Mostafa Dehghani
20 statements across 1 episodes · 9 bullish · 5 bearish · 1 people on the record · first statement Apr 2, 2026 by Mostafa Dehghani · across every show →
On the record as a speaker too: Mostafa Dehghani's record, appearances and statements → this page counts the times other people say the name.
Everything said about Mostafa Dehghani, oldest first
Apr 2, 2026
Dehghani initially thought Google's Transformer architecture was random and would die
“And I was like, I don't know if I want to go with this team. It's just like, they're doing something random. Like who, like everybody's doing LST. I'm like, why should I go and work with like a group of people who are working on this like random architecture, …”
Apr 2, 2026 positive
Apr 2, 2026 negative
AI continual learning research still lacks a proven recipe for productionization
“Like one side is, I think like the research is not like yet to a very, like to a point that you think that, oh, you know, this is the recipe, you know, I just need to kind of like, you know, exploit it and push productionization.”
Apr 2, 2026 positive
Vision Transformers benefited heavily from software infrastructure built for language models
“There was also benefit of doing that simply because the rest of the, that the machine learning field, which was working on, on, on language, they were using this, like architecture. So they were building infra for it, making it faster. And, you know, like the,…”
Apr 2, 2026 positive
Unified Transformer architectures simplified the training of natively multimodal AI models
“Even if this is not, like, the only architecture that would be, like, in a multi-model, but it made it really simple to train these models, like, natively, because you have, like, a single architecture and you can have all the modalities in, during training.”
Apr 2, 2026 bullish
Apr 2, 2026 positive
Building specialist models is currently the fastest path toward generalist AI
“Short term I would say like building a specialist model is like probably the fastest way to learn like what is actually possible. And in, in many cases, these like specialized model are becoming Stepping stone toward a generalist model, which is like super val…”
Apr 2, 2026 negative
AI research lacks evaluations to measure progress toward true self-improvement loops
“And the fact that we don't have evals that like, or like even defining evals that, that can maybe measure, oh, not how close we are to the point that we can actually get, get a self-improvement loop. It's just like, we don't have that. And it's just making it …”
Apr 2, 2026 negative
Apr 2, 2026 bearish
Real-world physical grounding will become the bottleneck for AI self-improvement
“As I said, you know, like soon, like the concept of data, like, you know, how to kind of like, you know, enable these models to kind of like, you know, be very good at like self-improvement becomes, how can I ground these models in, in real world? So this is d…”
Apr 2, 2026 neutral
AI data work will shift toward physical world grounding and interactive environments
“At the end of the day, I think, like, the work that we're doing on the data side most likely is going to shift toward building environments or making sure that these models can interact with physical words, and then it becomes more of a problem of, okay, how c…”
Apr 2, 2026 bullish
New pre-training techniques will drastically boost base AI model capabilities
“The way that we used to do pre-training, maybe, like, you know, like two, a year ago or two years ago maybe, like, you know, diminishing return is, like, obvious, but I can see how new ideas are bringing, like, you know, fresh, fresh energy into the pre-traini…”
Apr 2, 2026 negative
Safe execution environments are currently the bottleneck for autonomous AI development
“Like in a safe setup, you know, where they can like, because right now we definitely, we don't, we're not confident about, you know, them doing the right things all the time and measuring like how much they can push and how long they can push a task is very di…”
Apr 2, 2026 neutral
Apr 2, 2026
Apr 2, 2026 positive
Apr 2, 2026 positive
Apr 2, 2026
Single AI failures damage user trust more than average performance builds it
“People don't experience average performance of these models. They experience, like, the failures. If you have your model doing a dumb mistake, the damage in the trust that it makes is, like, bigger than, like, you know, the benefit of getting hundred things ri…”
Apr 2, 2026 bullish
Fully automated AI self-improvement will eliminate human bottlenecks and trigger breakthroughs
“The moment that we had this full automation, I would say we can close the loop of self-improvement and then it becomes the Like, you know, the problems become like, you know, mostly providing compute for these models to actually do what they want to do. And as…”
Apr 2, 2026 neutral
Anchoring self-improvement loops with real external signals prevents AI model collapse
“Model collapse mainly happens when you have a loop that is Completely closed. Right. And if you don't have any outside signal and just the model, for example, talking to itself or operating in a very like a restricted environment there's a good chance that you…”