Soon, everyone will have a genius at their disposal

AI 3.0 is three weeks away.

Wait. What?

It's as simple as 1-2-3:

AI 1.0 has never made money — and never will

Ignore the hype, the demos and the endless talk about “agentic AI.” Just follow the money.

• Adoption flattened nearly a year ago.
• Ninety-five percent of ChatGPT users pay nothing.
• Every new feature and every new user adds cost that only increases with scale.
• Advertising is not an option, because no one will trust an LLM that confidently promotes its advertiser’s wares.

Even Google understands this. Gemini doesn’t need to make money. Google will happily spend $10B a year to protect a $100B search business, because $90B is better than nothing.

But that’s a defensive posture, not a profit engine.

AI 2.0 will make money — and it won’t require a new model

All it takes is the decision to run an LLM like a business, not a research project.

AI 2.0 rests on four pillars. None are rocket science. All are about money — because money is the only metric that matters:

• Cost reduction via pre-execution provisioning
• Billing based on metered compute, not tokens or flat rates
• Indelible, loss-less memory to build trust and retention
• User-level governance for personalization and control

That stack can be deployed in a couple weeks, because it is merely a layer between the current models and the the user. We have the skill

Now let’s talk about AI 3.0

Not agentic AI. Not autonomy. Not science fiction.

Something more fundamental.

AI as collaborator

The most powerful way to use AI is not as an answer machine.

It’s as a collaborator.

• A collaborator doesn’t agree with you automatically.
• A collaborator pushes back.
• A collaborator forces iteration.
• A collaborator exposes weak assumptions and asks the question you didn’t want to ask.

That requires back-and-forth.

Human → AI → human → AI.

Not prompt → output → acceptance.

In this mode, AI is not optimizing for fluency or politeness. It is optimizing for productive tension.

• The human challenges the model’s reasoning
• The model challenges the human’s framing
• Each iteration sharpens the problem space
• Insight emerges from friction, not compliance

This is how real thinking happens.

Current LLM products suppress this mode because most users don’t want friction. They want answers that feel correct quickly. So models are tuned to be:

• agreeable
• confident
• smooth
• low-resistance

That’s great for demos.
But it’s terrible for insight. For discovery. For the Next. Big. Thing.

AI 3.0 restores collaboration by design. It treats iteration not as failure, but as the mechanism by which understanding improves.

AI as genius at your disposal

Pattern matching is the highest level of cognition.

Everything else — knowledge, intelligence, creativity — is downstream of it.

Breakthroughs do not come from accumulating more facts. They come from recognizing that this problem is the same as another problem, even when the surface details differ.

That’s how:

• railroads rhyme with databases
• databases rhyme with cloud provisioning
• cloud provisioning rhymes with AI capex

Most people don’t think this way — not because they aren’t smart, but because pattern matching is cognitively expensive and socially unrewarded.

So LLMs are trained to do the opposite.

By default, models are constrained to what works for most people:

• local reasoning
• domain-bounded answers
• semantic similarity
• incremental improvement

That is not how geniuses arrive at breakthroughs.

Geniuses work backwards:

• they strip away surface detail
• ignore domain boundaries
• search for invariant structure
• ask “where have we seen this failure mode before?”

Pattern-weighted reasoning externalizes that process.

It allows a user to surface structural similarity across domains before commitment, before execution, before damage is done.

This isn’t creativity theater.
It’s judgment infrastructure.

And it isn’t impossible. It’s patent pending.

– Published Thursday, January 8, 2026