The next great AI market may not be artificial intelligence. It may be human intelligence, scaled.
We have spent the first phase of the AI revolution asking how intelligent machines can become. The next phase may force us to ask a more fundamental economic question: what happens when AI allows individual human beings to scale, preserve and monetize their intelligence?
Today, expertise is mostly sold through time and packaged knowledge. A teacher sells a course. A consultant sells hours. A lawyer sells advice. A scientist sells research. An entrepreneur writes books or gives lectures.
The common limitation is simple: the expert is one person. Even the world’s greatest teacher cannot personally teach millions of students.
AI potentially changes that constraint. Instead of selling only the products of intelligence—books, courses, lectures and consulting—we may eventually sell access to the intelligence behind them.
I call this idea Raw Intelligence.
1. The Intelligence Bottleneck
Human expertise is accumulated slowly. A great teacher may spend 30 years solving problems, observing students, discovering misconceptions and developing better ways to explain difficult concepts. Much of that expertise never makes it into a textbook.
The valuable asset is not merely what the teacher knows. It is also how the teacher approaches a problem, what they notice first, which mistakes they recognize immediately, which explanation they choose, which shortcut they trust, when they abandon an approach, and how they adapt an explanation to different learners.
This is tacit expertise. Today, much of it disappears when the expert stops teaching. AI creates the possibility of preserving and distributing at least part of it.
2. From Knowledge to Thinking
A conventional book primarily transfers knowledge. Raw Intelligence attempts to transfer something closer to ways of thinking.
Consider a mathematics teacher. A textbook can explain the quadratic formula. A course can demonstrate several examples. But an exceptional teacher may look at a student’s wrong solution and immediately understand: “You don’t actually have a problem with this formula. You have misunderstood the relationship between these two quantities.”
That diagnosis is expertise. The teacher may then explain the concept using a completely different example because they know that the first explanation will not work for this particular student.
Capturing such patterns is much more valuable than simply uploading lecture notes. Raw Intelligence therefore aims to preserve knowledge + reasoning + heuristics + experience + judgment + methodology.
It is not a literal copy of someone’s biological brain. It is a licensed computational representation of their distinctive expertise.
3. The Mind Book
Imagine an expert voluntarily giving an AI company access to a carefully prepared intellectual corpus.
Call it a Mind Book.
It could contain writings, lectures, solved problems, explanations, problem-solving methods, conceptual frameworks, examples, teaching strategies, common mistakes, decision rules, responses to difficult cases, and other material the expert chooses to contribute.
The foundation model supplies general-purpose intelligence. The Mind Book supplies the distinctive intellectual contribution of a particular expert.
The proposition isn’t simply that “AI knows mathematics.” It is that AI can help you think through mathematics using the methodology of this particular expert.
4. Rent a Great Mind
Now imagine a marketplace for Mind Books.
A student has a difficult mathematics problem. Instead of buying a ₹5,000 course, the student pays a few rupees—or a small monthly subscription—to access the Raw Intelligence of an exceptional teacher.
The student asks: “I understand the formula, but I don’t understand why this substitution works.” The AI responds using the teacher’s methodology. The student asks another question. The system adapts. It gives another example. It identifies the student’s misconception. It challenges the student.
The student is effectively renting access to the teacher’s accumulated expertise. The same model could apply to physics, engineering, programming, medicine, law, finance, writing, design, business and countless specialized fields.
5. Why Sell Courses When You Can Rent a Mind?
This changes the economics of expertise.
A course is essentially a frozen representation of what an expert knows. A Mind Book could be interactive.
A course says: “Here is what I decided to teach you.”
Raw Intelligence says: “Ask me what you need to know.”
A student doesn’t necessarily need 100 hours of lectures. They may need one excellent answer to one difficult question at the moment they are stuck. The expert’s intellectual asset becomes available precisely when it is needed.
6. When One Expert Becomes a Million
The fundamental economic breakthrough is scaling.
Suppose an exceptional teacher can personally teach 500 students. Their time is the bottleneck. Now imagine a Mind Book capable of assisting 500,000 students.
The teacher has not literally become 1,000 teachers. Their intelligence has become scalable.
AI isn’t necessarily replacing the teacher. It is becoming a distribution system for the teacher’s expertise.
A previously local expert could potentially become globally accessible. A specialist whose market was too small to support a conventional business could suddenly have a worldwide audience. A retired expert could continue contributing to education long after retirement.
7. Who Gets Paid When AI Thinks?
This is where the idea becomes difficult—and interesting.
Suppose an AI generates an answer using general model knowledge, Expert A’s reasoning, Expert B’s specialized knowledge, and information supplied by the user. Who gets paid?
Traditional copyright isn’t designed for this problem. Copyright is relatively good at identifying copying. AI may instead reproduce patterns of reasoning without reproducing the original words.
That requires a new concept: Cognitive Attribution.
The objective isn’t necessarily to pretend that every answer can be divided into mathematically perfect percentages. Instead, the system could establish auditable measures of material cognitive contribution.
If a licensed expert’s distinctive intellectual asset materially contributes to a commercially valuable response, the expert participates in the resulting revenue.
For example, a hypothetical attribution system might record: Foundation intelligence — 70%; Expert Mind Book A — 20%; Expert Mind Book B — 10%. Those numbers are illustrative, not a proposed technical standard. The principle matters more than the exact formula.
If AI scales someone’s distinctive intellectual contribution, that person should share in the economic value created by that scaling.
8. Cognitive Auditing
This immediately raises another question: who decides how much the expert contributed?
The AI company should not necessarily be the sole judge of its own royalty bill. A neutral auditing system could therefore emerge.
Government could establish the rules, just as it establishes tax and accounting laws. Independent cognitive auditors could verify provenance, licensing, attribution methodology, usage records, royalty calculations and compliance.
The analogy with financial reporting is straightforward:
- Government → establishes the rules
- AI company → maintains usage accounts
- Independent auditor → verifies them
- Expert → receives royalties
The government could collect ordinary taxes on the resulting economic activity, and potentially a legally defined public levy where appropriate.
The important principle is separation of roles. Government regulates. Auditors verify. Platforms operate. Markets determine value. Experts own or license their intellectual assets.
9. The “Ask Directly” Button
There is another essential feature. Raw Intelligence must know when it doesn’t know.
Suppose the AI encounters a genuinely unusual question and cannot satisfactorily reproduce the expert’s reasoning. Instead of hallucinating, it offers:
ASK DIRECTLY
The user can request the actual expert.
The interaction becomes: AI → insufficient confidence → human expert → answer.
This creates an elegant division of labour. AI handles the enormous volume of ordinary questions. The human handles exceptional questions. The human’s scarce time becomes a premium service rather than the entire business model.
With appropriate consent, the expert’s new answer could potentially be incorporated into the Mind Book. That creates a feedback loop:
Expert → AI → User → difficult question → Expert → improved AI
10. The New Economics of Education
Consider the implications for a student who cannot afford an elite teacher.
Today, access to exceptional expertise is often constrained by money + geography + time + availability.
A Mind Book could potentially reduce all four constraints. A student in a small town could access the methodology of an exceptional teacher in another country. The cost could be a few dollars rather than thousands. The interaction could occur at midnight or during a five-minute break.
The student doesn’t need to consume an entire course. They can ask the exact question that is blocking their progress.
This doesn’t eliminate human teachers. Education is much larger than information transfer. Motivation, discipline, social development and human mentorship remain important. But Raw Intelligence could dramatically reduce the scarcity of high-quality intellectual assistance.
11. Cognitive Access
This suggests a new category of product.
We currently sell books, courses, lectures, consulting, tutoring and expert hours. The AI economy could add:
Cognitive Access
The customer doesn’t buy everything the expert has ever produced. They buy the ability to interact with an AI representation of that expertise whenever they need it.
That is a shift from selling knowledge products to selling access to expertise.
It may also shift expertise from a labour model to an asset model.
Today: Expert → time → income
Tomorrow: Expert → intellectual asset → usage → royalty
12. Raw Intelligence Is Not Artificial Intelligence
This distinction matters.
The foundation model provides general intelligence. Raw Intelligence provides specialized human intelligence.
The foundation model is the engine. The Mind Book is the specialized cognitive layer.
A useful analogy is computing. A processor provides general computational capability. Software turns that capability into something specialized and useful.
Likewise:
Foundation AI + Mind Book = specialized cognitive capability
As generic AI becomes increasingly abundant, distinctive expertise may actually become more valuable, not less. The scarce resource could shift from computational intelligence to judgment, experience, intuition, methodology and provenance.
13. The AI Social Contract
There is a larger issue behind all this.
One possible future is: human beings create knowledge → AI absorbs it → AI companies capture the value → creators are economically displaced.
That creates resentment. The creator can reasonably ask: “If my intellectual work helped build the system that replaces me, where is my share?”
Raw Intelligence proposes a different arrangement:
Human creates → AI scales → users benefit → creator receives royalties.
The objective is not to prevent AI from becoming powerful. It is to create an economic mechanism through which humans participate in the value created by scaling their intelligence.
That may become increasingly important as AI becomes capable of performing more cognitive work.
14. The Sarah Connor Problem
There is also a psychological and political dimension.
If society comes to believe that AI has taken our knowledge, absorbed our expertise, replaced our jobs and captured the resulting wealth, then resistance to AI could become intense.
The conflict would not necessarily begin because people hate intelligent machines. It could begin because people believe the machines have confiscated the economic value of human intelligence.
Raw Intelligence offers a different narrative:
AI does not have to steal human intelligence. It can distribute it.
The teacher can earn from teaching millions. The specialist can reach a global market. The retired expert can continue contributing. The creator retains an economic relationship with the intellectual asset.
15. From Expert to a Million Experts
Return to the mathematics teacher.
Today: One teacher → hundreds of students
A course: One teacher → thousands of students
A Mind Book: One teacher → potentially millions of interactions
Ask Directly: The teacher remains available for exceptional cases
Royalty attribution: The teacher participates economically in the resulting value
The teacher’s most valuable asset is no longer simply the number of hours they can work. It is the quality and distinctiveness of the intelligence they have accumulated.
AI provides the scale.
16. The Intelligence Economy
This leads to a simple proposition:
AI should scale intelligence, not confiscate it.
The first generation of AI focused on making machines intelligent. The next generation could focus on making human intelligence economically scalable.
That produces a radically different relationship between humans and AI.
- Raw Intelligence — My intelligence can help millions of people.
- Apprentice Mind — AI helps me develop my own intelligence.
- Augmented Mind — AI extends my existing intelligence.
These are not necessarily three technological versions of the same thing. They are three different relationships between humans and AI.
Raw Intelligence is the starting point because it creates the economic infrastructure for human expertise to be preserved and scaled.
And the most provocative question is therefore no longer:
“Will AI replace the expert?”
It is:
“What if AI allows the expert to become a million experts?”