Where Is All the A.I.-Driven Scientific Progress?
Episode
39 min
Read time
2 min
Topics
Fundraising & VC, Leadership, Design & UX
AI-Generated Summary
Key Takeaways
- ✓AI Agent Performance: Cosmos writes 42,000 lines of code and reads 1,500 research papers per run, completing analysis tasks that take human PhD researchers three to six months, validated through academic collaborator testing with unpublished datasets.
- ✓Scientific Bottlenecks: Clinical trials remain the primary constraint for medical breakthroughs, not computational analysis. Even with perfect drug candidates today, proving efficacy requires five to ten years of human testing, making decade-long disease cure promises unrealistic despite AI advances.
- ✓Generative Biology Models: De novo antibody design and organism creation represent 2024's breakthrough capability, allowing scientists to generate novel proteins and organisms from scratch by specifying target characteristics, eliminating months of traditional experimental iteration and design work.
- ✓Research Validation Requirements: AI-generated scientific findings require extensive human verification through cross-referencing, additional experiments, and manual analysis. Scientists spend significant time understanding and validating AI outputs before publication, similar to checking colleague work, with approximately 80 percent accuracy rates.
What It Covers
Sam Rodriguez, CEO of Future House, explains where AI is actually accelerating scientific discovery versus hype, discussing his AI scientist tool Cosmos that replicates six months of research work overnight at $200 per run.
Key Questions Answered
- •AI Agent Performance: Cosmos writes 42,000 lines of code and reads 1,500 research papers per run, completing analysis tasks that take human PhD researchers three to six months, validated through academic collaborator testing with unpublished datasets.
- •Scientific Bottlenecks: Clinical trials remain the primary constraint for medical breakthroughs, not computational analysis. Even with perfect drug candidates today, proving efficacy requires five to ten years of human testing, making decade-long disease cure promises unrealistic despite AI advances.
- •Generative Biology Models: De novo antibody design and organism creation represent 2024's breakthrough capability, allowing scientists to generate novel proteins and organisms from scratch by specifying target characteristics, eliminating months of traditional experimental iteration and design work.
- •Research Validation Requirements: AI-generated scientific findings require extensive human verification through cross-referencing, additional experiments, and manual analysis. Scientists spend significant time understanding and validating AI outputs before publication, similar to checking colleague work, with approximately 80 percent accuracy rates.
Notable Moment
Rodriguez reveals his AI scientist discovered a novel genetic mechanism for type two diabetes by analyzing raw variant data and identifying how a specific protein binding site affects insulin secretion in the pancreas, representing genuinely new scientific knowledge.
Episode Transcript
Cynthia Erivo is the best singer in the world. She's incredible. I don't know what it is about her voice, but it, like, brings me to tears, like, every single fucking time I hear. She has the most incredibly, like, emotional voice. I don't I I was, like, trying to figure out what it was, but it's just, like, she just has like because, like, she obviously she has the power, but there's all these, like, textures in there. Did you did you see the, designed to go viral clip of her visiting her old school? Everyone I obviously lost my shit. I was The absolute best is that the students start singing, and they just sound like shit. No worries. Nightmare sweet. Just imagine you're one of these kids. You're not that it's just it's just like after school class. You know, it's a little club. You're just doing it for a little bit of enrichment, and, like, you're just kinda plotting along trying to get through the day. And then fucking Cynthia Riva shows up, and they're like, alright, kid. You're up next. What do you got? No. Thanks. No. It was so sweet. I would dwell up. So sweet. I'm Kevin Roose, a tech columnist at the New York Times. I'm Casey Neud from Platformer. And this is Hard Fork. This week, Future House CEO Sam Rodriguez joins us in the studio to separate the hype from the reality of AI science. Well, Casey, it's time for some science. Yeah. Give me a second, Kevin. I'm just gonna put on my lab coat here, get up my Bunsen burner, and, see what you've got cooking for us today. So I have been obsessed with this question of what AI is and isn't doing for science and scientific discovery. Obviously, this is something we hear a lot about from the leaders of the big AI companies, people like Dario Amade, Sam Altman, Demis Hassabis. They have all been saying things in recent months about how close they believe we are to solving new scientific problems and curing diseases and fixing the climate with all of these new AI tools that they're building. And some of that is obviously hype or at least has this sort of markings of hype, but there's actually a lot of real stuff going on in AI and science that I just do not feel personally qualified to evaluate. Yeah. And I would also say that science has become one of the main ways that the leaders of these tech companies want us to evaluate them. Because whenever one of their models does something horrible, the message we basically get back in response is, don't worry. We're about to cure cancer. Just hang on tight. I know that this chatbot might be driving you to madness, but if you could just give us a few more releases, we're gonna do some really good stuff. Yes. And this is something that we're also hearing now …
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