[BSOhealth]

These figures are for a very small number of potential people. This leaves out that frontier AI is being developed by an incredibly small number of extremely smart people who have migrated between big tech, frontier AI, and others.

Yes, the figures are nuts. But compare them to F1 or soccer salaries for top athletes. A single big name can drive billions in that context at least, and much more in the context of AI. $50M-$100M/year, particularly when some or most is stock, is rational.

[stocksinsmocks]

It’s just a matter of taste, but I am pleased to see publicity on people with compensation packages that greatly exceed actors and athletes. It’s about time the nerds got some recognition. My hope is that researchers get the level of celebrity that they deserve and inspire young people to put their minds to building great things.

[godelski]

I think I'm mostly with you but it also depends how it exactly plays out.

Like I definitely think it is better for society if the economic forces are incentivizing pursuit of knowledge more than pursuit of pure entertainment[0]. But I think we also need to be a bit careful here. You need some celebrities to be the embodiment of an idea but the distribution can be too sharp and undermine, what I think we both agree on is, the goal.

Yeah, I think, on average, a $100M researcher is generating more net good for a society (and world) than a $100M sports player or actor. Maybe not in every instance, but I feel pretty confident about this on average. But at the same time, do we get more with one $100M researcher or 100 $1M researchers? It's important to recognize that we're talking about such large sums of money that at any of these levels people would be living in extreme luxury. Even in SV the per capita income is <$150k/yr, while the median income is medium income is like half that. You'd be easily in the top 1%. (The top 10% for San Jose is $275k/yr)

I think we also need to be a bit careful in recognizing how motivation can misalign incentives and goals. Is the money encouraging more to do research and push humanity's knowledge forward? Or is the money now just another means for people that just want money to exploit, who have no interest in advancing humanity's knowledge? Obviously it is a lot more complicated and both are happening but I think it is worth recognizing that if things shift towards the latter than they actually make it harder to achieve the original goals.

So on paper, I'm 100% with you. But I'm not exactly sure the paper is matching reality.

[0] To be clear, I don't think entertainment has no value. It has a lot and it plays a critical role in society.

[fdr]

I think it's pretty funny because, for example, Katalin Karikó was thought to be working in some backwater, on this "mRNA" thing, that could barely get published before COVID...and, the original LLM/transformer people were well qualified but not pulling quarter billion dollars kicking around trying to improve machine translation of languages, a time-honored AI endeavor going back to the 1950s. The came upon something with outstanding empirical properties.

For whatever reason, remuneration seems more concentrated than fundamentals. I don't begrudge those involved their good luck, though: I've had more than my fair share of good luck in my life, it wouldn't be me with the standing to complain.

[godelski]

  > Katalin Karikó was thought to be working in some backwater, on this "mRNA" thing, that could barely get published

There's a ton of examples like this, and it is quite common in Nobel level work. You don't make breakthroughs by maintaining the status quo. Unfortunately that means to do great things you can't just "play it safe"

[yreg]

Intel made an ad series based on a similar idea in ~2010.

“Our Rock Stars Aren't Like Your Rock Stars”

https://youtu.be/7l_oTgKMi-s

[moomin]

It’s closer to actors and athletes than we’d all hope, in that most people get a pittance or are out of work while a select few make figures that hit newspapers.

[bamboozled]

Agree and it's as if these "super smart top researchers" aren't feeding off the insanely hard work of open source contributors who basically got paid nothing to do a bunch of the work they're profiting off.

None of these models are operating in a vacuum.

[gherkinnn]

Sounds vindictive. And yet. According to Forbes, the top 8 richest people have a tech background, most of whom are "nerdy" by some definition.

[hkt]

Those are nerds who did founding rather than being an employee, though. Maybe that's the distinction they're trying to make?

[Waterluvian]

I don’t think this distinction actually exists. At that salary this person is moving from being a founder of his own company to being a founder of his own business unit inside Facebook.

[layer8]

The money these millions are coming from is already based on nerds having gotten incredibly rich (i.e. big tech). The recognition is arguably yet to follow.

[vitaflo]

Nerds run the entire world how much recognition do they need?!

[xorcist]

Not really the same, is it? Actors are hired to act. Athletes get paid to improve the sport. It's not like nerds are poached to do academic research or nerd out at their hearts desire. This is a business transaction that Zuck intends to make money from.

Locking up more of the world's information behind their login wall, or increase their ad sales slightly is not enough to make that kind of money. We can only speculate, of course, but at the same time I think the general idea is pretty clear: AI will soon have a lot of power, and control over that power is thought to be valuable.

The bit about "building great things" certainly rings true. Just not in the same way artists or scientists do.

[dbacar]

how do you know they are nerds?

[cm2187]

What I don't understand in this AI race is that the #2 or #3 is not years behind #1, I understand it is months behind at worst. Does that headstart really matter to justify those crazy comps? Will takes years for large corporations to integrate those things. Also takes years for the general public to change their habits. And if the .com era taught us anything, it is that none of the ultimate winners were the first to market.

[peterlk]

There is a group of wealthy individuals who have bought in to the idea that the singularity (AIs improving themselves faster than humans can) is months away. Whoever gets there first will get compound growth first, and no one will be able to catch up.

If you do not believe this narrative, then your .com era comment is a pretty good analysis.

[godelski]

  > There is a group of wealthy individuals who have bought in to the idea that the singularity is months away.

My question is "how many months need to pass until they realize it isn't months away?"

What, it used to be 2025? Then 2027? Now 2030? I know these are not all the same people but there are trends of to keep pushing it back. I guess Elon has been saying full self-driving is a year away since 2016 so maybe this belief can sustain itself for quite some time.

So my second question is: does the expectation of achievements being so close lengthen the time to make such achievements?

I don't think it is insane to think it could. If you think it is really close you'd underestimate the size of certain problems. Claim people are making mountains out of molehills. So you put efforts elsewhere, only to find that those things weren't molehills after all.

Predictions are hard and I think a lot of people confuse critiques with lack of motivation. Some people do find flaws and use them as excuses to claim everything is fruitless. But I think most people that find flaws are doing so in an effort to actually push things forward. I mean isn't that the job of any engineer or scientist? You can't solve problems if you can't identify problems. Triaging and prioritizing problems is a whole other mess, but it is harder to do when you're working at the edge of known knowledge. Little details are often not so little.

[overfeed]

> My question is "how many months need to pass until they realize it isn't months away?"

It's going to persist until shareholders punish them for it. My guess is it's going to be some near-random-trigger, such as a little-known AI company declaring bankruptcy, but becoming widely reported. Suddenly, investing in AI with no roadmap to profitability will become unfashionable, budget cuts, down-rounds, bankruptcies and consolidation will follow. But there's no telling when this will be, as there's elite convergence to keep the hype going for now.

[xorcist]

Indeed, this will continue as long as the market allows it to. There is a well known quote about how long markets can stay irrational, but I would like to remind where we are right now:

Telco capex was $100 billion at the peak of the IT bubble, give or take. There's going to be $400 billion investments in AI in 2025.

[tonyhart7]

why we acting like this group wealthy individuals don't know what happening???

they know it may be or not gonna happen because months its ridiculous, but they still need to do it anyway since if you not gonna ride it, you are gonna miss the wave

stock market has not been rational since??? forever??? like stop pumping and dumping happen all the time

[godelski]

What I don't understand is with such small of a gap why this isn't a huge boon for research.

While there's a lot of money going towards research, there's less than there was years ago. There's been a shift towards engineering research and ML Engineer hiring. Fewer positions for lower level research than there were just a few years ago. I'm not saying don't do the higher level research, just that it seems weird to not do the lower level when the gap is so small.

I really suspect that the winner is going to be the one that isn't putting speed above all else. Like you said, first to market isn't everything. But if first to market is all the matters then you're also more likely to just be responding to noise in the system. The noisy signal of figuring out what that market is in the first place. It's really easy to get off track with that and lose sight of the actual directions you need to pursue.

[storus]

LLaMA 4 is barely better than LLaMA 3.3 so a year of development didn't bring any worthy gains for Meta, and execs are likely panicking in order not to slip further given what even a resource-constrained DeepSeek did to them.

[godelski]

  > given what even a resource-constrained DeepSeek did to them.

I think a lot of people have a grave misunderstanding of DeepSeek. The conversation is usually framed comparing to OpenAI. But this would be like comparing how much it cost to make the first iPhone (the literal first working one, not how much each Gen 1 iPhone cost to make) with the cost to make any smartphone a few years later. It's a lot easier and cheaper to make something when you have an example in hand. Just like it is a lot easier to learn Calculus than it is to invent calculus.

Which that framing weirdly undermines DeepSeek's own accomplishments. They did do some impressive stuff. But that's much more technical and less exciting of a story (at least to the average person. It definitely is exciting to other AI researchers).

[p1esk]

If comparing Deepseek to OpenAI, sure. But OP is comparing DeepSeek to Meta: both are followers.

[godelski]

Yes, but there's also more to what I said than just leader-follower.

[wiseowise]

But muh China.

[IshKebab]

Yeah this makes zero sense. Also unlike a pop star or even a footballer who are at least reasonably reliable, AI research is like 95% luck. It's very unlikely that any AI researcher that has had a big breakthrough will have a second one.

Remember capsule networks?

[bbminner]

Hm, I thought that these salaries were offered to actual "giants" like Jeff Dean or someone extremely knowledgeable in the specifics of how the "business side" of AI might look like (CEOs, etc). Can someone clarify what is so special about this specific person? He is not a "top tier athlete" - I looked at his academic profile and it does not seem impressive to me by any measure. He'd make an alright (not even particularly great) assistant professor in a second tier university - which is impressive, but is by no means unique enough to explain this compensation.

[sbinnee]

I think the key was multimodality. Meta made a big move in combining texts, audio, images. I remember imagebind was pretty cool. Allen AI has published some notable models, and Matt seems to have expertise in multimodal models. Molmo looks really cool.

[bbminner]

Looking at Molmo description:

"Our key innovation is a new collection of datasets called PixMo that includes a novel highly-detailed image caption dataset collected entirely from human annotators using speech-based descriptions, and a diverse mixture of fine-tuning datasets that enable new capabilities. Notably, PixMo includes innovative 2D pointing data that enables Molmo to answer questions not just using natural language but also using non verbal cues. We believe this opens up important future directions for VLMs enabling agents to interact in virtual and physical worlds. The success of our approach relies on careful choices for the model architecture details, a well-tuned training pipeline, and most critically the quality of our newly collected datasets, all of which we have released."

This is a solid engineering project with a research component - they collected some data that ended up being quite useful when combined with pre-existing tech. But this is not rocket science and not a unique insight. And I don't want to devalue the importance of solid engineering work, but you normally don't get paid as much for non-unique engineering expertise. This by no means sounds unique to me. This seem like a good senior-staff research eng project in a big tech company these days. You don't get paid 250M for that kind of work. I know very talented people who do this kind of work in big tech, and from what I can tell, many of them appear to have much more fundamental insight and experience, and led larger teams of engineers, and their comp does not surpass 1-2M tops (taking a very generous upper bound).

[bbminner]

A PhD dropout with an alright (passable) academic record, who worked in a 1.5-tier lab on a fairly pedestrian project (multimodal llms and agents, sure), and started a startup.. Reallyttrying to not sound bitter, good for him, I guess, but does it indicate that there's something really fucked up with how talent is being acquired?

[parineum]

> and started a startup

You bring up the only relevant data point at the end, as a throw in. Nobody outside of academia cares about your PhD and work history if you have a startup that is impressive to them. That's the only reason he's being paid.

[naveen99]

Molmo was pretty slick

[TrackerFF]

Frontier AI that scales – these people all have extensive experience with developing systems that operate with hundreds of millions of users.

Don’t get me wrong, they are smart people - but so are thousands of other researchers you find in academia etc. - difference here is scale of the operation.

[torginus]

Yeah, I guess if you have a datacenter that costs $100B, even hiring a humble CUDA assembly wizard that can optimize your code to run 10% faster is worth $10B to the company.

[cadamsdotcom]

10% is an enormous amount. Let’s say 1%.

Even if it’s 1% at the scale you’re talking that’s 1B to the company. So still worth it.

Wild.

[diamond559]

Yeah but then you factor in the costs of building and running the machines vs the revenue and realize they are actually burning money for the blind hope of major breakthroughs in cognitive understanding that could be centuries away.

[perks_12]

I can print jersey with Neymars name on it and drive revenue. i can't do that with some ai researcher. they have to actually deliver and i don't see how a person with $100M net-worth will do anything other than coast.

[magic_man]

Top athletes they have stats to measure. I guess for these researchers I guess there are papers? How do you know who did what with multiple authors? How do you figure out who is Jordan vs Steve Kerr?

[thefaux]

Yeah, who knew that Kerr would have the more successful overall career in basketball?

[neeeeees]

This is not remotely true. The gap between them as players far exceeds the gap elsewhere, combined, many times over.

People forget Kerr was a bad GM

[AIPedant]

A very major difference is that top athletes bring in real tangible money via ticket / merch sales and sponsorships, whereas top AI researchers bring in pseudo-money via investor speculation. The AI money is far more likely to vanish.

[brandall10]

It's best to look at this as expected value. A top AI research has the potential to bring in a lot more $$ than a top athlete, but of course there is a big risk factor on top of that.

[AIPedant]

The expected value is itself a random variable, there is always a chance you mischaracterized the underlying distribution. For sports stars the variance in the expected value is extremely small, even if the variance in the sample value is quite large - it might be hard to predict how an individual sports star will do, but there is enough data to get a sense of the overall distribution and identify potential outliers.

For AI researchers pursuing AGI, this variance between distributions is arguably even worse than the distribution between samples - there's no past data whatsoever to build estimates, it's all vibes.

[brandall10]

We’ve seen $T+ scale impacts from AI over the past few years.

You can argue the distribution is hard to pin down (hence my note on risk), but let’s not pretend there’s zero precedent.

If it turns out to be another winter at least it will have been a fucking blizzard.

[AIPedant]

The distribution is merely tricky to pin down when looking at overall AI spend, i.e. these "$T+ scale impacts."

But the distribution for individual researcher salaries really is pure guesswork. How does the datapoint of "Attention Is All You Need?" fit in to this distribution? The authors had very comfortable Google salaries but certainly not 9-figure contracts. And OpenAI and Anthropic (along with NVIDIA's elevated valuation) are founded on their work.

[brandall10]

When Attention is All You Need was published, the market as it stands didn't exist. It's like comparing the pre-Jordan NBA to post. Same game, different league.

I'd argue the top individual researchers figure into the overall AI spend. They are the people leading teams/labs and are a marketable asset in a number of ways. Extrapolate this further outward - why does Jony Ive deserve to be part of a $6B aquihire? Why does Mira Murati deserve to be leading a 5 month old company valued at $12B with only 50 employees? Neither contributed fundamental research leading to where we are today.

[jgalt212]

If you imagine hard enough, you can expect anything. e.g. Extraordinary Popular Delusions and the Madness of Crowds

[brandall10]

Sure, but the idea these hires could pay out big is within the realm of actual reality, even if AGI itself remains a pipe dream. It’s not like AI hasn’t already had a massive impact on global commerce and markets.

[ojbyrne]

My understanding is that the bulk of revenue comes from television contracts. There has been speculation that that could easily shrink in the future if the charges become more granular and non-sports watching people stop subsidizing the sports watching people. That seems analogous to AI money.

[ignoramous]

Another major difference is, BigTech is bigger than these global sporting institutions.

How much revenue does Google make in a day? £700m+.

[positron26]

OOf. Trying awfully hard to have a bad day there eh?

[user____name]

Rational inside a deeply olligopolistic and speculative market.

[ulfw]

F1 or soccer salaries are high because these are MARKETABLE people. The people themselves are a marketable brand.

They're not high because of performance/results alone.