Ethan He
16 statements across 1 episodes · 7 bullish · 0 bearish · 1 people on the record · first statement Jun 1, 2026 by Ethan He · said 2 times in 1 episodes since 2026 · across every show →
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Everything said about Ethan He, oldest first
Jun 1, 2026 positive
He: AI first-principles planning calculates the theoretical minimum days to ship
“If you think about some limitation, for example, the current data, like how, how fast can we acquire the videos? And if you think about training the models, like what's the iteration speed? For training a model end-to-end and how, how would adding more GPUs ac…”
Jun 1, 2026 bullish
Jun 1, 2026 positive
Ethan He: Pipeline bug fixes drive more model gains than new algorithms
“And often I find that this is kind of boring, but like a lot of the improvements does not come from new algorithms. It comes from finding small bugs here and there in the data pipeline, in the model training pipeline. Those gave the biggest boost to the model …”
Jun 1, 2026
Ethan He: Video Models Must Bootstrap From Image Diffusion Models for Semantic Understanding
“After you train such model, such image model, the reason it's a foundation for video models is that image, image models are Cheaper to train and they have much denser connection between language and text. So, sorry, language and images. For example, you train …”
Jun 1, 2026 neutral
Ethan He left xAI because changing corporate priorities limited LLM research
“For me there's a lot of research you want to do that you cannot do at, as a company. And also like the priorities and objective, the, for company typically can change very fast. It is, it's also the same for XAI. So, so now it's kind of like the time to, there…”
Jun 1, 2026 positive
Jun 1, 2026 neutral
Ethan He: Training transformers directly on raw image pixels is impossible
“If you're trying, if you can technically, theoretically train image or video models on pure pixels, but the problem is that the, it's a lot of tokens. So like one image, like it's a thousand by a thousand is like one million tokens, one million pixels. It's im…”
Jun 1, 2026 neutral
Jun 1, 2026 neutral
Ethan He: Diffusion transformer training closely mirrors LLM training architecture
“So now the training, training of the diffusion transformer, you already generated models use diffusion transformers. It is actually quite standard. It's very similar to how you train a language transformer models. It's not that much difference. It's just the t…”
Jun 1, 2026
Ethan He: Video models require image foundations and 100% synthetic caption pairs
“Building a video model. You actually need to build a image model first and building, building these two models. The data you need is a hundred percent synthetic pair of language and image or language to video because on the internet, actually the videos Don't …”
Jun 1, 2026
Ethan He: NVIDIA Cosmos required labelers to describe videos for blind reconstruction
“So that's in the protocol of Cosmos labeling. We required the objective we gave to the labelers was that you have to describe the video as detailed as possible, such that a blind person hears a blob of text, can reconstruct what the video is like from their he…”
Jun 1, 2026
Ethan He: Coding Models Shift Research Bottlenecks Back to Compute
“Compute might become a bottleneck again, because previously, like if you want to train a new model, say you want to generate new synthetic data and then, or write a new algorithm, it might take a few weeks. And during that period of time, you don't, you might …”
Jun 1, 2026 neutral
Jun 1, 2026 positive
Jun 1, 2026 positive
Ethan He: Powerful video AI will naturally learn to control physical robots
“Once these models can use computers and understand the future state of computer extremely well, the robots might be Might be one of the tools a very powerful AI can use. So the powerful AI might just be able to control the physical embodiment naturally.”
Jun 1, 2026 positive
Ethan He: Daily iteration speed is the top factor in model training
“When I look at like training models, I don't so actually the top important thing is like how many how many iterations can you do like per, per day? And the more iteration can you do, you can train the model much faster. So if you have a very strong infra and y…”