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.

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.

How do we know when an entire organization is performing well – not just its people or its teams, but the system as a whole? We have spreadsheets full of individual KPIs, agile dashboards that chart team velocity, 9‑box grids for talent. Yet when we try to define what “organizational effectiveness” looks like at the company, division, or functional level, you get one of two things: a hand‑wavey answer, or an exclusively business-defined set of metrics that average out to contribution margin.

What tools do we have today? One pretty good example is B Corp certification: it’s a valuable badge for what I would consider organizational responsibility, but its scoring rubric doesn't adequately weight things like product-market fit, customer connection, and community energy. A company can ace the B Corp audit while still being slow and siloed. Another alternative is to look at baskets of measures focused on the employee experience. Measures of diversity, equity and inclusion are tremendously important. So is employee engagement. So are predictive measures of attrition, a data-driven understanding of manager quality, and a deep-dive on organizational network connectedness (using ONA to measure collaboration distance, spotlight silos, and reveal overloaded hubs).

I'd argue that these are not enough, and I hope you're nodding along.

I would also argue that an effort to distinguish "traditional organizations" from "next-gen" or "responsive" organizations is not serving our movement, nor are these definitions even really true, since firms are anything but monolithic. My experience so far is that this is actually not an issue of a separate operating model that some companies have adopted, but rather that there are performance gains on the table for every organization if the humans in that organization collectively decide to do something about it. That in mind, can I convince you to pre-order my book?

Hidden Patterns

A book about fixing the invisible stuff that makes work suck. Not the obvious stuff like bad bosses or dumb policies, but the deeper patterns that slow teams down, kill good ideas, and waste everyone's time.

It gives you small moves that actually help work get better, no matter where you sit in the org.

Pre-Order on Amazon

Craven self-promotion complete! Back to the content.

So when it comes to designed human systems what counts as performance? Put another way, what are the things we’re trying to maximize entirely, even at the cost of other things?

A meta analysis is a handy tool here, since loads of really smart folks – sometimes individuals, sometimes groups – have given the world lists of performance criteria that can guide the conversation. I gathered a bunch of these lists, put each individual item on cards, and with the help of a few colleagues, put them into groups. I sourced 102 criteria in total, distilled from fourteen seminal references that span industrial design, urbanism, organizational science, and global governance. The list pulls equally from evergreen classics and contemporary fieldwork: Dieter Rams’ Ten Principles of Good Design; the original Agile Manifesto; Christopher Alexander’s Quality Without a Name; Salim Ismail’s growth blueprint Exponential Organizations; Jacob Morgan’s Future of Work; the worker-centric approach of Zeynep Ton’s Good Jobs Institute; Jane Jacobs’ urban vitality lessons from The Death and Life of Great American Cities; Kevin Lynch’s criteria on good city form in The Image of the City; Frederic Laloux’s teal and green development stages in Reinventing Organizations; the peer-to-peer ethos of the Responsive Org movement; the sustainability-forward leadership agenda of The B Team; UN guidelines on good governance and public administration; the high-performance team research of Wageman, Hackman & Lehman; and Yves Morieux’s Six Simple Rules. If you have some additional sources, send them my way!

Here they are, mapped from the individual examples on the outside to the seven performance criteria on the inside. Super-performance systems are purposeful, fit, vital, powerful, fair & just, connected, and safe. In each case, more is better. In the chart below, you can narrow the sources down with the dropdown, and read some of the background narrative from each source by hovering.

Purposeful

A purposeful organization knows exactly why it exists and makes that reason visible in day-to-day choices. People can repeat it in plain language, customers can feel it in the product, and partners see it in every negotiation. When purpose is this clear, alignment comes free: teams waste less time arguing over priorities because the destination is already agreed. Many of the source texts use the word purpose specifically, but I also count Jane Jacobs' Density and and Dieter Rams' Good design is honest as examples of purposeful criteria.

It's the world's most obvious example, but it's a good one. Patagonia’s purpose – protect our home planet – shows up everywhere: lifetime repairs, the “1% for the Planet” pledge, and a marketing budget that doubles as environmental activism. The purpose acts as a north-star filter on growth bets and cost cuts. (Link)

Further learning

• Years later, I still feel like Jim Collins captured the practical part of setting vision here (PDF) in his Vision Framework. I prefer "purpose" to "vision" or "mission" but I'm not going to fight you on it. Heck, sometimes I end up using both in a text if I'm trying not to repeat myself.
• Ikigai is another tool that works especially well for people, but also for organizations (and even for helping determine what a team should build next!).

Fit

An organization that scores high on Fitness delivers work that meets two conditions: quality you can measure and relevance you can feel. Users adopt the product without hand-holding, and teammates see/eliminate waste before anyone points it out. Fit turns continuous improvement into a reflex rather than a slogan. Christopher Alexander's Exact, Zeynep Ton's Achievement, and High Quality Output as Judged by Customers from Wageman, Hackman and Lehman are standouts here.

Figma launched multiplayer editing when most design tools were still single-player. The quality bar (speed, no version conflicts) was obvious, but its relevance came from solving a coordination headache designers had simply tolerated. Fitness showed up in usage: teams that tried multiplayer kept it on, and paying seats expanded inside each customer without a sales push. Orgs can display fitness, but so can (and must!) teams, people, products, features, etc. And fitness is always contextual and changing.

How Figma’s multiplayer technology works | Figma Blog

A peek into the homegrown solution we built as the first design tool with live collaborative editing.

Figma

Further learning

• Inspired by Marty Cagan
• How Miro Builds Product with Varun Parmar on Lenny's Newsletter
• Gibson Biddle’s Product Strategy

Vital

Vitality shows up when people leave work with more spark than they arrived. You sense it in the pace of Slack threads, the buzz around demos, the quick uptake of a colleague’s side project. High-vitality teams ship, observe, learn, and fold lessons back into the next cycle without ceremony. Vital organizations are alive, their culture is contagious and probably worth marketing to the outside world. They are interesting. I'm especially compelled by the language Exponential Orgs uses around Engagement here.

Basecamp’s “Shape Up” rhythm bakes vitality into the calendar. Every six-week build cycle is followed by a two-week cool-down where engineers chase curiosities, kill nagging bugs, or prototype wild ideas. Because exploration time is guaranteed, energy stays high through the build window; people know they’ll soon get space to tinker and recharge. The practice keeps release velocity steady while preventing burnout.

The Betting Table | Shape Up

Shape UpRyan Singer

Further learning

• Alive at Work by Daniel Cable
• WorkLife with Adam Grant
• Julie Zhou's The Looking Glass

Powerful

A powerful organization lets the people closest to the problem decide how to solve it. Authority flows outward and upward, so teams can move fast without begging for sign-offs. Disciplined empowerment backed by tight feedback loops and crystal-clear guardrails gives execs, leaders, managers, individual contributors – that is, everyone! – better control over the organization’s future. This idea comes straight from Yves Morieux's Expand the amount of power available, and is the least common theme among existing frameworks, but IMO one of the most essential to high performance and is only starting to be deeply understood by managers.

Haier’s “Rendanheyi” model turned a 90,000-person appliance giant into thousands of self-managed micro-enterprises. A product team that spots a market gap can form a contract with manufacturing, spin up a P&L, and launch in months – no executive committee needed. Because revenue shares and career growth hinge on the team’s own results, their empowerment is very real.

Shattering the status quo: A conversation with Haier’s Zhang Ruimin

Having built Haier into a global powerhouse, its CEO now wants to overthrow 100 years of organizational orthodoxy.

McKinsey & CompanyAaron De Smet

Further learning

• Humanocracy by Gary Hamel & Michele Zanini
• The Bayer DSO Case from HBS
• Hear it direct from Bill Anderson on At Work with The Ready

Fair & Just

A Fair & Just organization uses human dignity as performance infrastructure. Pay, feedback, promotions, and everyday courtesies flow without bias – no extra favors for title, tenure, or who you had lunch with. Policies are transparent and applied the same Tuesday morning as they are Friday at 5 p.m. When people trust the rules, they spend their energy on the work, not on second-guessing whether or not they can tell each other the truth. This one is pretty straightforward and mentioned pretty directly in quite a few of the sources.

This can and should extend to the product, too! Airbnb’s Project Lighthouse uses privacy-safe data to detect racial gaps in booking approvals; product tweaks since 2020 have cut the Black-vs-white booking-success gap nearly in half, with every group now above a 94 % success rate.

How we’re using data to make travel more open for all

Product and policy updates informed by Project Lighthouse have supported hundreds of thousands of bookings in the US over two years.

Airbnb NewsroomAirbnb

Further learning

• Kim Scott's books here are indispensable
• Some nice stuff in Increment by Lara Hogan
• Great research from Project Include
• Stop. Breathe. We can't keep working like this. (The Ezra Klein Show)

Connected

A Connected organization treats information like a renewable resource rather than something worth hoarding: teams keep internal work publicly visible; customers and partners are invited to critique early versions; insights flow both directions. When something breaks, the story spreads faster than the fix, so everyone patches similar faults before they hurt users. An important theme in the research is that a super-performance organization should be connected inside and outside. This concept is by far the most supported in the source material, but probably the most challenging for the companies I've worked with.

GitLab runs its entire OS – road-maps, policies, even salary formulas – off a 2,000-page public handbook. Issues, merge requests, and post-mortems default to “public,” so customers and strangers can spot gaps, file fixes, and learn alongside internal teams. That openness makes information flow both ways: engineers see real-time feedback from the dev community, while outsiders reuse GitLab’s processes verbatim, creating a virtuous cycle of improvement.

The GitLab Handbook

Learn more about GitLab and what makes us tick.

The GitLab Handbook

Further learning

• Team of Teams by Stanley McChrystal
• Brie Wolfson's The Kool-Aid Factory
• Gergely Orosz’s newsletter The Pragmatic Engineer

Safe

A team performs best when nobody is bracing for backlash. Safe means workloads are humane, tools are reliable, and people can dissent, surface mistakes, or ask for help without risking status or livelihood. Candor becomes a default, so risks and weak signals show up early, long before they snowball into crises. A standout piece of inspo for this one from the source material is Yves Morieux's Increase the need for reciprocity.

Pixar’s “Braintrust” sessions invite directors to screen rough cuts to a room of peers who offer unfiltered critique. Directors are free to accept or ignore any suggestion, which keeps feedback sharp while preserving creative ownership. Because reputations aren’t threatened by early missteps, teams expose half-finished ideas sooner and fix flaws before animation costs spike. The studio’s hit rate – 23 Oscars and counting – shows how safety underwrites creativity and quality simultaneously. I don't want to hear about Elio in the comments but, companies are always rising and falling, yanno?

How Pixar Fosters Collective Creativity

Reprint: R0809D Many people believe that good ideas are rarer and more valuable than good people. Ed Catmull, president of Pixar and Disney Animation Studios, couldn’t disagree more. That notion, he says, is rooted in a misguided view of creativity that exaggerates the importance of the initial idea in developing an original product. And it reflects a profound misunderstanding of how to manage the large risks inherent in producing breakthroughs. In filmmaking and many other kinds of complex product development, creativity involves a large number of people from different disciplines working effectively together to solve a great many inherently unforeseeable problems. The trick to fostering collective creativity, Catmull says, is threefold: Place the creative authority for product development firmly in the hands of the project leaders (as opposed to corporate executives); build a culture and processes that encourage people to share their work-in-progress and support one another as peers; and dismantle the natural barriers that divide disciplines. Mindful of the rise and fall of so many tech companies, Catmull has also sought ways to continually challenge Pixar’s assumptions and search for the flaws that could destroy its culture. Clear values, constant communication, routine postmortems, and the regular injection of outsiders who will challenge the status quo are necessary but not enough to stay on the rails. Strong leadership is essential to make sure people don’t pay lip service to those standards. For example, Catmull comes to the orientation sessions for all new hires, where he talks about the mistakes Pixar has made so people don’t assume that just because the company is successful, everything it does is right.

Harvard Business ReviewEd Catmull

Further learning

• All of Amy's books
• Google's Project Aristotle
• Psychsafety.com is pretty indispensable; subscribe to their weekly newsletter

So, what do we do with this? I would offer that we could start using a new survey, combine that with a battery of observable data points in service of a running external tracker for super-performance people, teams, and organizations. We could start tracking this globally as a replacement for the trackers of employee engagement. Longitudinal as it is, I don't think the below is telling us much about how our movement toward better ways of working and organizing is doing:

I find this framework equally useful for assessing 👋 future-ready leadership competencies 👋, team performance, and organizational performance. I'll admit that the survey questions below are not perfect, but they're at least directionally correct; you could certainly build a good instrument on top of these ideas if you wanted to, but in the meantime I think they're useful at face value.

Building on that, the observable data points turn out to be, again, pretty useful for spotting where work needs to be done. These measures do tend to elevate organizations that are legally able to work in public, but part of me thinks that might be a bit of a cop-out, so I'm going with it. You could create your own internal version of this if you wanted to, using measures that you think are better proxies for or indicators of the base ideas.

• Purposeful: Ratio of purpose references to total paragraphs in the CEO letter, 10-K intro, and latest earnings-call transcripts
• Fit: Net Expansion Rate disclosed in investor materials (revenue growth from existing customers YoY)
• Vital: Count of employee-authored tech/design blog posts, conference talks, or open-source commits per 1,000 employees over the last 12 months
• Powerful: Median days from publicly reported bug to merged patch in company-owned GitHub repos (or disclosed engineering tracker)
• Fair & Just: Unadjusted median gender pay gap (%) published in ESG or pay-equity reports.
• Connected: Share of merged pull requests opened by external contributors (non-employees) on company public repos over the last 12 months
• Safe: Annual Glassdoor review percentage that contains negative safety keywords (“retaliation,” “can’t speak up,” etc.)

I tried this myself with five public companies that shall remain nameless; I had ChatGPT gather the data and then I used my expert assessment to normalize what the computer was able to gather into a legible score, with 10 points as the maximum and 1 point as the minimum. This does flatten some of the assessment, but sometimes you gotta make tradeoffs for the sake of a prototype.

"SaaS A" is a company that makes software for people who make software. This software‑for‑developers firm runs on radical openness: monthly releases, 25 % of code merged from outsiders, public salary formulas, and incident write‑ups within a day. Those practices push every score into the top decile, so it runs the table on the assessment. Great company to work for. Great product. Good for the world.

"SaaS B" is another toolmaker for teams. It continually reiterates its purpose and keeps net retention high, but external collaboration is limited and its gender‑pay gap is still large (though disclosed and tied to exec bonuses). Hence strong 8s and 9s on Purposeful, Fit, Vital, Powerful, but yet middling 5–7s on Connected, Fair & Just, Safe.

"Consumer Tech A" is a commerce platform known for shipping fast – the median pull‑request merges in five hours – but it recently cut DEI funding and has so‑so Glassdoor scores. That explains a perfect 10 on Powerful and solid 8s elsewhere, offset by a 3 on Fair & Just and 4 on Safe.

"Consumer Tech B" is a global streaming giant with the world’s lowest churn and a culture that prizes candor. Purpose references saturate its ESG report, pay equity is near parity, but it releases post‑mortems slowly. Thus 10s in Purposeful and Fit, strong 8s and 9s in most criteria, and a 7 in Connected.

"Financial Services" is a tech‑forward bank: clear social purpose, high customer NPS, and 90% “great place to work” scores. But regulatory friction slows code merges, diversity gaps persist in international teams, and incident transparency is expectedly thin. Result: high Purposeful and Safe (9s), fair mid‑table Fit and Vital (7s), but 4s in Fair & Just and Connected.

To me, this a) scans and b) is useful. I'm fairly certain it would work well with teams, too.

What next?

So, to the comments and maybe email responses here: Would you use this? Should we do something like this? Are these criteria working for you?

Have you ever slogged through a consulting project that promised transformation (or perhaps worse, where you promised transformation) – then changed nothing?

Have you ever looked around a room full of smart, well-paid adults and wondered why work still feels like 1995, but with faster Wi-Fi?

Same. Saaaaaame.

I wrote Hidden Patterns because most solutions aim at the symptoms – new org charts, pep talks, shinier collaboration apps – while the real trouble lives deeper, in the invisible routines that shape how we gather, decide, and learn. When you actually start changing those patterns, I've seen cultures flip almost overnight.

Below are three of my favorite patterns from the book. You can run any one of them before lunch tomorrow. No reorg required.

Distributed Management (14)

Break the manager role into bite-size tasks (getting things done, coaching, assignments, vision-setting, development) and hand each to the teammate best suited for it. That clears bottlenecks and spreads real authority.

To start, try listing every recurring manager task on a whiteboard. Let the team claim ownership where they have energy + competence. Capture the hand-offs in a Team Charter (43) or Logbook (57). Build on this pattern with Elections (15, rotate key tasks every 6–12 months), Colleague Letter of Understanding (40, document the new agreements), and Expanded Available Power (4, give task-owners budget/time to act).

Do the Right Thing (26)

Worry about doing things right only after you’re sure they’re the right things. Formalize how the team defines value, chooses work, and course-corrects.

Put this into play by running a 30-minute “Stop-Doing Sprint” where everyone writes each active project on a sticky, sorts them into Advances Purpose, Unsure, or Doesn’t, then redesigns at least one “Doesn’t” item on the spot, and gives every “Unsure” item a two-week value test. (If there's no value, kill the work.) Build on this pattern with Strategy Heuristic (29, create a one-sentence rule of thumb), Active Steering (27, revisit direction every two weeks), Structured Decision-Making (30, add clear go/stop criteria).

Length Limit (64)

Put an explicit expiry date on roles, processes, even goals. Regular renewal keeps things fresh and prevents zombie work.

Try this by picking one live commitment (say, the weekly status meeting) and giving it a sunset date: “we'll stop doing this meeting in 90 days unless we decide to keep going.” Note the date in the calendar now. From there, you might improve your practice with Retrospectives (66, schedule a look-back before each expiration), and Health Checks (59, monthly pulse to catch drift early), both of which build toward a mindset of Dissolvability (9, treating every team and project as temporary).

These are small moves. You might be doing some version of these already. But stack a few together and you will start to feel systemic progress. That’s the promise! Tweak the code, not the people, and watch work get good again.

What are those numbers?

This project was inspired by A Pattern Language, which names 253 patterns for redesigning the built environment around us. I started collecting my own pattern library circa 2013, and finally finished the collection at 75, ranging from deep foundations to the changeable aspects of our physical workspace.

If you're nodding along here, now’s the moment to add to cart at Amazon, Bookshop.org, wherever you buy your books. Direct from the publisher? Barnes & Noble? Hudson Booksellers? Yes, even Walmart.

Hidden Patterns

A book about fixing the invisible stuff that makes work suck. Not the obvious stuff like bad bosses or dumb policies, but the deeper patterns that slow teams down, kill good ideas, and waste everyone's time.

It gives you small moves that actually help work get better, no matter where you sit in the org.

Pre-Order on Amazon

A quick thing on pre-orders, for those of you who someday want to write a book: pre-orders are a cheat code. As I understand it, every copy you buy before launch rolls into week-one sales, which drives bestseller lists, unlocks bookstore placement, and convinces my publisher to keep the lights on for a second print run. You might even see this in an airport.

A little bribe for individual buyers

I’m printing a deck of 75 Practice Cards: one for each pattern, plus quick-start instructions. The first 100 people who DM or email me a pre-order receipt will get a set. One per human while supplies last.

"I work at a company. Can I buy in bulk? What do I get in exchange?"

That's amazing, too. Here's a link where you can do that, and a different offer. Email or DM me a receipt, and...

• For any bulk buy (10+ copies through the link below) → I’ll host a private virtual workshop for your team.
• For a big bulk buy, like... 500+ copies? (and you cover travel) → I’ll show up in person, sharpies and sticky notes in hand, and we’ll redesign your team/org/whatever, together using the cards.

Thanks, friends. For subscribing, and maybe even pre-ordering this thing!

Goals

• Think through what's required of people who aim to make change within a business. Changemakers are sometimes consultants. Sometimes they're internal folks. Sometimes they're people with Org/Industrial Psych degrees. Sometimes (way more often than some would expect) they're Information Architects who became UX people who became Service Design people who, as they stared deeper into the abyss, decided to do something about it. How do we do it? How do we get better at it?
• Think about what I personally need to work on next.
• Help me be a better partner to my colleagues as they develop.
• Help people who want to get into this field find their way in from the adjacencies. A ton of the skills on the original SMM are applicable here. True heads will recognize Research and Counsel are lightly edited or directly lifted, respectively, from the original. Presentation has shifted here to Facilitation. Making (old) and Content (new) are pretty similar – with the adjustment mostly focusing on the spreadability and usage of the stuff you make, rather than just being able to move pixels tastefully (and fast). I have a little bit of a dream of making a bunch of these SMMs and hosting them in a single place for all of us to see where our skills overlap and where they don't, but that's a separate post.
• Make some shareable content so more people know who I am, because I'm writing a book. There, I said it! Hopefully out next year on Matt Holt Books/Benbella.

Anyway, here it is, with colors from Flexoki:

And just like the original, here's a short description of the skills.

1/ Research

The ability to uncover and synthesize critical information about organizational dynamics and industry trends. You can’t rely on gut feeling alone. Whether via academic journals, 1:1 interviews, or just a slightly more culturally in-tune morning commute, changemakers must be information sponges. You surface insights that spark innovation and fuel transformation.

2/ Corporate Strategy:

The capacity to develop and implement strategies that steer an organization’s growth. It’s about connecting the dots, creating a clear path forward, and ensuring that change initiatives align with the broader strategic agenda (ideally one you’ve had a hand in shaping).

3/ Organizational Design:

Expertise in designing teams and processes to drive adaptability and performance. Whether restructuring hierarchies, forming new teams, or changing how groups make decisions, changemakers know that structures shape behavior. And they’re ready to reimagine the operating model when necessary.

4/ Content:

The ability to create and manage materials that drive engagement, clarify processes, and support strategic goals. Every function in a business ultimately produces content – templates, presentations, videos, etc. – shapes how ideas are communicated and change is implemented. Changemakers must excel (lol) at crafting content that educates, influences, and moves organizations forward.

5/ Business Functions:

Understanding the inner workings of business functions and how they interact. You need to speak every department’s language. To drive change, you must know which levers to pull, and which ones might break if you pull too hard.

6/ Learning & Development:

The ability to inspire growth, resilience, and continuous learning while guiding others through change. Whether coaching, mentoring, or cheerleading, changemakers understand that driving change starts with empowering people.

7/ Change Movements:

Mobilizing people and resources to drive sustained transformation. Great ideas are only the first step. This skill is about overcoming resistance, maintaining momentum, and turning initiatives into movements that take on a life of their own. Note: knowing about things like ADKAR (the WWWWWH of change, let's say) is different than, and a contributor to, the Change Movements skill. Maybe it's a precursor, but I'm specifically not interested in calling this a Change Management skill. FWIW.

8/ Facilitation:

Guiding groups through complex discussions and decision-making. Priorities and perspectives will always compete. Facilitation is the ability to create space for productive dialogue, to help groups through their stickiest moments, and helping folks embody the change (as contrasted with simply thinking about, or considering a mindset shift). Things like conflict management live here, too.

9/ Counsel:

The ability to provide senior-level advice across all business areas. The highest level of this skill involves fostering long-term partnerships with clients (and let's be real – even if you're on an internal team, you have clients), helping them navigate change over time and becoming an integral advisor beyond any single engagement.

As before, each of these skills can be a business unto itself. We always thought that the best thing we could do was to strive for Level 7 across all of the skills; over-indexing on something and becoming known for one thing globally would be bad business (and bad for our brains, hearts, etc.).

Anyway.

• Send me your thoughts/reactions. I'd love to hear them. Did you know there is a comment field on the site? Try it?
• Let me know if you want to talk about this live. My schedule is less packed than you might think.

Let me know if you want to work with me on my dream project of getting multiple SMMs into one (beautiful, interactive, intentional, e.g. something that isn't just Google Docs) place.

A Pattern Language continues to be one of my all-time favorite books and IMO is a must-read for anyone working in organization design. It’s about building good cities and towns and buildings and rooms and lives and it’s packed full of patterns ranging from massive (Agricultural Valleys) to tiny (Different Chairs). This ↓ is a really nicely digitized version of it.

List of Patterns

See the README for an explanation and discussion about this project and how to use it. The Patterns A pattern language has the structure of a network. […] The sequence of patterns is both a summary of the language, and at the same time, an index to the patterns.

It’s missing some of the richer parts of the printed original – little descriptions of why the patterns work, but it does have nice graphs of the interconnections between the patterns. Shoutout to my favorite pattern, Six-Foot Balconies.

Side note: mapping pattern relationships is a good diagnostic practice. Here’s an old example of what that looks like in practice ↓

RTO makes senior employees quit

Speaking of identifying causal (or at least interesting) relationships in OD work, here’s a pretty impressive examination of the impact of Return to Office (RTO) mandates and retention of senior-level employees. The unsurprising conclusion, in the lede of coverage from Ars Technica:

Return to office (RTO) mandates can lead to a higher rate of employees, especially senior-level ones, leaving the company, often to work at competitors.

My stance here:

1. These kinds of mandates are obviously bad management practice that we will look back on with extreme prejudice. Accidentally losing senior talent to competitors is both expensive and bad for business.
2. Working in physical proximity to your colleagues is good. Human connection is important for mental and emotional health. Developing a shared language is doable online, but harder.
3. Remote work isn’t new. Executives in large corporates have been working primarily remotely for a LONG time, and it’s been…fine. How else could you manage a multinational company?
4. This is the latest in a long line of managers doing things that actively reduce performance but increase the feeling of control.
5. The biggest problem here is that RTO is one of the most boring mandates you can make, and boring strategic calls are extremely disengaging for senior talent. Mandates should be exciting, game-changing, challenging things. Like this.

Anyway, this is a dumb thing that companies should stop doing. And the methods in this report are fascinating.

This is amazing

This is zero percent an org design thing, but maybe you’re looking for a diversion? Digitized old maps projected into 3D. So cool. You could lose hours in here!

Hi-Res Interactive Map of Mt St Helens, Cowlitz County, WA in 1919 (1919 ed.) | Pastmaps

Explore a hi-res interactive 1919 map of Cowlitz County. See changes over time, compare to present day, or buy a museum-quality print.

Pastmaps

Values are diverging worldwide

Worldwide divergence of values - Nature Communications

The authors test whether social values have become converged or diverged across national cultures over the last 40 years using a 76-country analysis of the World Values Survey. They show that values have diverged, especially between high-income Western countries and the rest of the world.

NatureJoshua Conrad Jackson

I hadn’t heard of “sacred vs. secular” and “emancipative vs. obedient” values before – that is, values that uphold or reject tradition, and values that expand or restrict individual freedom from the group, and it’s fascinating that the world is especially divergent on especially emancipative values. And there’s an interesting set of convergent values here:

1. More tech
2. More feelings of responsibility for kids
3. Greater respect for authority
4. Less importance placed on work

We’re also finding in some research we’re doing with marketing teams that the teams that report having “best-in-class CX” are those that also report being able to “feel the vision/values of the company in the products/services.” Here’s to more opinionated firms, I guess?

Extremely spicy, public exit interviews

Mat Baxter’s Huge turnaround job

Ex-Huge CEO Mat Baxter talks about how to give creative agencies a shot at a sustainable future

ContagiousJames Swift /

There’s a lot of really interesting, good stuff in here but at least for me this misses the mark:

Now, we all know that there’s a very small relationship between brand awareness and performance. There are lots of brands in the world with 95% brand awareness who can’t make their sales numbers. Coca-Cola has very high brand awareness. Is Coke happy with the amount of Coke Red it’s selling right now across the world? No, they’re fucking not.

Having grown small businesses from zero to something, and having been inside large organizations with sophisticated economic models that predict future revenue growth, I can tell you that a) awareness isn’t the thing executives are necessarily concerned about, but the broader basket of brand measures are definitely important, b) awareness absolutely matters, and c) any reason why an exec enjoying high awareness might bemoan lower sales is going to be much more complex than “oh, there’s no relationship there.”

Also, $KO is close to all-time highs as of this writing.

Tim Brown (IDEO, Neol) proposes a few reasons why creativity seems so unreliable: we don’t ask good enough questions; we don’t have enough diversity; and we don’t focus enough on implementation. There’s an interesting connection in that last idea to what seems to be the central thesis of this thorough, mostly fair examination of design thinking that came out a few months before Brown’s post. He’s cited in the article, as is Cyd Harrell (18F, City of SF): “There’s a mismatch between the short-cycle evaluations [in commercial design] and the long-cycle evaluations for policy.” This reminds me of the “Social Origins of Good Ideas” paper, which explored the hypothesis that “people who live in the intersection of social worlds are at higher risk of having good ideas.” Life is hard between pace layers, but it’s where the magic happens.

An unspoken bit in here that I think is worth exploring: how can organizations that need innovation a) recruit folks that have ideas and b) keep them around long enough to see the ideas through to execution.

Related to all of this is this research from 2017 on the value of creativity. Embedded within is a finding that the most creative companies are those that make decisions quickly. We have to have good ideas, and we have to be able to act on those good ideas. Helping either side of that equation is valuable, and I don’t think it’s helpful to say that either the idea or the execution is more or less important the other.

11 Laws of Showrunning

Separate from the above but deeply related is this essay from Javier Grillo-Marxuach. The Laws themselves are going to resonate deeply with anyone who works in or around (waves hands) creativity, but I especially like the emphasis on the writer being the one who has to bring the thing to life – not some special genius in the corner who relies on a business-person to produce, but the productive force at the center of a show.

Opinionated Palettes

I’m super fascinated by these highly opinionated color palettes.

Poline is an algorithmic palette generator that “harnesses the mystical witchcraft of polar coordinates. Its methodology, defying conventional color science, is steeped in the esoteric knowledge of the early 20th century.” Okay!

Flexoki is a palette “inspired by analog printing inks and warm shades of paper.” I like that it’s a passion project of the CEO of Obsidian. I like when CEOs have cool passion projects.

Both of these remind me of an exhibition I recently saw at the Danish Design Museum that featured a color palette as an organizing principle for a collection of the Museum’s objects. The palette was created by Margarethe Odgaard and each color has a deeper, somewhat unexpected meaning. “Ivory” is a black created by charring ivory. “Humans” is a human-made cobalt green. “Love” is a red of a particular frequency that raises blood pressure. Neat.

Also: anyone remember Dopplr’s logo and constantly changing color palette, based on where you traveled? Ahead of its time.

Potent Downloadables

I really enjoyed these three PDFs: created with care, extremely specific, helpful. Send these to me!

New Creative Era

A new creative era awaits.

New Creative Era

Cultural Vibrations — Hannah Grey

Hannah Grey

Download the Playbook — Recess

Recess0

Randomness

Fictional Brands Archive

Speaking of passion projects!

Visual design rules you can safely follow every time

This is an absolute must-bookmark for anyone who makes a deck or a website.

Also on LinkedIn. BANNED on TikTok.

More on this topic:

Pace Layers for Organization

Pace Layers help visualize, distinguish, and discuss different kinds of work and teams within an organization. Here, I bring together a bunch of great thinking into a single construct. Enjoy!

CPJ.FYIClay Parker Jones

Skills Maturity Matrix + Organizational Pace Layers

What happens when we cross the Skills Maturity Matrix with the Org Pace Layers, and start getting real.

CPJ.FYIClay Parker Jones

Organizational health is (still) the key to long-term performance

We look at why companies that prioritize organizational health tend to be more resilient, operate more effectively, and generate better financial results.

McKinsey & CompanyArne Gast

This just in folks! Water is wet, apple pie is delicious, and organizational health is essential for firm performance.

I don’t know how we’re still at the point where we need to prove this to people, but it seems like it’s still the case. After all, it seems like some companies are taking a “beatings will continue until morale improves” approach when it comes to creating innovation and delivering for customers. Nonetheless, McKinsey studied over 2,500 firms in “every industry” and found that healthy organizations delivered 3x the returns as they are unhealthy peers. And this isn’t just a rich companies are good at things situation: companies that improved their organizational health drove 18% higher profit the next year. Good for sending to your boss!

The Challenges of Becoming a Less Hierarchical Company

More and more organizations are looking to create flatter, less hierarchical models to increase collaboration, agility, and employee empowerment. But recent research at a food processing company in Colombia outlines some stumbling blocks companies might face when trying to change their structure. Specifically, the researchers and company CEO highlight a series of structural and people dynamics leaders should look out for in their own efforts.

Harvard Business ReviewEric M. Anicich,

In probably one of the more nuanced critiques of self-management/new ways of organizing, HBR dives deep into the Avinsa case – with the aid of the CEO of the company as a co-author. I continue to be a little bit confused about two things that tend to show up in reports like this:

1. The emphasis that everyone has on flattening or reducing hierarchy. Overly deep organizations have some drawbacks (coordination costs, etc.) as do overly flat organizations (concentration of power in leadership, etc.). 
2. The critique always trends toward “this new way of organizing is hard and weird,” and there’s usually only a passing comparison to “traditional orgs.” The challenges listed in the article – zombie structures, integration challenges, uneven progress, change fatigue, empowered leaders, jargon, reskilling, and balancing short- and long-term goals – are big problems in “regular” orgs. And regular orgs don’t have good tools for resolving those problems, but self-organizing firms do. 

Anyway. This is an important report, but I’d love to see one that compares what’s happening at an Avinsa to, say, an average company in the food-processing space.

The Man Who Killed Google Search

Wanna listen to this story instead? Check out this week’s Better Offline podcast, “The Man That Destroyed Google Search,” available on Apple Podcasts, Spotify, and anywhere else you get your podcasts. This is the story of how Google Search died, and the people responsible for killing it. The story begins

Ed Zitron's Where's Your Ed AtEdward Zitron

A few things here:

1. One of those rare case-studies favor of? siloes/strong boundaries between some areas of a business that might seem to be well-served by integration. Church/state, Ad sales/newsroom, etc. 
2. Why hiring is important
3. Why every company needs decision-making methods that deprioritize (to the extent possible) political will, coercion, etc.

It’s a fascinating and sorta sad read.

CIPD | High-performing teams: An evidence review

CIPD

This summary and accompanying evidence review cover a meta-analysis of 70 studies of team performance. The two tables here are the real meat of the study: showing that the strongest effects on team performance are Psychological Safety and Shared Thinking (‘cognitive consensus’…referring to “whether team members define and think about issues in a similar way.” love that!), and that the interventions with the strongest effects on team performance are Teambuilding (vague), Teamwork Training (nice), and Group Goal-Setting.

The thing about shared thinking gives some language and definition to stuff we do but don’t get enough credit for when we coach teams.

Platform Teams in the wild

I like this report from Platform Thinking Labs on the The Building Blocks Thesis. Some good diagrams and such to lift and shift in here!

Digital building blocks are standardized and modular units of value creation, which, when combined, have the power to drive exponential value creation in an ecosystem.

This reminds me a lot of the approach that Stephan Gans and the PepsiCo insights team have taken, documented here:

Reimagining consumer insights at PepsiCo

Stephan Gans, PepsiCo’s Chief Consumer Insights and Analytics Officer, wants to bake real-time, data-rich insights into the food-and-beverage giant’s commercial decision-making processes.

Strategy+businessVishal Garg and Tom Fleming

My thinking was that if we really want to change the game, we’re more able to do that with the smaller, specialized firms that are more incentivized, agile, and ambitious. Let’s build something with them. So we built a series of partnerships where we jointly created the IP and the consumer insights tools. And we are happy to sell those tools to other companies that don’t compete with us—there’s been huge uptake. It’s not only a learning loop but also an entrepreneurial approach, with money coming in that has enabled us to accelerate the journey we’re on.

So…to summarize:

1. They’re developing tools that they use to develop insights.
2. The insights go on to inform marketing (product/promotion) decisions.
3. The tools earn revenue from non-competitive companies who pay to use them.
4. The tools get better as a result, helping PepsiCo teams win.

That’s cool.

Also on LinkedIn and YouTube. BANNED on TikTok.

But we’ve all been on teams where the mission was great, but the performance was decidedly not.

Here are four ways teams can go astray even if they have a fantastic, visionary mission – and what you can do about it.

1/ There’s another team with an identical mission, and oops, you were working on the same thing

This happens so frequently, and happens in every possible area that you can imagine. Two departments buying the same software. Two teams creating a new product that targets the same consumer. Two agencies buying media against the same target audience, bidding against each other and driving up their CPMs.

The usual answer: stay quiet and avoid the problem, because dealing with it is too painful, and potentially risky! If you flag the duplication to someone with power, what if you either a) are made redundant or b) held accountable for allowing the duplication?

The fix: 

• Accept the duplication and create a way to learn from divergent approaches. This also means you need a fast way to kill the projects that aren’t working, and to scale the stuff that works.
• Implement an org-wide solution for finding out who works on what, and develop a culture where people do go search for this kind of information before they start working. This is obviously a lot harder than the first fix, and we don’t really have time to get into it here.

2/ Everyone on the team has a different boss, and those bosses don’t agree

Okay, so, you’re on a team, and you all agree on a path forward. You bring that to your steering committee – usually a group of people who are the bosses of the people on your team – and stalemate ensues. Brand doesn’t agree with innovation, and finance doesn’t agree with either of them. Insights is wondering why they weren’t included from the jump. Legal is 20 minutes late to the meeting and cancels the whole project.

The fix: Be sure everyone is there for the kickoff and make the kickoff consequential. Be sure you surface disagreements early, with a pre-mortem or a Stinky Fish exercise. Perhaps re-think the steering committee and their decision rights altogether.

3/ The way promotions work is all about individual achievement, so, nobody works together 

Sam from Insights is hunting for a promotion, and the key – according to their manager – is the launch of a new reporting platform. For Sidney from the brand team, it’s growth against the new launch in the premium category. For Austin from media, it’s standing up a new LTA model. And all of these people are on one team, with a clear mission: “Get the core business back to growth.” But that mission isn’t part of their annual goals, and it definitely has nothing to do with their incentive scheme.

So at the end of the year when the core business isn’t in growth mode, who’s to blame?

The fix: 

• For the team: harmonize the goals with each other. There’s no reason individual achievement can’t work toward something important. Yep, that’s right! Discuss your goals with your teammates.
• For the leaders: stop rewarding individual achievement. Stop saying, “Laurie did such an amazing job on the new brand launch, isn’t she so smart!” and start saying “The brand team absolutely crushed the launch – that’s the kind of performance we’re looking for.” Stop giving individual awards at the end of the year, and start giving awards to whole teams. Simple, but hard.

4/ You have a big mission, but no authority to do anything related to the mission

Let’s say you have been given a big business mission. Something chunky, challenging, and clear: “Win with Gen Z.”

But you can’t approve any creative. You can’t decide to work with anyone aside from the old agency that was grandfathered-in. The brand guidelines are unclear or unwritten, so any movement in a new direction, perhaps as directed by what you learn from audience research, sends up a red flag and halts progress. You’re not allowed to talk to customers, so good luck having any kind of idea of what to do in order to win. 

The fix: Flag this early. Describe the authority that would be needed – and any conditions to that authority – and go raise it directly with the person who already has the authority in question. Ask them for direct, written permission. If no one has explicit authority and everyone is just using soft power and influence to move things along, we should talk more.

A great mission is the right starting point… but it’s still a starting point. Additional work and effort goes into making it practical. Bring the mission to life in the work and don't be afraid to evolve it (or the broader organization) if it is set up to fail.

Also on LinkedIn, YouTube and TikTok, where it was very popular, considering that it’s a video about prioritization.

Explainer here:

Strategic Compression

Strategic Compression is a way to improve the usability of strategic thought. If your business, project, function, division or team are facing high uncertainty, you need an adaptable, usable approach.

CPJ.FYIClay Parker Jones

Also on LinkedIn, TikTok and YouTube.

Robust guide here:

Role-Based Team Structure

Role-Based Team Structure is the best way to articulate expectations for your team. It provides durability, flexibility, and clarity. It builds a playbook for running your team. But perhaps most importantly, it helps divvy up the work in a more equitable, sane way.

CPJ.FYIClay Parker Jones

Also on LinkedIn and TikTok.

Also on LinkedIn. BANNED by TikTok for being too practical.

Also on LinkedIn. BANNED by TikTok.

Article version:

Use DICE instead of RACI

RACI is vague, hard to use, and reinforces the “what the hell is happening here” status quo. DICE is specific, easy to use, and shines a bright light on dysfunction.

CPJ.FYIClay Parker Jones

Also on LinkedIn and TikTok.

More here:

How your org structure is leaving millions on the table

An approach that yields unbelievable levels of productivity, driven by a focus on the work that matters most for growth. In practice, this initially feels like cheating.

Black GlassClay Parker Jones

Also on LinkedIn and TikTok.

The CEO came to us with a problem:

"We're doing everything we can to empower people. They just don't want to take responsibility."

The senior team met weekly and had done a great job describing their ways of working – they had a clear charter for not just the LT, but the way they wanted to meet, and how they wanted to interact with the org.

But even with a high level of intention, and the work ethic to back it up, they had a problem with team empowerment and engagement in the levels below the SLT.

We dug in further to see why they weren't getting what they wanted, because there's usually something else going on when you hear a quote like this.

After asking around, it was clear that there was something else going on. Examples:

1. Despite explicit decision rights being given to an individual VP, when the time came to make a smart call about opening a new location, they were held up at the last second to wait for approval from another set of hidden stakeholders (that team lost the deal for the real estate as a result).
2. Whenever it came time to make a decision, another additional committee appeared, needing to see the slides.
3. When teams operated with transparency and shared their plans, other portions of the organization would raise concerns to leadership and slow their progress.

This was obviously not a situation where teams were shirking their given empowerment.

What was happening with the SLT?

We asked the SLT for all their agendas from the past year, and this is what it yielded: 48 items, which we were able to sort quickly and play back to them. We popped these into a survey with a simple question for each decision: should this topic have made it to the SLT table, based on our charter?

In a few minutes we had our number: more than 70% of the agenda items shouldn't have made it into the room; they should have been decided at a lower level of the organization, faster, and with richer data.

This means that out of the 2,400 hours that this most senior team in the org met that year, 1,680 of those hours (or so) could have been spent on other decisions.

So what do you do about it?

Our first idea – giving the organization practices to make better decisions – was helpful but ultimately didn't solve the problem.

We tend to think in terms of decision-rights (RACIs, DICEs, RAPIDs) for problems like this. This is almost always the wrong idea, because project-level reality is more complicated than we think.

But projects only look like this on paper:

Big, chunky phases with pretty easy slicing of responsibility.

In reality, we know they look like this:

Try making a RACI for that, and you’ll end up spending all your time documenting, and not much time making actual progress; as you get further from the center or top of the organization, the decisions get smaller, more frequent, and harder to accurately describe.

A better idea is to give people better practices for making decisions.

In general, there are three things we’re trying to do here:

1. Helping people notice what sort of decision they're facing.
2. Helping people deciding how to decide.
3. Helping people managing conflict between two good options.

All of these are under-described and under-practiced.

We offered some shared language, tooling, and guidebooks to help teams through this:

Each of these kinds of decisions is driven by the kind of relationship you want between different parts of the organization. This is adapted from work by Susan Finerty, Gregory Kesler and Amy Kates. Grab their books!

We heard a lot of great feedback from teams on these ideas, but after a few weeks of trying them on, there still wasn't much change.

We needed something bigger.

What we found was that teams are escalating things that don't need escalating to avoid pain.

It's painful to find out there are 5 more committee meetings that need to see the slides. It's painful to get held up from making a good business decision – and to lose out on something important as a result. It's painful to hear heckles from the peanut gallery while you're being a good corporate citizen.

Instead, teams chuck it up to the SLT and let them hash it out.

Our strong hypothesis was that most of this was due to confusion on who could make what call, rather than malicious compliance.

There are a few ways to sort out decision-making among senior leaders. All of them result in naming winners and losers: some people take over the decisions that others were making before. As a result of this work, the organization is wins some new effectiveness, and some of the individuals are losing stature. So even though this is a zero-sum game between the business and its people, the alternative isn’t one we can accept.

The answer here is to go slow, be methodical, and help people deal with the losses.

Here's what we recommended.

There are two cohorts to work with here: first, the office of the CEO, which consisted of the CEO himself, his Chief of Staff, and Executive Assistant; second, the group of eight direct reports, which were a mix of divisional and functional leaders. We settled on a call-and-response pattern between the two groups:

1. Invite to change, with a clear explanation of what could change, the process being used, and how to participate.
2. Self-evaluate: We asked each direct report to individually consider two prompts. “What do I own?” and “What do I think I should own?” We gave them a template to help structure their response, but if you are doing something similar, a simple spreadsheet would do.
3. Synthesize: Gather up all the inputs from each direct report, and work to understand overlaps and gaps.
4. Declare: Decide which senior leader should own which thing, and workshop those decisions with the direct reports. This stage is deeply political and precarious, as there will always be winners and losers. It’s important to allocate time and attention here. Be ready to reiterate that the old system wasn’t working, and keep the corporate strategy near at hand to help give legitimacy to transfers of power.
5. Try: Give the new operating system a go for a reasonable amount of time.
6. Sense: Notice where breakdowns and reversions to habit happen.
7. Edit: Using what the directs have noticed, edit the governance. (Keep doing 5, 6, and 7 … more or less forever.)

Wrapping Up

This stuff requires constant attention, and the only ways I’ve seen firms stick to consistent, market-leading levels of “decision quality” is by making it part of what it means to be a member of the group.

At Amazon, if you don’t operate according to the Leadership Principles (LPs), you’ll quickly find your way out of a job.

Here’s their LP covering this topic:

Have Backbone; Disagree and Commit. Leaders are obligated to respectfully challenge decisions when they disagree, even when doing so is uncomfortable or exhausting. Leaders have conviction and are tenacious. They do not compromise for the sake of social cohesion. Once a decision is determined, they commit wholly.

At NeXT (and I assume this is something he carried forward to Apple) Steve Jobs brought passionate commitment not just to good choices, but to the governance framework that would consistently produce those good choices.

For anyone leading a team – global LTs and working teams alike – this ought to occupy something like 30% of your attention.

Anyway.

Around a year later, I caught up with a member of the LT from the story above and their reflection was that the process was painful but necessary: they used their improved governance to respond more effectively to COVID-19. Which is, I guess, what all of this stuff is about. Why spend a bunch of time on decision-rights? Why go through painful redistribution of authority?

Because all those thousands of hours of bad escalations compound something like 10-100x across the org. There aren’t a ton of chances for any org to instantly multiply their productivity without spending any more money. This is one of them.

For employees and managers in matrixed roles, life is hard.

That's because consensus is hard, and the whole dual-reporting thing was designed around consensus.

But in general, managers tend to focus on the individual, rather than the system.

What’s going on with so-and-so? Why can’t they keep up?

So we might start with role definition and performance improvement. We could fill out something like the role description below, and hope that things get better.

But what contributes to good performance in a matrix is less about the individual, and more about the ecosystem of goals, incentives, relationships that surround them.

What instructions is the individual receiving? Are those instructions aligned? If not, what happens?

In practice, the immediate relationship between functional requirements (e.g. “what do we think 'good' looks like") and business outcomes (e.g. “did the campaign perform to expectations”) matter most but often aren’t designed with intention.

NB: my usual preference would be to reconfigure the working group into a series of real teams, rather than sticking with a matrix, but that's not always practical or possible.

Enter: the Matrix Design Canvas.

The Canvas is a tool for design, and can be used by managers and individual contributors alike, either to improve their own situation, or to help someone else.

How to use it

1. Current State: Fill in the canvas based on your best understanding of how things work today, using stickies or text fields in your preferred working environment. Get the truth out there, no matter what you think of it.
2. Find the Gaps: In almost every Current State, you’ll find some gaps. A missing or unclear expectation; a broken relationship; goals and incentives that are out of alignment.
3. Reflect: How does the current state of the system drive individual performance (positively and negatively)? What might need to change?
4. Adjust: Using a new color of sticky-note or text, capture what will need to change in order to get more of the performance that you want. The canvas will make your next steps obvious.

Glossary

Further Reading

The Founding Flaw of Matrix Organizing

There’s an old bug inside the system, and it’ll never go away.

CPJ.FYIClay Parker Jones

Activist Presentations & Corporate Structure

TLDR: Activist investors want lean SBUs, and they don’t love that matrix

CPJ.FYIClay Parker Jones

How technology is redrawing the boundaries of the firm

Companies are reorganising themselves in the wake of digital upheaval

The EconomistThe Economist

I am always equally annoyed and surprised by how often Basic Technologies (like Zoom, Slack/Teams, email, PowerPoint) end up as the core topic of conversation when it comes to organization design.

I should stop being like that.

For one, technology shifts strategy:

For example, between the 1980s and the 2010s, globalisation and the IT boom boosted economies of scale, which encouraged market concentration. But they also increased competitive pressures and cut the cost of communication and collaboration between firms. The net result was for many companies to shrink their scopes. In research published last year Lorenz Ekerdt and Kai-Jie Wu of the University of Rochester found that the average number of sectors in which American manufacturers were active fell by half between 1977 and 2017.

For two: technology changes how we think of the barriers of the firm. That's heady, for sure, but it's real.

Besides making it easier to tap non-employees, technology is enabling companies to collaborate more seamlessly with other businesses. In 2020 Slack, the messaging platform of choice in many a workplace, launched a feature that lets users communicate with outside firms as they would within their own organisations. More than 70% of the Fortune 100 list of America’s biggest firms by revenue use the feature.

Two lols in here: 1) I love how The Economist describes stuff like this; 2) 70% of the Fortune 100 use Slack Connect!? Such a slap in the face to Microsoft.

I guess you could say how you chat is a strategic advantage.

Which takes me to...

Patterns Hidden Inside the Org Chart

How Microsoft researchers used machine learning to map collaboration networks that extend beyond the org chart

WorkLabAll StoriesLeadershipCultureInnovationCollaborationPerformanceWellbeing

This is some fascinating research from Microsoft, showing the real organization inside of the organization, and how it differs from the written organization design.

• Some leaders have "unified teams" that operate as wholly separate, isolated units (they don't email or chat much).
• Reorganizations can be measured by assessing pre- and post-reorg communications patterns (do new patterns form, or nah).
• In general, organizations don't much look like what we write down! They're organic, ever-changing things.

Also: the graphical representation of the org here is just really pleasant.

This is an inward view, though. What's an alternative?

Turn Your Teams Inside Out

Externally focused x-teams can drive innovation, performance, and distributed leadership but require a shift in mindset.

MIT Sloan Management ReviewDeborah Ancona and Henrik Bresman

I've written about this before, but this article is full of useful tips for how to push teams from being inwardly focused (usually on being good at something) to being externally focused (helping a customer be good at something).

None of this will be earth-shattering news for anyone who has been following along here, but there is one story that will be easy to port into a deck: Takeda created externally focused, experimental and remarkably democratic Spin-In teams that actually produced new molecules for their portfolio.

They did some things that would feel "soft" in most corporate cultures – sensemaking; ambassadorship; task coordination; clear exploration, experimentation, and execution phases; distributed leadership – but are actually key ingredients to being properly customer-focused.

Customer focus is key – even internal teams need to have a clear understanding of who their customer is, and what their real needs are.

Another lens I wish more teams would adopt is service orientation. And by that I don't mean "teams should be good at customer service" but rather "teams should intentionally design the services they offer."

How to build service design capability, step by step — Good Services

5 steps to raise your organisation’s service design capability, and a free tool to help you plan how to do it

Good ServicesLou Downe

Number one: buy Lou's book. It's good!

Number two: Lou and Sarah have a ton of amazing resources on their site. This one 👆 isn't too far off from a ~normal change management process (pick from Lewin, Lippitt-Knoster, Kotter, Prosci), but the details provided are helpful, and the tool (Google Sheet here) is quality.

By and large, big change initiatives skip over the learning bit. They do this because big organizations and the people that inhabit them have been trained that learning means "log into the intranet and complete your required annual anti-discrimination training."

It can be better!

Netflix, Democracy, and ~Operational Excellence

Why Netflix’s approach to complexity – going all the way back to 2001 – creates better results.

CPJ.FYIClay Parker Jones

Which leads me to something I wrote a while back now on democratic versus autocratic organizations and how something closer to democracy at work leads to more learning and better, deeper teaching.

Learning: Organization-level learning can happen through things like metrics and dashboards, of course, but the stickiest version of it happens when leaders listen to teams, and teams listen to each other.

Teaching: When leaders are focused on providing robust context for decisions, rather than simple mandates, individual contributors (who are usually but not always less experienced than leadership) learn more about the fundamentals of the business.

Quick note here for the yeah right crew out there: you absolutely do not need to have a comprehensive org change effort to make "more democratic" approaches work. You can just apply some good practices locally! Everyone around you will get smarter, and your team will be regarded as astoundingly effective.

I reviewed the academic literature on AI and machine learning's potential to automate professional tasks, then compared and ranked those functions against the modern marketing org. A mere 7% of it is strongly resistant to automation.

The Functions Least Likely to Be Automated

Strategy and new brands. Category strategy and innovation. Brand management.

The more strategic, less brand-aligned functions scored highest in defensibility against automation. Strategy and new brands held the largest concentration of highly defensible tasks. Brand management and category strategy and innovation each held a smaller but significant share of highly defensible work.

The reason isn't mysterious. These roles make decisions about future investments, with risk and perception in play, and they generate new ideas – work that depends heavily on social and creative intelligence.

The Functions Most Likely to Be Automated

Insights. Media. Internal agency.

The roles that scored lower in defensibility were concentrated on day-to-day growth or management of the existing business, including receiving and maintaining brand positioning. Internal agency, insights, and media roles are the ones I'd expect to be less resilient as AI and machine learning are further deployed in the marketing process.

How I Got There

I started with a meta-analysis of the academic literature, which converged on six capabilities least susceptible to automation:

1. Perception. Tasks that require complex human perception – assessing a landscape or situation, identifying proximity and properties, and making decisions based on that perception – are too difficult for machines or algorithms to replicate.
2. Creative Decisions. Generative AI has proven valuable in the creative brainstorming process and in speeding up the execution of creative or messaging, but it can't replace actual creativity – "the ability to come up with ideas or artifacts that are novel and valuable."
3. Social Tasks. Tasks that require social intelligence, like negotiation and persuasion, require an iterative process of assessment of and reaction to human emotion. Some algorithms can replicate aspects of this process, like predicting likely responses, but they fail to reproduce the real-time analysis and reaction of a human.
4. Development of Positioning. Technology-enabled segmentation and targeting can certainly inform positioning, but strategic positioning that communicates a value or message that resonates at a personal, emotional level with consumers remains a uniquely human ability.
5. High-level Product Decisions. Research suggests that high-level product decisions based on assessments of consumer behavior and identity are too difficult to automate; low-level product tasks like brand logo design can be supported by AI.
6. Entrepreneurship. Creative entrepreneurship – the process of identifying, pursuing, and capturing new opportunities for business innovation, creation, and growth – requires more creative intelligence and dexterity than AI can currently provide.

To understand how these tasks distribute across an actual marketing org, I ran them against a large global beverage company. Using ChatGPT, I generated 73 unique job descriptions covering the company's 233 marketing positions.

I then classified each of those descriptions against the six difficult-to-automate functions, giving each a rating of 1 (low requirement), 2 (medium), or 3 (high).

Averaging the ratings produced a final score for each job, and a scale to evaluate the org against:

• Jobs scoring 2.5 or above: high defensibility against automation.
• Jobs scoring between 1.51 and 2.49: medium defensibility.
• Jobs scoring below 1.5: low defensibility.

Low and medium defensibility doesn't necessarily mean these jobs will be replaced by AI. It does mean that many of the functions currently associated with these roles could be improved or streamlined using AI.

The average score across the full marketing org was 1.67. The distribution:

• 16 jobs (7%) scored 2.5 or above.
• 99 jobs (43%) fell between 1.5 and 2.49.
• 118 jobs (50%) came in at 1.5 or below.

This research only scratches the surface of how the gradual evolution of technology and the marketing org will affect one another, but it does point at where we'll need to rethink, realign, and reskill:

• Enterprising marketing departments will invest in areas like media and insights, where AI offers the greatest potential for automation and ROI. They'll simultaneously look at how to take some automatable responsibilities off the plate of professionals in human-intensive roles, freeing up more of their time for creativity, strategy, and innovation.
• We need to research and consider how this breakdown of defensible roles affects different segments of the workforce – across tenure, education, gender, and race.
• We need to seriously weigh the costs of automation against the benefits. All of these jobs, regardless of their scores, are good jobs. Replacing employees with AI may benefit the bottom line, but it can do long-term damage to the workforce by eliminating the valuable early-career experience that prepares professionals to take on the more complex tasks that can't be automated later in their careers.

From Defending the Budget to Defending the Job

Forrester recently estimated that AI will replace 7.5% of agency jobs by 2030. I think the impact is going to be much more substantial.

There's more to the story than the raw distribution above. Underneath the unsettling proportions of replaceable marketing work sit some worrying indicators.

Start with gender. The 136 women in the organization tended to hold jobs with less defensibility – an average score of 1.63 – than the 73 men, who averaged 1.73. The defensibility gap is smaller than the salary gap in this organization, where men's average salary was $161k and women's was $131k. The conclusion is unflattering: women in this org aren't only underpaid, they're also disproportionately holding jobs with fewer defenses against replacement.

The story doesn't get better when you look at tenure and education. Of the 217 people in jobs with medium-to-low defense against AI replacement, 107 had over ten years of experience, and 65 had MBAs. According to Forbes, the average cost of an MBA is $61,800. Multiply that out, and the org has spent roughly $4 million on additional education for jobs that may not survive the next reorganization.

Roll it all up. The annual salary cost of the 99 medium-defensibility jobs is $15.4 million; the 118 low-defensibility jobs cost $12.8 million. Eliminating half the jobs from each category would yield $14.4 million in annual savings – a 10-basis-point improvement to net income.

From a strategic point of view, this is an exciting moment. Companies that lean into artificial intelligence will have a hiring advantage for high-potential talent, a competitive edge against their peers in product differentiation and unit costs, and the ability to unlock new, previously unreasonable ways of doing business. That much is obvious to anyone who has even dabbled with these tools, and it's supported by a growing body of academic research.

From a humane point of view, it's more complicated. Each slice of that graph is a livelihood, and the chart couldn't be a better depiction of inequality if it tried: most of those jobs have a limited defense against replacement by a sufficiently well-trained computer.

So, Now What?

I didn't do all of this research to prove out a bleak future. I did it to get clarity on the pitfalls and the wins sitting in plain sight.

And the analysis somewhat misses the point. We should automate the work that gives us little joy and where humans have a limited advantage over a computer. We should replace what we automate with better, fulfilling, growth-oriented work.

• Innovations that serve underserved and underrepresented communities.
• Breaking the systemic problems and patterns in place in advertising and marketing.
• Developing and launching new – and more sustainable – products.
• Creating deeper relationships with customers and partners.
• Supporting psychological safety with and for peers.
• Coaching teammates, building new skills and capabilities.
• Impacting cultures and society toward forward change.
• Deep learning and research.
• Going home on time, and focusing on health and wellness.

Most of us only have a playbook suited for a pre-AI world, and our job descriptions reflect that playbook. The good news is that playbooks and job descriptions are well within our control. Better, more future-ready versions are within our grasp. We just need the will to make the leap.

Four New Structures

Beyond directly rewriting the job descriptions that are under threat to emphasize growth, fulfillment, and humanity, leaders can adopt new structures to solve the problems that AI will create. I see four:

• Multi-Tenure Teams, solving the talent drain.
• AI Councils, keeping the business in line.
• Platform Teams, bringing up the level.
• Coaches, keeping the human side in touch.

Bad jobs for incoming top talent produce a weak long-term leadership succession plan. As I've shown above, AI will hollow out the roles new graduates have historically filled. Those new grads are more likely than anyone to be power-users of AI tools, but they'll be joining firms that don't have much of a place for them – most of the routine tasks that have provided training opportunities for junior talent will be automated or handed to a computer entirely. Top talent will simply stop considering roles that don't offer immediate access to intra- and entrepreneurship, strategic leadership, and innovation.

In the early days of social media, there was an uptick in reverse-mentorship, where junior employees coached senior executives on the digital revolution. Companies are going to need to go beyond mentorship and develop Multi-Tenure Teams, where new employees, senior executives, and middle managers break hierarchical boundaries and work as a single, cohesive team.

The result: new grads get better jobs. Companies maintain their talent pipelines. Tenured employees get a diffusion of knowledge from the younger generation.

In the mid-aughts, companies struggled to write effective social media policies. Legal departments are still struggling to handle "Shadow IT," the tendency for business units to acquire and use their own software systems outside of their company's governance framework. This issue will compound dramatically as generative AI explodes – and helps internal non-developers build their own tooling.

Companies will need markedly better governance forums than the ones they rely on today. Traditionally, governance has focused solely on data integrity and security. There's an opportunity now to create AI Councils that offer more holistic guidance, balancing business needs and ground truth.

To pull this off, AI Councils will need greater proximity to operations, with more council members coming from junior ranks rather than the executive layer, and from operating units rather than HQ. They'll also need more inclusive, digital tools for vetting, deciding on, and communicating new rules and regulations – tools that go beyond MS Word, PowerPoint, and traditional intranets.

Generative AI tools will eliminate the idea that an entire function requires centralization. Instead, we'll see dedicated teams turning their skills into software. These Platform Teams will take functions like "Marketing Insights" and break them down into individual pieces – a Concept Screener for assessing the validity of marketing ideas before they receive budget allocation, an Audience Optimizer for improving a media plan's targeting.

Every department I've observed has five to ten human-intensive tasks that require intense, inter-team agreements to spin up but are necessary to the proper functioning of the business. I've seen this administrative overhead consume around 70% of a team's available working time inside large organizations. Platform Teams turn those tasks into simple, digitized workflows that take a few clicks instead of a multi-quarter agreement.

In the short term, this lets distributed or decentralized teams maintain quality and business alignment without paying the administrative tax. In the long term, these capability-as-a-service implementations become a core differentiator for the firms that had the guts to invest in them today.

Like the previous structures, the coach role isn't 100% new. But with the changes ahead, it will need to – and finally be able to – expand into a first-class, insourced member of the firm. Coaches will be essential for bridging the gaps between people, forming and scaling high-performing teams, and developing the human side of the business.

I see an increased emphasis for coaching in three domains:

1. Leadership Coaches, focused on nurturing the leadership skills of the new workforce, ensuring a smoother transition into roles of responsibility and influence. Leadership coaches will guide individuals in decision-making, conflict resolution, and strategic planning, ensuring the cultivation of a robust leadership pipeline for the organization.
2. Team Coaches, focused not just on inter- and intra-team dynamics but on the relationship between AI tools and human teams. They'll work on enhancing collaboration, communication, and problem-solving skills, ensuring that the human-team-AI integration is smooth and productive.
3. Skill Coaches, focused on the continuous development of technical and soft skills – ensuring the workforce stays abreast of the evolving technological landscape while maintaining the human touch in their interactions and operations.

As we enter an era where the research tells us many roles are replaceable, the role of coaches becomes irreplaceable – and probably powered by a knowledge-and-coachee database that lets fewer coaches work with more of the workforce.

Wrapping Up

For leaders, this is not the time to adopt a wait-and-see approach. Generative AI is already actively in use across the corporate world, from approved and intentional approaches like Bain's OpenAI alliance and Expedia's ChatGPT integration to grassroots applications at the edges of the firm. These tools are already driving labor disputes, like the recently concluded months-long Writer's Strike.

There are pessimistic views of the impact of this new technology on our workforce, ranging from minimal returns on investment to drastic job loss. I take a different view. I hope we'll land in a future where AI has helped us build dramatically more productive workplaces, and where it has helped us elevate the human side of work.

But as with any new technology, the future we get relies on the decisions we make.

Let's dig in.

Earlier versions of this analysis were originally published in three parts on Black Glass and at Forbes.

In 18 weeks, a traditional 30-person marketing team finishes around eight important projects. With a few small adjustments to how they’re structured, leading to substantial changes in how they spend their time, they could easily finish 100 projects of the same scale without spending a single additional dollar or adding people to the team.

Let me explain.

Here’s a simplified, traditional marketing org:

Reporting to the CMO are six Centers of Excellence (COEs). The leaders of the COEs are in the black boxes. The folks reporting into those leaders are listed in the white boxes. Each of those individuals works with other folks in the other COEs to get work done, but they report to their COE leader. These COEs are important, but come at a cost: increased coordination.

Here’s how the COE Leaders spend their time.

The majority of the manager’s time is spent on meetings about future work and administrative meetings for their COE, the marketing org, the business and 1:1s. None of their time is spent on actively advancing the ball on key projects, unless you count status updates and addressing/surfacing issues that lead to re-work (I don’t count this as advancing the ball).

Let’s look at their direct reports:

The crucial difference between these folks and their managers is the addition of six hours of focused working time (usually broken into smaller bits in the calendar), but there’s also more time spent on status updates, rework and dealing with problems in current projects.

Their weekly calendars probably look something like this.

All-in, these individuals are spending around 30% of their time on “maker” activities that are related to growth. 60% of their time is spent on management/admin activities. And around 10% goes to unavoidable losses (like tech/tool issues or taking breaks).

When I ask teams how they’d like to spend their time, they usually give me the opposite kind of allocation: 70% for growth, 20% for admin, 10% for the other stuff.

This undesired overspend on management and administration is driven by the siloed COE structure: work gets done in single-capability teams that have to pass work products back and forth, so status updates, org-related meetings and substantial pre-planning work have to happen before undertaking anything new. This is compounded by the number of discrete projects taken on by each individual and team. For this example, I assume that each individual takes on 4 projects at once and shares these projects equally with others on the team. In practice, I’ve seen that this number is usually substantially greater than 4.

And all of this has a massive impact on productivity.

If I assume a standard project duration of 500 hours, teams with this kind of structure require between ~50 and 100 days to finish that work, or around 10-20 weeks. This means that almost nothing of substantial value gets done within the quarter where it’s planned, so these teams are always working on opportunities that were pre-selected months or (yes!) years in advance.

The alternative is to forego the COE structure and replace its hierarchy with a series of multidisciplinary teams. For convenience, I’ve used the exact same number of people and types of roles in the example below.

With this new structure, the team is able to afford one more team than before – they no longer have the management “layer,” because they’ve redistributed these folks to the working teams.

I’ve observed that this shift has a massive impact on the nature of the work and how everyone invests their time.

• Reduce the amount of COE, Org, and 1:1 meetings required to get the work done and keep teams on track – to less than half what’s required in a traditional organization
• Reduce switching costs by working on fewer overall projects; by cutting the projects in active progress by 50%, there are less mental shifts throughout the day
• Nearly eliminate the status update work, getting it done within one hour per week
• Reduce rework (and escalations) by 75%
• Substantially reduce the frequency and intensity of future planning sessions
• Take more frequent, meaningful breaks during the day

All of these shifts are enabled and reinforced by the structure, and give each individual the ability to spend 50% of their working week doing deep work on important projects.

These calendars are much more palatable (more, longer stretches of purple = good):

This approach yields unbelievable levels of productivity, driven by a focus on the work that matters most for growth. In practice, this initially feels like cheating. 

Switching the focus from Management Time to Maker/Growth Time is straightforward and feels relatively linear, but the impact compounds. To show how this works, let’s assume again that each of these “Important Projects” requires 500 hours of focused work to complete. With more than half the week spent focused on only two important projects, it only takes these teams around 8.3 days to finish the work.

At the end of 18 weeks, each of our focused, multidisciplinary teams will have completed twenty 500 hour projects. There are five of these teams, so the org has shipped 100 important projects. To put this in even finer relief, in the span of eight weeks, one team of six will have done more important work than the entire department used to complete in 18 weeks.

Launching 100 things vs. Launching 8

Imagine for a moment that there are 300 potential projects that either of these teams can take on. These projects vary in terms of their financial impact, ranging from a few worth $100 million to many more worth only $50,000. It’s obvious that the team with greater productivity will make more money, but…how much more?

To make a fair comparison between the two teams, I ran a Monte Carlo simulation. This statistical method models the probability of different outcomes in a process that is unpredictable due to the randomness. The simulation involves running a large number of iterations (e.g., 10,000) and averaging the results. In each iteration, the simulation randomly selects projects for each team without replacement, which means that the same project cannot be selected twice.

After running the simulation, I found that on average, the “Traditional” Team’s earnings for a 50-week period amounted to $33 million, while the Multidisciplinary Team’s earnings reached $420 million.

However, this probably understates the potential value of some of these projects. I’ve seen work done by marketing teams drive upward of $750 million in incremental revenue. And if I swap out just one of the 50k projects for one worth 750 million, the average outcome for the Multidisciplinary team goes up to well over one billion dollars.

Here’s a direct quote I’ve heard from one of my past clients: “This billion-dollar brand wouldn’t have even been approved if not for this new way of working.” Don’t kill the big ideas because they take too long to make it through the system. Instead, change the way you work and start stacking checks.

The question: how long can you wait to find out whether you have a 50k outcome or a 750m outcome?

Learning 100 things vs. Learning 8

On a personal level, think about how much smarter you’d be if you read 100 books in a given timeframe vs. just reading eight.

(Fun fact, it takes around 14 hours to read 500 pages, so in the organizational context that we’re discussing, our Multidisciplinary Team would be putting away 2,100 books per year.)

A finished project doesn’t just yield a business result, it makes everyone involved smarter than they were before the project started. With all the pressure around us, we can’t afford to not get smarter faster than our competitors. Changing to a better structure for focused work will ensure that we get smart about the right stuff – our market and our customers.

But we can quantify this, using the experience curves that I’ve discussed in previous posts. If I use the learning rate from that example, by the end of 18 weeks, both Traditional and Multidisciplinary Teams will be operating at a higher level than they were before.

It seems ridiculous to say that the same people can be 20x better at their jobs just by changing their org structure, but I see this every day in my work. People get better at stuff with repetition, and so do teams. More repetitions: more better. And when those same teams are enabled to focus more on making vs. managing, the efficiency compounds. The structure not only needs to support this, it needs to be designed around making from the ground up.

Career Growth

If you’re reading this far, you probably have a great team and are wishing you had more options for how every employee could grow as fast as they want. This structural shift will be a huge part of your toolkit.

With the multidisciplinary team structure, employees have the opportunity to focus more on their growth and development as professionals. This enables them to hone their skills, learn new ones, and gain invaluable experience in their field. As a result, employees can make significant strides in their careers, not only enhancing their performance but also increasing their value to the organization.

When employees are provided with a working environment that fosters growth, collaboration, and innovation, they become more motivated and engaged in their work. This creates a positive feedback loop, where employees who are growing and developing are more likely to stay with the company and contribute to its success, while the company benefits from a highly skilled, knowledgeable, and motivated workforce.

Wrapping Up

Adopting a multidisciplinary team structure can have a transformative impact on an organization's productivity, profitability, and overall success. By breaking down silos, fostering collaboration, and empowering employees to focus on deep, meaningful work, companies can unlock tremendous value and unleash the full potential of their teams. This structural shift not only leads to better business outcomes but also supports the personal and professional growth of every team member. It's time to move away from traditional, hierarchical structures and embrace a more agile, flexible, and growth-oriented way of working. The future of work is here, and it's time to adapt or risk being left behind.

We've known this forever, but we haven't always acted on it. The chart to the right is a measurement of the productivity of an assembly line making Lockheed L-1011 airplanes.

As the team made more planes, they got better at making planes: the first plane took more than 1.1 million hours to make, and the 100th took fewer than 300,000 hours. But eventually, after they made around 150 planes, the organization started forgetting what it learned.

Despite robust guides for "how to make the plane," the teams associated with the manufacturing still had lots to learn, and lots to remember.

Bruce Henderson made this famous as the "Experience Curve" in the 1960s, building on what T.P. Wright had called the Learning Curve thirty years earlier.

But there are other ways that organizations learn. Well before the L-1011 was even an idea, manufacturing leaders were coming up with ways to make their teams more effective. On one hand, you could give folks instruction on how to do something. On the other hand, there was an idea that you could ask the team what they should do, and let them make a decision about it.

You'll never guess which worked better.

So with all of that in mind, what is your organization learning? Is it learning good lessons or bad ones? How is it learning those lessons?

I believe there is an alternative approach to L&D that is more efficient and effective than traditional methods. I refer to this approach as "Emergent Learning & Development," contrasting with traditional, centrally led approaches.

Both modes of change are important, but I'm shifting to value Emergent L&D over Traditional approaches. Here's why:

Emergent L&D Brings Teams Closer

This approach requires individuals and teams to share their ideas, knowledge and experiences. The natural side-effect of more sharing is more collaboration and inclusiveness, more learning from one another, all of which create a deeper connection to the overall mission of the organization. I'm more connected to these people; I'm invested in our collective success.

Emergent L&D is Real-World Learning

Prioritizing flexibility and adaptability to changing needs of organizations and individuals eliminates the "one-size-fits-none" problem inherent to traditional change management. Further, Emergent L&D focuses on the real problems felt by teams in the organization, rather than those that are assumed by central leadership. This reduces wasted time and expense, and with good "gardening," can also eliminate duplicative projects (i.e. each region comes up with their own approach to Problem X).

Emergent L&D Improves Daily

Because the idea is to focus on doing the right things right, rather than just following a process, the constant cycle of try, measure, iterate results in an organization that gets better constantly, not just once. On the other hand, because Traditional L&D focuses more on enforcement of a new "thing," at least a portion of the organization is likely to think the new "thing" is bad, unwarranted, or ill-advised.

Requirements

Not every organization is ready for Emergent L&D. I can think of at least three core requirements for orgs (and leaders) that want to try working in this new way.

1. Trust: Leaders must foster a culture of trust and collaboration—not just on the surface or through malicious/passive compliance. Psychological safety is a must; otherwise new and novel approaches to important problems won't get raised, so the emergent flow of ideas will feel more like a trickle. As a result, teams won't have anything new and interesting to adopt, and the new approach will be a failure.
2. Infrastructure: Emergent L&D was possible before good digital knowledge management tools existed—both of the examples I cite are pre-internet! But good learning infrastructure—including tools and processes that allow for collaboration, knowledge sharing and real-time feedback—is a requirement for scaling this approach to tens of thousands of people. These tools need to be configurable and usable by everyone in the organization, not just by a central team.
3. Measurement: Leaders leaning into emergence need the right metrics in place to track the spread of good ideas, and to document performance improvements that come from these ideas. This can be as simple as a shared whiteboard to track pre- and post-adoption measures, and as complex as an ERP system fully integrated into the LMS I just described.

Change Sprints

If you're thinking of trying this out... good. Here are three weeklong sprints that you can think of as "mini change modules" to try to bring this to life.

Collaboration Design Sprint 

• Goal: A documented approach to collaboration that can inspire others to work in a new way.
• Activities: Bring a small number of teams from different departments to work together on a specific project or challenge. But don't just solve the problem at hand—plan to document how they shared their knowledge, skills, and experiences, and the benefits that came from this approach.

Accessibility Innovation Sprint 

• Goal: Produce an emergent, open library of resources and programs to support employee development.
• Activities: Bring 2-3 teams together to focus on making all their existing knowledge, including training materials and online courses, searchable and accessible to everyone in the organization.

Adoption Sprint 

• Goal: Align on a few ways that change-makers can track adoption in your organization.
• Activities: Bring a handful of crafty digital innovators together (ask around, and don't worry about hierarchy or location) for a sprint to sort out how to measure uptake of the new changes. Is it views of a Loom video or Canva document? Is it signups for a coaching course or attendees of a virtual workshop? Is it active users of a new tool?

Alongside the chart was a phrase that I've now learned was apocryphal:

The reason the American Army does so well in war is because war is chaos and the American Army practices chaos on a daily basis.

So: democracy underperforms dictatorship under simple conditions. As complexity increases, democracy gains a relative advantage. (I've written about this here, here and here.)

Of course, very few organizations operate with anything close to democracy; the most common mode is something like a patchwork of dictatorships with decent trade agreements. And most versions of organizational democracy feel Kafka-esque.

This all made me think of The Netflix Culture Deck from 2001 – specifically these three slides:

The idea in the deck is that growth creates complexity, people put procedures in place to stop the complexity, and this drives out top performers. The alternative is to give high-performing employees the space they need to do the things they were hired to do – to help the company make better things for its customers.

I'd worried for a while that Netflix had lost its way in the Streaming Wars, but it seems like it's all good. Current reporting is that Netflix kept its culture as it scaled, and as time and turnover drifted away from that deck.

Receipts? Check out this description of Netflix culture on Hacker News, inspired by Stratechery's Netflix's New Chapter, and intended to explain this phenomenon of Netflix's culture and Reed Hastings: "To say that Hastings excelled at execution is a dramatic understatement; indeed, the speed with which the company rolled out its advertising product in 2022...is a testament that Hastings’ imprint on the company’s ability to execute remains."

I particularly love this bit:

We kept hiring great engineers and saying "if we do it this way it will be a better experience for the customers" and for us internal teams, our customers were other engineers, so we made the best internal tools we could to enable developers.

Such an underrated mindset.

PS: the "Netflix's New Chapter" article is an instant classic.

Experience Curves & Rising Complexity

Switching gears for a moment, there's a phenomenon in manufacturing called the Experience Curve. The idea is that with each new unit you produce, the labor unit cost for the next one you make will be lower. Because of learning, process improvements, growth in individual laborer skill, etc.

(That bump at the end is from another thing that I should write about another time – how organizations "forget" through turnover.)

But then there's this recent research highlighted by Ethan Mollick that looks at the entrepreneurial version of similar measures. What happens to successful entrepreneurs when they move on to a second, third, or fourth project? They add complexity to their offer, and their time-to-deliver goes up.

Launching new things like, say "streaming movies over the internet" or "adding advertising to a subscription-based service" isn't the same thing as "produce an incremental L-1011."

Why democratic organizations fare better

Democratic organizations fare better than autocratic ones for at least two reasons:

1. They're more efficient learning machines
2. They teach more of their people more about "the business"

Learning: Organization-level learning can happen through things like metrics and dashboards, of course, but the stickiest version of it happens when leaders listen to teams, and teams listen to each other. Quoting from Jedberg again:

When some of the engineers said, "we could do this a lot faster and eventually worldwide if we hitch our wagon to this new AWS thing". And so management got out of the way and said "if that's what you think is best, we will support that".

Listener, that's psychological safety. Imagine if the leaders had said, "Nah, Reed is better friends with Bill, so we're going to use Azure." The engineers would stop speaking up, they'd probably quit, doom spiral, etc.

Teaching: When leaders are focused on providing robust context for decisions, rather than simple mandates, individual contributors (who are usually but not always less experienced than leadership) learn more about the fundamentals of the business.

How do we make money? How do we decide? What does the board care about? What behaviors are encouraged? What are the implications of choosing technology X? What happens when we change our process?

I've seen this firsthand. When we swapped over to self-management at Undercurrent in 2013-2014, we were forced to start teaching everyone about our approaches to finance, resourcing, project selection, scopewriting, escalations, pricing, conflict management, etc. Before that shift, we'd often answer questions about these topics with something like, "don't worry about that, focus on your craft." That was no longer acceptable, nor good business practice.

Mental overhead for everyone shot up.* Back to the slide at the top: our normal operating mode was more chaotic, as policies, practices, and teams were liable to change at a moment's notice. But after the all the core business teaching happened – and not all from "leadership" – we were much more effective.

*Anecdotal, but if you were there, I'm sure you feel me

Distributed organization at Yumemi (300 people, Japan, Software development)

Decentralization in the Workplace: How Distributed Management is…

Breadcrumbs list

Corporate Rebels

Yumemi is a software development company that sells to other businesses (read: agency/consultancy); their products are in use by 50 million people. So a going concern!

They are organized into Guilds, which are composed of Teams of no more than 7 people. People management happens on a volunteer basis at the Guild level. Proposals for changes work via a form of structured consent. The operate with radical transparency (here's their org chart in Miro).

Their Guilds include:

1. Android
2. iOS
3. Frontend
4. Serverside
5. Sales and Marketing
6. Design
7. Project Management
8. Corporate

Everyone can choose which teams they work on. The process works like this:

The process starts when the sales team (as the client contact point) acquires a new project opportunity. (Note: this is just an opportunity, not yet a project.) Before the sales team can turn the opportunity into a project, it must first find an engineering team in the firm willing to take on this work and willing to accept the proposal acquired by the sales team. Once this happens, the sales team can start introducing the engineering team to the client, and the project can finally kick off.

This is the way most large consultancies work anyway, but adding all of these structural safety measures, I'd imagine, substantially improves psychological safety, reduces anxiety, and improves overall engineering/client outcomes. The organization made this shift in 2018, so it seems like it's going OK.

None of this is newfangled stuff – a lot of it looks like a localized, simpler version of Sociocratic ideas that are proven to work.

And yet, for most, collaboration is dead

This, by Simon Terry, argues that collaboration is dead. Jeff Bezos is famous for saying that communication is a bug, not a feature. So...this is definitely a thing, not just a local annoyance.

Simon suggests a few better paths forward than "new technology" or "new process" – my favorite is "begin and end with everyday work."

Video is exciting. Events are exciting. Campaigns, features, incentives and more can help educate employees but begin and end your work by improving everyday work. Make that better and your efforts have ongoing value to the grassroots in the organisation.

What do the teams actually do? Where do they spend their time? How can the work get better and easier? Examples like Yumemi address the reality of value creation, without the window-dressing that comes with most change programs. This means that the changing gets easier, better, and cheaper with time – so the organization gets better without requiring top-down intervention.

An alternative is to elbow your way into the spaces that matter

Katie Bauer is Head of Data for GlossGenius (they make an app for beauty pros) and wrote this awesome post about how data can gain a seat at the table.

Elbows of data

It seems like it happened slowly, then all at once. For some time it was the consensus that new data tools and technologies were going to be the thing that finally helped data teams break through, get executive buy-in, and drive the successful outcomes that we all knew they could. But in the past ye…

Wrong But UsefulKatie Bauer

Companies may not be sure how to best incorporate data teams into their processes of value creation, but that doesn’t mean we can’t elbow our way in. The most impactful data folks I’ve worked with in my career have not been iconoclastic or revolutionary heads of data, nor have they been analytically brilliant data scientists or mastermind architect data engineers. They’ve been elbows of data—folks who have insisted on being involved in driving the company forward, whether they were invited to or not.

How?

1. Make a habit of fact finding (aka "Know the business cold")
2. Think about the second life of your work (aka "Understand the horizontal, re-usable aspect of each project")
3. Don’t wait to be asked (self-explanatory)
4. Be proactive about explaining your constraints and asking for what you need (same – but in practice this means having high-quality, accessible material about what you do and why, and the state of your backlog)
5. Don’t suffer in silence (same, but worth saying)

Speaking of the second life of your work...

Create good documents

6 Rules of Good Documentation | Variance

Documentation was always critical for company- and culture-building. Now, as the world moves more remote, it’s only become more important.

VariancePLG Content Marketing

Oldie but goodie from Noah.

1. Fit for context (know who you're writing the document for, why they'll use it, and don't be shy about flagging this context)
2. Clearly written and to the point (break out your Elements of Style and pay attention to it)
3. Visual where possible (documents require more than writing – see Yumemi's Miro example, above)
4. Skimmable (Use formatting)
5. Up to date (obvious, but tag when updates are made)
6. Discoverable & Tracked (I would go beyond this: you can't just make a document public, you have to publicize it)

Beyond documentation, running realtime/synchronous workshops are a must-have skill

Pilar Esteban Gómez wrote this exceptional guide to running workshops:

Tips to design & run inclusive workshops

Workshops mean to be collaborative. But just by using post-its on a board, we are not being inclusive: we need to consciously create a safe…

EverestEngineeringPilar Esteban Gómez

These are all good.

Like, really good! Someday I'll have to make some of these of my own, but in the meantime, each one of these workshop facilitation methods is super memorable and easy to run. You'll seem like a workshop genius if you commit a few to memory and pull them out in a meeting that's going sideways (or not going anywhere).

Beyond documentation, collaborative working sessions like the ones Pilar described are a critical ingredient in fixing the working relationships between and inside groups. Function leads (and individual contributors, of course!) need these tools in their kit to help decide on shared paths forward and to build emergent wisdom.

So yeah. Collaboration is dead. But it's all we've got, and we have the tools to design the structures that help healthy collaboration thrive. I think we're just missing the will to do it, and awareness of what's possible.

HMU if you want to try any of this! I'll help!