Andreas Stuhlmüller, co-founder and CEO of AI research assistant Elicit, explains the company's product philosophy and engineering approach.
Insight
Stuhlmüller: AI requires deeper world models to make novel scientific discoveries
“Having deeper models of how, let's see, what are the underlying structures of different domains, how they're related or not related, I think will be an important ingredient for models actually being able to make novel contributions.”
Insight
Stuhlmüller: Academia cannot build great software tools due to paper timelines
“It's really hard to actually build interesting tools as an academic. You can't really hire great engineers. Everything is kind of on a paper to paper timeline.”
Prediction Not checkable as stated
Stuhlmüller: Generalist AI research platforms will be a winner-take-all market
“So I think there will be, at least within research, I think there will be, like, one best platform, more or less for this type of generalist research. I think there may still be, like, some particular tools, like, for genomics, like, particular types of module…”
Opinion
Stuhlmüller: Trading inference compute for answer accuracy is undervalued in AI
“Being able to invest more or less compute into getting more or less accurate answers is, I think, one of the core things we care about, and that I think is currently undervalued in the AI space.”
Insight
Stuhlmüller: Pure long-context LLMs are significantly harder to debug than RAG
“In one sense, I think you're right that the throw everything into the context window thing is easier to maintain because you just can swap out a model. In another sense, it's, if things go wrong, it's harder to debug, where, like, if you know, here's the proce…”
Disclosure
Stuhlmüller: Seed VCs urged Elicit to build legal AI over research
“We did encounter, I guess talking to VCs for our seed round. A lot of VCs were like, you know, researchers, they don't have any money. Why don't you build a legal assistant?”