This result, by itself, does not generalize to open-ended problems, though, whether in business or in research in general. Discovering the specification to build is often the majority of the battle. LLMs aren't bad at this, per se, but they're nowhere near as reliably groundbreaking as they are on verifiable problems.

From my reading yes, but I think I am likely reading the statement differently than you are.

> from first principles

Doing things from first principles is a known strategy, so is guess and check, brute force search, and so on.

For an llm to follow a first principles strategy I would expect it to take in a body of research, come up with some first principles or guess at them, then iteratively construct and tower of reasonings/findings/experiments.

Constructing a solid tower is where things are currently improving for existing models in my mind, but when I try openai or anthropic chat interface neither do a good job for long, not independently at least.

Humans also often have a hard time with this in general it is not a skill that everyone has and I think you can be a successful scientist without ever heavily developing first principles problem solving.

The process you’re describing is humans extending our collective distribution through a series of smaller steps. That’s what the “shoulders of giants” means. The result is we are able to do things further and further outside the initial distribution.

So it depends on if you’re comparing individual steps or just the starting/ending distributions.

  > Can humans actually do that? 

Seriously, think about it for a second...

If that were true then science should have accelerated a lot faster. Science would have happened differently and researchers would have optimized to trying to ingest as many papers as they can.

Dig deep into things and you'll find that there are often leaps of faith that need to be made. Guesses, hunches, and outright conjectures. Remember, there are paradigm shifts that happen. There are plenty of things in physics (including classical) that cannot be determined from observation alone. Or more accurately, cannot be differentiated from alternative hypotheses through observation alone.

I think the problem is when teaching science we generally teach it very linearly. As if things easily follow. But in reality there is generally constant iterative improvements but they more look like a plateau, then there are these leaps. They happen for a variety of reasons but no paradigm shift would be contentious if it was obvious and clearly in distribution. It would always be met with the same response that typical iterative improvements are met with "well that's obvious, is this even novel enough to be published? Everybody already knew this" (hell, look at the response to the top comment and my reply... that's classic "Reviewer #2" behavior). If it was always in distribution progress would be nearly frictionless. Again, with history in how we teach science we make an error in teaching things like Galileo, as if The Church was the only opposition. There were many scientists that objected, and on reasonable grounds. It is also a problem we continually make in how we view the world. If you're sticking with "it works" you'll end up with a geocentric model rather than a heliocentric model. It is true that the geocentric model had limits but so did the original heliocentric model and that's the reason it took time to be adopted.

By viewing things at too high of a level we often fool ourselves. While I'm criticizing how we teach I'll also admit it is a tough thing to balance. It is difficult to get nuanced and in teaching we must be time effective and cover a lot of material. But I think it is important to teach the history of science so that people better understand how it actually evolves and how discoveries were actually made. Without that it is hard to learn how to actually do those things yourself, and this is a frequent problem faced by many who enter PhD programs (and beyond).

  > We are always building on the shoulders of giants.

It's probably worth reading The Unreasonable Effectiveness of Mathematics in the Natural Sciences. It might seem obvious now but read carefully. If you truly think it is obvious that you can sit in a room armed with only pen and paper and make accurate predictions about the world, you have fooled yourself. You have not questioned why this is true. You have not questioned when this actually became true. You have not questioned how this could be true.

https://www.hep.upenn.edu/~johnda/Papers/wignerUnreasonableE...

  You are greater than the sum of your parts

Is this so different?

But I’ve successfully made it build me a great Poker training app, a specific form that also didn’t exist, but the ingredients are well represented on the internet.

And I’m not trying to imply AI is inherently incapable, it’s just an empirical (and anecdotal) observation for me. Maybe tomorrow it’ll figure it out. I have no dogmatic ideology on the matter.

CEOs/decision makers would rather give all their labour budget to tokens if they could just to validate this belief. They are bitter that anyone from a lower class could hold any bargaining chips, and thus any influence over them. It has nothing to do with saving money, they would gladly pay the exact same engineering budget to Anthropic for tokens (just like the ruling class in times past would gladly pay for slaves) if it can patch that bitterness they have for the working class's influence over them.

The inference companies (who are also from this same class of people) know this, and are exploiting this desire. They know if they create the idea that AI progress is at an unstoppable velocity decision makers will begin handing them their engineering budgets. These things don't even have to work well, they just need to be perceived as effective, or soon to be for decision makers to start laying people off.

I suspect this is going to backfire on them in one of two ways.

1. French Revolution V2, they all get their heads cutoff in 15 years, or an early retirement on a concrete floor.

2. Many decisions makers will make fools of themselves, destroy their businesses and come begging to the working class for our labor, giving the working class more bargaining chips in the process.

Either outcome is going to be painful for everyone, lets hope people wake up before we push this dumb experiment too far.

They just want people to think the barrier of entry has dropped to the ground and that value of labour is getting squashed, so society writes a permission slip for them to completely depress wages and remove bargaining chips from the working class.

Don't fall for this, they want to destroy any labor that deals with computer I/0, not just SWE. This is the only value "agentic tooling" provides to society, slaves for the ruling class. They yearn for the opportunity to own slaves again.

It can't do most of your work, and you know that if you work on anything serious. But If C-suite who hasn't dealt with code in two decades, thinks this is the case because everyone is running around saying its true they're going to make sure they replace humans with these bot slaves, they really do just want slaves, they have no intention of innovating with these slaves. People need to work to eat, now unless LLMs are creating new types of machines that need new types of jobs, like previous forms of automation, then I don't see why they should be replacing the human input.

If these things are so good for business, and are pushing software development velocity.. Why is everything falling apart? Why does the bulk of low stakes software suck. Why is Windows 11 so bad? Why aren't top hedge funds, medical device manufactures (places where software quality is high stakes) replacing all their labor? Where are the new industries? They don't do anything novel, they only serve to replace inputs previously supplied by humans so the ruling class can finally get back to good old feeling of having slaves that can't complain.

When the systems turn into something trivial to manage with the new tooling, humans build more complex or add more layers on the existing systems.