Enterprise users of AI have already begun questioning the cost benefit as they realise the cost of tokens (sort of unit of AI use) is not commensurate to the value they are deriving. Whilst more efficient models and better chips will likely bring down token costs in due course, there is another rather implicit cost to the user. When we individuals use AI, we might submit personal information, say medical test reports to help improve our personal health and hence risk losing our privacy. For enterprises that input proprietary data into AI to make the most of it, the cost is losing trade secrets and thereby competitive advantage. At the same time, the model companies who we individuals and enterprises feed data actually benefit from it as our feeds help them get better. Satya Nadella, the CEO of Microsoft in this post on X, talks about this paradigm as the reverse information paradox, based on Nobel prize winning economist, Kenneth Arrow, who described a paradox in the market for information: ““Its value for the purchaser is not known until he has the information, but then he has in effect acquired it without cost.” In Arrow’s “Information Paradox,” the seller risks giving away knowledge in order to sell it.
AI creates the reverse problem. In the AI age, the buyer risks giving away knowledge, just in order to use what they bought.
You essentially pay for intelligence twice, once with money, and again with something even more valuable: the proprietary knowledge you must reveal to make that intelligence useful. The better you want the model to perform, the more of that knowledge you have to feed it!
Over time, the information asymmetry becomes increasingly skewed. The seller learns more and more about you as you use what you purchased, while you learn very little about what the seller is learning in return.
That is what I think of as the Reverse Information Paradox.”
Nadella argues that whilst patents were a way to protect sellers of information from the Information Paradox, we need a regulatory equivalent of patents to protect buyers of AI from the Reverse Information Paradox.
More importantly, Nadella’s proposed solution points to an increasingly distributed AI infrastructure in protected environments of enterprises.
“While the great innovation that comes from model providers having fair use rights to train models on public data is needed, I find it ironic that the status quo is to then turn around and impose restrictive terms on distillation, and to reserve the right to learn from customer usage and interaction data. If learning flows in only one direction, economic value converges toward the owners of the learning infrastructure rather than the creators of the knowledge itself. Therefore, it’s imperative that we distribute the learning infrastructure to every firm so that they can control their own learning loop.
As Alex Karp put it: “What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the means of production, and it’s not being transferred to someone else.” The current regime does precisely the transfer Karp and companies fear.
That is why enterprises need a real trust boundary for their human capital and token capital to compound. It is where an organization’s data, traces, evals, adapted weights, and memory accumulate and improve together. And it is a hard boundary across which nothing crosses, not even the intelligence exhaust, without consent. Enterprises will demand the rights to use model outputs to fine tune and/or train their own models. I think of this as every firm’s right to align models to their enterprise accountability obligations.”
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