Three men who compete for the same enterprise budget said the same thing within two weeks of each other. There’s a new narrative around information and domain knowledge as a competitive advantage that’s important to explain. Some parts are accurate, but what they all leave out is a deception that could cost enterprises everything. (View Highlight)
Satya Nadella published a long post on X this weekend that got 3.7 million views. He coined the phrase the ‘Reverse Information Paradox’. His thesis is you pay for AI twice, once with money and once with the proprietary knowledge you have to reveal to make the model useful. (View Highlight)
He borrowed Alex Karp’s demand that customers should own the means of production, and Karp made the case on CNBC. He told enterprises that token-metered AI was a deal where “something has gone completely wrong.” You pay for the tokens and the labs keep your IP. (View Highlight)
Marc Benioff has been saying a gentler version of that for a year. He says that your data is not Salesforce’s product. Salesforce sells an agentic platform, but Benioff is positioning its Data Cloud as the enterprise system of record for everything that touches the customer without transferring that data into Salesforce’s hands. (View Highlight)
All three are dancing around the same perception shift. Models are commoditizing, and the moat is moving from the model to the proprietary knowledge that makes models and agents valuable. Nadella calls that knowledge tacit, stored in private evals and corrections. Benioff calls it context and grounding. Karp calls it the ontology and the alpha. It’s the same asset and competitive advantage with three different brands that suddenly find they have a common interest. (View Highlight)
However, they are only describing the easy half of the problem. It’s enough to get you to buy the platform, so that’s where these vendors stop. Unfortunately, this partial narrative is dangerous to businesses that need to get value out of their information and AI investments. It hides the additional work required to actually get what those vendors are promising. (View Highlight)
The same assumption hides underneath each narrative: that the knowledge already exists. It is sitting in your datasets and your employees’ heads right now, valuable and intact, waiting for you to wall it off and monetize it.
‘Protect your context’ only makes sense if you have context worth protecting.
‘Own your learning loop’ assumes the loop is already producing something.
Nadella’s term for it is revealing. He calls the valuable byproduct exhaust. It’s the traces, corrections, and evals that leak out of the engine as you work. Exhaust is what you get when you are not capturing information intentionally. (View Highlight)
Here is what I see working with a range of clients, and others I talk to have a similar experience. Most start with datasets that contain almost none of the information that would make an agent valuable. The transactions are all there, but the context that explains them is not.
You have a record that a deal closed at a discount, but nothing about who pushed for it, what the customer threatened, which competitor was in the room, or whether the rep would do it the same way again. You have the output of a thousand decisions without the reasoning behind any of them. The knowledge the three CEOs want you to protect is rarely captured because nobody engineered the systems to do it.
That is the part the vendors have every incentive to understate. “You already have the context, we’ll help you unlock it” is a much easier sale than “your pipelines throw away the most valuable thing they touch, and we need to rebuild them.” The first is a platform purchase. The second is a strategy and architecture problem. Guess which one shows up in the keynote. (View Highlight)
Most of my engagements start with the unglamorous work of re-engineering data pipelines so they gather data contextually in the first place. Context requires capturing the decision, correction, reason, and the outcome alongside the transaction, rather than the transaction alone. Then turning that captured context into a knowledge graph and structural causal model an agent can reason over, instead of a warehouse it can only query. The raw material of the moat is a byproduct, and byproducts only accumulate when you build the machine to collect them deliberately. Left alone, they vent into the air like exhaust.
This reframes what the high-value information capability actually is. Protecting information is a control problem, and it is largely solved with zero-retention tiers, tenant boundaries, and on-prem weights. That is the platform the three of them are selling. (View Highlight)
The capability that separates businesses over the next decade is acquiring information, especially new information. That means getting good at:
Engineering access to the processes and workflows that generate high-value information
• Designing the decision workflow so the reasoning is transparent
• Capturing corrections the moment a human overrides the model
• Running experiments that tell you something you did not already know
• Wiring the outcome back to the action that caused it
Every new information set you can manufacture this way makes the business more valuable. When the information set is genuinely novel, something no competitor holds and none can easily reconstruct, it stops being an asset and becomes an advantage. That is where the moat gets built. Not by protecting the context you have, but by engineering the flow of context you do not have yet and protecting that engineering work from being easily copied by competitors. (View Highlight)
Information Flywheels Are The Strategy
This is the piece Nadella hints at with his fifth C, Compound, and then leaves as an incomplete thought. Compounding is not a property you declare. It is a machine you build and an iterative action: The Information Flywheel.
The mechanics are simple to state and hard to implement, so I can’t blame them for stepping over them. You engineer a workflow so that using it generates information you did not previously have. That information feeds back into the system (the knowledge graph, structural causal model, evals, and models) and makes the next use more valuable, which drives more use, which generates more information.
Turn the wheel and the asset compounds, and the gap between you and a competitor who started later widens as the product or agent’s usage scales. The three CEOs describe the asset sitting at the end of The Information Flywheel. It is the difference between owning a reservoir that’s stagnating and owning the mechanism that continuously replenishes it. (View Highlight)
Which is why the real strategic question is not the defensive one every vendor is currently posing: How do we keep our data from leaking to the labs? Enterprises must go on offense to generate more revenue: What flywheels can we build that our competitors structurally cannot duplicate easily?
Protecting a static asset has become table stakes, and that’s what most vendors are afraid to confront. Manufacturing compounding information architecture is the endgame. You can patch the leak, and still have nothing worth protecting. You can win the second and make the first fight irrelevant, because a competitor who copies your data as of today is copying a snapshot that has already moved.
Information Flywheels are critical drivers of decision and transformation dominance. (View Highlight)
Where This Lives: The Learn Layer Of Orchestration
In the agentic orchestration architecture I’ve been using and writing about, this capability maps to one of four layers: Represent, Decide, Act, and Learn. (View Highlight)
Everything Nadella, Benioff, and Karp describe lives in Represent. Grounding context, protecting the ontology, owning the semantic layer, and keeping the evals inside the boundary are all the representation problem. That is the right place to start. It is also the place most enterprise AI work ends, which is why this narrative is so dangerous.
Information Flywheels live in Learn. It’s the layer that turns action-outcome feedback into information the graph did not contain before. Represent protects the knowledge you have. Learn manufactures the knowledge you don’t. One is a shallow moat you inherited from BI. The other is a moat you compound to enable agents and AI. Learn is, by a wide margin, the hardest of the four to build correctly, for a reason most teams discover only after they have shipped something that looks like it is learning, but isn’t. (View Highlight)
look at your own stack honestly. How much of it is built to protect what you already know, and how much of it is built to manufacture what you don’t? Have you completed an information assessment that details what context exists in your datasets? Have you done a gap analysis between what you have and what agents will need to support a workflow and deliver an outcome?
If your business is like most that are early in their information and AI maturity journey, there isn’t enough context for monetization to be feasible. It is better to understand that upfront than to build high castle walls around data that isn’t worth protecting. (View Highlight)
