Every time one of these articles come up, you can recognize that silicon valley is treating these people badly, but you should remember that everyone else is treating them worse
But you can't ignore how much modern Big Tech has sucked away from academia compared to the tech companies of the Cold War era. Microsoft Research and Google Research have some impressive folks, but even combined they are a scientific pittance compared to the might of Bell Labs, and there is far more interference from the business side. This despite the fact that the executives of those companies are vastly wealthier than anyone from Bell Labs in the 20th century, even adjusting for inflation.
And of course it's not just the executives: every 7-figure Google software engineer should get a >$100k pay cut, and that money goes to a STEM PhD to pursue nonprofit research at Google Labs. Believe it or not, $100k is still pretty competitive for a young PhD mathematician (similar to assistant professor at a selective state school). Even if it's chump change for a guy who fine tunes AdSense.
It's not like the current demand for scientists is somehow a completely natural value, arrived at objectively and with no human biases involved.
And the private sector is heavily to blame for that. In ways that you even describe, as well as others (as another commenter noted, regulatory capture is one).
Correct, this is what the article points out.
Their options were squashed when SV was praising DOGE and the cuts to national research grants based on keywords like “inequalities”.
Nobody had the time to check that mathematicians also use the term.
We wrecked our research and the vultures got cheap labor to put lipstick on their slop machines.
We have a $1.78T deficit. The ducks and the mathematicians will need to take a cut at this point.
The fatal assumptions many people thinking about government spending from the outside make are that
a) money is limited
and
b) money is redistributed (~to a cause of their choice) after funding for something else gets cut
Since money is limited, and we’re spending in a deficit, money shouldn’t be redistributed to another bad cause after something gets cut. Unfortunately, and all too often, it does.
One side cuts taxes to spend on blowing up the world’s energy market, another side raises taxes to buy votes among the people who pay 0 in taxes (or fund a study on if ducks like Mozart). They’re both wrong, and people are too blinded by the sports-team nature of politics to recognize this.
The US had a balanced budget as little as 30 years ago. This current state of fiscal profligacy isn’t inevitable, except for the fact that both parties realized they could buy votes with your children’s financial wellbeing.
On the question on music and ducks, music seems something that humans universally get, but it is not clear if animals do so. Why we should not research it? What if music is the secret to human consciousness?
I do wonder how minor this foundation has been laid w where graduate students may be conditioned exploited by colleges.
Resident libertarian moron: uuuuhhhhhh have you considered that they voluntarily consented to being treated poorly? Actually this is the least poorly they could possibly be treated.
Why? Serious question. Surely the only people using the LLM for such specific STEM domains are the exact same people who are "chasing grants instead of researching." Certainly I can see how training an LLM on this stuff can help automate the process of grant-chasing, and maybe OpenAI can expand their homework cheating business to graduate schools. But I do not see how this stuff helps honest researchers, except a bit around the margins (e.g. perhaps Claude isn't so good at the Perl used in bioinformatics, that's a use case justifying some RLHF from a PhD).
It really seems like the main utility of this stuff is getting a higher score on Humanity's Last Exam and showing the customers/investors that actually Opus 4.9 is 2% smarter than GPT 5.5. Separately there are AlphaProof/etc-style LLMs for solving real research problems in math and CS, but those techniques don't even work for theoretical physics, let alone biology.
(I mean, OpenAI released GPT-Rosalind just yesterday, and - surprise - it's not meant for chasing grants.)
It's not 2023 anymore, it's 2026. LLMs are good enough to be useful. They have been for at least a year, and they keep getting better. You need to be living under a rock for the past few years to not notice that.
If you're saying stuff like this with a straight face then you are clearly not a scientist and you don't know what you're talking about. In 2021 there were maybe 10 papers with fictional citations. Even the publication mills at least linked to other junk papers. Now there are hundreds of thousands of papers with dishonest and useless bibliographies. This is because LLMs are an unbeatable force multiplier for dishonest and useless scientific work.
I am sure some legitimate academics are getting real use out of them. I am also sure that the net effect of LLMs on science is enormously negative, and it will take decades to fix the mess.
Hallucinated citations == strong evidence of scientific fraud. Name and shame and don't publish.
Alas, what's happening only shows the emperor has no clothes. If anyone slept through the replication crisis, they surely can't ignore it now. Can't really blame LLMs for lighting up the structural corruption of scientific process for everyone to see.
If anything, it's doing us a favor - if the journal gatekeeping and peer review can't handle people putting literal, obvious bullshit in their papers today, think what else they aren't handling either, and for how long this has been the case.