Long story short, I think we’re in a phase where the organizational value function is lagging behind the tech. A “proof of concept” used to be correlated with “proof of work” and some amount of domain understanding, but I think now what we need is a focus on “proof of understanding” or else you’re probably just wasting tokens on a baby version of the problem. A decent proxy right now is that if you have zero external dependencies then your solution is probably a toy.

Honestly, when I ran this interview I didn’t care much about the specifics of what you memorized beforehand. I care if you can read and write code a bit. I care more whether we can have a productive conversation. If you learn something new from me or the problem, how does that look and feel? If I make a mistake, how do you react? Are we able to communicate technical ideas to each other? Are we able to productively work through conflict?

We’re not computing many medians day-to-day, but we’re doing all those other things constantly.

The more pernicious effect I’ve been seeing is that we’re living in the golden age of LLMs, but eventually that’ll fade. Tokens are subsidized and cheap, model capabilities leap forward regularly, and there’s competition driving it all. But even now there’s stories about frontier models suddenly becoming less capable, or providers switching to usage-based billing, and new model releases feel a bit more sluggish and less dramatic. (Fable/Mythos notwithstanding.)

Eventually the models are going to settle into a rut of being just “good enough” to earn a living rather than all this hoopla. A lot of people will be re-hired. And we’ll do it all again for the next wave.

The trick to getting good at using LLMs for software is to learn how to make _all_ projects low-stakes.

I also think you might be discounting exactly how much compute is used to train these monsters. A single 1ghz processor would take about 100,000,000 years to train something in this class. Even with on the order of 25k GPUs training GPT3 size models takes a couple months. The anemic RAM on GPUs a decade ago (I think we had k80 GPUs with 12GB vs 100’s of GBs on H100/H200 today) and it was actually completely impossible to train a large transformer model prior to the early 2020s.

I’m even reminded how much gamers complained in the late 2010s about GPU prices skyrocketing because of ML use.

If I wrote about “how to paint great art” or “how to cook great meals” or “how to build great things” then it would be silly to say “have good taste”—even if that’s part of the answer. It won’t help anyone else to improve in any of those endeavors.

I’ll admit that I may just be immature at research as almost all my experience has either been attempting to replicate research or to put it into practice in production systems.