You're Not Using AI. AI Is Using You.

When we use AI in its most common form, we come to realize something about it. The large language models (LLMs) behind it have been trained on public knowledge, not on the knowledge your organization, business, or institution owns.

We can provide a model with our documents to process, but this does not change the model. It does not learn that way. The exchange is one-directional in a way that is easy to miss. The model answers our prompts and forgets us, but the data we send does not simply disappear. It is used. Not to make the model better understand our business, but for something else entirely.

That something else is the point of this article.

The drive for AGI

What is happening right now is a drive toward artificial general intelligence, or AGI. This is not a backdrop to AI development. It is its organizing goal. The work is to make intelligence broader, more general, and more capable, and every major provider is steering toward it.

However, that drive has hit a wall.

The large language models have been trained on very nearly everything humanity has written down, and the supply of fresh, human-generated public text is all but exhausted. To keep expanding a general intelligence, the frontier model providers need a new source. This source contains the knowledge that was never public in the first place, the knowledge held privately inside organizations: institutional knowledge.

This is why your data is wanted, and it is worth being exact about the purpose.

Institutional knowledge is being harvested not to make any particular organization more capable, but to advance the general project. It is wanted precisely because it is what the general intelligence lacks. Fed into the general model, an organization's distinctive, hard-won knowledge becomes the material from which the next general intelligence is built, and then sold back to everyone, including you.

AI development has run out of public knowledge. The knowledge it needs next is yours.

This puts a question to every organization that it has not been asked to consider.

Are you adding AI to your intelligence, or adding your intelligence to theirs?

The tale of two intelligences

To answer it, we have to be clear about what intelligence is. Intelligence is not a thing one possesses. It is the capacity to apply knowledge to a situation in order to decide how to act, and it does not exist apart from that application. Knowledge must be applied before it can be considered as intelligence.

Every organization is already an intelligence by this measure. In many ways it is the first artificial intelligence that humans created.

An organization applies what it knows to the situations it faces, decides, acts, and answers for the result. This intelligence is built from the organization's values, its accumulated knowledge and experience, and the aggregated judgment of the people who work within it. That applied knowledge is its substance, and it is exactly what the general models now want. Call it institutional intelligence.

Artificial intelligence in the form of large language models is a general intelligence in name, but by this measure it is not yet intelligent at all. It is trained on the world's public knowledge, built to serve everyone, and committed to no organization's ends. It knows nothing of your business, your history, or your way of working.

An LLM treats your knowledge as data to be processed and acquired. It is capability waiting to be applied, and it becomes intelligence only when it is brought to bear on a real situation that someone must act in and answer for. The intelligence that matters does not live in the model. It lives wherever and whenever the model is applied.

So which of the two intelligences is the relevant one for your organization? The general intelligence knows a great deal in general and nothing about you. The institutional intelligence is the only one that knows your situation, carries your constraints, and has to answer for what it decides. For your organization, the relevant intelligence is your own.

The model can inform it, but it should not replace it.

Science versus engineering

There is an older distinction that is helpful for understanding what is going on right now. It is the difference between the scientific method and the engineering method.

Science expands what we know. Engineering applies it. Neither discipline outranks the other, but they are not the same work, and they do not answer for the same things. Science produces knowledge for everyone and is accountable to no particular outcome. Engineering takes that knowledge, applies it to a particular problem under particular constraints, and answers for whether the result stands or fails.

The drive for AGI is following the scientific method. It expands a general intelligence, and it is a legitimate and powerful enterprise. But it is not the work of any single organization, and its benefit does not accrue to any single organization. Feeding it your knowledge is participating in someone else's science research and experimentation.

The work that matters for an organization follows the engineering method, applying knowledge to achieve a particular end or objective. When it comes to AI engineering, the task is to take the general capability and apply it to your own mission, your own constraints, and your own ends, and to answer for the result. The textbook contains the knowledge. The engineer applies it, and answers for whether the bridge stands.

The drive for AGI follows the scientific method. Applying it to your business follows the engineering method.

This engineering cannot be left to the AGI provider. The frontier provider builds the science, the general model. Applying it is a different discipline, and it belongs to the party that has the particular knowledge, carries the constraints, and answers for the outcome. The frontier provider has none of these, and its interest runs the other way. It gains when your knowledge is taken up into the general model, not when that knowledge stays applied to your mission alone.

Waiting for frontier model providers to deliver applied AI is to ask the science to do the engineering, and to ask the party that profits from your knowledge to act against its own interest. The work is the organization's, because the stake is the organization's.

One could also object that the frontier providers are doing both, science and engineering. It is true that they engineer, and heavily. But their engineering serves their mission, which is to advance the general intelligence, not yours. Engineering always answers to whoever holds the stake, and for the frontier provider that party is never you. There are two engineering efforts here, pointed at two different ends, and only one of them is pointed at your mission.

AI adoption versus applied AI

AI adoption is the use of the general intelligence as it is delivered. The organization brings its prompts, its documents, and its way of working to a general model, and in doing so it adds its intelligence to the model's. No engineering is done, the beneficiary is the AGI provider, and the knowledge flows toward AGI. This is the default, the thing that happens when nothing further is done.

There is a second cost, quieter than the first. As an organization leans on the general model, it begins to adapt itself to the model rather than the other way around. Its people defer to the general answer, and its own judgment, the particular way it knows and decides, falls out of use. Adoption depletes institutional intelligence twice over. It feeds the general intelligence, and it lets the institutional one wither from disuse. The organization drifts toward AGI and away from itself.

Applied AI is the engineering of that general capability into the organization. The model is applied to the organization's own mission, under its own judgment, toward its own ends. The knowledge stays where it belongs and is put to work there. The engineering is done, the beneficiary is the organization, and the intelligence that results is its own. This does not happen on its own. It is work, and it is the work most organizations have not yet done.

It is also more than pointing a general model at a problem. For AI to be applied, the model has to be operationalized and contextualized within the business: brought into its work, its decisions, and its constraints, and made to operate there. At present this rarely happens. A general model, used as delivered, processes information about the organization without ever coming to know it. It reads what is put in front of it and keeps nothing. A model that knows you is one that has been shaped by your institutional knowledge and carries it. The end of applied AI is a model of your own, one that knows you, not a general one that processes information about you.

A model that processes information about you is not a model that knows you.

Most organizations believe they have applied AI. Most have only adopted it.

Engineer your own intelligence

The choice in front of every organization is this. You can adopt AI and let your knowledge feed a general intelligence that serves mostly the AI provider, or you can apply AI and build an intelligence that is your own.

The first looks like the easier path, but it is not a free one. You pay for it, you supply the knowledge that makes it worth more, and the intelligence it builds belongs to someone else. The second is work. It is the engineering of a general capability into your mission, your constraints, and your ends, until what you have is not a borrowed model but an intelligence that knows you and answers to you.

That work will not be done for you by the AGI providers, whose interest runs the other way, and it will not happen on its own. It is yours to do, because the intelligence it builds is yours to keep.