think AI is going to be like turning on the lights at a party, and a lot of organizations are not going to love what they see. When you remove the grunt work, what's left is the stuff that was always underneath. The real blockers. And they're not technical.

The bottleneck was never effort. It was never the tools. It was never that people didn't have enough hours. It was permission. Permission to move. Permission to speak up. Permission to select your leaders. Permission to ship without asking. Permission to fail.

That's the bottleneck. It always has been.

Patterns even over plans

Those three breakthroughs from our journey were structural changes; I've spent the last decade trying to understand what others can learn specifically and do specifically in order to bring more of this yes, finally energy to their workplaces. What I have come to understand is that the real magic is in the small, repeatable moves that unlock organizations. These are specifically not grand operating models. They are specifically not reorganizations, and I am eager to design for a future where there are fewer of those, not more. Instead, these are moves, like a chess opening or Jordan's signature shimmy. They are recipes, but more like mirepoix than Joel Robuchon's pommes puree: they are humble, repeatable, waiting for you to adapt them.

I ended up with seventy-five of them, each one connected to the others, a system. And I wrote a book about it, because everybody talks about the big things (psychological safety, self-management, empowerment) but nobody tells you how to actually get there. It's great to say you want psychological safety. But what has to be true before that's even possible? You might want rounds for managing discussion. You might want consent-based decision-making. You might want to make a distinction between your role and who you are, so people aren't fused with their title. Each of those is a concrete thing you can do right now.

Or take elections: peer-driven leadership selection. Sounds radical, because it is! But why? Because you probably don't have structured decision-making yet. You don't have team charters. You don't have advice processes. Elections sit at the top of a web of dependencies, and if you try to bolt them on without the foundation, they collapse. The whole point of the book is to make those dependencies visible, to break the big, aspirational stuff down into the small moves that make it real.

I'm not alone in this. There's a whole shelf of us now! Bree Groff's Today Was Fun, Aaron Dignan's Brave New Work, Jurriaan Kamer's Unblocked, Karina Mangu-Ward's Teams That Meet the Moment, the Corporate Rebels, Amanda Litman's When We're In Charge. Different angles, but with the same core insight: the way most organizations work is broken, and there are better patterns hiding in plain sight.

The data

In preparation for this talk, I got curious: do organizations that actually run on these patterns outperform the ones that don't? Is this real, or is it just a nice framework that looks (very, IMO) good on a bookshelf?

So I built a survey. I got one hundred and seventy-four respondents across twelve industries. I split them into high-performing organizations (growing revenue, engaged employees, strong employer brand) and everyone else.

Of course companies that apply more of these patterns are more successful. Of course they are. But how much more successful? How much more do they apply these patterns?

High performers adopt these patterns at 1.44 times the rate of everyone else (95% CI: 1.27x–1.64x). The overall effect size is a Cohen's d of 0.85, which means the average high-performing respondent scores higher than eighty percent of the comparison group (Mann-Whitney U, p < 0.0001). That's describing a different world!

The distribution of the data is, perhaps predictably, bimodal. Two normal distributions, with the high performers shifted to the right. It would appear to me that organizations don't gradually adopt these patterns, gradually drifting toward better ways of working. You either have the system or you don't.

Eagle-eyed observers will note that nearly a third of the lower-performing group scores above 3.5 overall, or well into the territory where you'd expect to see high performance. They're doing a lot of the right things, but they're doing them in isolation. They're stuck in between: too much new stuff to go back, not enough to reach the other side. It's like asking teams to adapt quickly inside a company that takes nine months to approve a budget change: the agility is cosmetic, and the system rejects the transplant.

We've seen this in a number of studies over the years: organizations resist change during long stable periods, then undergo concerted, dramatic shifts across multiple variables at once. The complementarity in the data supports this: the return on one practice depends on having the others. Partial adoption is inherently unstable.

Reader, I did not expect this. I hoped to write a book that presupposed that you could do some of these things, but not all of them, and that would be fine. That you could work your way toward a better world. What I am learning is that perhaps you cannot. Perhaps it is a whole-of-culture, whole-of-organization thing, and maybe that's OK.

The differentiators are about power

Everybody does advice-seeking, demos, adaptive agendas. Many organizations reported the presence of self-managed teams, or at least what most organizations call self-managed teams, which is usually "teams with some autonomy but a manager still makes the calls." These practices are table stakes. And notice what they have in common: you can adopt every one of them without fundamentally changing who gets promoted, who gets paid, or who makes the final call. They're additive. They make work feel better. They don't threaten anyone.

The practices that actually separate high performers from everyone else are all about power:

Elections. Structured, peer-driven leadership selection; not a suggestion box, but a real process with nominations, rounds, and consent. High performers scored 3.43 on this. Everyone else: 2.10.

Talent Marketplace. The organization works like an internal labor market. People can see where the work is and move toward it without needing a manager's permission. High performers: 3.67. Everyone else: 2.12.

Team Incentives. The team wins together or not at all. High performers: 4.00. Everyone else: 2.81.

Who gets to decide. Who gets to move. Who benefits when something works.

Where do you start?

Elections and talent marketplaces sound wonderful. But you can't walk in Monday morning and tell your CEO you're doing peer-elected leadership. But every one of these patterns has smaller patterns underneath it, and you're probably already doing some of them. You just stopped walking before you got anywhere.

To get to Elections, start with advice-seeking, since most teams already do this. Now make the process explicit: who has input, who decides, what counts as a valid objection, when a decision is final. That's structured decision-making. Add rounds: everyone gets an uninterrupted turn to speak. Add consent: proposals move forward unless someone raises a real objection. Now you have the infrastructure to safely elect a leader. Not because elections are easy, but because you built the foundation that makes them possible.

To get to Talent Marketplace, start with showing work, via demos, reviews, whatever you call them. Now make that the default across all teams, not just within yours. Work in public. When work is visible, people can see where they're needed. Treat roles and teams as temporary, designed to be dissolved and reformed as the work changes, not permanent fixtures. Give people real authority to move toward the work. And here's what happens next: when people start moving between roles, they stop fusing their identity with their job title. That's role-soul distinction, and it's not a prerequisite, but rather something that emerges when you let people flow.

To get to Team Incentives, start with retrospectives, because you probably already reflect as a team. Now write it down: a real team charter with purpose, roles, and decision rights. Clarify what the team owns: its domains, its assets, its standards. Start measuring team outcomes with metrics reviews, not individual output. And then tie rewards to those outcomes. Most organizations try team incentives without doing any of the work underneath. They slap a team bonus on a group of people who don't have shared goals, shared ownership, or a shared way to decide anything. Of course it doesn't work.

None of these paths requires a reorg. All of them require a decision that someone with authority has to make, and then not take back.

The accelerant

So...why all of this at a tech conference? AI is an accelerant, and accelerants amplify dynamics that already exist in the system.

If your organization has the structural permission for people to move, to lead from the work, to share what they discover, AI makes all of that faster. Someone builds a prototype in a day instead of a quarter. A team of four does what used to take a department. And the organization lets them, because the permission was already there; the plumbing already works.

But if your organization doesn't have that? Someone on your team is going to discover a genuinely new way to solve a problem, and it's going to die in a review cycle. Or worse! They're going to hoard it. If you reward individuals and someone figures out how to ten-x their output, why on earth would they share that with the team? They'd be diluting their own advantage.

The bimodal distribution is about to get very painful. The gap between the two groups is about to widen faster than it ever has. And if you're stuck in between, doing some of the right things but not enough, I'm worried that the path is toward low performance, not toward ~acceptable performance.

The organizations that win with AI won't be the ones with the best models or the biggest budgets, although those things will help. They'll be the ones where permission isn't something you ask for. The ones where permission is baked into how the system already works.

The cleared calendar

Come back to London with me. That cramped conference room, the thermostat that lies, four people and a forbidden Google spreadsheet.

AI is clearing the calendar at a scale we've never seen. The hours are coming back. Millions of them. And the only question that matters is: can your people move? Can they focus? Can they try something without asking? And when it works, does the team benefit, or just the person who happened to discover it?

The hard part is what it's always been. Are you willing to redistribute power? To remove the invisible barriers? So that when the calendar clears, people can actually move?

• It comes up when there are disputes about propriety of a given action. Should so-and-so have done what they did?
• When there are questions about whether individuals can do their best work. Does so-and-so have a good idea of what's expected of them?
• When there are worries about people's ability to grow. Do managers know what it takes to become a leader?
• When execs wonder if they're hiring right. Are we confident this JD is what we actually need?

Role clarity, or the lack of it, gets blamed in every case.

The classic organizational psychology research (Robert Kahn and colleagues in 1964, John Rizzo and colleagues in 1970) treats role ambiguity as almost uniformly destructive: it lowers satisfaction; it reduces motivation; it drives emotional exhaustion. A Rutgers meta-analysis confirmed a moderate negative relationship between role ambiguity and job performance. This is the received wisdom, and I think it's increasingly wrong.

Eleven years ago I wrote about my dim view of this idea, and I truly believe knowledge workers can now leave it behind, break Jevons' Paradox, and kill Baumol's Cost Disease in the process.

Huh?

William Stanley Jevons observed in 1865 that Watt's more efficient steam engine actually massively increased coal consumption, because efficiency made coal viable in far more applications. This happened in knowledge work too, where collaborative tools that were supposed to simplify communication multiplied channels instead. Every time we make administration easier, we produce more administration: more meta-work about work.

Economist William Baumol noticed in the 1960s that some sectors just can't get more productive: a string quartet still needs four people and forty minutes to play Beethoven. Costs in those sectors rise anyway, because they have to compete for labor with sectors that are getting more productive. Management has been a Baumol sector, and that sucks for everyone involved, including (Bane voice) you, the people.

So why can we leave role clarity behind?

TL;DR: AI lets us do a lot more with a lot less → every team can be smaller → smaller teams want and need less role clarity.

I'm not going to try to convince you whether the AI part of this argument is real. I know it is because I've seen it. If you're not yet there, fine! You can still believe that teams can and should be smaller.

I spoke recently with Michelle Peng at Charter about Hidden Patterns, and among many good questions, she asked: "What do you think companies will look like, how will they be organized, if they apply all of the ideas in the book?"

My expectation is that organizations will both be smaller and feel smaller, feel more local, even while achieving the same or better outcomes. They'll be made of networks of teams. Yes, those teams will in many cases be part of a bigger thing, but I think that'll feel more abstract and federated, and the thing you'll care about is the small(er) team around you.

Smaller teams need and want less clarity. There's more overlap between roles, more closeness with teammates, more exploration of what you can do, what they can do, and genuine optimism about what might be possible tomorrow. Obsessing over where I stop and where you pick up freezes what's possible.

Role clarity is a symptom of relational poverty

Richard Hackman's research found the optimal team size is roughly 4.6 members, because coordination costs grow exponentially with team size, and as a result he never allowed teams larger than six in his Harvard classes. Jennifer Mueller found that in larger teams, individuals perceive less available support, or "relational loss." Fewer people means more of each other.

Consider CLOUs, or Colleague Letters of Understanding, used at Morning Star, the world's largest tomato processor, since the 1990s. Each employee writes a Personal Commercial Mission and specifies 20-30 distinct roles with deliverables, levels of authority, and performance metrics, all negotiated peer-to-peer, creating extreme, bottom-up role clarity. The most generous version of the idea you could design. Morning Star is highly profitable, and yet roughly 50% of senior hires leave within two years because they can't adapt to the system. Paul Green Jr., co-founder of the Self-Management Institute at Morning Star, attributed the washout not to incompetence but to inability to adapt to self-management. The people who most embody the instinct toward role clarity, those who have spent careers defining their remit and operating within it, are exactly the ones who fail.

So even the best possible version of role clarity, one that is negotiated not imposed, transparent not hidden...still breaks experienced people. The problem is role clarity itself. It's a bureaucratization of what should be a living relationship.

If you do knowledge work, stop wanting role clarity. Instead, do what IDEO founder David Kelley recommends: "see what you can get away with." Kelley's philosophy is that the primary obstacle to creative work is fear of failure and of judgment. Your job description is the floor, and you define the ceiling. Push past your formal mandate to find the real limits, rather than pre-constraining yourself. This only works when there's trust, and trust is easier in small teams.

The counterargument from the research is worth taking seriously: autonomy within a clear mission is productive; ambiguity about what the mission is tends to be destructive, or "be loose on how, and tight on what." A 2022 study in Frontiers in Psychology tried to show that role ambiguity promotes creative problem-solving, but the positive pathway wasn't empirically supported. Only the negative path held up: ambiguity → rumination → stress → reduced creativity. I think they were measuring the wrong thing. They were looking at individual role ambiguity inside organizations that otherwise demanded clarity. Imagine being the one person without a job description at Deloitte. Of course that's stressful. What I'm more curious about is what happens when the whole team agrees to operate with less definition.

If role clarity is out, then team clarity is in. The distinction matters: role clarity asks "What am I supposed to do?" while purpose clarity asks "What are we trying to accomplish?" Role clarity only becomes meaningful inside a container of collective purpose. Without understanding the actual work first, job descriptions are abstract exercises. Google's Project Aristotle found that "structure and clarity" was one of five factors for team effectiveness, but their definition blended individual expectations with collective purpose. The research suggests these are hierarchical: mission clarity is the container; individual role clarity is a downstream detail that small teams can often leave implicit.

Bbbut without role clarity we can't have psychological safety! Let's look at what Amy Edmondson says in Teaming. A team is a stable object, a fixed group with defined roles; Teaming is a verb, the activity of coordinating across shifting boundaries. Her argument is that in fast-moving knowledge work, you can't rely on stable teams with clear roles; you need people who are skilled at teaming across those boundaries. Teaming is what you do when role clarity runs out, and in her view it's the more important capability. She defines psychological safety not as comfort but as "permission for candor": speaking up across boundaries of status and expertise without fear of penalty. That's the condition I want to design for. Some of that candor, if not most of it, is going to come from people willing to go beyond what was already made clear.

Where am I wrong? Where is role clarity essential, actually? Safety-critical environments: you want the surgeon and the anesthetist to have very clear roles, and the same thing applies to captain and first officer. Regulated industries or functions (legal, compliance, finance) often need auditability, and auditability requires named accountabilities. In large-scale manufacturing or logistics, where hundreds of people must coordinate tightly, formal role definition reduces catastrophic error. My thesis is strongest in knowledge work, creative work, product development; the strategic side of any of those regulated industries. The irony is that these are exactly the contexts where leaders most loudly demand role clarity. They want certainty in a domain that doesn't offer it. (Or they're looking for someone to declare them a leader and someone else a follower, and that's not how that works.)

The organizations that perform well under conditions of speed and ambiguity are the ones with more developed relational capacity. For Edmondson, this is psychological safety. For Mueller, it's relational support. For Morning Star, it's encoded in a letter of understanding. All three are pointing at the same thing: shared context and trust let people act without checking their job description first. Why not just make trust easier by shrinking teams and making more of them.

So what gets harder?

More small teams. More humanity within them. More joy from having better relationships with colleagues. More of the outcomes we want. More output! But it has to go somewhere.

Further to that, it's a lot of more, and the received wisdom on strategy is that it's fundamentally about less, about saying no. Michael Porter's canonical 1996 argument: "The essence of strategy is choosing what not to do." Steve Jobs, returning to a nearly bankrupt Apple in 1997 with 40+ products, cut the line to 4. "Focus means saying no to the hundred other good ideas." If you believe that structure is downstream of strategy, then capabilities and teams optimized for one strategic position are inherently suboptimal for another. As an example: Continental Lite tried to be both full-service and low-cost; they lost hundreds of millions and fired the CEO.

I'll grant that this is absolutely true when building things is expensive, when it was hard to turn the machine on. When it took months and a full team to ship a feature, you had to choose carefully. Prioritization was a scarce-resource allocation problem, and all the OKRs, roadmaps, sprint planning sessions, and quarterly reviews were coordination mechanisms that protected that scarcity, and compression algorithms for communications overhead. Fred Brooks showed that communication channels grow as n(n-1)/2: five people have ten channels, fifty people have 1,225. (Also a small-team stan, Fred would tell you that adding more software engineers to an already late project will only make it later).

When building gets cheap

A friend who leads engineering at a midsize tech firm texted me recently:

I think 3-4 months from now we will have a serious problem where we've run out of roadmap. We'll be shipping things faster than sales, marketing, customer success, and operations can keep up. We don't have to spend months doing annual planning of what we'll build vs. what we won't. We just build it all.

Okay so...the people who build things are telling you the building stopped being the problem. This is a head of engineering telling you that we will run out of “shovel ready“ work to do. CircleCI's 2026 State of Software Delivery report found that daily workflow runs increased 59% year-over-year, driven by AI code generation. Feature branch activity increased 15% while main branch throughput – code actually reaching production – declined 7%. Teams are generating more code than ever and shipping less of it. Review, validation, integration, and recovery are now the scarce thing.

Absorption failure

Sometimes this really, really does not work. Samsung rushed the Galaxy Note 7 to beat the iPhone 7 to market. When the battery spec changed mid-development, the QA team wasn't informed; their tests were based on the original design. Everybody knows this story by now, courtesy two global recalls, and phones banned from every airline on earth. The $5.3 billion in losses can be chalked up to absorption failure. They could build it; the organization couldn't safely release it.

"Yes, but if you ship a new feature without appropriately onboarding legal, you'll risk a lawsuit. Without onboarding CS, you'll have a quality escape."

So the constraint moved downstream, to the folks maintaining the (typically, but not always) non-software systems in the business. And I don't think the answer here is "don't build that," but instead something like "don't ship it yet."

Wesley Cohen and Daniel Levinthal's 1990 paper on absorptive capacity showed that the ability to exploit new knowledge is a function of prior related knowledge. You can only absorb what you're already partly prepared to absorb. Organizations that haven't built integration, onboarding, legal-review, and change-management muscle arrive at the doubling without the capacity to absorb it. Shaker Zahra and Gerard George's 2002 update splits this into potential capacity (recognizing and acquiring knowledge) and realized capacity (transforming and exploiting it). Our potential capacity has gone way, way up, and not just in technology teams. The question is whether the we have the realized capacity to use what's being shipped.

There's a darker concept in the original Cohen and Levinthal paper called "lockout": if you fail to invest in absorptive capacity during a fast-moving period, you may never catch up, because you won't even recognize the signals that you're falling behind. You can't see what you're missing. The evidence of competitive disadvantage stops reaching you because you no longer have the prior knowledge to recognize it as a signal.

The way I see it, prioritization alignment is evolving into two disciplines, with both pieces being harder than what they replaced.

The first question: are we doing the right things? When you can build everything, "everything" could include a lot of garbage. The scarcity of engineering capacity used to be a natural filter. Without it, you need real judgment about what matters: a single person with genuine taste, or formalized direction-setting, or structured environmental scanning. (And for the stuff that won't actually cause a legal/quality/safety disaster, maybe just stop worrying and let speed be your new moat.)

The second question: can the organization absorb what we're shipping? Even if you're doing the right things, doing them faster than legal, CS, ops, and marketing can keep up is its own failure mode. This is the "say not yet" discipline. the thing is working, it's ready, and you have to not ship it.

The old prioritization question was "what should we build?" and scarcity answered it for you. The new questions are "what actually matters?" and "can we hold it together long enough to ship it well?" Nobody has a framework for those yet. Cool; frameworks are exactly how we got into this mess.

So where do the old ways still have value? When you genuinely can't do two things at once. Even if building two things is cheap, sometimes the market will only let you be one thing. Porter was right about Continental Lite, and he's still right about companies trying to serve contradictory positions simultaneously. For early-stage companies where every person IS the absorptive capacity, saying no to ideas is still the discipline. The 'say not yet' frame assumes you have capacity downstream of the builders. A lot of companies don't, and for them, the old prioritization is still the whole game.

Strategy is saying no not yet

Wrapping up!

1. On role clarity: Shared context and trust that let people act without checking their job descriptions. Make that easier by encouraging smaller, stronger teams.
2. On prioritization: The solution to absolute abundance is organizational capacity: the systems that allow all teams to absorb what other teams can produce.

You'll probably be thinking, as I am, that both of these are leadership problems. It's hard for teams to get small all on their own. It's normally impossible for them to decide on their own what they should be doing. You will probably also note that most leadership teams are not ready for either of these. They're still trying to solve role clarity where it's not needed or wanted, with tools that actually prevent the real problem – relational poverty – from getting solved. Meanwhile teams are quickly accomplishing astonishing things, outpacing the absorptive capacity of the org.

If you take one thing from this: stop asking, "who does what?" and start asking, "can we service what we're building?" The answer is probably no. It'd be wrong to slow down; the right reaction here is to build capacity.

Aaaaand that's the work.

The essay is about the gap between designers and management. Rand identifies three causes of poor design:

1. Management's indifference to quality;
2. Market researchers' vested interests; and
3. Designers' lack of authority or competence.

Buried in the middle is this sentence:

"I believe that design quality is proportionately related to the distance that exists between the designer and the management at the top. The closer this relationship, the more likely chances are for a meaningful design."

OD!

What should we take from this?

• Proximity to decision-makers determines quality. The closer the practitioner is to leadership, the less the work is filtered through layers of opinion from people without relevant expertise. Rand illustrates this with Hermann Bahlsen and the Bahlsen biscuit company—a manufacturer who, in Rand's telling, "combined art and his work in the most thorough fashion," sustaining a direct relationship with designer Martel Schwichtenberg over time and producing work of consistent excellence.
• Committee work degrades creative output. "Group design or design by committee, although occasionally useful, deprives the designer of the distinct pleasure of personal accomplishment and self-realization" and prevents ideas from having "time to develop." This is about the kinds of conditions that produce good work.
• Management unawareness is structural. People who approve design work usually haven't been trained to assess it. Rand is careful not to be contemptuous here; he notes that "lay people who have an instinctive sense for design... leave design to the experts."
• Design is an ethical practice. "A badly designed product that works is no less unethical than a beautiful product that doesn't." Whether organizations can develop something like taste (felt judgment even over criteria) is a related question.

The org design angle

Rand's core argument maps onto Expanded Available Power, or the idea that organizations generate better outcomes when they create more power to distribute throughout the organization, rather than concentrating all of it at the top. The designer-to-management distance Rand describes is a proxy for exactly this. When design decisions travel up a long chain of people without design backgrounds, power from expertise into preference, from quality into safety.

Rand's own practice is instructive here in ways he didn't quite acknowledge. His IBM work was a group project, and it predates the essay by thirty years. Eliot Noyes assembled the program starting in 1956, with Rand for graphics, Eero Saarinen for architecture, Charles and Ray Eames for exhibitions and films. Each operated with real authority in their domain, or a working example of Emergent Leadership, where authority follows expertise even over title. They designed in parallel, under shared principles, with a client relationship close enough to keep everything cohesive. This was what we'd now call a federated structure, and by the time he wrote the essay, it had three decades of results behind it.

The practice Rand was observing in 1987—mostly focused on graphic design, logos, print—has since expanded into something he probably wouldn't have anticipated. The design of a major digital or physical product today (happily!) involves researchers, interaction designers, visual designers, content strategists, accessibility specialists, and engineers, all making decisions that compound over time across millions of touchpoints. The scale is categorically different, and the shape of the federation (if not the size!) needs to match.

The design systems movement is one answer. The model, developed explicitly at companies like Google and Salesforce, and now the de facto standard for any design org of meaningful size, works like this: a small, usually seniorish core team sets direction and maintains standards; contributors embedded in product teams participate in evolving the system without ceding their autonomy. It's neither individual authorship nor design-by-committee. Rand would recognize the instinct to preserve the integrity of judgment and resist dilution, even if the structure is new. He just had a logo where we have a living codebase.

What the design systems movement has built is something close to the Guilds pattern: knowledge networked across a distributed organization, maintained through shared standards rather than shared location or reporting lines.

Rand's core intuition is unchanged: distance between designer and decision-maker is where quality goes to die. In a federated design organization, the relevant distance is between the standards-setting function and the people executing against it. Keep that distance short and the relationship substantive, and scaled coherence is the result. Let it grow, and you get what Rand feared: aesthetic judgment replaced by political judgment, quality replaced by safety.

"Any system that sees aesthetics as irrelevant, that separates the artist from [their] product, that fragments the work of the individual, or creates by committee, or makes mincemeat of the creative process will, in the long run, diminish not only the product but the maker as well."

Source: Paul Rand, A Designer's Art, 1985

New CEO Philipp Navratil, who took over in September 2025, headlined the press release "accelerating strategic change." The numbers were actually decent, with 3.5% organic growth, and CHF 9.2 billion in free cash flow, but the structure news swamped the financials. The portfolio is being trimmed down to Coffee, Petcare, Nutrition, and Food & Snacks, with everything else either being folded in, de-consolidated, or shopped. A company that once had 200 operating companies and subsidiaries in 80 countries is consciously narrowing to a handful of global category bets.

What made this move possible?

A lot of work, over a long, long time.

In June 2000, Nestlé SA signed what was then a "much-publicized $200M contract with SAP" (plus $80M in consulting and maintenance) to install a global ERP system. They called the broader initiative GLOBE (Global Business Excellence). The idea was to take all those autonomous operating companies (all running on different systems, with different processes, in different countries) and make them all run the same way.

Nestlé USA had already tried a version of this domestically, code-named BEST (business excellence through systems technology). A backronym hates to see IT coming. The CIO at the time put it bluntly: "I took eight or nine autonomous divisions and said we are going to use common processes, systems and organization structures." It took six years and over $200 million. She compared it to what Nestlé's global parent was attempting: "They're just taking it up a notch." (Why does everything cost $200m in this category? Spending limits? Rounding?)

GLOBE took more than a decade to roll out globally. By 2005, Nestlé was presenting it to investors as a platform—not just for efficiency, but for business agility. By 2009, it was the backbone for a global shared services organization. By the early 2010s, Nestlé Business Services (NBS) was handling finance, HR, and IT across dozens of markets. Recently, they migrated to SAP S/4HANA, the current-generation version of SAP's enterprise software.

The whole arc was expensive, disruptive, and deeply unpopular with the managers who lost autonomy. (See the same CIO article. It's full of juicy detail.). And it created the conditions for what happened this week.

The Org Design Angle

It's much easier to cleanly exit a business if the underlying systems let you carve it out. Dissolvability is usually framed more narrowly, in terms of sunsetting projects or disbanding teams gracefully, but it absolutely can apply to entire business units. If your ice cream operations are entangled with your coffee operations at the data, process, and finance layer, it's hard to sell ice cream without selling everything. GLOBE, for all its pain, created modularity; it separated Nestlé's businesses into legible, portable units.

The companion pattern here is Domains, Assets & Standards. What GLOBE built was a shared standard layer: common processes, common master data, common reporting. Standardization even over Centralization: Nestlé's businesses still operated with considerable autonomy in terms of products, marketing, and local execution. What GLOBE took away was the freedom to run a different version of the books. Infrastructure can be Rule of Law for the enterprise.

Navratil is explicit about this in the FY2025 press release. Nestlé is now simplifying around "nine end-to-end business processes, such as procure-to-pay and hire-to-retire." The press release notes that while these processes are "underpinned by consistent IT infrastructure, they vary considerably market to market"—and that variation is what slows them down and limits the value of their data. Nestlé is accelerating shared services, standardizing and automating, just like GLOBE intended to do, but with a clearer logic around service design.

Is everything a Pace Layers story? Sorta! The infrastructure layer (GLOBE, SAP) moves slowly and expensively...decades of investment and pain. But that slow layer is precisely what enables the strategy layer to move fast(er). Navratil's "accelerating strategic change" is riding on infrastructure and culture built between 2000 and 2025.

What to Watch

• Whether the carve-outs are actually clean. Water is targeting deconsolidation from 2027. If shared services dependencies cause friction, we'll see it in scope creep, delay, and one-off costs.
• The Nestlé Health Science integration. NHS is being folded into Nutrition rather than sold off, with the idea of finding synergies and simplification. The CEO also is stepping down. Watch whether this is real integration or a rebranding exercise.
• The phased headcount reduction. Around 16,000 roles targeted by end-2027, including ~12,000 white-collar, tied to CHF 1B in annual savings. 20% of that CHF 1B savings target has already been delivered, ahead of schedule. How much of what remains is enabled by the shared services expansion (where you centralize first, then automate and reduce, and sometimes return to the BUs as a strategic embed...perhaps this time via software?) versus harder cuts in operating units?

Company as Code

Company as Code

Reimagining organisational structure for the digital age.

42futuresDaniel Rothmann

Daniel Rothmann proposes treating your entire organizational structure as version-controlled code. A company manifest, showing who reports to whom, what policies apply where, which roles carry which accountabilities, written in a domain-specific language, diff-able, branchable, queryable. Want to model what happens if you merge two departments? Spin up a branch, run the impact analysis, and merge when ready.

I wrote more about this last week, but the short version is: this idea has tons of precedent (GlassFrog, Team Topologies' TeamAPI, the whole DAO movement, Open Policy Agent) and almost zero adoption. This is the real question! It's still hard to get leaders to use Tableau; expressing reporting relationships in a programming language is a different kind of behavior change. (Is it also a ngmi thing?)

If your org structure is machine-readable, an LLM can reason about it. "Show me every role that touches customer data." "What happens to escalations if we merge these two teams?" The combination of structured org data plus AI querying is powerful, and it connects directly to Transparency and Rule of Law. Most organizations run on a fog of half-remembered org announcements and outdated wiki pages. An all-seeing Logbook would fix that.

But should we push this metaphor all the way? Because...

A City is Not a Computer

A City Is Not a Computer

A bold reassessment of “smart cities” that reveals what is lost when we conceive of our urban spaces as computers

Princeton University PressShannon Mattern

Shannon Mattern's book is a corrective to the exact impulse that makes "company as code" so seductive. She's writing about cities, but it's basically the same thing: technofuturists treat complex human systems as engineering problems and import assumptions about what counts as knowledge. Citizens become users, residents become rows in a database.

My favorite chapter is about libraries. Where dashboards watch, libraries serve. Where digital twins model behavior from above, libraries grow knowledge from below. Libraries are built around care even at the cost of efficiency—librarians who can interpret a vague question, community rooms where strangers meet, collections no algorithm would assemble. Her alternative to the computer metaphor is gardening-adjacent, about branching what already exists...systems that layer and grow together imperfectly over time. She points toward maintenance and repair as values worth designing for.

What might that "care infrastructure" look like inside a company? Probably office hours with no agenda, Slack channels where "I don't know" is a normal answer, documentation maintained as a commons.

So yes, make organizations more legible and inspectable. And remember that the code will never capture the whole system, that the informal network always expands beyond the written org chart, and that some of the most valuable infrastructure in any organization is the stuff that looks like inefficiency from a dashboard.

OK so that's the philosophy. What's AI actually doing to the social fabric of work? Two new field experiments, two very different answers.

The Cybernetic Teammate

The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise

Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.

NBERFabrizio Dell’Acqua

This is the latest from the Dell'Acqua/Mollick crew, and the headline finding is striking: individuals working with AI matched the performance of two-person teams working without it.

776 P&G professionals were randomly assigned to work on real product innovation challenges, either alone or in cross-functional pairs (one R&D, one Commercial), with or without GPT-4. Individuals with AI (+0.37 SD) performed at essentially the same level as human teams without AI (+0.24 SD). Teams with AI did a tiny bit better (+0.39 SD), but they were a whole lot better at having breakthrough outcomes (keep reading).

Even more interesting is what happened to expertise boundaries. Without AI, R&D people proposed technical solutions and Commercial people proposed commercial ones, so functional silos doing what functional silos do. With AI, that distinction vanished. Both groups produced balanced, cross-functional solutions regardless of background. AI broke the silo without requiring the team.

The emotional findings are wild: people reported more positive emotions (excitement, energy, enthusiasm) working with AI than working alone, matching the emotional lift typically associated with having a human teammate. The researchers call this the "cybernetic teammate" effect, after Norbert Wiener. AI is starting to provide some of what we've always said only teamwork provides—performance, expertise sharing, and social engagement. All three pillars, from one interface.

This is an uncomfortable org design question: if AI can substitute for the benefits of teamwork, what's the justification for teams? The paper's answer, which I think is right, is that teams + AI still produce the best exceptional outcomes (they were THREE TIMES more likely to land in the top decile). At least for now, breakthrough performance still needs humans and AI together.

Now here's where it gets interesting, because a different experiment found something nearly opposite...

The Impact of Generative AI Adoption on Organizational Networks

Where the P&G experiment asks "can AI replace a teammate?" this randomized controlled trial goes after "what happens to the org chart when you give people AI?" and gets a surprisingly social answer:people talked to each other more, not less.

The study covered 316 employees across 42 teams in an European tech services firm. Half got a RAG-powered GenAI assistant grounded in the company's knowledge base. Half didn't. Three months later, collaboration ties jumped (+7.77 degree centrality vs. +1.12 for control). Knowledge-sharing ties showed similar gains. The network visualizations are striking—treatment group nodes go from scattered clusters to a dense, interconnected mesh.

Specialists became knowledge magnets. Technical experts saw the biggest jump in being sought out for knowledge. The AI made deep expertise more valuable, not less—it helped people find and access the right expert faster. That's basically what Guilds are supposed to do, achieved through tooling rather than structure. Generalists shipped more. Sales staff completed roughly 28% more projects. The AI handled enough coordination overhead that integrators could actually integrate (Expanded Available Power in action).

So how do you reconcile this with P&G? I think the difference is context grounding. P&G used generic GPT-4. INSEAD used a RAG system embedded in the firm's own knowledge base—CRM data, internal docs, meeting recordings. Generic AI might substitute for teammates; grounded AI might make teammates more valuable by lowering the cost of finding and consulting them. The tool you build determines the social structure you get.

The unresolved tension in the INSEAD paper: knowledge In-Degree and project output were negatively correlated. The people everyone consults aren't the ones shipping the most projects. If specialists become knowledge magnets, do they eventually drown?

When it Starts Feeling Like a Video Game

When It Starts Feeling Like a Video Game

The system goes on-line August 4th, 1997. Human decisions are removed from strategic defense. Skynet begins to learn at a geometric rate. It becomes self-aware at 2:14 a.m. Eastern time, August 29th. In a panic, they try to pull the plug.

Clay Parker JonesClay Parker Jones

I built three functional things in a weekend with Claude as my teammate—a research survey, a team charter tool, a book promotion page—and it felt like playing a video game. The bad friction was gone. The space between "I want this to exist" and "it exists" collapsed to almost nothing.

This reminds me of Jane McGonigal's framework for why people play, or put in purely economic terms, why people do things for free: to accomplish satisfying work, to spend time with people you like, to get good at something, to be part of something bigger. AI hits three of four. The second one, "spend time with people you like," is where it gets weird. IMO/IME the best teammates have always been the ones whose easy-mode is your hard-mode, and Claude's easy-mode is expansive. But it does not give a shit about my easy-mode. It does not notice whether I'm good at having opinions about what should exist. That asymmetry is what makes it work and what makes it not quite collaboration.

This is basically Coase's Law playing out in real time. When the cost of building drops below the cost of buying, the buy market collapses. A lot of mid-tier SaaS exists not because the idea is hard, but because the building was hard. When the building takes a weekend, that value proposition evaporates.

What? What would that look like?

• It would look like a company manifest. A single, declarative source of truth for how the organization works—who reports to whom, what policies apply where, which roles carry which accountabilities—written in a purpose-built programming language (a "domain-specific language," or DSL) modeled after infrastructure-as-code tools like Terraform.
• It would look like diff-able governance. Policy changes can be tracked like code changes, so you can see who changed what, when, and why. Charthop already “does this,” but without "all the other org context" built into a diff.
• It would look like a staging environment for your org. Want to model what happens if you merge two departments? If this existed, you’d spin up a branch, run the impact analysis, and merge when ready. (Again a thing Charthop does but without "awareness” and with a bunch of seemingly GUI-related latency. Funny that we forgive this kind of latency for LLMs but not for client-server interactions.)
• It would feel like compliance with a simple query. Rothmann's team spent "hundreds of additional person-hours" prepping for ISO 27001 by gathering evidence and mapping controls to policies so that they could prove to auditors that the company does what it says it does. If the policies were machine-readable from the start, most of that becomes a database query: show me every control that maps to Appendix A.8, and the last time each was reviewed. Minutes are better than weeks!

Context

This idea has a lot of precedent. Holacracy's GlassFrog has been storing governance as structured data since the early 2010s—roles, circles, accountabilities, domains, all queryable. The entire DAO movement—Aragon and others—took it further: the organization is the code, executed on-chain. On the more pragmatic side, Blue Yonder manages its GitHub organization structure as JSON files in a GitOps repo—teams, permissions, segments, all submitted as pull requests. Team Topologies' TeamAPI defines team interfaces as structured specs. The Policy as Code movement has tools like Open Policy Agent (OPA) that let you write access controls and compliance rules as code instead of Word documents. Zhamak Dehghani's Data Mesh even bakes "federated computational governance" into its architecture—dual human/machine-readable governance artifacts, checked into Git.

My self-promotional angle

This connects to several ideas in Hidden Patterns, but the most important is Transparency. Most organizations run on a fog of half-remembered org announcements and outdated wiki pages, leading to questions that are really just unsure statements, like... "I think Clay owns thaaat? Now?" A company manifest would make the actual operating structure legible to everyone.

But my org charts are already in Google Docs!

With transparency comes easier adherence to the Rule of Law. When governance is prose in a Google Doc, it’s not always clear whether its words are currently in force, and over whom. When it's code that's object-oriented, version-controlled, and auditable, I think it's probably easier to can hold everyone to the same standards because the standards are inspectable and tied to roles.

Unresolved issues

Rothmann is honest about the core tension: "Buildable? Yes. Viable? I don't have the answer." Organizations are messy because humans are interesting. A Kubernetes cluster doesn't have feelings about being redeployed. People do, and better file formats don’t change how we feel about a merger between two departments.

There's also the question of adoption. It's still hard to get leaders to use Tableau; getting them to express reporting relationships in a programming language is a different kind of behavior change. Maybe this changes with the magic that comes from LLMs in the command line? Maybe not.

What to watch

• Who builds it first. This feels like a space where a startup (or an ambitious internal tools team) could assemble existing pieces into something usable.
• Whether AI changes the equation. If your org structure is machine-readable, an LLM can reason about it. "Show me every role that touches customer data." "What happens to escalations if we merge these two teams?" The combination of structured org data and AI querying is powerful.
• Adoption path. Does this start with compliance-heavy industries (finance, healthcare) where the pain of audit prep is acute enough to justify the investment? Or does it grow bottom-up from engineering organizations that already live in Git?

What this is

It's a survey I built to answer a simple question: do the 75 patterns in Hidden Patterns actually correlate with organizational performance? I know that I like them and I have good, company-by-company data that they work (excerpted from the Intro):

One of my clients that adopted similar patterns saw a 40x improvement in project completion pace on like-for-like work. Another particularly stuck team saw a 200x improvement.

[...another] client put these patterns to work and saw a 40-point jump in autonomy, customer connection, iteration, and decision-making, a 17-point gain in inclusion of diverse views, and an 87-point Net Promoter Score (NPS) for the patterns themselves.

...Patterns like these underpinned ideas that delivered $750 million in new revenue in a single category and drove $400 million in share gains with zero extra spend.

...My client teams cut escalations to leadership by 64% and reversed a profit slide, ending up at 2x their previous growth rate.

...Media teams I’ve helped use these patterns outperformed industry peers by 2x, while a category president told us, “Without this, we couldn’t compete against clever, fast-moving outliers.” Oh, and teams also spent 70% less time on non-value-added busywork.

...but do the companies that adopt these patterns measurably outperform the ones that don't? Or was it just luck?

Survey design: respondents self-report on three health indicators (revenue growth, employee engagement, and advocacy), which classifies them as High Performing or Everyone Else. Then they rate 15 randomly assigned patterns on a 5-point Likert scale. It takes five minutes, and it's anonymous. Each respondent sees a different subset, so as volume grows I get coverage across all 75.

So far, so good

High Performing organizations agree with the pattern indicators at 2.1x the rate of everyone else. Agreement rate (4+ on a 5-point scale): High Performing = 65.7%, Everyone Else = 32.0%. With n = 17, this is suuuuper early and directional, not conclusive. But it's a wide gap for a first look.

Learning is the biggest differentiator. Patterns like Experimentation, Flow State Work, and Length Limit show a 70-percentage-point gap in agreement (79% High Performing vs. 9% Everyone Else).

The patterns come as a bundle. Foundations and Learning are tightly correlated at r = .79. Does this suggest it's the whole system or not much at all? I argue in the book that patterns reinforce each other, but I'll be surprised (perhaps not pleasantly) if this whole-system thing continues to play out.

The patterns that showed the strongest High Performance signal so far are a mix I find encouraging: Elections (+4.0 mean gap), Kanban (+4.0), Structural & Psychological Safety (+3.7), Do No Harm (+3.7), and Check In & Out (+3.3). Governance, visibility, daily practice, ah, the boring social technology of a well-run organization.

Honest caveats

Seventeen respondents across seven industries. Seven High Performers vs. ten Everyone Else. One block has zero High Performing respondents, so 15 patterns have no comparison data yet. Three of the seven High Performing respondents are from small consulting firms, which may self-select for these patterns. Self-report bias is real, so the same optimism that drives High Performing classification is probably inflating pattern agreement, but the prompts are behavioral, so...I'm not too worried about that.

If you work in an organization, take the survey. Give me five minutes. Send it to a friend. The more data I collect, the sharper every one of these signals gets, and I'll be turning this into a research report in a few weeks' time.

In Yeo’s piece, Fukasawa is at a design trade event, trying to speak while a room full of seasoned designers talks over him. Then he gets the mic:

“The rational mind makes mistakes as the mind can only focus on one thing at a time. But feeling is different. It’s simultaneous. The feeling of the chair, the carpet, the drink, the flushness of your face and temperature of the room. It can all be felt at once. And if you cannot feel anything, you cannot be creative.”

The org design version of the same problem

• Most org design work is relentlessly cognitive, focused on operating models, domains, decision rights, frameworks and canvases and matrices. A lot of that is important, good, rigorous work. And it's typically sequential, rational analysis that can only hold one thing at a time.
• Feeling is instantaneous and multi-threaded. You can sense the temperature of a meeting, the weight of a reorg announcement, the texture of how two people actually collaborate all at once, without a framework. If your org design practice has no feeling in it, no sensitivity to the lived, material, embodied experience of work, is it design at all?
• Wholeness is often exclusively focused on the idea that people should be able to bring their, whole best self to work. "Whole” has to include the sensing, feeling, bodily self and the cognitive slice, but practices that cultivate this kind of sensitivity tend to land as either corporate cringe or unserious, spiritual, self-help. And yet the Bauhaus had students doing breathing exercises and movement work before they touched materials. We’ve so thoroughly internalized the idea that work is cognitive output (at least in corporate spaces) that anything involving the body or the senses feels like it doesn’t belong. A product designer who refuses to touch prototypes, who only works in wireframes and specs, would be rightly called out for being disconnected from their craft, like an architect who never visits the site or a chef who won't taste. Sensory and emotional engagement is the discipline.
• We org designers have material too: meetings, conversations, the felt experience of showing up to work on a Monday. Patterns like Check In & Out are a small part of how teams develop the perceptual skill to notice what’s actually happening before they take action. Dismiss these rituals at your own risk.

Bauhaus as org design

The Bauhaus was a design school and a way of organizing, with Masters and apprentices working together over shared meals in shared studios. Practicing, breaking bread, merrymaking, learning, all woven together. Community is method is community.

This is what I mean when I talk about "new ways of working and organizing," a super intentional phrase I use constantly that I’m not sure really registers with anyone. “New ways of working” is familiar enough to nod along to. Sure, async! Agile! Stand-ups! But “new ways of organizing” the structure itself, not just the habits within it, requires seeing that method as designable; designing it well requires the same kind of embodied sensitivity that any good design practice demands. The Bauhaus didn’t teach people new techniques inside an existing school structure, it reinvented the school! You can’t get to Psychological Safety by running a workshop inside a structure that punishes vulnerability.

How ideas spread

Japanese students study at the Bauhaus and return home. Architect Renshichirō Kawakita, who never visited the school himself, works from their contacts and translated manuscripts, organizing meetings, contextualizing the ideas for a Japanese setting, and establishing Kōsei education. It moves from architectural pedagogy into art education through Fukujiro Gōtō, and decades later, it emerges in Fukasawa’s “Without Thought” philosophy and Muji’s wall-mounted CD player, which turns the memory of pulling a fan cord into a musical experience.

This is how patterns spread, mandate-free and market-driven, through people who inhabit an idea, carry it somewhere new, and let it adapt. Whenever you see an org design or people team try to “scale” a way of working by writing a playbook and emailing it to 500 people, I want you to think about how Kawakita did it instead: translating, convening, contextualizing, and letting it become something new.

Good further reading here in AIGA.

Have a great week!

1. survey.hd-pt.com, a research survey for my book, collecting data on 75 organizational patterns across six dimensions. Anonymous, five minutes, designed to find correlations between pattern adoption and business performance. (Quick aside here: will you please take this survey, tell your friends to do so, too?)
2. ccccharter.com, a better version of a team charter tool I've been wanting for years. New UI, cleaner flow, actually works the way I always wanted it to. Now has organizational support, so you can have a private repo of team charters that everyone can see. Looks great, works great. If you like Glassfrog but don’t like Holacracy, this is for you.
3. cpj.fyi/buy, an honestly quite good page to promote Hidden Patterns. Building this was actually the hardest, despite it being the simplest architecturally.

Three functional things, one weekend, with Claude as my teammate. And to echo a thing I saw on Threads, it really did feel like playing a video game. Not in a cheap, gamification sense; there are no points, badges, or leaderboard telling me I'm a 10x developer (I am not devloper). Mostly just that the bad friction was gone. The space between "I want this to exist" and "it exists" collapsed to almost nothing, and I found myself properly working at 8am on a Saturday while making eggs for Emily not because I had to, but because I was having fun.

"I had fun building websites" is not an essay. But I think there's something underneath it that matters, and it connects to stuff we were thinking about fifteen years ago that eventually ends up meaning something to Coase's Law...if you squint.

In 2011, we were deep into collective action design. Mike, Bud and I were working on frameworks for how crowds get motivated to do things together—pulling from Jane McGonigal's game design research, Daren Brabham's work on Threadless, Malone and Laubacher and Dellarocas at MIT. We were trying to understand why some platforms and communities generated huge levels of participation and others didn't, and we had clients that let us apply that thinking to their digital strategy.

McGonigal had this framework from a New Yorker Conference talk, with four reasons people play:

1. Accomplish satisfying work
2. Spend time with people I like
3. Get good at something
4. Be part of something bigger

She was talking about games, but to us these were just about motivation. Games just happen to be the best technology humans have ever built for activating all four simultaneously, and that's what makes them sticky. The graphics and the competition are nice, but the fact that you are doing satisfying things, with people you like, while getting better, in service of something that matters...is what matters.

Satisfying work: yes, obviously. Shipping is satisfying. When I asked "can you re-architect this so that it works with Vercel and Supabase instead of Replit" and it just... did it... that's satisfying. The thing I wanted to exist now existed, the way I wanted it to exist.

Spend time with people I like: okay, this one's complicated and probably the most interesting. More on this in a second.

Get good at something: I was learning in real time. I'm not really learning how to code, but at a certain point all of the architecture chat starts making more and more sense. And it changes the way you think about and look at technology—I already knew a lot of the basics but now I really know. This is a skill, and I got better at it over the weekend. Check!

Be part of something bigger: The survey feeds the research. The book site feeds...me? The charter tool serves teams I work with.

Which takes me to Brabham's crowd motivation spectrum with making money on one end and getting addicted on the other end. This weekend wasn't really about making money. Maybe someday I make money off the things from this weekend, but the engagement was all about addiction to my own work.

And now we're back at McGonigal's second point—spending time with people you like. I have a philosophy about collaboration that I've held for a long time, and it goes like this:

The best teammates are the ones whose easy-mode is your hard-mode.

When someone has a skill that is:

• Effortless for them (feels like play, they'd do it for free, it's just how their brain works); and
• Looks like magic to you (you genuinely cannot believe they just did that)

...you're in the sweet spot. The collaboration is pure surplus. You're not competing or second-guessing, you're both a little in awe of each other, and the work is better than either of you could do alone.

The opposite is when you're good at the same thing your teammate is good at. You'd think overlapping expertise would be a strength, but it's often poison. Because when you're also good at the thing they're doing, you have opinions about how they're doing it. You know how you'd do it, and their way isn't your way. Instead of awe, you get criticism. Not because they're wrong, but because you're also right, and that's worse. I've seen this kill projects and teams. Two designers who are both great at interaction design will nitpick each other's work into dust. Two strategists will debate frameworks forever. Two strong writers will edit each other's sentences until everything goes blah.

Respect to my teammates past and present, but Claude Code is the best example of pure teamwork that I've ever experienced, fully recognizing that this sounds insane.

Its easy-mode—writing code, remembering how seventeen different APIs work, debugging, iterating, throwing away an hour of work without complaint when I say "actually, no, start over"—is expansive. Most of the things you can do, it can do as good or better. (I've tried to learn to code probably a dozen times. It doesn't stick. Not because I'm stupid but because it's not how my brain works. Every time I've tried, it feels like homework. Like drudgery. Like the opposite of play. npm install what the hell are you talking about) You constantly feel amazing because you're constantly making progress. The outputs just keep getting better.

My easy-mode? It does not give a shit about my easy mode. It does not notice whether I'm good at having opinions about what should exist, knowing what good feels like without being able to articulate the rules, understanding organizational problems deeply enough to build tools that actually address them, or even having taste in product. It does not care and will build you anything, beautifully, and have absolutely no idea whether it was worth building. And the AI never gets tired. Never gets defensive. Never says "well I liked the first version better" in a tone that means something else entirely. It just... builds. And rebuilds. And rebuilds again. At 2am on a Sunday, it has exactly as much energy as it did at 9am. I realize that it's peak hype cycle to talk about AI not getting tired, and not discounting its sycophantic behavior, but when you're making a thing, this sorta matters.

Good collaboration is mostly just being down

Bigger picture: SaaS appears to be over. The market dip this week, it seems, was at least partly a reaction to Claude Code. If one person with an AI can build a charter tool, a survey platform, and a book promotion site in a weekend, what does that mean for the companies charging $29/month for each of those things separately? A lot of mid-tier SaaS exists not because the idea is hard, but because the building was hard. The value proposition was: "we did the annoying technical thing so you don't have to." When the annoying technical thing takes a weekend instead of a year, that value proposition evaporates.

This is basically Coase's Law playing out in real time—when the cost of building drops below the cost of buying, the buy market collapses.

I'd expect that anything that was a wrapper around effort is toast. The stuff with real network effects, proprietary data, irreplaceable expertise (What counts here? Figma? Notion? Akamai?) probably lives on much longer. But "nice form builder" might not, when making a nice form builder is a Saturday afternoon.

I want to be careful here because "work should be fun" is a sentence that's easy to agree with and equally easy to misunderstand. Not fun like a pizza party, or like a ping-pong table in the break room. I mean fun like: the thing you're doing is intrinsically satisfying, you're getting better at it, you can see the results, and the friction between your intention and the outcome is low enough that you stay in flow.

That's what games get right! It's the feel of an immediate feedback loop. Do a thing, see the result, adjust, do it again, lose track of time. Most work isn't like this. Most work is: you have an idea, you write a brief, you wait for someone to read the brief, they misunderstand the brief, you have a meeting about the brief, you revise the brief, it goes into a backlog, it gets deprioritized, it comes back three sprints later looking nothing like what you wanted.

I don't know what this means for individual jobs or companies, but I do know that this is going to spread to more and more people and when it does, friction disappears, motivation changes, and what's possible feels completely different from one day to the next.

A few things are true simultaneously:

• I've had some very fun jobs and this weekend was very fun. The things I built are good. Not "good for AI-generated." Good good. I would put them in front of clients and feel proud.
• Something is also being lost, or at least rearranged, and I'm not sure what or how to think about it. Is the struggle part of the value? Is human-made important? For what categories of things, and why?
• What happens when the hard parts that are fun for some people just aren't hard anymore? My fun weekend is someone's displaced livelihood. I don't have a good answer for this, and I'm suspicious of anyone who does.
• Is "it feels like a video game" a warning sign? Games are also addictive. They're also designed to exploit dopamine loops. They're also, sometimes, a way to feel productive without actually producing anything that matters. I don't think that's what happened this weekend, but the feeling of flow can be a lie. You can be in flow doing something pointless.

Anyway. Everything probably changed in the last few months. For the better or for the worse, I'm not totally sure. I do know that it is really fun, and I'm going to go build more things that I've wanted to see in the world until I know which end is up.

She's in good company here. Rita McGrath at Columbia has argued since 2013 that sustainable competitive advantage is dead and that we should think in "arenas" rather than "industries." Martin Reeves at BCG showed in 2012 that Porter's framework belongs in one quadrant of a much larger strategic palette—the stable, predictable quadrant—and that applying it elsewhere is actively harmful. Last year, Brandenburger and Nalebuff renewed their 30-year argument that the framework can't even see complements—products that increase the value of yours—making it structurally blind to platform economics. Porter himself reaffirmed the framework in 2008, dismissed all proposed additions, and hasn't published a comparable update since. That was eighteen years ago.

The examples that disprove Porter's strategic validity are vivid. Volkswagen had a textbook-perfect Porter position in 2014, and then Dieselgate erased half the company's market value overnight. So not new entrants or a substitutes, but regulatory and social accountability risk, categories absent from Porter's framework. I wrote at the time that "people can do amazing things together, but they can also perpetrate deeply unnerving evil." A bit bold, maybe, but a 600,000-person organization systematically cheating emissions tests is not really an industry-dynamics story. Huawei was the world's largest telecom equipment maker, well-positioned by every industry metric, until the U.S. government placed it on the Entity List and cut off access to Android and critical semiconductors. Geopolitics ate my homework, I guess.

The OD angle

As with all frameworks, this new one is wrong but useful, and short on the prescription. It names five macro forces but doesn't offer much guidance on what organizations should do about them. The question is whether your organization can sense what's coming and respond in close-to-real-time. I've been skeptical for some time that there are many industries or moments left where stable strategy is desirable or even possible. Average tenure of a company on the S&P 500 was 33 years in 1964, dropped to 24 by 2016, and is forecast to hit 12 years by 2027. If half the S&P 500 will be replaced in a decade, stable positioning within a stable industry is becoming less and less realistic.

In Hidden Patterns' Do the Right Thing I argue that organizations and teams should only worry about doing things right only once absolutely certain they're the right things. For most organizations right now, the right things are shifting faster than any planning cycle can accommodate. I see three systems for staying oriented:

1. one person decides (not ideal, but possible in some cases);
2. formalized direction (think like guidelines for what's right and what's wrong... "by-right zoning" is an example of this in a city, and "design systems" are an example of this in corporate);
3. intentional environmental scanning (now, near, next, maybe via Wardley Mapping?).

That third system explicitly calls for tracking weak signals of change in society, technology, economics, environment, and politics. So, Gafni's new five forces, built into a pattern that tells you what to do with them.

But scanning means nothing if you can't act on what you find, so I'd recommend actually adopting a structure like Network of Teams such that the business can reconfigure itself as conditions change. Teams can form, merge, split, or dissolve based on emerging needs rather than through agonizingly slow top-down reorgs. With strong teams whose explicit job is scanning the environment (as compared to a "strategy department" that produces annual decks, say) signals could be routed immediately to the people who can do something about it.

What to watch

• Whether "new forces" thinking produces action or stays conceptual. We've had macro-environmental scanning frameworks for nearly six decades. For that entire time we've held on to structures that can't quickly respond to the data that come out of them.
• Sensing teams. A January 2026 BCG/WEF/IMD report found fewer than 1 in 5 companies have a dedicated geopolitics function. They identified four OD archetypes for building one: Watchtower, Influence Network, Command Cell, Nerve Center.
• The speed of structural response. When the next macro shock hits, watch whether companies can reconfigure their networks or whether they're still waiting for the annual planning cycle to catch up.

In 2016, Y Combinator announced it would build cities from scratch and asked Twitter what a city should "optimize for." The internet suggested fish tacos. This is roughly the level of discourse that Shannon Mattern, an anthropologist and professor at UPenn, pushed back against in 2021 with A City Is Not a Computer. The book started as a series of essays for Places Journal, and it reads that way—each chapter works on its own, the writing is more exploratory than prescriptive, and the footnotes are generous.

I like this book.

Its target is this technofuturist metaphor used by planners to treat cities as computers (sensors on, data in, optimal decisions out) that imports a whole set of assumptions about what counts as knowledge. It's a pattern we see at work, too: "If you can't measure it, it doesn't exist. If you can measure it, you understand it." Citizens become users, objects, or worse: rows in a database. Mattern traces this thinking through municipal "control rooms" covered in real-time dashboards, through Alphabet's failed Sidewalk Toronto project, through the digital twins that promise to simulate entire cities in code. In each case, she finds the same mistake: confusing the map for the territory.^[1]

My favorite chapter is the one about libraries. Where dashboards watch, libraries serve; where digital twins model behavior from above, libraries grow knowledge from below. Libraries offer Wi-Fi, sure, but also librarians who can interpret a vague question, community rooms where strangers meet, and collections that no algorithm would assemble because they don't rely on advertising revenue, because libraries are built around care even at the cost of efficiency.

Her alternative is a gardening metaphor for systems that layer and grow together imperfectly over time. It's deliberately messier than the metaphor it replaces, setting up a conclusion that points toward maintenance and repair as values worth designing for. Care, maintenance, repair. I like those, and I'll be adding them to my personal library of design strategies worth using.

Why this book stays with me

I kept finding "our work" in these pages, even though Mattern is writing about cities and we spend our days thinking about organizations.

Dashboards

I've seen plenty of leadership teams stare at metrics that tell them everything except what's actually going on. The numbers are clean; the reality is messy. I don't think any of us, Mattern included, are trying to argue that measurement is bad, but rather that measurement choices are political, and they shape what we're able to notice.

Local knowledge

Mattern shows how smart-city projects repeatedly steamroll the people who actually live somewhere, treating residents as subjects rather than sources. The same thing happens when anyone parachutes into organizations with frameworks that ignore what people on the ground already know.

Care infrastructure

What might "infrastructure for care" look like inside a company? Probably actual spaces—physical and social—where people can learn, ask dumb questions, and help each other without being optimized. Office hours with no agenda. Slack channels where "I don't know" is a normal answer. Onboarding that's human-led. Documentation maintained as a commons. The library works because it's being looked after by someone who gives a shit about quality even at the cost of optimization.

Layered histories

Organizations, like cities, are layered. They carry old systems, workarounds, informal networks, and institutional memory that org charts struggle to capture, partly because they're hard to visualize in 16:9. I spent a long time discussing this yesterday with John Cutler: that venues John's played over the span of years, even under new ownership, carry on some sort of institutional or architectural memory. My feeling is that organizations are the same; even though the people are always changing, they remember their past, and this memory changes how they operate. Designing with that complexity, rather than pretending you can start from scratch, feels right.

This book is a reminder that we should not treat human systems as engineering problems, and that we shouldn't ignore our senses, our bodies, or our communities when we design. Whether you're redesigning a city or a team, the same questions apply: What are we choosing to measure? Whose knowledge counts? Who gets to decide what "better" means? These are political questions, and dashboards can't answer them for you.

^^ that link keeps breaking, here's a PDF

What happened

Researchers ran a randomized controlled trial at a European technology services firm, with 316 employees across 42 teams. Half got access to a RAG-powered GenAI assistant grounded in the company's own knowledge base: CRM data, internal docs, meeting recordings, email history. The other half kept working as usual. Three months later, they measured what changed.

What they found

• People talked to each other more, not less. Employees with the AI tool gained significantly more collaboration ties (+7.77 degree centrality vs. +1.12 for control) and knowledge-sharing ties (+5.21 vs. +0.84). The AI made human interaction more worthwhile. Good.
• Specialists became knowledge magnets. Technical experts saw the biggest jump in being sought out for knowledge (In-Degree up 5.92 vs. 1.96 for generalists). The AI made deep expertise more valuable, not less; it helped people find and access the right expert faster.
• Generalists shipped more. Sales staff (the generalists) saw the biggest productivity gains, completing roughly 28% more projects. The AI handled enough coordination overhead that integrators could actually integrate.
• The network itself changed shape. The before/after network visualizations are striking: treatment group nodes (red) go from scattered clusters to a dense, interconnected mesh. The org's informal structure literally rewired in three months.

The org design angle

Most of the AI-and-work discourse is stuck at the individual level: will AI make me faster? Will it take my job? This paper shifts the unit of analysis to the network, and the findings map directly onto patterns I think about constantly.

The first is Expanded Available Power. When coordination costs drop, more people can participate in more decisions and exchanges. Increasing intelligence (artificial and human) distributed capacity. Specialists who were previously bottlenecked by translation overhead suddenly became accessible. Generalists who spent their days chasing down context could focus on synthesis. Everyone's effective range expanded.

The second is Guilds, or more precisely, what guilds are trying to solve. The paper's "knowledge catalyst" finding is basically: AI can do what communities of practice are supposed to do by surfacing expertise and making it findable. Specialists' In-Degree centrality jumped because the AI made their knowledge legible to the rest of the org. That's the promise of cross-cutting knowledge networks, achieved through tooling rather than (or in addition to) structure.

And there's a Network of Teams implication here too. The researchers found that the increase in ties wasn't random: routine, low-value exchanges got replaced by targeted, high-value ones. The network got denser but also more purposeful. That's what good network design does: better connections where they matter is more valuable than just "more connections everywhere."

What's unresolved

A few things the paper can't answer yet. The study ran for three months; long enough to see effects, short enough that novelty could be doing some work. Does the organic rewiring persist, or do networks settle back? The researchers acknowledge this.

There's also the overload question. If specialists become knowledge magnets, do they eventually drown in requests? Does this mean that we actually need more specialists, or do the specialists need to build tools that extend their capacity? The paper found that knowledge In-Degree and project output were negatively correlated—the people everyone consults aren't the ones shipping the most projects. That tension doesn't go away with AI and it sounds like it might get worse.

And the sample is a single firm in Central Europe, with self-reported network data. Strong design, genuine randomization, but one context. The mechanism for this experiment is plausible and well-theorized, but we need more replications before treating it as settled.

What to watch

• Network effects over time. Whether the increased connectivity persists past the novelty phase, or whether organizations need to actively maintain the conditions that produced it.
• Specialist overload. Whether knowledge magnets burn out when the whole org can suddenly find them. The negative correlation between being consulted and shipping projects is a strong signal.
• Grounded vs. generic AI. This study used a RAG system embedded in firm-specific knowledge. Generic ChatGPT, Claude, or even Glean, aren't likely to produce the same network effects. Watch whether the context is doing most of the work (I suspect that it is).
• Structural response. Whether organizations that see these network shifts redesign in response—smaller teams, fewer coordination roles, new knowledge-sharing rituals—or just pocket the productivity gains and move on.

What happened

Organizations are flattening. Average span of control in the U.S. jumped from 10.9 to 12.1 in a single year—a nearly 50% increase since 2013. Middle managers are being cut at disproportionate rates, a trend Gallup now calls the "Great Flattening." Meanwhile, enterprises are pouring money into AI-powered decision tools. IDC finds 88% of large organizations have implemented or are piloting "decision intelligence" initiatives. And Planview's benchmark of 800 global firms shows that strategy execution leaders are 9.5x more likely than laggards to say they can adapt quickly to market changes.

The pitch is simple: fewer managers, smarter tools, faster decisions.

What the data actually says

Quite a lot, it turns out.

• Flattening without support backfires. Gallup's meta-analysis shows that larger teams can work, but only when managers have the right capacities, spend less than 40% of their time on individual contributor work, and give meaningful feedback weekly. Strip those conditions away and engagement drops as span widens. 97% of managers already have individual contributor responsibilities. The median manager already spends 40% of their time on non-management work, so increasing their reports without reducing this IC workload doesn't seem to be a great idea.
• Too much intake, not enough processing power. IDC found that 26% of decisions are still made primarily on intuition, and another 25% on deliberate data analysis. Only 11% are driven by AI recommendations. Organizations generate insights faster than they can execute on them, with "a growing gap between information and execution." 87% rely on spreadsheets, 74% on BI dashboards, both mostly disconnected from each other. So our bottleneck appears to be in the structure, in the connections through which organizations process intelligence.
• Speed without governance makes things worse. Planview's data is striking: complex approval processes remain the #1 barrier to strategy execution, unchanged since 2021. The leaders in their study solve this not by removing governance but by centralizing coordination (52% vs. 37% of laggards) and making governance "supportive" rather than "obstructive." 78% of leaders say governance supports their ability to respond to change. For laggards, it's 31%.

OD angles

I've written before about the trouble with hierarchy, specifically how it concentrates learning at the top and redirects teams' attention from customers to internal politics. I'm sympathetic to flattening. But these three reports, taken together, suggest that the current wave of flattening is optimizing for the wrong variable.

The challenge here is the quality of decision infrastructure—the systems, habits, and frameworks that turn information into action. Structured Decision-Making, Active Steering, and Expanded Available Power all help with this by pushing genuine decision authority—along with the data, tools, and feedback loops to use it—closer to the work. Planview's leaders get this. They review strategy monthly, use integrated platforms, and standardize goal frameworks. As a result, they exceed revenue goals by 12% while laggards break even.

The Gallup finding on feedback is maybe the most important data point in the bunch: when managers give meaningful feedback weekly, roughly 70% of employees are highly engaged, regardless of team size. When they don't, it's one in four. The mechanism that makes decisions good is the conversation.

What to watch

• Manager engagement trends. Gallup reports managers are already struggling more than in recent years, with more burnout, more stress, more job-seeking. If flattening accelerates without support, expect this to get worse before it gets better.
• Decision execution rates. IDC's leading FMCG company auto-accepted 74% of AI recommendations. Watch whether that number climbs industry-wide, and whether outcomes improve or just velocity does.
• The feedback habit. Gallup's weekly meaningful conversation finding is a leading indicator for everything else. Organizations that institutionalize it will handle whatever comes next.

This paper's still got a lot of juice.

The title is the whole argument: the picture of the thing is not the thing, an org chart is not an organization, the map is not the territory, etc. Strategy and structure alone cannot produce results.

TL;DR:

1. The image of the organization is not the organization. Strategy decks and org charts are representations. The actual organization is the sum of behaviors, relationships, and capabilities that exist regardless of what's on paper.
2. Soft doesn't mean unimportant. It means hard to see, hard to measure, and hard to change. Those are reasons to take it more seriously, not less.
3. You can't change one S and expect the others to follow. The elements are interdependent. A new strategy with the same skills, style, and systems will produce the same results.
4. The pace of change is gated by the slowest S, usually either skills or shared values. Announcing a new structure takes a week. Building the skills to make it work takes years.
5. This was obvious to some people in 1980. The insight isn't new. What's new is pretending we've never heard it—reorganizing the boxes every few years while leaving the soft stuff untouched, then wondering why nothing changes.

Where it came from

Ron Daniel, McKinsey's managing director at the time, noticed that even sophisticated strategies kept failing in execution. 👋 He gave a young Tom Peters—freshly back from a Stanford PhD in organizational behavior—a small assignment to figure out why. Peters had been pulling from Jim March, Herbert Simon, Karl Weick—ideas like bounded rationality and satisficing. The framework crystallized in a two-day session in San Francisco where they landed on an alliterative model with seven S's. Peters thought it was gimmicky, but he was wrong. Cringe wins! Forty-five years later, people still remember the framework. Long version here.

The argument

The 7-S model divides into "hard" and "soft" elements:

Hard: Strategy, Structure, Systems. These are the ones consultants (and executives) usually focus on, because they're legible, and they make nice, fancy decks.

Soft: Style, Staff, Skills, Shared Values (originally called "Superordinate Goals," lol). These actually determine whether anything works. They're harder to see and harder to change.

Other notable ideas

• "Hard is soft. Soft is hard." The financials and the org chart look solid but are actually malleable. The culture and the capabilities look fuzzy but are actually stubborn.
• The authors were explicit: "We believe that style, systems, skills, superordinate goals can be observed directly, even measured—if only they are taken seriously."
• And: "A shift in systems, a major retraining program for staff, or the generation of top-to-bottom enthusiasm around a new superordinate goal could take years. Changes in strategy and structure, on the surface, may happen more quickly. But the pace of real change is geared to all seven S's."

Why this (still) matters for OD

This paper sits at a hinge point in org design history. McKinsey's bread and butter had been strategy and structure—boxes and lines, portfolio theory, the rational redesign of the firm. Peters and Waterman were insiders arguing that the firm's core toolkit was insufficient. You could get the structure right and still fail, because the soft stuff would eat your beautiful design.

What they didn't quite do—and what I've been thinking through in a long essay I'm writing right now on this very topic—is resolve the tension between the two approaches. The 7-S framework includes soft elements, but it doesn't tell you how to work on them. It's still fundamentally a diagnostic tool, a checklist, a way to notice what you're missing. It doesn't give you a method for changing culture, building skills, or shifting style.

That's the gap that the behaviorally focused "side" of Org Design was supposed to fill. In the decades since this paper was published, the two sides of OD mostly talked past each other. Structure people drew operating models. Behavior people ran workshops. Neither could do the other's job. The 7-S framework was a structuralist firm acknowledging the limits of structuralism. That acknowledgment was and still is important, but this is very much unfinished work.

What’s happening

• Titles are getting specific. Less “President, Operations” but President, Space & Mission Systems or EVP, Ice Cream. Not necessarily “not functions” but also not not functions. Specific sources of advantage.
• P&L accountability is unmistakable. Each of these new units is supposed to own outcomes end-to-end.
• The center is shrinking. Fewer generic corporate leaders, thinner HQ, strategy living in the businesses rather than above them.

My former coworker Michael Cata’s research from 2018–2020 comes to mind here. He found that firms with specialized, strategy-specific leadership roles outperformed peers by roughly 1–2 percentage points in net margin. That sounds modest, but in public markets that delta is the difference between “well-run” and “category leader.” (Michael, if you're reading this, I can't find a place to link to for this work. HMU.)

Generally speaking the trend is NOT toward this design—titles are becoming more generic over time, not less, likely out of some kind of uncoordinated but secular trend toward inter-firm transferability—so it’s interesting to see these edge cases.

The org design angle

As with yesterday’s bit about the unbundling of the CFO, this is Domains, Assets & Standards playing out at the executive layer. This pattern asks leaders What do you own? In more generic-leaning top teams, the answer can be accordingly vague: shared accountability, escalations, passive-voice performance explanations rather than ownership.

There’s a second pattern underneath: Lean Teams. The companies making this move are shrinking the center as they create these new operating units, with smaller rooms, clearer interfaces, less coordination cost, and decisions that happen closer to the market. Whether this stays true (it probably won't) is another question entirely, but it's a good idea that these firms should stick to if they can.

What’s unresolved

The obvious failure mode is creating “presidents” without real authority, or layering them on top of unchanged corporate functions, which just adds cost and confusion.

Ubisoft may be a cautionary case here—their Creative Houses have “full financial ownership” but haven’t named the leaders yet. The structure is announced, but the accountability is TBD. Unless presidents get true control over capital and talent, I’d see this as rebranding rather than redesign. We’ll see!

What to watch

• Change cadence. Whether the new leaders get installed quickly, or the announcement runs ahead of the reality. I assume that markets prefer KO’s approach here, where long-tenured leaders are announced with meaningful structure changes.
• Corporate shrinkage. Whether functional EVP roles are actually being downleveled into the business units, or being recreated as shadow units off the side of the C-suite.
• Margin lift. Whether these companies show the Cata-predicted 1–2 point improvement the research predicts in 18–24 months.
• Industry diffusion. Whether this spreads beyond these examples—tech is conspicuously absent from these examples.

TL;DR: What it is

• A domain unbundling. Murphy keeps finance, strategy, investor relations, and enterprise services. Digital goes to new CDO Sedef Salingan Sahin. Customer and commercial goes to CMO Manolo Arroyo (now “Chief Marketing and Customer Commercial Officer”). What was once one portfolio is now three.
• An elevation of digital to the C-suite; CDO ≠ CIO ≠ CTO. Sahin will lead “digital strategy, data, and operational excellence”—the consumer-facing stuff, not the infrastructure stuff. She reports directly to the CEO.
• A bet on internal talent. Sahin has been at Coca-Cola since 2003. She ran Eurasia and Middle East operations, led the nutrition/juice/dairy category, and by all accounts drove digital transformation in both roles. Arroyo is a boomerang! One 19-year stint that started in 1995 as a Brand Manager, a couple years in the outside world, and then back to Coke, where he took on a series of unit president roles starting in 2017.
• I’m at the reorg. I’m at the succession plan. I’m at the combination reorg and succession plan. Incoming CEO Braun (also joined KO in 1996, like Quincey) gets to reshape his leadership team on day one. In 30 years at the company, he's worked in supply chain, new business development, marketing, innovation, general management, and bottling operations across North America, Europe, Latin America, and Asia before becoming COO. Quincey moves to Chairman.

How Quincey did

A quick sidebar on the outgoing CEO: James Quincey had a genuinely successful run. He took over in 2016 with a mandate to turn Coca-Cola from a soda company into a “total beverage company”—and largely delivered. He killed about 200 underperforming brands (roughly half the portfolio), added more than 10 billion-dollar brands including BodyArmor, Topo Chico, and Fairlife, completed the refranchising of the North American bottling network, and pushed into alcohol through partnerships with Molson Coors and Brown-Forman. Warren Buffett publicly endorsed him. Yale gave him a Legend in Leadership Award. The stock is near all-time highs.

So as mentioned, this has been in the works for a long time. Quincey is stepping back at 61 after nine years, staying on as Executive Chairman, and handing the reins to his COO with the board’s blessing. The reorg happening simultaneously is the new CEO putting his fingerprints on the structure. 

What “customer commercial” actually means

“Customer” in CPG-speak means the retailer, not the end consumer.

“Customer commercial” is the function that manages those retail relationships. It includes: negotiating shelf placement and slotting fees, designing trade promotions (e.g. BOGO), coordinating with retail media networks (Walmart Connect, Kroger’s 84.51°), and managing the army of account managers who call on each retail partner. It’s where the marketing strategy meets the point of sale.

This function used to live under the CFO because trade promotion is *expensive* — it’s one of the largest line items in any CPG budget. Having it under finance made sense when the job was mostly managing spend. But retail has gotten more complex, and the line between “marketing” and “commercial” has blurred. Moving it to the CMO signals that Coca-Cola now sees this as a growth lever.

Context

The CDO role has had a bumpy decade. A 2019 Deloitte report famously predicted the role would cease to exist by 2020—the idea being that once digital becomes everyone’s job, you don’t need a dedicated leader for it. That didn’t happen. Per MIT Sloan, 84% of organizations now have a CDO or CDAO, up from 12% in 2012. But tenure is brutal: more than half serve less than three years.

Coca-Cola’s version is interesting because of where it’s carving from. The CFO-as-digital-strategist model made sense for a while—digital transformation is expensive. But there’s a limit to how much any one executive can span. Murphy was overseeing: finance, strategy, corporate development, investor relations, customer relationships, commercial leadership, *and* digital strategy. That’s a lot of domains for one portfolio.

The OD angle

Coca-Cola is essentially saying: digital is now big enough and distinct enough to be its own domain. It can’t be a part-time responsibility bolted onto finance. And the person who runs it needs to be able to make decisions without navigating through another executive’s priorities.

There’s also a Pace Layers dimension here. Finance operates at a certain cadence — quarterly reporting, annual budgets, multi-year planning. Digital consumer engagement operates at a completely different speed. Putting both under the same leader creates an unrewarded collision. Separating them lets each move at its natural rhythm.

And notice who *got* the responsibilities: not outside hires but internal operators who’d already demonstrated they could drive transformation in their previous roles. IMO this is a great example of Emergent Leadership at work — leadership emerging from demonstrated competence rather than being assigned by title.

Unresolved issues

The obvious tension: Arroyo now owns marketing *and* customer commercial. Sahin owns digital strategy. But digital marketing is… what, exactly? And who owns the AI-generated content Coca-Cola has been experimenting with? The boundary between “digital” and “marketing” is porous at best. There will be turf negotiations.

There’s also the question of whether creating a CDO is a forward-looking move or a catching-up move. Coca-Cola’s $1.1 billion Microsoft deal in 2024 suggests they’ve been serious about AI and cloud for a while. But appointing a CDO in 2026, when some companies are already sunsetting the role, raises the question: is this the beginning of Coca-Cola’s digital chapter, or are they institutionalizing what others have already integrated?

What to watch

• The Arroyo-Sahin boundary. Will marketing and digital operate as partners or rivals? The test will be who leads integrated campaigns where the line between “content” and “tech” disappears.
• Sahin’s mandate scope. She’s been asked to “assess how to organize the teams responsible for digital across the enterprise.” That’s either an invitation to restructure or a delay tactic. Six months from now, has the org chart changed?
• CFO Murphy’s portfolio. He still has a lot. Strategy, corporate development, investor relations, enterprise services. If the unbundling continues, that’s the next place to look.
• Tenure. CDO is a short-lived role industry-wide. Does Sahin last three years? Five? If she’s there in 2030, Coca-Cola will have beaten the odds.

The argument

• The heroic leader myth is the obstacle. Widely shared beliefs about leadership—that it requires commanding and inspiring others—actively prevent organizations from developing effective leaders.
• Followership and leadership share the same foundation. Meta-analyses show that personality traits predicting good followership are nearly identical to those predicting good leadership. This is not coincidence.
• Five capabilities matter most: Active listening, prioritizing purpose over personal credit, reliable execution, critical dissent, and coachability.
• Our conditions favor the shift. Complexity, specialization, and AI are eroding the value of command-based authority. Emotional intelligence and the capacity to synthesize diverse perspectives become relatively more important.

(I do wonder about this last point, though. It strikes me that we are either entering into, or already in, a state that feels like interdependence is eroding, where model-supported generalists can do more than they ever could before, and where winner-takes-all dynamics are accelerating. So while I agree with this article on a principled basis, I wonder if the conditions thing is...accurate.)

The OD implications

The authors recommend selecting and developing leaders with followership capabilities—but they stop short of asking how, given that the selectors themselves rose through the heroic model. Two structural interventions follow from their argument that they do not name:

• Design forums that require senior leaders to follow. The tension the article leaves unexamined—followership as stage versus followership as permanent practice—has structural solutions. Distributed Management breaks the manager role into component tasks assigned to the right person, creating regular opportunities for positional leaders to defer. Upward Representation brings elected team representatives into leadership meetings with equal speaking rights. Structured Decision-Making forces leaders to integrate input rather than simply collect it. Without scaffolding like this, "leaders should model good follower behavior" remains exhortation.
• Change who does the selecting. If those doing the hiring and promoting reached their positions by exhibiting heroic behaviors, telling them to select for humility is unlikely to work. Elections—peer selection of leaders, as W.L. Gore has practiced for decades—put the decision in the hands of people who have experienced candidates as followers, not just as leaders.

What to watch

• Leadership development programs. Most remain designed around executive presence and strategic vision. Whether they shift toward listening, feedback-seeking, and coachability will indicate how seriously organizations take this argument.
• Selection criteria for senior roles. The substantive test is whether organizations begin screening for humility and learning orientation alongside track record and industry expertise.
• The AI factor. If AI makes traditional expertise less exclusive, as the authors argue, I'd imagine that emotional intelligence and collaboration become relatively more valuable—and the followership model gains ground. But we'll see.

What it is

• A governing text for a model, written primarily for the model. Anthropic says the document is optimized for precision over readability, and that it “directly shapes” Claude’s behavior in training.
• A transparency move. Anthropic argues that publishing the document helps outsiders distinguish intended behavior from accidents, and says it will document gaps between intentions and outcomes in system cards.
• Freely reusable. It is released under Creative Commons CC0 (effectively public domain), explicitly inviting reuse.

What it says (in brief)

Anthropic lays out four core priorities and states that Claude should generally resolve conflicts in that order:

1. Broadly safe, defined partly as not undermining appropriate human oversight
2. Broadly ethical
3. Compliant with Anthropic’s guidelines
4. Genuinely helpful

The document also explains why Anthropic prefers value-and-judgement cultivation over rigid checklists, while still allowing “hard constraints” for high-stakes cases.

Context: this is an evolution of “Constitutional AI”

Anthropic has been using a “constitutional” approach for years, but earlier versions were closer to a list of principles. In a 2023 explainer, Anthropic said its constitution drew from sources including the UN Universal Declaration of Human Rights, trust-and-safety practice, other labs’ principles, and even platform terms (it cites Apple’s terms of service as inspiration for some modern digital issues).

Anthropic has also experimented with gathering public input and translating it into principles suitable for constitutional training—an exercise that it notes involves subjective mapping.

This exactly is the pattern that should show up whenever a system becomes too consequential to run on discretion: write down the constitution, make it suspenseful powerful, and force decisions to run through a stable hierarchy.

(Reader, I am still getting used to this. I make this point in the Rule of Law pattern in my forthcoming book Hidden Patterns: governance scales when it is legible, consistent, and auditable—when it is not “whatever the most persuasive person in the room thinks today”. Shoutout to Macron's bespectacled rule of law speech at Davos this week. It's a good place.)

Anthropic is trying to apply that logic to a model that must generalize to messy edge cases. A written constitution:

• Concentrates authority in an explicit text (even over a rotating set of product prompts).
• Constrains discretion without pretending discretion can be eliminated (principles even over rules).
• Creates an audit ~surface: a standard against which behavior can be tested and discrepancies documented.

The unresolved issue: legitimacy

Human constitutions are meant to draw authority from some form of consent. An AI constitution is authored by a private firm and imposed on a tool used by outsiders. CC0 publication improves scrutiny, but it does not settle who gets a say in revisions, or what happens when commercial incentives pull against the stated hierarchy. Constitutions which costs little to adopt may cost little to discard, and I feel confident that Anthropic's process to arrive at this document will keep it safe for some time.

What to watch

• Revision cadence and governance: how often the constitution changes, and whether changes are explained in a disciplined way. 
• Evidence of enforcement: whether system cards and evaluations show meaningful alignment with the constitution, rather than selective examples. 
• Diffusion into the industry: CCo makes it easy for rivals to adopt the text...AND for regulators and buyers to start expecting “model constitutions” as standard documentation. Wouldn't that be a win for constitutional organizing!

Growth is replacing “productivity” as the headline: Accenture’s latest Pulse of Change reporting says 78% of organizations now see AI as more beneficial to revenue growth than cost reduction. As a former consultant, I view this mostly as a shift in what leaders are trying to buy. And that matters.

Data advantage is the real moat (if you already have it): Aramco’s CEO said the firm expects $3–5bn in “technology realized value” in 2025, up sharply versus prior years. The underlying claim, echoed in coverage and company statements, is that the edge is less about compute than deep operational data built over decades.  

The talent bottleneck is domain expertise, not ML engineers: Aramco and its executives have repeatedly stressed that scaling depends on training subject-matter experts (engineers, operators, geologists) to generate the use-case pipeline; the company has pointed to training 6,000+ people in AI-related capability.  

Visa is positioning “agentic commerce” as a new transaction regime: Visa’s push is infrastructure. They’re building protocols to let merchants distinguish legitimate shopping agents from bots, plus standards for agent–merchant communication during checkout. In parallel, Visa is marketing “Intelligent Commerce” as a toolkit of tokenization, authentication and transaction controls intended to let agents transact safely on a user’s behalf (noting the product is still being deployed).  

Clinician time is the sell in healthcare, not “efficiency”: Philips has framed the value as shifting time from admin to care, arguing that nurses can spend 10–15 minutes each hour on administrative work, and that automation should return that time to patients. I’ll note here that Buurtzorg outdoes these numbers by a long ways without AI, but that’s for another day. 

Leadership literacy is an adoption constraint: Broad access to tools seems to produce little change until senior leaders build with them directly. After all, governance, risk appetite and prioritization sit at the top in most firms

What to watch

• Whether “AI value” shows up as revenue-linked measures (conversion, retention, pricing power) rather than time-saved.
• Whether firms build a repeatable kill / pilot / scale operating cadence (rather than function-by-function demos).
• In payments, whether “trusted agent” standards become default plumbing for online checkout.  
• In industrials, whether incumbents’ historical data + first-mover advantage actually converts into defensible performance gaps.

Sources

• World Economic Forum: Scaling AI: Now Comes the Hard Part
• Accenture: Pulse of Change
• Saudi Aramco: corporate update on Davos / CEO panel participation
• Bloomberg: Aramco expects $3–5B “technology realized value” in 2025
• World Economic Forum: Roy Jakobs (Philips) on freeing clinician time for patients
• Visa: Trusted Agent Protocol announcement (Investor Relations / Business Wire)
• Visa: Trusted Agent Protocol technical documentation (developer portal)
• Visa: Intelligent Commerce product page

This paper is also good, but it's different and more constrained to specific agents, not a broader general purpose technology like CC is turning out to be. This NBER working paper surveying 776 P&G employees indicates that these tools replace some of the core benefits of teamwork, including performance, cross-functional expertise, and even some social/emotional lift.

• Engineers reported using Claude for about 60% of their work and self‑assessed ~50% average productivity gains compared with traditional workflows.
• Usage data indicates Claude Code can now execute roughly ~20 consecutive actions before human input is needed, and the share of feature‑implementation tasks climbed from ~14% to ~37% over six months, signalling deeper utility beyond trivial tasks.
• Most employees felt they could fully delegate only about 0–20% of their work to Claude, meaning most tasks still require human verification, oversight, or involvement.

Of course the thing is good at doing stuff. Not always, and not perfectly, but it works. The meat for org designers is in the qualitative feedback:

• Engineers describe evolving into AI collaborators: often becoming more full‑stack as Claude helps them step into domains outside their original expertise. “It did a way better job than I ever would’ve. I would not have been able to do it, definitely not on time... [The designers] were like ‘wait, you did this?’ I said “No, Claude did this - I just prompted it.’"
• But they also report fewer mentorship interactions and reduced team touchpoints, as colleagues often turn to Claude first rather than each other.
• Concerns about skill atrophy and social/organizational change are emerging alongside the productivity gains.

What this means beyond Anthropic: teams and OD leaders will need new guardrails around agent delegation, and reinforced apprenticeship loops to ensure skills, collaboration, and social capital don’t erode even as AI boosts output. I also foresee AI-native organizations caring substantially less about things like "role clarity," and "opportunities for growth and development." AI has made these concepts obsolete and in the case of the former, perhaps even undesirable or stifling.

Not for nothing, one of my favorite findings for my particular brain chemistry is this bit:

In general, people were enthused by their new ability to prototype quickly, parallelize work, reduce toil, and generally raise their level of ambition. One senior engineer told us, “The tools are definitely making junior engineers more productive and more bold with the types of projects they will take on.” Some also said that the reduced “activation energy” of using Claude enabled them to defeat procrastination more easily, “dramatically decreas[ing] the energy required for me to want to start tackling a problem and therefore I'm willing to tackle so many additional things.”

That is so extremely on the nose for me.

Early in '24 I was working on an OD project and, as we were recommending a 13. Network of Teams model, I had the idea that we should create a comprehensive give-get matrix for each of the 47 teams we'd imagined for the future. Asking a human teammate to do this would have been...punitive. This sort of thing exceeds the "context window" for any mortal being; it would have taken weeks. AI has no problem with this kind of thing.

A team gets re-org'd (again). Someone makes a deck (of course). Everyone nods. The deck gets filed into the corporate catacombs. Two weeks later the same team is fighting about scope, authority, and who owns the gnarly work. Again.

In 2020 I wrote a post called "Software for Scaled Organizing," about how PowerPoint is a terrible tool for organization design, and leader- or HR-owned maps of reporting lines aren't the answer. So I finally did the thing I've been whining about for years: I built a small web app that lets any team create a charter that's easy to access, easy to edit, and hard to lose. Shoutout to my friend, Cullen MacDonald, for trying to help me make this a couple years ago. We were busy at that time, so we didn't really finish.

A new version of this idea is live at ccccharter.com. (Here's a dummy charter so you can go see a filled example.)

Why this tool exists

There's good evidence that team structure predicts outcomes, including code quality. But most orgs treat structure as something that gets decided during a reorg and then frozen until the next one. The people doing the work don't get to adjust the machine they're inside.

Part of the problem is the artifacts. Org charts, RACI matrices, slide decks: these formats look authoritative, but they're hard to edit, hard to keep current, and usually locked behind someone else's permissions. There's usually multiple versions of the same thing. All of this conspires to make org design feel like something that happens to you, not something you do.

Team charters: the artifact and the practice

A team charter is a compact guidebook for five things:

1. Purpose: why this team exists
2. Focus Areas: what we do, what we don't, where we're spending attention
3. Measures: how we know we're winning (metrics + behaviors)
4. Decision Rights: what we decide autonomously, where we consult, what we depend on (DICE format, because of reasons)
5. Roles: the distinct accountabilities the team needs

A charter is an artifact and a repeated practice. Make it together. Test it against real decisions. Review it on a cadence (65). Keep it accessible to stakeholders.

You don't need permission to clarify how you work

A team charter is supposed to be written by the people doing the work. But in most orgs, the "official" org system is locked behind HR admin workflows, licenses, or IT. So teams end up writing their charter somewhere else, like Notion, Google Docs, or Confluence.

Those tools can work. But they have two failure modes. First, access friction: the person who most needs the knowledge can't get at it (no account, no permissions, wrong workspace). Second, legitimacy gravity: everyone treats the charter as "unofficial" because it isn't in Workday. So the charter becomes a container for vibes.

But here's the thing: Workday was never going to answer these questions anyway. The org chart is usually a system of record for administrative facts: who reports to whom, job titles, headcount. A charter answers operational questions: what this team does, how we make decisions, who owns what work. These are different categories. Waiting for the org chart to clarify your team's scope is like waiting for your birth certificate to tell you what to do with your life.

What I wanted: a charter that is one URL, editable by the team without IT, easy to share across boundaries (contractors, partners, new hires), versioned so updates feel safe, and designed to be the thing you actually point to, instead of a "shadow doc" that defers to some "real" system that will never contain this information.

That's ccccharter.com.

A note on access: charters are public-by-URL but not discoverable. Security by obscurity isn't perfect, but it matches how teams actually share docs; a contractor can read your charter five minutes after you send the link, instead of five days after IT provisions an account. If you want tighter control, you can restrict editing to specific people while keeping view access open. Useful if you want a holacracy-style setup where only the secretary edits, but everyone can always see the current state.

How to use ccccharter.com

You can use the tool two ways: solo drafting or an actual workshop. Workshop is better.

Option A: The 2–3 hour charter workshop (recommended)

Create a new charter at ccccharter.com. Put it on a projector, share the link in the meeting chat. As a group, fill in the team name and mission, 3–6 focus areas, a handful of measures (mix outcomes and behaviors), decision rights (be explicit—this is where teams stop stepping on each other), and roles (real accountabilities, not resume lines). Assign one person to keep the doc real over time. Stewardship even over ownership.

Option B: Solo draft → team review (meh, but still workable)

Draft the first pass yourself. Share the link and ask people to add missing scope, decisions, roles. In the next team meeting, stress-test the charter using real examples: "Does this clarify what we say no to?" "Who decides this?" "Which role owns that?" Save. Revisit monthly or quarterly.

How I'd actually deploy this inside a team: Pin the link in Slack or Teams. Add it to onboarding. Link it from every project kickoff doc. When conflict appears, point to the charter like it's a constitution, because it kind of is. See rule of law (2).

BTW, there's also a Team Guidelines section that supports markdown, which is where I put the stuff that doesn't fit cleanly into decision rights or roles, like norms, rituals, "how we work" prose. It's the closest thing to a team README. All of the roles have little additional detail fields, if you need to add more context.

How it got built: Replit + ChatGPT

I built this working version in Replit, with ChatGPT to help me build technical specs for the things I wanted in the app.

I wrote a PRD and designed screens (clear UX, clear objects, clear constraints). I gave those to ChatGPT, which helped turn those specs into schemas, routes, components, and edge case checklists. I then used Replit to iterate fast, deploy instantly, and share my messy progress with a few friends.

The thing I keep learning about AI-assisted building: it's incredible at acceleration if you're willing to be the editor-in-chief. If you let it drive unsupervised, it will happily build a plausible-looking haunted house. It will build things (like you can see me troubleshooting in the image above) that "work" but don't work work. So you have to stay in the room, and your personal taste for what a thing should look like, feel like, function like ... it really matters.

Replit is also exactly what it should be: a prototype engine. It's great for shipping the first thing. It's not necessarily where you want to live forever if you care about uptime, scale, or private data. The long-term plan is the normal boring grown-up stack: GitHub, real hosting, proper database, proper auth. I'll probably end up using Claude Code for that.

But to get from zero to "it works," Replit + ChatGPT is a cheat code.

What's next: decisions that leave a trail

The feature I'm most excited to build is a proper decision record. Right now charters capture what a team can decide. But they don't capture what the team did decide, or how.

The plan is to build an objection-first decision workflow. Facilitator posts a proposal tied to a specific decision right. Participants join via QR, respond with one of three options (endorse, safe-to-try, objection), and objections require two fields: what will likely go wrong, and what would make it safe. When the facilitator reveals, objections show first, with names attached. Resolve them, record a final decision, and it becomes a permanent entry in the charter's decision history, otherwise known as a team logbook (57).

It's consent-based decision-making with an audit trail. Am still speccing it out, but if that sounds useful, let me know.

Try it, steal it, tell me what breaks

If you want to try the tool, go to ccccharter.com. Make a new one. Give it your email. Make note of the URL.

If you want the deeper pattern language behind it, that's in Hidden Patterns. including the Team Charter chapter and a bunch of other patterns that make charters stick.

And if you have opinions (you do! I know you do!), send them. This tool is early, and I'm building it in public on purpose.

The whole point is to stop letting our org design levers die in slide decks.

It's for the person in your life who has opinions about things: supply chains, rice cookers, where you buy your gifts and what the gifts are.

It is a gift guide containing objects and experiences where the logic is part of the pleasure: systems that reward attention, tools that assume you'll keep them for decades, books that change how you see the world. I am unsure if these things are on sale. Most of them are not timely, nor are they trendy. I personally think they're all cool, and I would happily give and get these gifts, but that's not exactly the point. Some are inexpensive. Others are costly. All of them hold up to scrutiny, which is the point.

They are broken into categories: books; desk & workspace; for young systems thinkers; home objects; stays & experiences; subscriptions & memberships; tools & repair; food & pantry. A fermentation jar is a tool, a toy, and a lil' lesson in emergent systems. A craft workshop is an experience, but also a chance to see how an institution transfers knowledge. If someone on your list would appreciate that kind of slippage (or if you're shopping for yourself and looking for a bit of permission) you're in the right place!

1. Books

Giving books is chic. Here's a selection of designy, occasionally weird titles that will delight those who read the colophon at a range of price-points.

Is there a known optimum gate size for the dual control of cattle and sheep? by Sofia Nannini

CCA Bookstore $18 CAD / Yep! That's the title. This is an essay on Cedric Price’s reconfigurable livestock pen that doubles as a picnic ground, using gate sizes and animal movement as an entry point into questions of control.

Material Reform: Building for a Post-Carbon Future by Material Cultures

MACK, $22 / A systems book about supply chains, resources and time. Ideal for someone who wants to understand why ideas like “low carbon” require an entire reconfiguration of how we build. 

Visible upon Breakdown, ed. by Justinien Tribillon, Offshore (Isabel Seiffert & Christoph Miler)

Spector Books, $34 / A photographic and essayistic (?) tour of infrastructure’s hidden wiring (rail lines, pipes, data cables) made visible when things fail. Great for someone who obsesses about things like the BQE Triple Cantilever Repair.

Designing Programmes by Karl Gerstner

Lars Müller, ₣40 / Gerstner shows how to design systems using grids, permutations and constraints as engines for creativity. It’s also a beautiful object unto itself, with tight typography, and rigorous layout that will train the eye every time it's opened.

Walking as Research Practice by Roma Publications

Perimeter Books, $52 / A cluster of essays and projects that treat walking as a method for mapping the city: rhythms, signage, thresholds, maintenance. Good for the person who already notices curbs, drain covers and wayfinding but wants a vocabulary for it. 

System Process Form by MuirMcNeil

Thames & Hudson, $95 or Unit Editions (Collector’s Edition) $203 / Essentially a catalog of parametric thinking applied to type and pattern: letterforms generated by rules, rather than taste or tradition. For anyone (even non-type-nerds) who gets a kick out of watching simple systems explode into complexity. 

Manuals One: Design & Identity Guidelines by Unit Editions

About $350 used if you know How to Win at eBay / A survey of corporate identity manuals from the era when designers assumed their work would last decades. The pleasure here is seeing how grids, ratios and usage rules build a coherent world. There’s also a Manuals Two, which is also sold out. Maybe try to find an extravagant set? 

2. Desk & Workspace

For messy desks and clean desks. For messy minds and clean minds. Also sometimes the office is an airplane.

This Stalogy Notebook

JetPens, $26 / Essentially a dateless (or date-ful) planner that feels like a lab notebook: ultra-thin paper, subtle timeline on each page, tiny grid. It’s for the person who wants to log everything – projects, experiments, life admin – in a single continuous run.

Six Pen Hard Case

Present & Correct, $115 / I like the Undyed Natural finish here, as it’ll get darker and more uniquely theirs over time. Holds six pens for travel and/or safekeeping. Great for the on-the-go facilitator (hi!), though I don’t think the dimensions are right for standard Expo dry-erase markers.

Anglepoise Desk Lamp

Anglepoise, $235 / Iconic for a reason! The spring and arm geometry means the shade stays exactly where you put it. The design goes back to 1950, comes in multiple colors and styles, and is still in production by the company that originally developed it, which is now a B Corp. 

Wms&Co. Blackened Bronze Custom Stamp

Wms&Co., also $235 / A dense little block of blackened bronze that turns a recurring mark – a symbol, a project code, a library stamp – into a personal ritual. It's probably overkill for something like "from the desk of," but would be cool for someone building their own internal cataloguing system; you could design the stamp with a few fields to fill in by hand? Also great if somebody recently started their own small business, and wanted to bring a perfectly-imperfect branding moment to outbound material.

3. For Young Systems Thinkers

Nominally these are for kids, but IMO they're for ... ever. Stay young, folks.

Carson MicroBrite Plus Pocket Microscope

Carson, $16 / Small enough for a coat pocket, powerful enough to make everyday things – paper fibers and ink dots – interesting. Maybe a gateway drug into “let’s see how everything is made” field trips around the house.

Majo Ideas

Majo Ideas, ~$70 for a bundle of 3 / An activity zine that teaches kids how pattern, contrast and repetition work by letting them build their own optical illusions. It's a coloring book that reinforces learning grids and variation. It's inspired by iconic artists, so there's an art history lesson, too. Maybe makes going to the museum with little ones even more rewarding? Also offered as a subscription.

Flensted Futura Mobile

Flensted, $104 / Six spheres orbit on a delicate counterweighted frame; the whole thing is basically a lesson in equilibrium disguised as nursery decor. Grows well with a child. Nothing baby-coded about it, but it is still a mobile.

Naef Cella Modular Wooden Puzzle

Naef, $217 / Nine nested shells become rooms, staircases, impossible cubes; spatial reasoning in Swiss hardwood. Rewards the kid (or adult! I want one!) who likes finding every possible configuration before putting it back on the shelf. I like the natural wood, but also comes in colors.

Community Playthings Introductory Unit Block Set

Community Playthings, $340 for an introductory set / 97 blocks in 17 shapes, all cut to the nearest 1/100 of an inch, so structures behave predictably under load. I don't think I really realized that precision manufacturing would be so important to making a thing that inspires play, but it makes sense! Expensive but will last forever and hand down across generations.

4. Home Objects

Cleanup, Store, Beautify

4. Home Objects

Redecker Copper Cloths

Boston General Store, $12 / Finely woven copper threads in double-layered cloths that strip burnt-on junk off pans without shredding the surface (use only when wet). Basically a reusable, recyclable, material-science-based answer to “why are we still buying green plastic scrubbers?” (The actual answer is you're using non-stick, when you could be preheating your stainless steel to the right temp before using it.)

Kamenoko Tawashi Round Brush

Tortoise General Store, $16 / A coconut-fiber donut that’s been doing dishes in Japan for over a century. Designed before commercially available surfactants, when the brush itself had to provide the cleaning power. This also means it's really good for things where you don't want to use soap. Lasts a long time, good for the world, lets you use solid soap if you like.

Redecker Horsehair Indoor Broom Head

Four Corners, $32 / I promise I'm not sponsored by Redecker. They just make cool stuff! Oiled beechwood, dense horsehair, designed to be pulled rather than shoved (TIL!) so the bristles don’t splay and die in a year. With basic care (wash, comb, hang) it’s a decades-long relationship with your floor. So much better than a Swiffer.

Riess Enamel Measuring Jug

Labour & Wait, $46 / An Austrian enamel jug with internal gradations, good enough to go from mixing batter to holding kitchen tools on the counter. In a pinch, you could cook something in it! It’s calibrated, robust, and made by a company that’s been enamelling metal for 450+ years. 

Arc Alarm Clock

Nanu Electrics, $299 / One of my favorite purchases of the year. It is very expensive for an alarm clock, but when you get it, you'll understand why. It's super heavy, for one, so it doubles as a bedside weapon for intruders, and every finish, bit of packaging, and even lightweight LED interaction just makes sense. The sound is incredible, soothing, and produced by percussion (not by a speaker, say). And the knob feel is refined.

5. Stays & Experiences

Architecture you can sleep in, systems you can inhabit.

Open House Architecture Weekend

Open House Worldwide, Free! Or Donation-Based! / Once a year, cities open up buildings—from infrastructure to private homes—that you’d never normally see. Plan a trip around one, or stay local.

Week(s)-Long Workshop at Penland School of Craft (North Carolina, US)

Penland School of Craft, ≥$1k / Immersive craft workshops in things like wood, metal, textiles, print, clay. The draw for a systems person isn’t just the making, it’s seeing an institution structured around skill transfer, mentorship and shared equipment. I like Intentional Warp Ikat ($2,200) and Greenwood Technologies ($6,700, yikes? but it's like a month long? you make a chair?), and I'm super impressed that so many of them take all skill levels!

Two Nights at Fogo Island Inn (Newfoundland, Canada)

High four figures for a long weekend, all-inclusive / What if a hotel was a social enterprise? 100% of their operating surpluses go back into the local community, and the architecture is tuned to landscape, weather and pattern of life on a remote Atlantic island. It’s basically a case study in regenerative systems disguised as a very good holiday. Board is included, as are most of their awesome experiences.

A few nights at Castelfalfi (Tuscany, Italy)

Also expensive, and not all-inclusive / A whole Tuscan hilltown converted into a working resort: medieval walls, organic vineyards, biomass plant—and then a proper spa, infinity pool and those stupidly good sunsets over the valley every night. Several restaurants on site (from white-tablecloth to wood-fired pizza in the piazza) mean you can treat it like a tiny, walkable city where everything good is within five minutes on foot. After dinner, you can go boar-sighting in their Land Rovers. Nice. We went here a couple years ago and LOVED it.

6. Subscriptions & Memberships

Ongoing inputs to keep your head and heart well-nourished.

Dossier Magazine

Direct from Dossier, $30/year / A long-running creative network disguised as a travel magazine: born in the indie fashion/art scene, now re-cast as a luxury travel and culture title by the ex-Departures crew. For someone who wants sharp, global stories with proper photography and edits (but none of the usual airport-lounge blandness).

Real Review

Real Review direct, ~£65/year / A tall, sized-to-be-easy-to-hold-in-one-hand sized magazine about “what it means to live today” covering an extremely wide range of topics in review format. A recent issue reviewed synthetic white pigment and run clubs, for example. Two issues per year, and comes with access to a private Discord server.

MacGuffin Magazine

Direct from their publisher? €54 / Each issue picks one thing (N° 15 is about "the stitch" for example…) and then dismantles it across history, manufacturing, sociology, and pure weirdness. For someone who enjoys seeing how a single everyday thing is actually a whole hidden network, it’s perfect. Also 2x per year. I sense a trend.

Local Design / Architecture Museum Membership

Nearest serious institution (Cooper Hewitt–type places), usually $60–150/year / Free admission is great, but the real value here is repeated exposure: seeing how exhibitions are built, how labels evolve, which designers keep showing up in the credits.

8. Tools & Repair

Things to fix other things.

3M Very High Bond (VHB) Tape

Hardware Stores, ~$25–40 / A roll or two of the terrifyingly strong double-sided stuff that holds the world together. A favorite of engineers everywhere; it’s not glamorous, but once you’ve used it to solve a problem, you never un-know it. Use with adult supervision! Comes in many widths. No link because the easiest ones to actually link to are ULINE and Amazon. Sorry!

iFixit Pro Tech Go Toolkit

iFixit, $40 / A compact tool-roll with precision bits (including all the weird Pentalobes), tweezers, picks and spudgers, about half the size of the classic Pro Tech kit, tuned for actual field repairs. Will change how the recipient thinks about “sealed” hardware. 

Put together a proper leather-care kit including Saphir Médaille d’Or Products

Where: Shoe repair shops, menswear/sneaker boutiques. / Don't do a pre-packaged one, but a hand-assembled set: cream polish, wax, conditioner, horsehair brushes, daubers, cloths, maybe a spoon-style shoehorn. It changes leather from “thing that wears out” to “thing that evolves with you.” By the way, this doesn't just have to be for shoes! Bags need care, too! Saphir products are the gold-standard for a reason. Youtube is your friend for learning how to use this stuff!

9. Food & Pantry

Process-and system-oriented things to make things for yourself, your family, your friends.

Anson Mills Heirloom Grains

Direct from Anson Mills, ~$12–18 per bag / They cold-mill to order from organic heirloom varieties and ship quickly for freshness, which is nerdy even by grain standards. For the systems-obsessed cook, it’s a tangible lesson in what happens when you treat supply chain and storage as design problems. 

Diaspora Co. Single-Origin Spice Tins

Diaspora Co., select grocers. from ~$12 per tin / Equitably sourced spices from South Asian farms, with farmers paid roughly four times commodity rates and shipped in stackable tins. The recipient will taste the difference, but they’ll also appreciate the business model and packaging. 

Yamaroku Barrel‑Aged Soy Sauce

Onggi, $45 for 500ml / Fermented 3–5 years in 100‑year‑old cedar barrels, this soy sauce answers the question, "What if condiments could teach you about infrastructure?": slow, layered and slightly ridiculous to use in a stir-fry you’re not paying attention to. Try it on vanilla ice cream! (A thing about me is I like a lil' sip of soy sauce when nobody's looking.)

Weck Jars + Fermentation Weights Starter Stack

Kitchen shops, canning suppliers / Straight-sided glass jars with glass lids and rubber gaskets, plus lids from the next size down as your weight: the basic primitives for kraut, pickles, sourdough starter, whatever. Fermentation is just a controlled system; this gives them the labware to run experiments on their own counter. Also these are great jars for plastic-free food storage and microwaving. Good to have on-hand, in general.

Brightland “The Duo” Olive Oil Gift Set (Alive + Awake)

Brightland, ~$70–80 / Two California extra‑virgin oils – one grassy, one robust – lab-tested to exceed California’s stricter EVOO standards and bottled in UV-protective opaque glass. It’s a nice illustration of how harvest timing, varietals and storage turn into flavor.



Heyyyy, is this marketing‽ Yes.

Hidden Patterns by Clay Parker Jones

Simon & Schuster, $32 / Give them the gift they’ve always wanted: dignity at work. They’ll get more done, happier, faster, and more creatively than ever before. 

The world of work is bleak rn fr fr; what if, in the year ahead, we all tried to make it a bit better and a *lot* more human? (They will have to wait until March to get a physical copy!)

Decoding Zuck’s Superintelligence Memo

Mark Zuckerberg, the chief executive of Meta (aka the company formerly known as Facebook), has published a memorandum about “superintelligence” and what it will mean not only for his co…

On my OmRead More

Mark Zuckerberg’s internal note feels a little bit like AI bingo. Universal tutors! Code copilots! Real-time research assistants! (I’m personally compelled by this, mostly because that’s what o3 feels like to me right now.)

That said, Om Malik points out that this is kindof Zuck’s go-to move in a crisis: 2019 was about privacy, 2021 the metaverse, now super-intelligence. This time, Meta’s head-count is already down roughly 21% since 2022, and the safety, infra, and product teams that used to absorb these big swings are (probably, guessing here!) leaner than normal. Maybe that’s fine (maybe the computers are indeed doing the work! maybe Fred Brooks was right!) but it’s hard to overstate the execution load here: launching a new product category, rebuilding trust, and shipping best-in-class safety systems…all with fewer humans. Big bet!

The people paid to safeguard users quietly head for the exits.

Is anyone left to defend trust and safety?

It’s under assault this year from users, politicians, and their own executives — but the industry has responded with silence

PlatformerCasey Newton

Trust & Safety teams once had enough political capital to veto risky growth ideas. At this year’s TrustCon, the function mostly shared burnout stories and exit strategies; panels on child exploitation and election integrity had few attendees; many veteran leads have left or are quiet quitting; the rest described silent mode, doing only what compliance dictates and nothing more. 

The takeaways here are grim; this is in some ways a darker version of platform enshittification. We’re wading into uncharted technological territory and the folks we need as a strategy reality check are underfunded or just plain gone. Viewing this from a designer’s chair, if we’re deleting trust and safety from a list of functions – and not at least automating some of the capabilities contained within, or outsourcing them to a partner – we should not expect organizations that are able to deliver products we trust or that will keep us safe (from each other, or from the products themselves). See my thing on Safety in “Super Performance.”

Whether raw compute can navigate the junk drawer.

The Bitter Lesson versus The Garbage Can

Does process matter? We are about to find out.

One Useful ThingEthan Mollick

Two models caught my eye here:

• Rich Sutton’s “Bitter Lesson” (2019) argues that raw compute plus simple algorithms beats clever hand-engineering;
• Michael D. Cohen, James G. March and Johan P. Olsen’s “Garbage-Can Model” (1972) says decisions in large organizations are random collisions of half-defined problems, partial solutions, shifting participants, and unpredictable timing. 

Ethan Mollick asks what happens when giant models are dropped into chaotic environments. If Sutton is right, you can ignore most process maps. Just specify the outcome, let the models iterate, and the computer will sort it out. If Cohen/March/Olsen are right, those models will burn cash chasing contradictory goals in undocumented mazes. 

Either way, the work is to the territory so the agents know which levers move what, and prevent ever-more-expensive AI thrashing. (That said, I’m not sure this is how people are actually using the tools inside work environments today. Seems like it’s more ‘do the things I don’t wanna, or don’t have the brain chemistry for at the moment.)

CEOs find Wall Street loves shrinking payrolls and frame head-count decline as visionary leadership.

CEOs Are Shrinking Their Workforces—and They Couldn’t Be Prouder (WSJ why won't you unfurl here?)

WSJ reporting on the trend toward bragging about reducing headcount and increasing productivity. FWIW I’m not entirely sure this is truly an emerging trend or just something that’s endemic to the companies they profile: Wells Fargo headcount down 23% in five years; Verizon’s headcount in continuous decline and “something we’re very, very good at”; Bank of America set a goal of getting below 200k employees after peaking at 300k in 2010. Intel, Union Pacific, and Loomis are also in there. Not exactly growth or user-experience titans here, but you’ve also got Tobi at Shopify effectively putting the company on a hiring freeze absent proof that you tried to get a computer to do the work first. (Meta’s headcount, referenced above is growing but down from peaks, so not really the same thing.)

We’re not cutting people; we’re embracing efficiency. Think of us (and value us) as an AI company! 

Welp. Rotations, up-skilling, and cross-functional fluency are important for everyone regardless of where you might sit. If you can’t prove how your role adds non-automatable value, it seems like (depending on the firm, of course!) you’re one earnings call away from becoming a data point. Again, grim.

There’s also a connection to this, from the other day. Don’t do this to my bank, or my cancer research, thanks!

And this, from Sam Altman, a while back.

“We’re gonna see 10-person billion-dollar companies. In my little group chat with my tech CEO friends, there’s this betting pool for the first year that there’s a one-person billion-dollar company, which would have been unimaginable without AI and now will happen.”

Peer-reviewed data confirm that the first targets are email jobs

Working with AI: Measuring the Occupational Implications of Generative AI

Given the rapid adoption of generative AI and its potential to impact a wide range of tasks, understanding the effects of AI on the economy is one of society’s most important questions. In this work, we take a step toward that goal by analyzing the work activities people do with AI, how successfully and broadly those activities are done, and combine that with data on what occupations do those activities. We analyze a dataset of 200k anonymized and privacy-scrubbed conversations between users and Microsoft Bing Copilot, a publicly available generative AI system. We find the most common work activities people seek AI assistance for involve gathering information and writing, while the most common activities that AI itself is performing are providing information and assistance, writing, teaching, and advising. Combining these activity classifications with measurements of task success and scope of impact, we compute an AI applicability score for each occupation. We find the highest AI applicability scores for knowledge work occupation groups such as computer and mathematical, and office and administrative support, as well as occupations such as sales whose work activities involve providing and communicating information. Additionally, we characterize the types of work activities performed most successfully, how wage and education correlate with AI applicability, and how real-world usage compares to predictions of occupational AI impact.

arXiv.orgKiran Tomlinson

Researchers sifted through 200,000 Copilot chats, overlaid them on U.S. Bureau of Labor Statistics data, and found occupations dominated by text, numbers, and routine coordination rank highest for AI “applicability.” That means roles like interpreters, writers, sales reps, recruiters, and office admins. Roles like machinists, roofers, nurses, and electricians remain more challenging environments for AI, but then, have you ever worked with a contractor? I’m not sure that the AI is going to be used in the direct work, like “Pour materials into or on designated areas,” but it sure seems applicable to “Estimate construction project labor requirements.” (From O*Net’s Roofer page.) 

Two things: 

• Jevon’s paradox probably applies here (ATMs ultimately increased bank-teller jobs). And knowing how much it costs to build things, and how hard communication and coordination is in this space, it seems to me that adding a smart client-engagement thing on top of the physical work would make things more manageable for everyone (and probably more profitable; for every $100 a remodeling contractor makes, their profit is only $4.70 on average.)
• Nothing new here. Frey and Osborne had this a decade ago.

The General Theory of Enshittification

It isn’t a new phenomenon, but it seems to matter more

Paul KrugmanPaul Krugman

Paul Krugman puts formal economics around Cory Doctorow’s now‑famous neologism: every two‑sided platform starts out user‑loving, shifts to seller‑loving, then bleeds both to feed shareholders. The upshot for org designers is clear. Left unchecked, the desire to optimize rots user experience and trust. Instead, we need some universal principles that apply as much to the product as to the way of working inside the firm: interoperability, low exit costs, and incentives that reward long‑term surplus even over quarterly rent‑seeking.

Microsoft’s Reset

Recommitting to our why, what, and how - The Official Microsoft Blog

Satya Nadella, Chairman and CEO, shared the below communication with Microsoft employees this morning. As we begin a new fiscal year, I’ve been reflecting on the road we’ve traveled together and the path ahead. Before anything else, I want to speak to what’s been weighing heavily on me, and what I know many of you…

The Official Microsoft BlogMicrosoft Corporate Blogs

In a recent public memo, Satya Nadella tells the company: “progress isn’t linear, success has no franchise value.” Translation: even a $3t juggernaut must unlearn and relearn, doubling down on security, quality, and AI while pruning head‑count. The vibe here is context even over hierarchy, and it reminds me of Mary Parker Follett's "Law of the Situation" (reminder, the below was written in 1925):

This is what does take place, what has to take place, when there is a question between two men in positions of equal authority. The head of the sales departments does not give orders to the head of the production department, or vice versa. Each studies the market and the final decision is made as the market demands. This is, ideally, what should take place between foremen and rank and file, between any head and his subordinates. One person should not give orders to another person, but both should agree to take their orders from the situation. If orders are simply part of the situation, the question of someone giving and someone receiving does not come up.

Remember Y2K? Cooperation at Industrial Scale.

How We Averted a Worldwide Crisis

A system of cooperation was key

IT ChroniclesJohn Passadino

For most people, most days, it feels pretty hard to get a simple project done if it requires spanning multiple teams. What if you had to do that across multiple organizations, and the fate of the world was on the line? John Passadino’s Y2K retrospective is a tale of cross‑organizational coordination going right: thousands of coders, a shared, date‑driven "definition of done," and relentless test cycles. The crisis never came because everyone fixed the same tiny field together. It’s a reminder that existential risk can sharpen focus and dissolve silos faster than structural change, and that even very big teams can be coordinated and focused if we drop the managerial-era bullshit. 

Medium’s “Fell in a Hole, Got Out.”

Fell in a hole, got out.

Medium’s recap, financial turnaround, and difficult path back to health.

The Coach LifeTony Stubblebine

CEO Tony Stubblebine drops us into Medium’s near‑death spiral: $2.6mm monthly burn, click‑bait swamp, and $37mm debt. Tony killed vanity metrics, re‑anchored incentives, slashed cloud spend, and renegotiated the cap table. What I love most about this story is that it’s a case of quality even over growth… but of course, growth was the end result. It’s also a live counter‑example to Krugman-Doctorow, above.

Medium is best when it is giving voice to people who aren’t trying to be professional content creators and we think those voices (you) often have the most valuable stories to tell. The internet can’t just be for media pros, influencers, hustlers, and content creators. There also has to be a place, here, that understands the value of user-generated content, of people sharing their professional or academic work, of the lessons that come from living interesting lives and writing about it.

Figma’s S‑1: The Routing Layer for Coordination

Figma: Saving the Best for Last

Plus! The Waymo Balance Sheet; Adverse Selection in Hiring; Adverse Selection in Marketing; Factors; Growth

The DiffByrne Hobart

Enterprise products aim for maximum "data gravity"—if they're the system of record for answering important questions (like "who works here, exactly?" or "how much money do we have?" or "what have we legally obligated ourselves to do?"), they're hard to remove. But Figma is fundamentally a transactional layer, where things exist when they're being planned but not finished. That makes the problem harder. What Figma has been able to do to work around this is that they have data gravity for the data that's in users' heads: tacit knowledge of the product development process. PMs and designers have a sense for how things should work and how they should look, and engineers have an idea of the technical constraints. There probably isn't one product where all of this information will get explicitly shared in a searchable format, but Figma's tools are basically a way for one kind of specialized worker to prompt another to share something from their specialty. Sort of a routing layer for a human-implemented mixture-of-experts model. However, Figma is more than a design tool, or a human coordination layer. It has the requisite characteristics to become the central “system of action” for product development: orchestrating human and AI workers to automate creation of the digital products that are all around us. What’s important to note is that this process of product development represents more and more of what “knowledge work” actually means in the modern economy and digital products make up a larger share of that economy every day. The lines between product management, design, and engineering are blurring and Figma benefits from this.

I love this. And you do see this in high-functioning design cultures! The canvas is a traffic‑controller, steering the right slice of a design to the right human expert (designer, PM, engineer) at the right moment, and increasingly handing that context to AI assistants that can handle the boilerplate. What makes that special is quality, not consensus:

• Domain‑specific context. Documents surface paragraphs and comments; Figma surfaces variables, components, constraints, and AI‑ready metadata via its new Dev Mode MCP server, which “brings Figma directly into the developer workflow to help LLMs achieve design‑informed code generation.” (from here)
• System‑of‑action, not record. Every enterprise suite wants to own the record of past work. Figma’s moat is that it owns the meaningful moments where quality and taste get embedded. In an AI future where throughput is cheap, taste is the ultimate scarce resource, and Figma is building the rails to keep it front and center.
• Tech‑literacy as a performance metric. In this world, if senior leaders can’t pick up Figma – navigate components, tweak tokens, run a prototype – they’re already falling behind. It’s the new baseline skill for product orgs: everyone, from C‑suite to customer success, needs to understand the canvas or risk driving blind.