The reverse information paradox: keep your crown jewels.

Satya Nadella published a short piece last week called “The Reverse Information Paradox.” If you work anywhere near AI strategy, it is worth the ten minutes. I want to unpack why it matters, because it puts language to a problem I run into almost every week with midsize enterprises.

Here is his core idea. Back in the 1960s, the economist Kenneth Arrow described a paradox in the market for information. A seller has no way to prove the value of what they know without revealing it, and once they reveal it, the buyer has it for free. AI flips this around, and the buyer becomes the party at risk. You pay for intelligence with money, and then you pay again with something far more valuable: the proprietary knowledge you have to hand over to make that intelligence useful. The better you want the model to perform, the more of your own knowledge you feed it. All the while, the seller is learning more about you than you learn about what they are collecting in return.

That second bill rarely gets read, and it is the one that deserves the most attention from leadership.

The midsize companies I talk to have finished debating whether to use AI. They are racing. Leadership wants wins on the board this quarter, so the business side sprints, and the governance and risk teams end up cast as the people slowing everyone down. Often the business wants AI without knowing where to start, so someone feeds Claude everything they have, gets a summary back, and has built no durable capability. A few minutes were saved, and the operating model is unchanged.

I have sympathy for both sides. The pressure to move is strong, and no company wants to be the one that lost ground because legal was still reviewing a data processing agreement. The governance teams have a point too: they can see what the sprint is giving away in the background, or at least the better ones can. Some are simply unaware, and that is its own problem, because progress stalls once they work out what happened and pieces like Satya's start circulating.

Here is where it gets structural. Nearly every one of these companies already has a large cloud agreement in place: committed spend, negotiated discounts, the whole thing. So when AI shows up, the path of least resistance is to buy the bundled capability from the platform you are already committed to. Swipe the card you already have. It is fast, it is easy, and it looks efficient on paper.

The trouble is that buying instead of building is a decision about who gets smarter over time. In the default setup, the vendor does.

Satya gets this right, and many organizations miss it. Models learn from more than the data you formally load in. They learn from the exhaust: the prompts your people write, the tools your agents reach for, and above all the corrections your experts make when the model gets something wrong.

Consider what a correction is. It is a senior person on your team, someone you have paid for fifteen years to develop judgment, telling the model how your business thinks about a hard call. That is institutional know-how, the kind a competitor has no way to buy at any price. In the buy-everything model it leaks out one trace at a time, so slowly that nobody files an incident report about it.

Nadella puts it well. If learning only ever flows in one direction, then over time the value piles up with whoever owns the learning infrastructure rather than with the company that generated the knowledge.

For a midsize enterprise, your data and your team's judgment are the whole ball game. They are what make you hard to copy. Handing them over as a byproduct of a convenient purchase is the most expensive mistake I see companies make without realizing they are making it. It is early, and staying on the off-the-shelf path will prove costly when the time comes to correct course, because by then the knowledge has already left.

So the market hands leaders what looks like a clean tradeoff. Move fast and buy, and accept the leakage. Or protect your data and build, and accept that you will be slow and it will turn into a science project.

I reject that tradeoff, and I think you should too. It is a false choice, and it exists mostly because the buy option is packaged so conveniently and the build option gets pitched as a two-year platform overhaul.

That false choice is why we built our approach at ModernOps the way we did, and why we partner with firms that expect outcomes quickly rather than large consulting bills.

We take a blended approach. The goal is to get you from proof of concept to a real business outcome in under twelve weeks, and to do it in a way that builds the foundation instead of digging a hole you have to climb out of later.

Speed and governance work together when the architecture is right. The tooling side of that boundary, and how much of it a Microsoft 365 tenant already owns, is covered in Field Notes No. 08. We get you moving fast on the use cases that matter, and at the same time we set the trust boundary so your sensitive data stays inside your four walls. Your evals, which are your own definition of what “good” looks like, stay yours. Your traces, your corrections and your institutional memory accumulate on your side of the line and compound into an asset instead of leaking out as exhaust.

We also keep the orchestration layer decoupled from any single model, which is a quiet but important point in Satya's piece. If the one model you built everything around gets pulled, repriced or deprecated tomorrow, can you still operate? Can you still hit your evals with a different model underneath? If the answer is no, you are renting the capability, and the landlord sets the terms.

Do this right and every AI initiative stops being a cost you re-pay every quarter and starts being IP you own. Your team's judgment gets captured, protected, and reused. The learning loop runs inside your walls and gets better every cycle. That is the difference between spending on AI and compounding on it.

Satya framed the problem cleanly: a company should be able to use a model without giving up the knowledge that makes it unique. That is the reverse information paradox, and it comes down to a set of decisions you are already making, knowingly or otherwise, every time your team picks the easy path.

You are going to pay for intelligence, and that is fine. Just make sure your crown jewels stay on your side of the line.