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.