Approaching the AI Event Horizon? Part 2, w/ Abhi Mahajan, Helen Toner, Jeremie Harris, @8teAPi

**Nathan Labenz** (0:00)
Hello, and welcome back to The Cognitive Revolution. Coming up, you'll hear part two of a marathon live show that I co-hosted with my friend Prakash, also known as Adapai on Twitter, in which we explore AI for science, recursive self-improvement, and geopolitical competition. I love doing full deep dive episodes, but I can only cover so many topics in that way. And so I am experimenting with higher intensity live shows as a way to deliver what I hope is the same high-quality analysis, but in a denser format. In the first half, which hit the feed yesterday, we talked to Professor James Zou of Stanford about his work on AI for science, Sam Hammond about how well the US administration is doing to manage international AI competition, and Shoshana Tachovsky about AI agent behavior in the wild. In this second half, we talked to Abhi Mahajan, also known as Owl Posting, about AI for biology and medicine, including the foundation models he's building at Noetik AI to better predict which patients will respond to which cancer treatments, and why, though he's skeptical of many AI for biology results that have been published to date, he does expect trends to continue to the point where AI is ultimately transformative for the field. Then we talked to Helen Toner about a report that CSET just put out called When AI Builds AI, which summarizes conversations from a closed-door workshop in which participants tried, but failed, to establish any consensus expectation about the impact of automated AI R&D, ultimately leading to the conclusion that automated AI R&D is simply a major source of potential strategic surprise. Then finally, we have Jeremie Harris, talking about the very challenging position we find ourselves in, where we lack both the technical means to reliably control superhuman AI systems, and the trust and coordination mechanisms needed for the US and China to address this problem collaboratively. Plus, a bit of discussion of how he maintains situational awareness, and how our respective personal productivity stacks are evolving. As you'll hear, the challenges of making sense of such massive disagreement among leading AI experts, and simply keeping up to date with AI developments coming at us daily comes up repeatedly in these conversations. And to be real, nobody seems to have perfect solutions. One partial solution that I can recommend, though, is using large language models to help identify blind spots. And for that purpose, I am really enjoying the blind spot finder recipe that I recently created on Granola. Granola works at the operating system level, so it can capture all of the audio into and out of your computer, including, if you wish, the contents of this episode. And its recipe feature can work across sessions to identify trends, opportunities, or yes, blind spots that only become apparent with that zoomed out view. Obviously, this is a tool that grows in value over time. But if you want to try it today, I suggest downloading the app, starting a session while you play this episode, and then asking it to identify blind spots based on this conversation. What is so cool about this feature, for active granola users at least, is that the blind spots it identifies for you will be different from the ones it identifies for me. As I said last time, this was fun for me, but especially because it is a new format, I would love your feedback. Do you feel that you got as much value from this more time-efficient approach as you usually do from our full deep dive episodes, or did we miss the mark in some way? Please let me know in the comments, or if you prefer by reaching out privately via our website, cognitiverevolution.ai, or by DMing me on your favorite social network. With that, I hope you enjoy the Cognitive Revolution live, from February 11th, co-hosted with Ada Pai.

**Erik Torenberg** (3:54)
I'm going to add Abhi Mahajan. Abhi is outposting online and he works on AI for Cancer at Noetik AI. Abhi, welcome.

**Abhi Mahajan** (4:06)
Yeah, great to meet you. Thanks for having me on.

**Nathan Labenz** (4:10)
You have the great distinction of being recommended to me as the Zvi for AI and biology and the intersection of those two. So big shoes to fill, big reputation to live up to, but excited to meet this actually the first time we've properly spoken.

**Erik Torenberg** (4:24)
Yeah, and I learned from Ron Alpha that you built an entire competitive intelligence platform, LLM-based, to feed the clinical analysis pipeline. So and also that Claude recommends every cancer drug it sees. So let's talk about that.

**Abhi Mahajan** (4:41)
Yeah, the typical way that a lot of, like increasingly a lot of biopharmers are interested in asset acquisition as opposed to just developing their drugs from scratch. This is partially because like China is bumping out a lot of very interesting preclinical assets. Why not just buy those for a few million dollars? They've already done the optimization. Let's just run those in patients. Most of the time, the way you look for these drugs is either you mine your personal network or you have these like clinical trial aggregation platforms that like help you do the job. Both of these are like obviously lossy and like a better way is just like scrape the entire semantic web yourself and annotate every single investigational drug you find with your company's priorities. What you think is like important to look for, modalities that are particularly interested in, organize that all into a table, rank it by some metric, and then you give that to the therapist to work off of. Obviously, there's still a human due diligence step. These models like still are not perfect, even like 5.2, 5.3, not perfect, but it's pretty good.