Comment by godelski

I've spent most of my career working, chatting and hanging out with what might be best described as "passionate weirdos" in various quantitative areas of research. I say "weirdos" because they're people driven by an obsession with a topic, but don't always fit the mold by having the ideal combination of background, credentials and personality to land them on a big tech company research team.

The other day I was spending some time with a researcher from Deep Mind and I was surprised to find that while they were sharp and curious to an extent, nearly every ounce of energy they expended on research was strategic. They didn't write about research they were fascinated by, they wrote and researched on topics they strategically felt had the highest probability getting into a major conference in a short period of time to earn them a promotion. While I was a bit disappointed, I certainly didn't judge them because they are just playing the game. This person probably earns more than many rooms of smart, passionate people I've been in, and that money isn't for smarts alone; it's for appealing to the interests of people with the money.

You can see this very clearly by comparing the work being done in the LLM space to that being done in the Image/Video diffusion model space. There's much more money in LLMs right now, and the field is flooded with papers on any random topic. If you dive in, most of them are not reproducible or make very questionable conclusions based on the data they present, but that's not of very much concern so long as the paper can be added to a CV.

In the stable diffusion world it's mostly people driven by personal interest (usually very non-commericial personal interests) and you see tons of innovation in that field but almost no papers. In fact, if you really want to understand a lot of the most novel work coming out of the image generation world you often need to dig into PRs made by an anonymous users with anime themed profile pic.

The bummer of course is that there are very hard limits on what any researcher can do with a home GPU training setup. It does lead to creative solutions to problems, but I can't help but wonder what the world would look like if more of these people had even a fraction of the resources available exclusively to people playing the game.

  > Someone has probably studied this

https://en.wikipedia.org/wiki/Goodhart%27s_law

But there is no way to know who is truly the 'best'. The people who position and market themselves to be viewed as the best are the only ones who even have a chance to be viewed as such. So if you're a great researcher but don't project yourself that way, no one will ever know you're a great researcher (except for the other great researchers who aren't really invested in communicating how great you are). The system seems to incentivize people to not only optimize for their output but also their image. This isn't a bad thing per se, but is sort of antithetical to the whole shoulder of giants ethos of science.

Yeah I think this is a general principle. Just look at the quality of US presidents over time, or generations of top physicists. I guess it’s just a numbers game: the number of genuinely interested people is relatively constant while the number of gamers grows with the compensation and perceived status of the activity. So when compensation and perceived status skyrockets the ratio between those numbers changes drastically.

I have seen absolutely incredible, best in the world type engineers, much smarter than myself, get fired from my FAANG because of the performance games.

I persist because I'm fantastic at politics while being good enough to do my job. Feels weird man.

It is pretty simple - if the rewards are great enough and the objective difficult enough, at some point it becomes more efficient to kneecap your competitors rather than to try to outrun them.

I genuinely thing science would be better served if scientist got paid modest salaries to pursue their own research interests and all results became public domain. So many Universities now fancy themselves startup factories, and startups are great for some things, no doubt, but I don't think pure research is always served by this strategy.

I would categorize people into 2 broad extremes. 1) those that care two hoots about what others or the system expects of them and in that sense are authentic and 2) those that only care about what others or the system expects of them, and in that sense are not authentic. There is a spectrum in there.

that's what happens at the top of most competitive domains. Just take a look at pro sports; guys are looking for millimeters to shave off and they turn to "playing the game" rather than merely improving athletic performance. Watching a football game (either kind) and a not-small portion of the action is guys trying to draw penalties or exploit the rules to get an edge.

Anytime a system gets hyper-competitive and the stakes are high, it starts selecting for people who are good at playing the system rather than just excelling at the underlying skill

This is an interesting theory. I think there is something to it. It is really hard to do good in a competitive environment. Very constrained.

Goodhart's law

Perhaps these companies just end up with so much money that they can't possibly find ways to spend all of it rationally for purely product driven work and just end up funding projects with no clear business case.

W.l gore and similar companies are excellent examples, of goretex fame and other chemicals. Super interesting management structure called open allocation which is exactly this, employees get to choose what they work on. Valve is similar but slightly less formal.

It paid off for PARC, iirc the laser printer justified lots of other things that Xerox didn't profit from but turned out to be incredibly important.

The problem here is management expecting researchers to dump out actionable insights like a chicken laying eggs. Researchers exist so that you can rifle through their notes and steal ideas.

Indeed. And it feels like there is this untold in-between where if you belong to an unknown applied AI team, you don’t have to deal with academia’s yak shaving, you don’t have to deal with Meta’s politics and you end up single handedly inventing TRMs.

How many patents did that research result in that paid off in terms of use, licensing and royalties?

  > I can't think of it ever really paying off

Also it is what big tech was doing until LLMs hit the scene

So I'm not sure what you mean by it never paying off. We were doing it right up till one of those things seemed to pay off and then hyper focused on it. I actually think this is a terrible thing we frequently do in tech. We find promise in a piece of tech, hyper focus on it. Specifically, hyper focus on how to monetizing it which ends up stunting the technology because it hasn't had time to mature and we're trying to monetize the alpha product instead of trying to get that thing to beta.

  > But from a business perspective it seems to almost never pay off.

Why is it that we like off the wall thinkers? That programmers used to be known as a bunch of nerds and weirdos. How many companies were started out of garages (Apple)? How many started as open source projects (Android)? Why did Google start giving work lifestyle perks and 20% time?

So I don't know what you're talking about. It has frequently paid off. Does it always pay off? Of course not! It frequently fails! But that is pretty true for everything. Maybe the company stocks are doing great[0], but let's be honest, the products are not. Look at the last 20 years and compare it to the 20 years before that. The last 20 years has been much slower. Now maybe it is a coincidence, but the biggest innovation in the last 20 years has been in AI and from 2012 to 2021 there were a lot of nice free reign AI research jobs at these big tech companies where researchers got paid well, had a lot of autonomy in research, and had a lot of resources at their disposal. It really might be a coincidence, but a number of times things like this have happened in history and they tend to be fairly productive. So idk, you be the judge. Hard to conclude that this is definitely what creates success, but I find it hard to rule this out.

  > I'd tell them to go become academics.. but all the academics I know are just busy herding their students and attending meetings

[0] https://news.ycombinator.com/item?id=45555175

For just 10% of those money a $100M mathematician can hire 10 $1M mathematicians or a whole math dept in some European university to do the work and the thinking for them and thus beat any pressure while resting and vesting on the remaining 90%.

A lot of people also don't know that many of the well known papers are just variations on small time papers with a fuck ton more compute thrown at the problem. Probably the strongest feature that correlates to successful researcher is compute. Many have taken this to claim that the GPU poor can't contribute but that ignores so many other valid explanations... and we wonder why innovation has slowed... It's also weird because if compute was all you need then there's a much cheaper option than Zuck paid. But he's paying for fame.

The reportings at the time said that he was Mark’s 5th choice or similar. It is fairly clear he would prefer Ilya, Murati, Mark Chen, and perhaps others, but they said no, and Alex Wang was the first one to say yes.

Alexandr Wang is not interesting and a few steps short of a fraud that Mark had to bail out because he was so co invested.

Shareholders should be livid if they knew a single thing about what was going on.