The Moat Is Coherence

For most of my career, the systems I built were valuable because they were hard to build.

At research labs and then inside large enterprises, my teams and I built machine learning systems that took months of work, rare expertise, real budgets, and long struggles for resources. The difficulty was not incidental to the value. It was the value. If a competitor wanted the same capability, they needed the same scarce people, the same time, and the same money. Scarcity of execution was the moat. We just never had to call it that, because it had always been true.

Looking back now, I feel two things at once. Pride, because the work was genuinely good. And vertigo, because a lot of it could be stood up today in a weekend, by a small team with a subscription. The capability I spent years of my life building is being commoditized.

Here is the part that took me longer to see. The part of that work that mattered most was never the part that was hard to build. It was knowing what to build, what the data actually meant, which requests to push back on, and which impressive system would quietly make things worse. That part has not commoditized. That part is what this piece is about, because the world’s highest-grossing law firm recently bet half a billion dollars on it.

The sentence every boardroom should read

Kirkland & Ellis, which booked $10.6 billion in revenue in 2025, recently announced it would spend roughly $500 million over the next few years building its own AI platform rather than relying only on the tools its competitors can buy. Its chairman, Jon Ballis, compressed the logic into one line. Widely available AI tools, he said, are “raising the floor for everyone.” But, he added, “we don’t get hired for the floor.”

That is one of the most clarifying things a business leader has said about AI strategy in two years, and it cuts against how most companies are spending. The prevailing assumption is that advantage comes from having the most capable AI. Buy the best models, deploy the most agents, automate fastest, and you win. This was a reasonable playbook for almost every previous technology. It is wrong about this one.

We have run this experiment before

It is wrong because capability is commoditizing, and we know what happens when capability commoditizes, because it has happened to every general-purpose technology in modern history. Electricity was an advantage for the firms that could generate it, until it became a utility and the advantage migrated to what firms did with it. Computing was an advantage for firms with mainframe access, until computing became accessible and the advantage migrated to data and process design.

Nicholas Carr made himself famous, and briefly infamous, by calling this pattern in 2003. His Harvard Business Review essay “IT Doesn’t Matter” argued that information technology was following electricity and the railroads from proprietary advantage into shared infrastructure. The argument was bitterly contested at the time. Two decades later it looks prescient. The firms that built durable advantage in the computing era were not the ones with better hardware. They were the ones that developed distinctive organizational capabilities for using what everyone could buy.

Michael Porter gave us the vocabulary for why. Operational effectiveness, doing the same things better, is necessary but not sufficient, because best practices diffuse. Strategy is doing things rivals cannot easily match. Tools that a thousand firms can license are operational effectiveness by definition. They raise everyone’s floor at once, and a tool that raises every floor confers advantage on none of them. That the operating model is the moat, more than the model, is a case I made in detail recently, building on McKinsey’s own evidence. If your AI is the same as the AI across the negotiating table, you have spent money to keep pace, not to pull ahead. Ballis’s floor and ceiling is Carr’s argument and Porter’s distinction, restated by a customer with $500 million on the table.

Even the people selling the technology concede the mechanism. Larry Ellison, whose company is staking its future on enterprise AI, argues that because every major model trains on the same public internet, model outputs are converging and differentiation is eroding. He is right. Shared inputs produce shared reasoning. The implication the vendors are less eager to draw is that buying more of a commoditizing capability is not a strategy. It is a subscription.

Where Ellison’s answer falls short

Ellison’s proposed moat is proprietary data. That is closer, but it imports a mistake. Every enterprise already has data, most of it fragmented across systems, contradictory between departments, disconnected from the outcomes it produced, and ungoverned. Pour that into a powerful reasoning engine and you do not get insight. You get fast, confident reasoning over an incoherent picture, which is worse than slow reasoning, because the confidence hides the incoherence. Having data is not scarce.

What is scarce is data made coherent: owned, reconciled across the organization, connected to the outcomes it produced, and trustworthy enough to reason over, joined to the judgment of the people who know what the numbers mean. I argued earlier in this series why no vendor can sell you this. The platform that stores and retrieves your data is plumbing, and plumbing commoditizes. The coherence is the asset, and it compounds.

What Kirkland is actually buying

Read the Kirkland decision through that lens and it stops looking like a technology splurge and starts looking like strategy. The firm is not paying $500 million for data it already owns or software it could license. Anyone can download its public filings. What cannot be downloaded is how the firm decides: the judgment of 250 of its lawyers, 100 of them partners, encoded in a form every lawyer can draw on for every matter. Kirkland is spending to make its institutional judgment coherent, and to keep it exclusive.

The tell is in the terms. The outside firms building the platform are barred from selling it to any other law firm. If the value were the technology, exclusivity would not matter. Kirkland insists on it because it believes the technology is commoditized and the value is the distinctive judgment the system encodes. A shared tool would dissolve exactly that. A shared tool is also a conduit. Every standard and correction a firm feeds into it can improve the version its rivals rent tomorrow, which is the leakage I traced in Your AI Usage Exhaust Is Someone Else’s Moat. Read that way, Kirkland’s exclusivity clause is that essay’s prescription written into a contract: close the loop, and keep what you encode inside your own walls.

Since the May announcement, the pattern has only hardened. Through June, Kirkland added two more exclusive builds, one for private-equity fund formation and one for litigation, and framed each the way it framed the platform itself: a way to capture the firm’s own judgment and knowledge and keep it exclusive to Kirkland. Three deals in roughly five weeks, and the constant across all of them is the insistence on owning what the tools encode.

None of this means Kirkland is certain to be right. Building rather than buying is a real bet, and it is possible that within a few years a purchasable platform, fed a firm’s own data and tuned to its standards, delivers most of the advantage at a fraction of the cost. Every executive now faces a version of that question. But notice what the question is actually about. It is not whether to have AI. It is what you are trying to own. Kirkland has decided the thing worth owning is not the model and not the data but the coherence that turns both into judgment competitors cannot replicate.

The wrong scoreboard

The firms that misread this will keep score by the wrong numbers. They will count agents deployed and measure speed of adoption, and they will mistake a rising floor for a rising position. I understand the pull of that scoreboard better than most, because I spent years on the other side of it, building the hard things the scoreboard rewarded. The hard things are cheap now.

The firms that read it correctly will ask a harder question: when the models are a commodity and the data is everywhere, what does our organization understand about itself that no competitor can buy?

That was never the intelligence. It is the coherence of the organization putting intelligence to work. Kirkland just put half a billion dollars behind that proposition. The rest of the market is still buying the floor.

This argument runs through my book, Coherence, arriving this Fall. If you want to follow the thinking as it develops, join the list at coherise.com. The one-page decision tool from the book is the first thing I send.