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Early in my career, at Mitsubishi Electric Research Labs, I watched a team solve a hard problem in a way that has stuck with me ever since.

The task was automatic highlight reels for baseball games. Real effort had gone into the sophisticated version: systems that could read the play, follow the ball, understand the game. Then someone on the audio side noticed something. Every moment worth keeping had one thing in common. The crowd roared. So they tried the simplest possible approach. Replay the few seconds around each spike in crowd noise. It produced a near-perfect highlight reel, built from a signal anyone could have used.

The sophisticated system was not the advantage. Noticing what mattered was.

I think about that a lot right now, because the enterprise is making the same mistake at scale. We have decided the advantage in AI is capability. The smartest model. The most copilots. The biggest deployment. It is not. And capability is about to stop being scarce at all.

For most of the past decade, the winning move was speed. Adopt faster, automate more, ship before the competition. That worked because building things was hard, and whoever removed that friction first pulled ahead. Agentic AI is ending that era, because it is making execution cheap. When anyone can build, automate, and deploy in an afternoon, speed stops being an edge. Everyone has it.

So what becomes scarce? Coherence. Whether your growing crowd of autonomous systems, and the people accountable for them, still pull in the same direction. When everyone can go fast, coherence is what wins.

The debt that never shows up on a dashboard

Here is what happens when execution gets cheap and no one is watching for this. Everyone makes more. Sales stands up an agent for lead qualification. Finance automates forecasting. HR wires up hiring. Operations builds a copilot for logistics. Each one works. Each one passes its own tests. On paper, the company gets more automated every month.

And it gets quietly harder to run. Decisions start flowing through systems no single person fully understands. Two agents act on assumptions that contradict each other. Every team sharpens its own corner while the whole thing loses its shape. I call this complexity debt, and its defining feature is that you cannot see it directly. You feel it later, as the strange sense that nobody can quite explain why the organization behaves the way it does.

I learned how this happens the embarrassing way, long before agents existed. At United Technologies, I built a data visualization I was proud of. It was dense, information-rich, technically elegant, the kind of thing that impresses other engineers. But users hated it. They found it unreadable, and they were right. I had optimized for the wrong thing. My system was correct at the level I cared about and useless at the level that mattered.

That gap is the whole problem, and it is about to repeat across the enterprise, one agent at a time. A system can be flawless at its task and still make the organization worse. Task-level correctness and organizational coherence are different things. Improving one does nothing for the other, and most companies are measuring only the first.

What coherence is

So what am I asking you to protect? Coherence is not a vibe or a culture slogan. It is structural, and you lose it in specific, recognizable ways.

A coherent organization shares context. Its systems and its people reason from the same facts, so a decision in one place does not silently undercut a decision somewhere else. It keeps its wiring legible, so pulling out one system does not trigger a chain reaction nobody saw coming. And it keeps a human line of accountability for every autonomous system: someone who owns it, knows what it is really doing, and can correct it when it drifts.

None of that means slowing down, and none of it means centralizing. A decentralized company can be perfectly coherent. A centralized one can be a mess. Coherence is not the absence of autonomy. It is what makes autonomy safe to scale.

You cannot supervise your way out of this

The instinct, once a leader feels this, is to watch everything. That instinct fails on contact. You cannot supervise a hundred agents by paying attention harder.

The leaders who handle this well build coherence into the structure instead. They set constraints so whole classes of incoherence cannot arise in the first place. They contain systems so the failures that do occur stay local rather than spreading. They spend their scarce human judgment on the few decisions that truly need it, and let the rest run inside guardrails. It is engineering, not vigilance. The difference is between a company that stays coherent because someone is always watching and one that stays coherent because it was built to.

The advantage no vendor can sell you

This is why I keep coming back to that baseball reel. Intelligence is commoditizing. Everyone will have capable models, mostly the same ones, at mostly the same price. The capability will not be your advantage, any more than the sophisticated summarizer was.

What will not commoditize is the ability to deploy all that intelligence coherently: the judgment to decline the automation that buys a local win at the cost of the whole, the architecture that lets you move aggressively without piling up debt, the discipline to keep the organization legible to itself as it fills with autonomous systems. That is hard, it is specific to you, and there is no vendor who can sell it to you.

So here is the question I would sit with. You can almost certainly tell me, right now, how accurate your models are and maybe, how many agents you have in production. But can you tell me whether your organization is still coherent? Do you have anything that would show you coherence breaking before it breaks something?

Most leaders do not. It is the most expensive blind spot in enterprise AI, and almost no one is looking at it.

That question is what my book, Coherence, is about, and it is what I will be working through here in the open over the coming months. The one-page tool I use to start answering it is the first thing I send when you join the list at coherise.com.

For thirty years, the fastest company usually won. I think that is about to stop being true.

Agentic AI is making execution cheap. When anyone can build, automate, and deploy in an afternoon, speed stops being an edge, because everyone has it. What becomes scarce is coherence: getting the growing crowd of autonomous systems, and the people responsible for them, to pull in the same direction. When everyone can go fast, coherence is what wins.

I did not arrive at that from a strategy deck. I arrived at it from a question I have been chasing since college.

For as long as I can remember, one thing has held my attention above all else: what makes something intelligent. That question pulled me out of discrete math and computer science and into computational neuroscience, where I spent years on a single puzzle. How does the brain turn billions of small, unreliable, independent units into one coherent mind? No neuron is in charge. No neuron can see the whole. And yet, somehow, thought happens. I did not know it at the time, but that puzzle would turn out to be the through-line of my whole career, and eventually, of this book.

I carried the question into industry back when there was no name yet for the job I was doing. I lived through the Big Data years, and then the Data Science years, each one arriving as the answer to everything and each one settling into something quieter and more useful. Then deep learning arrived, and it felt different in kind. The first time I saw what it could really do, I understood that the ground had moved under all of us. I joined Karthik to start Concentric AI on the strength of that conviction, and bet a company on it.

What I had watched build slowly for years then began moving at a pace I had never witnessed. Steady theoretical progress, faster hardware, and ever more compute compounded into generative AI. And now we are somewhere newer still, in the agentic era, where AI no longer just answers. It acts.

Which brings me back to the thing that unsettles me. In narrow domains like math and code, the progress is genuinely breathtaking. And yet inside most enterprises, that progress stubbornly refuses to turn into results. In conversation after conversation, what I hear from leaders is relentless pressure and a quiet fear of falling behind, all of it wrapped around a phrase nobody can quite define: “doing AI.”

Around that fear, the noise is deafening. On one side, breathless promises: networks of thousands of agents that will soon run entire departments on their own. On the other, a shrug dressed up as wisdom: the models will keep getting better, so whatever problem you see today will simply solve itself. Both cannot be true. And what is missing from all of it is not another confident prediction. It is a way to think clearly while everyone around you is loud and certain.

The serious thinking has mostly been elsewhere, and for understandable reasons. The researchers who build these systems are busy making the models, the theory, and the algorithms better. The engineers and tinkerers around them are focused on empirical evaluation, on benchmarks, and on getting individual systems to work. AI safety and responsible AI have concentrated on society and the long horizon, on where powerful models might eventually take us. Governance has approached AI through risk, security, and compliance. Economists and organizational theorists are only beginning to engage, and agentic AI is so new that there is little evidence yet to build rigorous work on. None of that is a failure. It just means the question I kept running into, how a company holds together as it fills with autonomous systems, has fallen into the gap between all of them, still largely unclaimed.

There is only one way I know to cut through noise like that. 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 changed is the cost of execution. It has collapsed toward zero. So I started there and followed it, step by step: to why firms exist at all, to what becomes scarce once doing things is nearly free, and to where advantage has to move next. The word I kept landing on was coherence. The same word from the brain, now describing the enterprise. I began writing to pin it down. Before long, I had a book.

So I am glad to share that my book arrives this Fall. It is called Coherence: What Wins When Everyone Can Go Fast. Its argument, reasoned from the ground up, is simple to state and uncomfortable to live by: capability is now for sale to everyone, so it can no longer be your edge. What cannot be bought, and what now decides who wins, is the coherence of the organization putting AI to work.

Between now and launch, I will be writing here regularly, thinking out loud through the ideas in it: why speed stopped winning, the hidden cost of making everything autonomous, and what a leader can actually do about it on a Monday morning. If any of this matches what you are seeing from where you sit, I would be glad to have you along.

You can read more, and sign up for launch-day access, at coherise.com. Everyone who joins also gets the one-page decision tool from the book, for sorting what to automate, what to augment, and what to keep in human hands.

This book cost me more weekends, and more self-doubt, than I bargained for. What kept me going was a small surprise that never quite wore off: the question I once asked about billions of neurons turned out to be the same one facing every leader now trying to hold a company together. I am glad it is nearly here.