https://lrner.io/classical

I’m not sure SV is prepared for what happens when you go down that road.

It’s not the model I don’t trust, it’s myself. The model is wrong _all the time_ because - it’s easy to verify the code - it’s hard to verify that I knew what I was talking about when I prompted it.

So the idea that you can broadly speaking take the human out of the loop. I think suggests to me a level of consistency in the contextual environment that would probably never exist.

At some point it’s politics. The model can come up with a better answer than my boss, and then my boss can just ignore it. Taking the human out of the loop broadly speaking implies that we all agree on what we’re trying to optimize.

Jensen Huang has been CEO of NVIDIA for 33 years. That is a role with a very specific type of information environment. He is sort of this weird combination of specialist and - he necessarily has to operate within a certain level of abstraction.

I’m somewhere in the middle, I’m a high achiever but not the highest. I would say I’m above average in my usage of AI at my tech job. The way I’ve learned systems thinking is by being a bit non-specific in what I learn. British history, psychology, software engineering, queuing theory, cooking.

The idea of learning systems but not basic math - the idea of being too discerning in what I’m willing to learn. The entire idea of passing up the ability to learn something like basic math.

So many mental models of the world are developed by engaging with things like basic math. How do you learn systems without learning patterns behind numbers?

If the whole argument is something like it’s now about taste or creativity or being a builder? The way you learn those skills is engagement with all the things. It’s not abandoning all the things to read a book on systems thinking and product management.

It’s not never focus, but if your default position is “maybe I shouldn’t be curious about that”. You’re operating from a deficit.

But the development of something like rails pushed forward the state of the art in ways that LLMs now replicate.

The point being like - someone has to keep writing programming languages and frameworks and libraries. Like - someone has to be thinking what the code looks like for LLMs to have patterns to match against, right?

It I were running YCombinator and I were writing next years request for startups. I would say:

Here are the top twenty household expenses / bill for the average person. Tell us which one you’re going to lower and how.

There are a few variations on questions you ask that get at the same thing, but while I understand the complaint of the poster, I also somewhat understand the dissent.

Why are you selling us this thing that maybe creatively in the right context could have an indirect impact on people’s lives. Solve one of the problems I have in front of me.

And there’s something super dystopian about the idea -

When I was a kid basically four things governed the emotion you felt about a car. What it looked like on the outside, how far the gas pedal was depressed whether or not the window was open and what was playing on the radio.

The experience of driving a car was one of the things we most looked forward to about growing up, and now we’re trying to abstract that away.

And I feel it myself - there is something exciting or stimulating in modern cars about - where the screens are, how big they are, what colors are on them. But how backwards that is to the whole original notion of design.

It is something I often think about in life that, inefficiency is where our humanity is.

If the four companies came together to start an industry organization and agree on safety protocols, I assume that would be legal done correctly.

And like - I think there’s a presumption you could make that AI models could overfit to asymptote towards just the capabilities and knowledge we currently have.

And that would be amazing! And crazy useful. And there are probably a whole world of complex problems that remain unsolved because they’re adjacent to knowledge we have but they haven’t been invested in.

But can a human reliably tell the difference between “can do 99.999% of the things we currently know how to do which includes a small subset of things we didn’t know we had the capacity to do” and “super intelligent math and science research pushing the frontier of what we know”

A physicist that knows all the things we currently know in excruciating detail feels like it should be able to make the leap beyond the frontier.

But since these are computer models it might just be that it can ride that line extraordinarily well while the line remains firm.