AI Research Labs
topic on 7 shows · 12 statements across 12 episodes
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12 statements about AI Research Labs, every show
Ambrosino: AI labs prioritized coding models because code directly accelerates AI research
“I also think that the labs historically invest in making their models good at things that accelerates AI research. And that in the era, the early era of coding models is very clear that the model being able to write correct code. Would accelerate research. In …”
O'Driscoll: Skeptical the Market Can Support Ten Separate AI Research Labs
“It will be interesting to see, can the world support 10? Research labs coming up with 10 different variants of AI, all of which have to build either commercial or consumer businesses. I think on aggregate I'm fairly skeptical, but it's less a quality of, I mea…”
fal hosts around 600 models compared to under 10 at AI labs
“So the number of models they have to host is like less than 10. And for us, it's like around 600, which is, complicates things like a lot.”
Mercor works with all top frontier AI research labs
“We work with All of the top research labs at the frontier of model capabilities, and we're not like the crowdsourcing companies in that we try to hide all the people on the platform, pay them low rates, et cetera.”
Chelsea Finn: Abundant resources can lead to wasteful compute usage
“Sometimes when you have a lot of resources, you don't actually think as carefully and as critically about what runs are going to be doing and so forth, and you end up being sometimes more wasteful of compute than if you were kind of more compute constrained.”
Schulhoff: External guardrail startups cannot solve core AI security issues
“There's no like external Product focused companies are like, oh, you know, I have the best guardrail now. It's not a realistic solution. It has to be the AI labs. It has to be, I think it has to be innovations in model architectures.”
Howard: Traditional AI labs favor paper citations over bold innovation
“It's very different to a research lab, which is like, hey, let's try and get lots of citations on a paper in a field that's so highly recognizable to its peers that they recognize it as being something they want to appear at their next conference, you know, wh…”
Bosworth predicts future AI breakthroughs will come from small labs
“A lot of these contributions aren't going to come from these big labs, like they're going to come from these little labs.”
Pomel: Datadog avoids dedicated AI labs because they set wrong expectations
“We, so we don't have a lab. In general, we're a little bit careful with labs. I think it's sets the wrong expectation.”
Srinivas: Only three or four AI labs can effectively scale models
“Those who do it right, those who get these 128 details right, are the ones who end up benefiting more from more scale. And that happens to be like three or four labs at this point.”
Masad: Major AI labs have sharply reduced publishing groundbreaking research
“A lot of the labs have sort of closed down how much they publish, and you might have noticed that. There isn't as much, as many groundbreaking papers. A lot of the papers are rehashing things. A lot of the papers are fake.”
Guo: Major AI Labs Insource Annotators Due to Vendor Quality Deficits
“One thing that I've seen with significant research labs is like still continued insourcing of annotators for both pre-training sets and LHF because some of the external services and marketplaces can't get to the level of quality that they're looking for in par…”