bayeslaw

Hey thanks for reading and for leaving a considered comment. You might be right, we'll see. In my mind, GDP growing isn't really that reassuring: 1/ GDP has been growing steadily while people's happiness and overall satisfaction is flat lined or finishing (so even if this was true it won't lead to better lives lived at scale) 2/ it's more interesting to look at the ratio of labour wages / GDP. I actually make a prediction about this and that has been shrinking already. If it continues (which I think it will) even if everything grows ppl will be unhappy and unsatisfied bc a large part of our self worth is coming from comparing ourselves w the ppl around us in the wider society. This is why more equal societies tend to be happier as well.

Finally a useful comment. Thank you :D

Fair point on the rhetoric: "no regression, no noise, just compounding" overstates it a bit. Individual family wealth does dissipate across generations. The model actually reflects this: η=0.85 inheritance fraction, threshold gating that blocks compounding below ~$20k, and a residual term that captures the noise and bad luck - which your sources are measuring.

The stronger claim of the essa is different from "any given dynasty compounds forever." It's Piketty's r > g: capital returns have systematically outpaced economic growth, so the wealth class maintains and grows its share even as specific families within it churn.

Cool, I'm a scientist too w a PhD and everything, I just don't make a big deal out of it - unlike you it seems. Granted, distributions don't get multiplied, random variables do. Funnily enough the essay's actual model (log(Y) = 0.35·zIQ + 0.45·zW + ...) is additive in log space, which is multiplicative in outcome space. The preface language is loose I grant you that but the formal model that appears later is well defined. Not sure what you want to say with a 2d histogram. We are modelling, not visualizing.

or two centuries, the credential system gave intelligence a route to heritable capital. Artificial intelligence is closing that route. This essay builds the argument from first principles - with probability theory, interactive simulations, and a prediction specific enough to be falsifiable - and puts a number on the window that remains.

Ok maybe I'm off by an order of magnitude. That's still 3 orders of magnitude of cheaper.

I'm an ML engineer/founder and I'm frankly amazed what has happened to the promo/intro video industry.. A few years ago this video that I just made in about 2 days would have required serious funding and whole group of 3D artists, cinama crew, voice actors..

Forget what the video is about. Focus on the fact that I - a non-pro - made this in 2 days for $100.

What will this do to dozens and dozens of jobs?

On the one hand I never felt more empowered as a bootstrapper, on the otherhand it doesn't taka a lot of thinking and foresight to start to fear my demise as a builder, coder, ML engineer.

How are you doing in 2026? Do you have friends who already lost their jobs?

Asked this here 2 years ago. Answer is: no

The equation for making money with software changed forever in 2026. But not the way you think. And not the way everyone says.

Everyone's celebrating that AI made building cheap. They're missing what it made expensive.

The equation was always: Demand × Product × Attention = Money

Here's what actually changed: Product collapsed to near-zero. Lovable ships MVPs in 2 hours. Cursor writes your backend in 30 minutes. Claude Code generates entire stacks. Building went from $50K and 6 months to $500 and 6 hours.

Incredible. Genuinely world-changing. So naturaly, billions are pouring into the AI application layer. The opportunity is massive and real.

But here's the part nobody's talking about: Demand got a lot harder. Not easier. HARDER.

Why? Because anyone can build an MVP now and everybody does. But the pool of good ideas didn't grow. The number of people chasing them grew 100x overnight.

Attention became nearly impossible. Thousands of new apps launch daily. Your customers are buried in options. Standing out went from hard to borderline impossible.

And here's the brutal part: 42% of startups still fail because they built something nobody wanted. Not slowly. Not badly. Just the wrong thing. That number isn't improving. It's getting worse.

Because everyone's optimizing the wrong variable. They're focused on: Shipping faster Building cheaper Iterating quicker

Almost nobody is focused on: Validating systematically Testing demand first Talking to customers before touching code

AI didn't solve the hard problem. It made the easy problem trivial and exposed what the hard problem always was: figuring out what people actually want.

Yet nobody is tackling demand testing and validation systematically. Well, almost nobody. We do at buildsherpa.ai.

Everyone's racing to build. Nobody's stopping to ask "should I?" So the discipline to validate first is the only edge left. Because in a world where everyone can ship an MVP in a weekend, knowing WHAT to build is the only remaining moat.

The beginning of the equation is now THE MOST IMPORTANT part.

Demand. Not Product. Not Attention.

Demand.

Because if Demand = 0, the rest doesn't matter: 0 × Easy × Hard = $0

You can build anything now. Which means you better know WHAT to build.

Validate first. Ship second. The speed at which you fail is not a metric.