huw
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I live for a high disagree-to-upvote ratio
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I think that the appropriate medium-term fit for the movement will be with organised labour (whether left or right!), as I’ve said before here. The economic impacts are not currently strong enough to have been felt in the unemployment rate, particularly since anti-inflationary policies typically prop up the employment rate a bit. But they will presumably be felt soon, and the natural home for those affected will be in the labour movement, which despite its currently weakened state will always be bigger and more mobile than, say, PauseAI.
(Specifically in tech, where I have more experience in labour organising, the largest political contingent among the workers has always been on the labour left. For example, [Bernie Sanders was far and away the most donated to candidate among big tech employees in 2020](https://www.theguardian.com/us-news/2020/mar/02/election-2020-tech-workers-donations-bernie-sanders).)
In that world, the best thing EAs can do is support that movement. Not necessarily explicitly or directly—I can see a world where Open Phil lobbies to strengthen the U.S. NLRB and overturn key Supreme Court decisions such as Janus. But, such a move will be perceived as highly political, and I wonder if the allergy to labour-left politics within EA precludes it.
Someone noted that at the rate of US GHD spending, this would cost ~12,000 counterfactual lives. A tremendous tragedy.
I think that’s a false dichotomy. It should be possible to have uncomfortable/weird ideas here while treating them with nuance and respect. (Are you instead trying to argue that having a higher bar for these kinds of posts is a bad idea?)
Equally, the original post doesn’t try to understand the perspective that abortion might be net good for the world. So I think the crux might actually be more about who you think should shoulder the burden of attempting-to-understand.
We legalise abortion because it helps people live their lives on their own terms, which is good (and some small cases where abortions are medical procedures that prevent death or physical harm directly). Young people can take risks and be stupid without it changing the course of their lives; or in more extreme cases, escape their abusers.
So, in the sort of Quixotic spirit of trying to avoid this thread getting out of hand, I want to be constructive. I think that such an obviously fraught and tense issue deserves more thought and care than a quick BOTEC. I get the broader point that you’re making, but you’re making it in a pretty crude way that feels insensitive to the very real harms people face due to restricted abortion access; I am not sure that the comparison was needed to make that point either.
Mmm, it is not merely the case that finance is drying up, but that according to OECD data, in 2023 net financial flows to the Global South were actually negative (i.e. they paid more in repayments than they received in new finance).
Nvidia’s moat comes from a few things. As you pointed out, they have CUDA, which is a proprietary set of APIs for running parallelised math operations. But they also have the best performing chips on the market by a long way. This is not merely a function of having strong optimisation on the software side (possibly replicable by o3 but I would need to see more evidence to be convinced that an LLM would be good at optimisation), or on the hardware side (much, MUCH trickier for an LLM given that a lot of the hardware has to operate on nanometre scale, which can be hard to simulate), but also because having the most money and a strong track record & relationship means they can get preferential access to next-gen fabs at TSMC.
It is also true that the recent boom has increased investment into running CUDA code on other GPUs. The SCALE project is one such example. This implies (a) the bottleneck is not about replicating CUDA’s functionality (which it does), but more about replicating its performance (they might have gains to make there) and/or (b) that the actual moat really does lie in the hardware. Again, probably a mix of both.
However, this hasn’t stopped other companies from making progress here. I think it’s indicative that Deepseek v3 was allegedly trained for less than $10m. If this is true, it suggests to me that:
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Frontier labs might be currently using their hardware very inefficiently, and if these efficiencies were to be capitalised on, demand for Nvidia hardware would reduce (both by using less of their GPUs, but also because you wouldn’t need the best of the best to do well)
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If it turns out to be cheap to train good LLMs, captured value might shift back to frontier labs, or even to downstream applications. This would reduce Nvidia’s pricing power.
Also, it looks like the competition is catching up anyway. It seems like it’s very reasonable to do inference on Apple or Google chips (Apple Intelligence runs on M2-series chips, these also have top TSMC node access; Google run a lot of inference on their own TPUs). I was particularly impressed that you can run a 600B+ parameter model on 8 Mac Minis, not even running Apple’s best chips. Even if it’s only inference, that’s a huge chunk of the market that might fall to competitors soon.
So I’m not exactly counting on Nvidia to hold, but I think it will be for other reasons than automation. Even if you are very AI-pilled, we still live in the world where market dynamics are much stronger than labour automation effects. For now :)
The Trump administration has indefinitely paused NIH grant review meetings, effectively halting US-government-funded biomedical research.
There are good criticisms of the NIH, but we are kidding ourselves if we believe that this is to do with anything but vindictiveness over COVID-19, or at best, a loss of public trust in health institutions from a minority of the US public. But this action will not rectify that. Instead of one public health institution with valid flaws that a minority of the public distrust, we have none now. Clinical trials have been paused too, so it’s likely that people will die from this.
I don’t have a great sense of what to do other than lament. Thankfully, there are good research funders globally—in my case, a lot of the research Kaya Guides relies on is funded by the WHO (😔) or the EU. We’re still waiting to see how the WHO withdrawal will affect us, but we’re lucky that there are other global leaders willing to pick up the slack. I hope that US philanthropic funding also doesn’t dry up over the coming years…