Nick Joseph
18 statements across 1 episodes · 6 bullish · 1 bearish · 1 people on the record · first statement Sep 30, 2025 by Nick Joseph · across every show →
On the record as a speaker too: Nick Joseph's record, appearances and statements → this page counts the times other people say the name.
Everything said about Nick Joseph, oldest first
Sep 30, 2025
Anthropic's Joseph: A single undetected bug can derail model training for months
“A single bug can like, Derail you for months. Yeah. And when you think about it, like you, the models take months to train. So you can kind of like lose a whole generation off of something that just looks like, ah, you know, it turns out like this piece of you…”
Sep 30, 2025 neutral
Joseph: Anthropic had to hack PyTorch profiler for large-scale GPU clusters
“The PyTorch profiler was, like, pretty good, actually, throughout for a single GPU. You want to, like, profile a GPU, the PyTorch profile would work. But if you wanted to profile a job on 100,000 of GPUs, that, like, hadn't really been done much, and then that…”
Sep 30, 2025
Joseph: Using multiple AI chip architectures multiplies engineering workload
“The downside of having multiple chips is that you have to write the thing multiple times. In theory, you could have abstractions across them, but they're different enough that it's pretty hard to do that. So you can sort of end up, if you do all the workloads …”
Sep 30, 2025
Joseph: Too many specialists forces managers to connect cross-domain optimizations
“I think if you get too many people who are specialists, you end up with a lot of effort has to come from the manager, from like the lead to connect everything, and to notice something like, Ah, if we change the architecture here, that would make this, like, ef…”
Sep 30, 2025
Anthropic's rate limits are caused by short-notice compute shortages
“Anthropic has rate limits constantly, and people complain about it a lot, and like the reason is like, there's only so much compute we can get on short notice, so you, like, making your inference more efficient is like the way you can serve more users.”
Sep 30, 2025 bullish
Joseph: AI progress relies on a revenue-to-compute positive feedback loop
“There's this positive feedback loop where you can train a model, You can use it to make something useful and sell that and get more money, use that to buy more compute, and then use that to train a better model. And we've sort of run that cycle over and over a…”
Sep 30, 2025 bearish
Anthropic's Joseph: Scaling alone likely will not achieve AGI without further paradigm shifts
“Like I think the sort of shift towards more RL is like one paradigm shift in the field, and I think it's, I think there will probably be more. I think a lot of people sort of argue about like, oh, it's like, you know, current paradigm's enough to get us to EGI…”
Sep 30, 2025
Joseph: Inference needs more HBM bandwidth while pre-training is FLOPs-intensive
“Inference as a workload in general. Tends to require more HBM bandwidth. You end up doing you sort of the simplest form of sampling since you're going one at a time. You have to load all the weights for every token. And that means you might want a lot of HBM b…”
Sep 30, 2025 positive
Joseph: Sparsely linked long-tail data may be most valuable for frontier AI
“And it might be that like, that data ends up more valuable because you, everything that's linked to a lot, you've already got. Like at some point, you're maybe like going for the tails, or you're going for the stuff that no one's ever, like, you know, it's onl…”
Sep 30, 2025 neutral
Anthropic: Frontier AI models are first-shot attempts due to chip limits
“But I do think the change is massive, and I think people, like, don't realize how chip-limited AI, like, research is, or something right now, like, the models that everyone uses, right? If you're using, like, Cloud Sonic four, Cloud Opus four, it's like, it's …”
Sep 30, 2025 neutral
Sep 30, 2025 neutral
Anthropic's Joseph: Very few engineers can debug ML from math to bytes
“I think one thing that's, like, surprisingly hard and there's very few people who can do is, like, kind of own that whole stack from, like, I understand how the ML is supposed to work and what the learning dynamics are, all the way down to, like, I know the by…”
Sep 30, 2025 bullish
Anthropic's Joseph: Certain AI Alignment Pieces Will Move to Pre-Training
“I do think at some point there will be, like, some pieces of alignment that, like, you do want to export back into pre-training because that might be a way to, like, Put them in with more strength, like, more robustness, kind of, or more core to the intelligen…”
Sep 30, 2025
Anthropic's Nick Joseph: Pre-training decisions determine whether inference can run fast
“Oh, no, I think a ton about inference, because it basically, like, The problem inference is solving, like, we basically determine the problem inference is solving. We give them a model, and they have to, like, run that fast, and it's very easy to give them a m…”
Sep 30, 2025 positive
Joseph: AI scaling laws predictably quantify loss reductions from compute and data
“There's this idea of scaling laws, which is that you can actually quantify, like, as you put in more compute, more, more data, more parameters, you get models in a very, you got a lower loss, a better prediction of the next word in a very predictable way.”
Sep 30, 2025 bullish
Anthropic's Joseph: Compute matters far more than pre-training objective details
“I think that, like, the one sort of general intuition I have is, like, compute is the thing that matters. So, like, I think if you throw enough compute at any of these objectives, you're gonna get something that's probably pretty good, and can kind of be fine …”
Sep 30, 2025 positive
Anthropic's Joseph: Do Everything Possible in Post-Training Over Pre-Training
“The way I usually think about it is anything you can do in post training, you probably should, because your iteration loop, like the ability to make progress is really fast. You can try something, you can try it again, you can try it again.”
Sep 30, 2025
Nick Joseph followed OpenAI safety leads to join Anthropic at founding
“Basically everyone I worked with, like all the safety leads left, which yeah, invited me to go to Anthropic, and that was sort of the reason I joined OpenAI, was because I cared about AI safety and wanted to work with them. So then I went with them to join Ant…”