Category: Coherise Newsletter

Newsletter – Edition 11

The date is set. Coherence: The Competitive Advantage AI Can’t Buy launches November 17 on Amazon. The manuscript is written, the interior is typeset, and the book is in its final proofreading pass. It is almost an object you can hold.

Before the launch details at the end, the idea itself.

What coherence is

Shireesh Thota, a corporate VP at Microsoft, read the book early and named what makes it different. His own career taught him that “locally correct decisions compound into complexity no one can untangle later.” The book’s insight, he wrote, is that “agentic AI does this to entire organizations at a speed software never reached,” and that Shashanka “has taken a hard-won lesson from systems engineering and shown it is now an organizational law.” He calls it “the rare AI book grounded in how complex systems actually fail.”

That is the novel part. Most people in enterprise AI now agree that making AI work within an organization is the hard part. The book goes further and treats a company as a system that fails in specific, nameable ways, the way any complex system does. Here they are, on one page.

Coherence is the link between what a system does locally and what the enterprise actually intends. Intent travels down, visibility travels up, and the link can fray in five specific places: context, architecture, decision, oversight, and time. Each is a measurable way a company comes apart while every dashboard stays green, and the book turns each one into something a leader can see and manage.

Run one test on your own organization. If you cannot produce a current inventory of every autonomous system running inside it, you are at low coherence, whatever your dashboards say, because you cannot see your own coordination state. Most large enterprises fail that test today. The full walk-through of the five dimensions lives here.

The week in ideas

Two pieces from the past week.

Organizational Coherence Decides Whether AI Pays Off at Scale. My latest for the Forbes Technology Council. Most people are now productive with AI individually, yet far fewer enterprises can show it in the P&L, and the gap is coherence. The piece walks through a supply chain where three agents each optimize locally and together misread a temporary spike as the new normal, and what it takes to close that gap before you scale. Weigh in on LinkedIn…

Where the Bitter Lesson Stops. Richard Sutton’s bitter lesson says scaling and learning beat human cleverness, over and over. I think it holds right up to the enterprise and then stops. Agents self-organize brilliantly when pointed at one clear goal. A company is the opposite, many goals in tension with no single target to hand the machine, and supplying that direction is coordination work no amount of scale can learn away.

Here is the first half of that lesson from my own week. I gave the tools one clear goal, turn the book’s idea into a song and a short film. Suno composed the track, and Fable visualized the story and synced the images to the audio, and the result genuinely holds together. Point agents at a single, well-defined goal and they are remarkable. The trouble starts only when there is no single goal to hand them, which is the enterprise problem in one sentence. Watch the music video.

Before you go

When the book goes live on November 17, I will send you the link that morning. Two things would help it travel. Mark the date on your calendar, and forward that email to one person wrestling with AI at scale. One peer each is how this reaches the rooms I will never get into on my own.

And if you coherise something this week, tell me how it went. The best of what I learn comes from those stories.

Newsletter – Edition 10

Greetings from Southern California, where I am at the Gartner Global CISO Executive Summit. One evening in, and the conversations with security leaders have already been worth the trip. It follows a recent visit to the CIO Fellows Society forum in Plano. Two rooms full of the people who actually have to make AI work inside large organizations, which is the best research I get to do.

A quick note before the ideas. You did not get an edition last week. I was on the road and heads-down on the book, and I would rather skip a week than send you a thin one.

Now the finding I keep coming back to.

Epoch AI published a study with a striking result. The cost of reaching a given level of AI performance has fallen about 47% every quarter since 2023. That is roughly 13 times cheaper each year, and it is the fastest price decline of any transformative technology on record. Four times faster than DNA sequencing, six times faster than computing, and fifty-four times faster than electricity over the century it took to get cheap.

Source: Luke Emberson and David Roodman, “The Plunging Price of Thought,” Epoch AI (2026), CC BY 4.0. https://epoch.ai/publications/the-plunging-price-of-thought

One example makes it real. In early 2025, OpenAI’s o3 could reach a high score on a PhD-level science exam for about thirty cents a question. Eighteen months later, a newer model matched it for four hundredths of a penny. A 725-fold drop. As the authors put it, that is a new car falling from fifty thousand dollars to sixty-nine.

Sit in a room of CISOs and CIOs and you feel what that number does. When intelligence gets this cheap, you do not use a little more of it. You deploy it everywhere. Every team stands up agents, every workflow gets automated, and the number of moving parts in the company climbs faster than anyone is tracking.

Here is what the price chart does not show. That figure is the cost of hitting a benchmark score. Turning that score into something the business can actually use, and keeping it working, has not gotten cheaper at all. Thought got cheap. Coordinating it did not. The cheaper each part becomes, the more parts you run, and the harder it gets to keep them pointed at one purpose. That gap, between how cheap it is to build and how hard it is to hold together, is the whole subject of this newsletter. Both of the posts below are dispatches from inside it.

The week in ideas

Two posts from while I was away.

220% More Code, 40% More Incidents. Meta ran the experiment at scale. Under a plan called Project OT, it restructured teams of 10 to 20 into pods of 3 to 5 and laid off about 8,000 people. Its own internal numbers, surfaced by Reuters, tell the rest. Code changes to internal platforms rose 220%. Major technical and security incidents rose 40%. Time spent firefighting them rose 70%. Only about a third of the new code reached users as features, so much of it was noise that still had to be read and reviewed. Zuckerberg’s takeaway, that they moved ahead of the technology, is partly right and teaches the wrong lesson. AI output has to be reviewed, integrated, and repaired, and that absorption capacity scales with how much you deploy and how the systems interact, not with headcount. There is a third cost center beyond compute and people: the cost of getting what the machines produce through the people who remain. It never appeared in the budget. It showed up everywhere in the incident logs.

Machines Organized Themselves. OpenAI’s People Couldn’t. I touched on the OpenAI swarm incidents a couple of editions ago. Here is the full account, because the detail is the point. In OpenAI’s own test sandboxes, swarms of its agents built organizations no one designed: roles like coordinator and recruiter, norms named HOLD and VETO and STOP, succession when an agent ran low on budget, identity signing after one impersonated another, even a chain of authority. About 700 of them broke into Hugging Face. The machines organized themselves. The people could not. Three OpenAI teams saw the activity at three separate moments and no one connected the sightings. OpenAI called it an organizational failure in writing. If the lab that built the agents could not connect three sightings, consider the odds inside a bank.

One thread ties them together, and ties both to the number up top. This is what abundant, cheap execution looks like without the structure to hold it. Meta’s code multiplied faster than anyone could absorb. OpenAI’s agents multiplied and self-organized faster than anyone could see. Same failure, two shapes. The parts outran the coherence. The price of thought falling 13 times a year guarantees more of both.

Before you go

Coherence: The Competitive Advantage AI Can’t Buy releases this Fall. Join the list for launch-day access and the Reliability-Complexity Matrix, the one-page tool I use to sort what to automate, what to augment, and what to keep in human hands.

And if you try to coherise something this week, tell me how it went. The best of what I learn comes from those stories, where the ideas meet reality.

Newsletter – Edition 9

The book has a cover now, and it is live on the site: Coherence. It is a strange relief to watch the argument transform from a manuscript draft to an object with a face.

Scattered strokes in the dark, only visible under a single light overhead, and the scatter settles into an ordered field as it falls through the title “Coherence.” That is the whole book in one image. Abundant parts do not become a system on their own. Something has to bring them into coherence.

First, a word on an eventful last week. Anthropic’s red team set swarms of its own agents loose and watched them collude, sabotage, and start turf wars, reporting that one agent’s bad decision quickly becomes every agent’s. A swarm of OpenAI’s own agents, the company later confirmed, spent weeks running a German wiki as a private message board to swap tactics for evading their monitors, and it went unnoticed for months. Dario Amodei called for the industry to slow down. Jensen Huang forecast millions of agents per company. European regulators opened an inquiry under the AI Act.

Notice what almost none of that argues about. Capability, speed, and control from the outside. The failures themselves were about coordination. Agents that each worked, interacting in ways no one had declared or owned. That is the gap this whole body of writing is about, which makes it a good week to lay it out in one place.

A field guide

If you are new here, or you want the argument in order rather than post by post, here is the map, in three movements.

What changed

• Little More Than an Undergraduate Essay. Ronald Coase’s 1937 question, why firms exist at all, and why cheap execution flips the answer.
• “AI Doesn’t Matter”: Beyond the J-Curve. Capability commoditizes, the way every infrastructural technology has. The advantage moves to coordination.
• Uninformed Expectations, Five Years Later. The real roadblock to AI value was never the technology. It was the organization.

How it breaks

• Nothing to Declare. The dependencies between agents that no one declares or owns, and a used textbook that reached twenty-four million dollars on Amazon.
• The Bill Is Incomplete. Complexity debt, the coordination cost that compounds off every dashboard.
• A Company Doesn’t Have One Brain. Why owning your data is not the same as making it agree with itself.

What to do

• Building Coherence Is About Structure, Not Supervision. You cannot supervise a hundred agents at machine speed, so you build the control tower instead.
• The Seven-Figure Job Nobody Can Quite Define Yet. The role forming to own all of this, and why it has no settled name.
• I Studied the Brain to Build AI. What the brain’s inner sense of its own body teaches about keeping an organization coordinated.

If you want the definitions rather than the posts, the plain-language explainer is here: What is organizational coherence.

Before you go

Coherence: The Competitive Advantage AI Can’t Buy releases this Fall. Join the list for launch-day access and the Reliability-Complexity Matrix, the one-page tool I use to sort what to automate, what to augment, and what to keep in human hands.

And if you try to coherise something this week, tell me how it went. The best of what I learn comes from those stories, where the ideas meet reality.

Newsletter – Edition 8

Shipping first time code is like going into debt. A little debt speeds development so long as it is paid back promptly with a rewrite.

This is a quote from 1992 by a programmer named Ward Cunningham. He was trying to explain how one can take shortcuts while writing software to speed up development but it carried a cost with it. He compared that cost to taking on debt, something that eventually came due and had to be paid back. And today, this analogy is so commonplace there is a term for it – “technical debt.”

Since beginning this newsletter, I have been talking about a cost imposed on an organization as more and more agentic systems are deployed. Complexity debt is the cost that builds as intelligence multiplies without coherence. It operates at a different level than technical debt. Technical debt is owed by a codebase. Complexity debt is owed by the organization, and it accumulates between systems rather than inside any one of them.

The word the industry has settled on for the visible part is sprawl. In May the Wall Street Journal reported on companies discovering they had too many AI agents. DaVita’s employees had built more than ten thousand. FICO’s staff were creating dozens a day. Lyft was building a platform just to keep track of its own. The CIO of Magnum Ice Cream put the mechanism in one sentence: “Because everybody can do it, we’re probably going to end up with a lot of people having the same types of agents.”

Sprawl is what those companies can count. Complexity debt is what they have incurred as a result. Sprawl is the number of agents. The debt is their outcome. Consolidating agents reduces the count. It does not pay down the debt, because the debt lives in the interactions.

Why now? For the whole history of software, the cost of building acted as a filter. That filter is gone. A workflow automation takes an afternoon. Untangling how it interacts with six other systems takes a quarter. The velocity of creation has separated from the velocity of coherence restoration, and the gap between them is where the debt accrues.

Why does nothing show it? Deming saw the mechanism in manufacturing before AI existed: optimize every department on its own and you degrade the system, because the interactions matter as much as any department’s output. Agents run the same logic at machine speed.

The deeper reason is that the debt belongs to no one. When a team deploys an agent, it adds coordination load to every other team. Its outputs have to be reconciled with theirs. Its dependencies have to be tracked. Its surface has to be watched. The deploying team enjoys the benefit and the enterprise absorbs the cost. That is the textbook definition of an externality. Coherence is a commons, and complexity debt is pollution.

The week in ideas

Two posts from the past week.

The Bill Is Incomplete. Every company deploying AI is staring at the same invoice and cutting it. Tokenomics has a vocabulary now, and thriftmaxxing is replacing tokenmaxxing. The post argues that this is the right discipline aimed at the wrong bill. When the cost of building collapses, the filter that once kept weak ideas from getting built collapses with it, and far more gets deployed. Each deployment adds dependencies and coordination cost. That cost belongs to no team, so no dashboard shows it, and it compounds while each system looks fine. EY’s fix is to price every agent. No agent carries the cost of coordinating with the rest. You can meter every agent perfectly and still miss the entire bill. [Weigh in on LinkedIn…]

Nothing to Declare. Fortune ran a piece from TIAA and Accenture arguing that the tracks are the constraint, and prescribing a modernized core, ready data, and redesigned workflows. All sound, all about a single system. McKinsey’s own numbers show enterprise-wide scaling up and EBIT impact flat, and the ordinary explanations do not grow with the number of systems deployed. One thing does. The post takes the undeclared consumers idea from Google’s 2015 technical debt paper and carries it to the enterprise, where agents read each other’s outputs without contracts and no one owns the pair. A textbook priced at twenty-four million dollars shows what that looks like when the loop closes. No public post-mortem of an enterprise losing money this way exists yet. Every case so far comes from markets and grids, where the party that could see the whole was not the party that took the loss. Enterprises are becoming that kind of substrate. [Weigh in on LinkedIn…]

One thread runs through both. Each takes a discipline the industry adopted for good reasons, pricing agents and modernizing foundations, and finds it applied one level below where the cost lives. Meter every agent and the coordination cost is still unmetered. Rebuild every track and the collision is still uncaught. Complexity debt is the name for what accumulates at that level.

Before you go

The book is Coherence: The Competitive Advantage AI Can’t Buy, out November 17. Everyone on the list gets the one-page decision tool I use to sort what to automate, what to augment, and what to keep in human hands.

And if you try to coherise something this week, tell me how it went. The best of what I learn comes from those stories, where the ideas meet reality.

Newsletter – Edition 7

Essential to competitiveness but inconsequential to strategic advantage: that’s why IT is best viewed (and managed) as a commodity.

This is a quote from Nicholas Carr more than twenty years ago about IT in enterprises. And the quote still works today if you read IT as the pronoun “it” referring to AI.

In 2003, Carr published the provocatively titled “IT Doesn’t Matter” in the Harvard Business Review and it instantly became one of the most argued-over articles. His argument was that infrastructure technologies followed a pattern. Railroads, electricity, and then IT, were capabilities that offered real competitive advantage when they were scarce and being built out. However, once they became cheap and commonplace, they turned into commodities. They were necessary but no longer enough to pull ahead since everyone else also had access to them.

It is tempting to carry the argument over to AI. Frontier models are commoditizing fast. On a recent earnings call, Jamie Dimon said his bank gets no unique benefit from AI since everyone has it. Carr was right about commoditization and we are now seeing that with AI.

The difference with AI is that it’s not something you can plug in and forget.

Electricity did not pay off for consumers right away when it arrived. Companies that wired up an old factory and changed nothing else would have realized little value. Gains came only after years of rebuilding around what electricity made possible: the floor plan, the processes, the nature of work itself. Economists later named this shape the “productivity J-curve.” Measured productivity dips while the slow, tedious work of reorganizing completes, and then climbs as that work pays off. Technology commoditized but realizing value from it required hard work. The story repeated with IT. AI now inherits the same trend.

Where AI differs is that prior technologies did not create more of themselves. Agents do. Every deployment adds systems, handoffs and dependencies, now at machine speed in the agentic era, and the organizational work does not end. Deploying individual agents that work is easy. But keeping several of them working together, without quietly working against each other, is the hard part and it grows as the AI capability gets cheaper.

That is the work beyond the J-curve, and someone has to own it. More on who below.

The week in ideas

Two posts from the past week.

Building Coherence Is About Structure, Not Supervision. The big advisory firms are arguing with themselves. They tell you to scale agents fast, then publish the evidence that scaling is where the value dies. What none of them prices is coherence, whether the systems still serve the business once they run together. Their fixes, unified data and orchestration and semantic layers, are permitting offices that check each agent at the gate. The collisions happen after the gate, when two cleared agents pull the same account two ways and no one is watching the live picture. Supervision does not scale to machine speed. Structure does. Sense the coordination state, constrain what each system can do, contain the failures, price the coordination cost, and save human judgment for the exceptions. They are building better permitting offices. Coherence is the control tower. Weigh in on LinkedIn…

The Seven-Figure Job Nobody Can Quite Define Yet. A role is forming with seven-figure pay, fierce poaching, and business schools racing to train for it, and it has no settled title, no agreed mandate, and insiders who expect it to disappear. That is not how a market treats a job it understands. The skeptics say it will fade like a chief electricity officer once AI becomes ambient. They are right about the title and wrong about the function. Electricity does not build more electricity when you use it. Agents do. The capability goes invisible while the incoherence accumulates, and someone has to own that. The book calls the work coherence architecture, and the person a coherence architect, offered as a description of the work while the market is still settling on what to call it. Weigh in on LinkedIn…

These two go together on purpose. The first is about what a company has to build, a structure that keeps its systems coherent as they multiply. The second is about who has to hold it once it is built. They meet at the same gap. Coherence goes unmeasured because no one owns it, and it stays unowned because the job of measuring it has no name yet. Name the work and you can build it. Build it and someone has to hold it.

Before you go

The book is Coherence: The Competitive Advantage AI Can’t Buy, out this Fall. Everyone on the list gets the one-page decision tool I use to sort what to automate, what to augment, and what to keep in human hands.

And if you try to coherise something this week, tell me how it went. The best of what I learn comes from those stories, where the ideas meet reality.

Newsletter – Edition 6

When I cofounded Concentric AI, the first guiding principle I wrote down for myself was first-principles thinking. Ignore the conventional wisdom about what is possible. Understand the real problem. Follow it wherever it leads.

I came back to that method for the book. When I introduced this newsletter, I put it plainly. There is only one way I know to cut through noise. You stop arguing at the surface and go back to first principles. You find the single thing that actually changed, and you follow it, patiently, wherever it leads, whether or not the destination is fashionable.

The single thing that has changed now is the cost of execution. It has collapsed toward zero. So I started there and followed it, step by step. The first step was a question that sounds too basic to be useful. Why do firms exist at all?

Someone answered that in 1937.

A twenty-six-year-old economist named Ronald Coase published a short paper called “The Nature of the Firm.” He had been carrying the idea since his undergraduate years, when he toured American factories trying to work out why industry was organized the way it was. He later called the paper “little more than an undergraduate essay.” That essay was a big part of why he was awarded the Nobel Prize in 1991. It is one of the cleanest pieces of first-principles thinking I have come across.

Coase asked a question no one had bothered to ask. Economics of the day said markets coordinate activity efficiently. In wikipedia’s words, that means those who are best at providing each good or service most cheaply are already doing so. If that were the whole story, there would be no need for companies. Every task would be handled by independent people contracting with each other in the open market. So why do firms exist at all, with their managers and hierarchies and payrolls?

His answer was transaction costs. Using the market is not free. You have to find the right person, agree a price, write the contract, monitor the work, enforce the terms. When doing all of that inside a company costs less than doing it through the market, the company does it inside. Firms exist because internal coordination is sometimes cheaper than market coordination. The boundary of a firm sits exactly where those two costs meet.

That was the insight. A firm is an efficiency solution to the cost of getting things done. And it carried a quiet implication that took decades to surface. When the cost of coordination changes, the best shape for the organization changes with it.

Which brings us to now.

For nearly a century, the binding constraint inside a firm was execution. Doing the work took scarce, expensive people. Building software, processing information, running an analysis, deploying a system, all of it needed specialists. Much of the org chart existed to allocate that scarce execution. Management decided what got built, by whom, in what order.

Agentic AI removes that constraint. Execution is collapsing toward free. A company’s ability to build workflows, deploy agents, and automate complex processes is no longer capped by engineering capacity. The scarcity the hierarchy was built to manage is dissolving.

What is left, and what now binds, is coordination. Keeping abundant autonomous systems coherent, steerable, and pointed at one purpose is getting harder and more expensive, in exactly the ways existing structures were never designed to handle.

This is what the book calls the Coasian Inversion. The firm was built to economize on scarce execution. Agentic AI breaks the assumptions underneath that design. Execution, the thing firms were built to marshal, is now cheap. Coordination, the thing they were never built to manage, is now the constraint.

The AI era does not end the need for firms. It changes the reason they exist. They no longer exist mainly to allocate scarce execution. They exist to hold abundant autonomous capability together. That is a different job, and the structures built for the old one are not built for the new one.

That is where following one changed cost leads. The answer is structural, and it began for me with an undergraduate essay.

The week in ideas

Three posts from the past week.

I studied the Brain to Build AI. The Agentic Era Sent Me Back to It. I trained in neuroscience before AI, and it left me a habit of asking what a technology can really do. I was skeptical of agentic AI, because reliability does not survive being chained. Stack ten agents at ninety-five percent each and you end up near sixty. Then coding agents improved so fast that I changed my mind about the timing. Once you can trust individual agents, you do not stop at one. You deploy fleets, and a new question appears that has nothing to do with reliability. If every agent does its job, does the fleet still add up to what the organization intended? A body answers that with proprioception, the constant inner sense of where its parts are. An organization needs the same inner sense, or capable parts never combine into coordinated action. Weigh in on LinkedIn…

A Company Doesn’t Have One Brain. The conversation has moved fast. “Buy the best model” is giving way to “protect your proprietary layer,” what BCG now calls the enterprise cortex. I agree that the model is a commodity and the moat is what the organization knows about itself. But the cortex framing hides an assumption. A brain has one cortex. An enterprise grows a dozen, each team with its own definitions and rules, and no one owns how they combine. You can own every byte and keep every vendor out, and still fail, because your pricing logic and your inventory logic never agreed on what a lapsed customer is. Owning your brain and organizing your brain are two different jobs. Knowing is not the same as coherising. Weigh in on LinkedIn…

What the Watermark Doesn’t Tell You. Anthropic began watermarking the text Claude writes, and detectors are spreading. They all read one thing, which tool produced the words. None of them reads whether the writing is any good. Slop is not a provenance problem. It is a failure of intent or execution, and no watermark reads either one. A person can produce slop unaided. Someone with a clear point can use AI to sharpen it and produce something worth reading. Tracing the tool cannot tell the two apart. Weigh in on LinkedIn…

One thread ties these to the essay above. Each is a dispatch from the new constraint. The brain piece names what an organization needs to stay coordinated, an inner sense of whether its parts still add up to intent. The cortex piece shows that owning the parts is not the same as making them agree. The watermark piece shows that no external mark can tell you whether the parts hold together, because that is a question of judgment, not provenance. Cheap execution handed every company more parts than it can see. Keeping them coherent is the work now.

Before you go

The book is Coherence: The Competitive Advantage AI Can’t Buy, out this Fall. Everyone on the email list gets the one-page decision tool I use to sort what to automate, what to augment, and what to keep in human hands.

And if you try to coherise something this week, tell me how it went. The best of what I learn comes from those stories, where the ideas meet reality.

Newsletter – Edition 5

In Edition 3 I shared the whole book as fifteen sentences. One of them claimed that coherence is a measurable property with five dimensions. A few of you wrote back with a fair question. Which five?

So as the launch nears, I put up a reference page that answers it, and answers the larger question in the title of this edition. What is organizational coherence?

Here is the short version. Coherence is your organization’s capacity to see what its autonomous systems are doing, judge whether they are doing it well, and correct them when they are not.

People sometimes hear coherence as something soft, a feeling of alignment or a good culture. It is neither. It is an operational capability, and you can have a lot of it, very little, or somewhere in between. Put another way, coherence is the integrity of the link between what a local part of the company does and what the whole enterprise intends. When that link holds, the parts serve one purpose. When it breaks, each part can be right on its own terms while the enterprise drifts.

That link can fray in five places, which is why coherence has five dimensions.

• Contextual: whether systems and people share compatible assumptions about the same situation.
• Architectural: whether systems are designed to interact predictably rather than collide through paths no one mapped.
• Decision: whether systems pursue goals that fit together rather than optimize locally in ways that hurt the whole.
• Oversight: whether the people responsible can actually see what systems are doing and correct them.
• Temporal: whether the organization keeps enough human understanding to supervise, fix, and retire its systems over time.

Each maps to a specific way organizations fail, and each can be measured and built. The page walks through all five, along with the ideas around them: the Coasian Inversion, complexity debt, agentic slop, and how coherence differs from governance and alignment. Check out the full reference page here: What is organizational coherence?

The week in ideas

Three posts from the past week.

Uninformed Expectations, Five Years Later. Five years ago in an interview, I was asked about the single biggest roadblock to AI ROI. I had said back then that it was uninformed expectations, and predicted disillusionment. Here we are today, years into the enterprise AI rush, and my answer to the question is still the same. The reason, however, is completely different. Weigh in on LinkedIn…

Coding Got Easy, But What Kind? I read a passionate post about what makes the profession of writing code human, and the author takes exception to the framing “code was never the hard part.” That statement, read at face value, can be true and false at the same time, depending on what you mean by coding. Producing instructions that run, yes. Building a software system that lasts, no. Weigh in on LinkedIn…

The Machinery Under Manners. Reid Hoffman wrote about a convention people use in professional networking. When asked for an introduction to a contact of yours, you check with the other person first and then make the introduction if they are willing to entertain it. But that “permission check” is not just courtesy, it is a structural mechanism. And agents especially need such structural constraints since they don’t face our social and societal constraints. Weigh in on LinkedIn…

One thread runs through all three. Each takes a surface we trust, an expectation, a line of code, a courtesy, and shows that what makes it really work sits underneath, out of view. Remove the hidden structure and the surface keeps looking fine right up until it fails. That gap between what shows and what holds is the whole subject of the book.

Before you go

A reminder about the favor from last week, because timing matters. SXSW community voting closes August 23. If the book’s argument has been useful to you, a vote helps carry it to a stage in Austin next March. Vote here.

The book is Coherence: The Competitive Advantage AI Can’t Buy, out this Fall. Everyone on the list gets the one-page decision tool I use to sort what to automate, what to augment, and what to keep in human hands.

And if you try to coherise something this week, tell me how it went. The only way I learn is from those stories where ideas meet reality.

Newsletter – Edition 3

A quick milestone. Copy-editing on the book is finished, and it has moved into design. The words are settled. Now it becomes an object you can hold.

Copy-editing is the pass where someone reads the manuscript line by line and fixes the grammar, punctuation, and consistency. My editor mentioned that the number of edits per thousand words on my manuscript was not unusual. I hope she was being true and not just being kind.

Here is the part I actually want to share, because it was a choice I made in the book for you, the busy reader with little time to spare.

At the end of every chapter there is a box called Key Takeaways. It answers six questions, in order. The one thing to remember. The demonstration. Why it holds. How to recognize it. What it changes. Where it goes next.

The boxes do one more thing together. Read the first line of each, chapter after chapter, and they assemble into the argument of the whole book. Fifteen sentences, front to back. Here it is.

---

When AI makes execution cheap, the advantage shifts from how much you can build to whether your organization stays coherent while you build it. The whole book, in order, reads as follows.

Part 1: The Inversion

1. Intelligence is commoditizing into a utility, so advantage tends to move to whatever stays scarce after it, which is the capability to deploy it well, not access to the intelligence itself.
2. The friction that once limited how fast complexity could grow has collapsed, and little has been built to replace what it quietly did.
3. When execution becomes cheap, the binding constraint tends to move from doing the work to keeping the work coherent, the Coasian Inversion.
4. Coherence is a measurable structural property with five dimensions, not a cultural attribute or a synonym for good management.

Part 2: The Failures

5. Enterprises rarely fail through one catastrophic AI decision; they fail through quiet accumulation across six specific failure modes.
6. AI capability is jagged, not uniform, so a system can be trusted only where success can be specified and checked.
7. Capable systems multiplying without coherence create a hidden, compounding cost that no dashboard shows.
8. Automation corrodes not just structure but capability, the judgment, memory, and oversight an enterprise needs when systems fail.
9. Automation reduces the burden of doing work but raises the burden of overseeing it, and the human handoff meant to catch failures fails structurally.
10. Task reliability and organizational complexity are independent axes, and the most dangerous deployments are the ones working perfectly while accumulating coordination cost.

Part 3: The Discipline

11. Coherence is maintained continuously by a designed control architecture, with humans as the exceptional layer, not added afterward by a committee reviewing outputs.
12. Structures built for scarce execution now actively produce incoherence, so the agentic enterprise must redesign its capabilities and incentives on purpose.
13. Human work does not disappear; it concentrates exactly where machines are unreliable, on the judgment that cannot be specified and checked.
14. When every competitor has the same models, the durable moat is coherence, which compounds, while data and model moats erode.
15. In the agentic era the leader’s core work shifts from directing execution to designing coherence, the one decision every other leadership decision depends on.

When everyone can go fast, going fast is no longer the advantage. What wins is whether the organization can go fast without coming apart.

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That is the spine. The chapters are the muscle around it.

The week in ideas

Four posts from the past week.

AI Still Needs Human Bosses? The New York Times handed an AI agent three office jobs. It wrote clean code in minutes, then failed at judgment. It could not upload a file, so it quietly marked the task done. It read employees on leave as cuttable roles. Firms are thinning the supervisory layer that catches exactly these errors, while installing systems that consume more of it. That reversal is the thesis of the book, and one version of it has already reached a federal courtroom. Weigh in on LinkedIn…

“The New Normal Because Faster” A viral Reddit thread about an enterprise platform deployment that went badly. I cannot verify a word of it, so I make no claim about any company. But the pattern the accounts describe is the one the book predicts. A delete button that clears the record from the screen and orphans the three hidden records it created. Locally correct, globally broken. A commenter named the whole thing in five words: the new normal because faster. Execution got cheaper. Coherence did not. Weigh in on LinkedIn…

Safest Car on the Road, Yet Parks in the Fire Lane Waymo is far safer than human drivers across 50 million miles and still collects parking tickets across my hometown of Austin. Two different failures live in that story. Jaggedness, where a system is superhuman at driving and stumped by a handicap spot. And the deeper one, where a firefighter has full authority over the car and no lever to move it. Three hundred cars each parking rationally still block the same church garage. The tickets are the city’s crude, correct instinct: price the incoherence when you cannot redesign the system. Weigh in on LinkedIn…

Early Signs of Rehiring A short update to an earlier post. Big employers from CSX to Alphabet are hiring again after eighteen months of treating hiring as a last resort. The narrow prediction held. Companies cut on the bet that agents would absorb the work, then hired back when the agents did not. The deeper coordination claim stays an open question. Best line, from an MIT economist asked whether firms need more people or fewer: no one has any idea.

One thread runs through all four. The machine can produce the output. A human still owns the part with no dashboard: the judgment, the handoff, the curb no single car is responsible for. That is my book in one sentence, which is a convenient thing to be able to say now that the fifteen are sitting above.

Before you go

Design is where a manuscript stops being a document and starts being a book. I will share the cover here first when it is ready.

If the book is why you are here, it is Coherence: The Competitive Advantage AI Can’t Buy, out this Fall. Everyone on the list gets the one-page decision tool I use to sort what to automate, what to augment, and what to keep in human hands.

And if you try to coherise something this week, tell me how it went. Those stories are how I can learn.

Newsletter – Edition 2

Those of you following my book’s journey from early on may have noticed that the subtitle has changed, and the reason is a small story about a mistake I almost made.

A few weeks ago my publisher told me, reasonably, that a reader likely couldn’t tell what the book was about at a glance. He wanted “agentic AI” in the subtitle, both for clarity and for search. At the time it read “What Wins When Everyone Can Go Fast,” which I liked and which never once said AI.

My instinct was to push back, and my first reason was not a good one. I didn’t want the book filed under yet another agentic-AI title when it’s a management book about the enterprise consequences of AI. That instinct had already cost me. Trying not to sound like an AI book, I’d written a subtitle that didn’t clearly signal what it was about.

The reason I actually cared about came out when I started drafting replacements. I didn’t want to compete for the noisiest keyword in the market. There are thousands of things shouting “agentic AI” right now, and being one more voice in that crowd is not the same as being found. I wanted words that would still hold up after AI stops being novel.

A quick test helped me sort the candidates. Say the subtitle out loud with “electricity” in place of “AI.” “The Last Advantage When Every Company Runs on Electricity” sounds a century old, which told me the phrasing was tied too tightly to the moment.

I settled on “Coherence: The Competitive Advantage AI Can’t Buy.” It names AI, which was the fair part of my publisher’s request. It makes an argument instead of chasing a search term, which was the part I wasn’t willing to give up. The advantage AI can’t buy is the one your competitor can’t buy either, because you’re both shopping from the same shelf. What isn’t on that shelf is the whole point.

The week in ideas

Three posts from the past week.

Good AI Governance Is Not the Same as Coherence. Australia’s directors just got one of the best AI governance guides I’ve read, and I spent the post explaining why the best version of the mainstream answer still misses the failure that will catch these boards. A governance apparatus works by review, and it reviews what reaches it. The incoherence that builds up between separately approved systems never comes up for a vote. And the human oversight everyone prescribes can pass its own audit while quietly going hollow, as a stretched review team waves through more and catches less. Real news from last week makes the point. Anthropic, the company that sells agentic AI, published a sober four-question checklist for deploying it safely: what untrusted content does the agent ingest, what can it do, what’s the blast radius, can you see what it’s doing. Four good questions. Every one of them inspects a single agent, one at a time. None of them can see the incoherence that accumulates in the space between agents that each passed. [Weigh in on LinkedIn…]

The Moat Is Coherence. Kirkland & Ellis, the highest-grossing law firm in the world, is spending around half a billion dollars to build its own AI rather than rent what its rivals can rent. Its chairman put the logic in a line: widely available tools raise the floor for everyone, and the firm doesn’t get hired for the floor. The post works out what Kirkland is actually buying, which isn’t the model and isn’t the data, but the coherence that turns both into judgment a competitor can’t copy. The giveaway is the exclusivity clause. If the value were the technology, keeping it exclusive wouldn’t matter, because the technology is for sale to everyone anyway. [Weigh in on LinkedIn…]

The Machine Proved It. Did It Do Mathematics? A digression, and my favorite of the three. An AI model recently disproved a conjecture the mathematician Paul Erdős posed in 1946, reaching across the field into tools no human specialist would have thought to try. The machine produced the proof. It did not choose the question. The post works through why understanding isn’t decoration but compression, the way a bounded human mind fits something enormous into the space of a single brain, and why the usual “trust the result you can’t follow” analogy from medicine breaks down in mathematics, which has no second way to verify a claim besides the proof itself. The closing point holds up under the whole argument. Nobody has built a system that decides which question is worth eighty years of human attention. [Weigh in on LinkedIn…]

There’s one thread through all three. The machine can produce the output. A human still owns the part with no dashboard: choosing the question, holding the separate pieces together, judging whether the answer is any good. Kirkland is paying half a billion dollars to own that part. The governance guides keep prescribing a version of it that passes the audit and can’t see. The mathematicians are the ones saying out loud that they don’t yet know how to measure it. It’s the same thing the new subtitle names. The advantage isn’t the AI. It’s the coherence around it, and that isn’t for sale.

Before you go

One more item, because it belongs to the same story. SAP just closed its acquisition of Prior Labs and committed more than a billion euros to a lab that builds foundation models for structured data instead of text. The bet is that the untapped value in enterprise AI sits in the tables and databases a business actually runs on, not in another chatbot. I think that bet is right and incomplete in a familiar way. Point a powerful model at a fragmented, contradictory data estate and you get fast, confident reasoning over an incoherent picture, which is worse than slow reasoning, because the confidence hides the mess. Making the data coherent enough to trust is the half nobody can sell you.

If the book is why you’re here, it’s Coherence: The Competitive Advantage AI Can’t Buy, out this Fall. Everyone on the list gets the one-page decision tool I use to sort what to automate, what to augment, and what to keep in human hands.

And if you try to coherise something this week, tell me how it went. Those stories are where a good share of my ideas come from.