Money-Printing AI Ideas

FUTURE

AI Businesses that Will Print Money

Welcome back! I’ve gone down the rabbit hole of AI’s most closely guarded secrets to give you a set of ideas that will shape the world to come, to the point I believe the resulting businesses will literally print money.

These aren’t ‘Agent for B2B Sales’ type of boring products we both know will fail, but ones that will actually transform entire markets, and which are a matter of time.

These are:

Agentic components, building what makes sense

Deterministic LLMs, how a billion-dollar star Lab may have solved the biggest mystery in AI: totally predictable outputs.

RLaaS, a technique that has been compared to AGI due to its transformative potential

Analog in-memory computing. You’ll learn what NVIDIA’s greatest weakness is (GPUs’ main limitation) and how an alternative that is 10,000x faster and cheaper has emerged.

We are covering them all today in words you will understand, no jargon, just the power of first-principles analysis.

I’ve written very few articles as packed with knowledge as this one. This post will change your mind about AI in the same way some of these ideas have changed mine.

Let’s dive in.

Agentic Components

This is the most obvious one, so I’ll cut to the chase. Besides, I’ve gone into this rabbit hole several times.

In a nutshell, most AI startups don’t make sense.

At all.

The Funny Thing about AI VCs

“It’s like ChatGPT, but for sales! “It’s like ChatGPT for HR, but smarter!”

If your startup’s description starts with “It’s like ChatGPT but…” I’m no longer interested because it’s very likely to fail.

The problem is that 95% of AI startups are that, because, funny thing, that’s precisely the pitch that gets you in the room with investors.

But this is misguided, your startup makes no sense, because you are building something that won’t be necessary in… months?

As we’ll see later in this post, it’s not about the scaffold (the techniques and methods built on top of AI models to make them smarter than they are), but about the data.

But then, what should be built?

Glorified Tool Callers

Most of the progress these days, at least in the frontier, is deeply correlated with one thing and one thing only: the amount of tool calls your AI makes per interaction.

When we say “reasoning,” what we actually mean is an AI doing two things:

Breaking down problems using a step-by-step approach

Profusely calling tools

If your AI model does only number one, it’s a Large Reasoner Model (LRM), or reasoning model. But if it also does number two, it’s what we call an ‘agent’.

But agents, which sound very cool, are essentially glorified tool callers. They're AIs whose sole job is to understand user requests, develop a plan, and execute tools to carry out the plan.

What this means is that AI Labs are solely focused on the logic side of things: building AIs that do better and better at the three things above…

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