Soumith Chintala
CTO, Thinking Machines Lab · 1 appearance on the record.
computed by AI from the episodes · how this works → · full disclaimer →
engineerscientistexecutiveinvestor@soumithchintala ↗LinkedIn ↗soumith.ch ↗
Soumith Chintala is best known as the co-creator and longtime engineering lead of PyTorch, the open-source deep learning framework. During his 11-year career at Meta AI, he also co-authored foundational research papers in generative adversarial networks, including DCGAN and Wasserstein GAN.
29statements → 15claims → 4claims resolved → 75%fully supported → 3.38/5average certainty → 2.52/5average debate potential → 6said about them ↓
3 supported 0 partly supported 1 contradicted 2 not yet assessed 9 not checkable as stated how the 15 claims stand · each chip opens the sources
9 predictions · 6 assertions · 6 opinions · 6 insights · 2 disclosures · every statement was checked. The predictions and assertions are the 15 claims: statements the public record can support or contradict. 4 are resolved, 2 are not yet assessed, and 9 name no date, number or outcome precise enough to check. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.
The record, in short
What the tape says about how Soumith argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.
Their most notable supported claim
Expressed certainty vs assessment result
weighted support: a fully supported claim counts one, a partly supported claim counts half. Each filled bar is clickable and opens exactly those claims; "none yet" means nothing said at that certainty level has resolved yet
Everything Soumith Chintala said on Latent Space that made the record, most notable first. Filter by type, assessment or year in the ledger →
Chintala: George Hotz's TinyGrad requires major breakthroughs to match PyTorch
“There's no, like, I don't think, like, unless we have, like, great breakthroughs, like, George's vision is achievable, like, or, like, he should be thinking about a narrower problem, such as, I'm only gonna make this for, like, work for self-driving car con ne…”
Chintala: Apple's MLX will fail server-side due to lack of differentiation
“If they end up expanding onto the server side, and they'll probably build something like PyTorch as well, right? Like, eventually, that'll where it will land. And I think there, they will kind of fail on the, like, lack of differentiation. Like, it wouldn't be…”
Chintala: Inference Moats from Fast CUDA Kernels Last Only Months
“I think, like, Together and Fireworks and all these people are trying to build some faster CUDA kernels and faster, like, you know, hardware kernels in general. But those modes only last for a month or two. Like, these ideas quickly propagate.”
Chintala: Simple AI frameworks must accept long compile times
“You can write a very simple framework but then you also should be willing to eat the long compile times of, like, searching for that optimal performance at runtime.”
Chintala: Nvidia's primary competitive moat is NVLink interconnect, not GPU silicon
“The mode that Nvidia has right now, I feel like, is that they're, they have the interconnect that no one else has. Like, AMD GPUs are pretty good. I'm sure there's very silicon that is not bad at all, but, like, the interconnect like, NVLink is uniquely awesom…”
Chintala: LLM inference market will become a low-margin laundromat business
“My view of the LLM inference market in general is that it's like the laundromat model. Like you, the margins are going to drive down towards the bare minimum, like It's gonna be all kinds of arbitrage between how much you can get the hardware for, and then how…”
Chintala: Synthetic data only works where humans already have symbolic models
“Outside of this, like, where we don't have good symbolic models, like, synthetic data obviously, like, doesn't make any sense. So synthetic data is not a magic wand where it'll work in all cases, in every case, you know, whatever. It's just where we as humans …”
Chintala: Childhood trust in institutions determines views on open AI safety
“And this might be a little controversial, but like, I find a lot of arguments, ah, based on whether, like, closed source models are safer, or open source models are safer, very much related to whether, what kind of cultural, ah, culture they grew up in, what k…”
Chintala: Aggregate open source AI model usage rivals GPT
“Maybe open source models are being as used as GPT is at this point in, like, all kinds of, in a very fragmented way. Like, in aggregate, all the open source models together are probably being used as much as GPT is. Maybe, you know, close to that.”
Chintala: Centralized feedback could trigger open source runaway over OpenAI
“If that central sinkhole is there, who's gonna go coordinate all of this integration across all of these, like, open source frontends? But I think if we do that, if that actually happens, I think that probably has a real chance of the open source models having…”
Chintala: Open source cannot beat Google's feedback distribution advantage
“Probably doesn't have a chance against Google, because, you know, Google has Android, and Chrome, and Gmail, and Google Docs, and everything, you know. So people just use that a lot.”
Chintala: Path to AGI will be continuous, not a sudden threshold
“The whole process of, like, how we think we got to AGI will be continuous and not, like, not discontinuous”
Chintala: Open source will reach AGI via pure natural selection
“The open source thing will be very much in line with getting to AGI, because open source has that, like natural selection effect. Like, if a better open source model comes, Really no one says, huh, I don't want to use it because there are ecosystem effects, I'…”
Chintala: LMSYS leaderboard is biased and misses major use cases
“The LLSS leaderboard is the best thing we have right now to understand whether a model is better or not versus another model, but it's also biased and only having a sliver of view into how people actually use these models. Like, the people who actually end up …”
Chintala: Home robotics is five to seven years away from commercialization
“Home robotics being, like, five to seven years away into commercialization. I think, like, It's not, like, next year or two years from now, but, like, five to seven years from now, I think, like, a lot more robotics companies might pop out.”
Chintala: Robotics requires engineering work rather than fundamental breakthroughs
“My view is actually hardware is still the bottleneck, and AI is also a little bit of bottleneck, but, like, I don't think there's any, like, obvious breakthroughs we need. I think it's just work.”
Chintala: Deep PyTorch-Mojo integration lacks synergy because Mojo replaces PyTorch frontend
“Mojo as a fundamental frontend would be replacing PyTorch, not, like, augmenting PyTorch. So, in that sense, I don't see a synergy in more deeply, like, integrating Mojo.”
Chintala: Hyperscalers build custom silicon to exploit vertical workload efficiencies
“Each large company has a sufficient enough set of verticalized workloads that have a pattern to them that, say, a more generic accelerator like an NVIDIA or an AMD GPU does not exploit. So there is some level of power efficiency that you're leaving on the tabl…”
Chintala: Meta built FAIR because AI capabilities rate-limited Meta's product development
“And for, then, like, the thesis was very simple. It was, like, AI is currently rate-limiting Meta's ability to do things. Our ability to build various product integrations, moderation, various other factors. Like, AI was the limiting factor, and we just wanted…”
Chintala: Less than 1% of open source AI usage yields feedback
“But the amount of feedback that is driving back into the open source ecosystem is, like, negligible. Maybe less than one percent of, like, the usage.”
Chintala: Meta will have over 600k H100 GPU equivalents by end of 2024
“That is by the end of this year, and 600 K H-One hundred equivalents. With 250 K H-one hundreds and including all of the other GPU or accelerator stuff, it would be 600 and something K aggregate capacity.”
Chintala: Time and data constrain Meta LLM releases more than GPUs
“So, I think the, it's all a matter of time. I think time is the biggest bottleneck. It's like, when do you stop training the previous one, and when do you start training the next one? And how do you make those decisions? The data, do you have net new data, bet…”
Chintala: No AI company currently feels they have sufficient compute
“If you don't have enough compute, you figure out how to make do with smaller models, but like, no one as of today, I think, would feel like they have enough compute. I don't think, like, I've heard any company within the AI space be like, oh yeah, like, we fee…”
Chintala: Smell and touch digitization is where images were in 1920
“When we think about audio, or images, or video, they're, like, so advanced that we have the concept of color spaces, we have the concept of, like, frequency spectrums, like, you know, we figured out how ears process, like frequencies in mouse spectrum, or what…”
The other half of the tape: Soumith Chintala's own voice is left out of every number here. Other people bring the name up 6 times in 4 episodes on Latent Space. every mention, with the transcript →
Who brings them up most Shawn Wang 3Alessio Fanelli 1
Every mention by year
tap a year for its mentions
2024 5 mentions in 3 episodes 2 per episode
Appearances (1)
| Episode | Date | Speaking time |
|---|---|---|
| Open Source AI is AI we can Trust — with Soumith Chintala of Meta AI | Mar 6, 2024 | 1h 9m |