Tag: organizational learning

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AI Business Technology

The Diffusion of Ordinary Work

A recent O’Reilly Radar piece has stayed with me longer than most: Jeff Ding’s diffusion theory of great-power competition applies just as well to AI adoption, and it suggests that companies chasing the frontier might be optimizing for the wrong thing.

Ding, a political scientist at George Washington University, pushes back on the standard story of technological power — that the country or company which first invents or dominates a glamorous new sector locks in lasting advantage. The historical record says otherwise. General-purpose technologies like steam, electricity, and computing produced durable national advantage not through invention but through diffusion: the slow, unglamorous work of embedding a technology into ordinary productive work across an entire economy. The infrastructure that mattered was never the breakthrough lab. It was the education and training systems that produced large numbers of competent, ordinary engineers who could put the technology to work. Ordinary engineers, in Ding’s framing, matter more than heroic inventors.

The same logic holds inside a company. Frontier models turn over every few months. Organizational know-how compounds.

Palantir makes the abstraction concrete. The company doesn’t train frontier models — it builds the layer underneath them: a live, machine-readable model of how a specific organization actually works, a data integration fabric, and a platform that connects whatever model a customer chooses to real operational decisions. It is deliberately model-agnostic. The value proposition is governance, context, and the accumulation of reusable logic rather than access to the newest weights. Practitioners embed with the customer, learn the domain, and configure the system against the customer’s own data and processes — diffusion as a job description.

Leadership has been unusually blunt about what this implies: frontier labs, they argue, are optimizing for benchmarks while under-delivering on what enterprises actually need. The clearest evidence for the argument is also the most citable one — there have been production cases where an unmodified open-weight model, running inside Palantir’s platform with customer-specific context, outperformed frontier models on the actual task. If true, and it appears to be, the implication is uncomfortable for anyone selling model quality as the whole story: the ground underneath the model — the ontology, the data, the accumulated rules — often determines outcomes more than the model itself.

Electrification is the closest historical analogue. Factories didn’t get more productive the day they installed electric motors. The gains showed up years later, once entire production systems had been redesigned around decentralized power. The lag was organizational, not technical. AI diffusion looks likely to follow the same shape — the bottleneck was never going to be model capability, it was going to be the patient, unglamorous work of redesigning how people actually work.

I don’t know who’s training the ordinary engineers right now — the ones who will spend the next decade doing the diffusion work rather than the invention work. I don’t think anyone’s tracking their names.

Categories
AI Business

The Reverse Information Paradox We’ve Always Had

Satya Nadella wrote recently about what he calls the Reverse Information Paradox: enterprises pay for AI intelligence twice. Once in money. Again in the proprietary knowledge they surrender through every prompt, correction, and evaluation. The better they use the model, the more of their own institutional understanding leaks into someone else’s system. The vendor ends up knowing more about the buyer’s business than the buyer knows about what the vendor retained.

Replace “model” with “employee” (or “consultant”) and the paradox is not new at all.

You pay for a person once with salary. You pay again with something harder to price: the context, relationships, and judgment they must absorb to become useful to you. The better they perform, the deeper the immersion, the more of your particular way of doing things moves into their head. Every correction and late-night conversation is another trace of institutional memory changing hands. When they leave, some of that memory leaves with them. Not always through theft. Usually just through the ordinary residue of good work.

The visible cost is salary; the invisible cost is the slow transfer of what makes you distinctive. High performers get more access precisely because they’re high performers, which means the leakage accelerates exactly when you can least afford it. The exhaust is just harder to see with people than with tokens — it moves through conversation and mental models instead of logs.

The analogy has a limit, and the limit matters. Employees bring knowledge in, not just absorb it. They have judgment and relationships a model doesn’t. Models are purely absorptive, and once something is inside them, it’s infinitely reproducible — a person can only be in one place, working for one employer, at a time. We’ve had a few hundred years to build tools for the human version of this problem: contracts, culture, non-competes. The model equivalent is still being invented in real time, which is exactly why Nadella felt the need to name it.

Apple’s recent legal action against former employees who joined OpenAI is this pattern in its sharpest form. Whatever the specifics, the shape is familiar: people who spent years inside one of the most sophisticated organizations in the world, carrying out knowledge that never appeared on any balance sheet and was hard to contain. No one fully anticipates what a mind absorbs simply by being in the room long enough.

That’s the real difference between the silicon case and the human one. You can try to take action to wall off knowledge flowing to a model. You cannot wall off what someone has learned to notice.