Alex LeBrun

💊 Where are all the AI drugs? 🧐 There’s been a lot of noise lately about AI “revolutionizing” drug discovery. A recent WIRED article by Veronique Greenwood captured the optimism well — suggesting that with tools like large-scale automation and generative models, we’re finally starting to see real traction. The article is an interesting and insightful take, I suggest you read it (link in the comments). However, two cents from me here. The reality is more sober. Despite the hype, very few companies have actually gotten AI-generated drug candidates into human trials. Fewer still into Phase II. In fact, the article only names two: Recursion and Insilico Medicine. ☝And here’s where it gets interesting. One of those programs mentioned, REC-994 from Recursion, was actually an in-licensed molecule that’s since been deprioritized after mixed clinical results, as far as I know. That’s not exactly an AI-born success story IMO. That leaves only Insilico’s ISM001-055, for idiopathic pulmonary fibrosis . Not only is it AI-designed, it’s still active in Phase II, with multiple other candidates moving through the pipeline — from target discovery to clinical testing, all driven by their end-to-end platform 🔥. That’s a critical distinction. There’s a difference between using AI to support R&D and building a platform that can consistently generate novel compounds and push them through real-world validation. So yes, AI is making progress in biotech — but we’re early, and success is still super rare. Which is why what Insilico is doing stands out, in my opinion, not because I am an investor in the company, but because it is the objective reality from what I see on the market at this moment. And by the way, since we touched AI drug discovery, Insilico also quietly rolled out a set of pretty serious upgrades and they explain them in their recent PharmaAI webinar (linking in the comments). There, things like faster physics-based modeling (Alchemistry 2.0), better tools for peptide screening and biologics design, and new features in PandaOmics and DORA that make omics analysis and research writing faster and more usable. They even opened up access to several aging clocks through an open-source package. Kudos to Alex Zhavoronkov and Alex Aliper and the team for pushing the boundaries! 👏

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🚨 Introducing the AI Apps 50: Startup Edition Ever wondered how startups are spending their money when it comes to AI? Our team at Andreessen Horowitz worked with Mercury to crunch the numbers and rank the top applications by spend. The list + what we learned from it ⬇️ - Horizontal apps have a slight lead over vertical (60% of the list). This includes general assistants (ex. Perplexity) and SIX different meeting support tools (ex. Fyxer AI). But, it also encompasses creative tools and vibe coding tools that are used in roles across orgs. - Vertical apps can augment human labor...or replace it. We're mostly seeing the former - but five companies on the list allow customers to "hire AI" (ex. Crosby Legal, Cognition, 11x). Labor augmenters mostly assist with customer service, sales, and recruiting. - Vibe coding has landed in enterprises. It's not just a prosumer trend! Number three on the list, below OpenAI and Anthropic? Replit. Other listmakers in the category include Lovable and Emergent, while Cursor made the ranks for more technical users. - Products are making the consumer -> enterprise jump. 12 cos also appeared in our most recent Consumer AI Top 100 - almost all of which started out B2C and have migrated B2B over time. In fact, 70% of listmakers are available for individual use (no enterprise license needed)! Check out the full report: https://lnkd.in/gmMvfvSv

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YC isn’t just AI anymore — it’s becoming the engine of America’s defense, energy, and industrial future. In this new episode of the Lobster Talks we unpack: • Why hardtech is back • How YC de-risks deeptech • Which partners to watch in S25 • And why most funds are already too late ➡️ Full video: https://lnkd.in/eyncPRRq

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Voice AI startups: I have been meeting a ton of voice AI startups in the last few weeks. Amazing progress and most of them are almost there. Almost all use multiple systems stitching together - STT (Speech to text), TTL (Text to Text LLM) and TTS (Text to speech). Lots of engineering to address latency and accuracy using caching, reducing context, pathways, etc. Most of them are clocking sub 1 sec latency. A few are attempting speech-to-speech models and it looks like it is still not there for 15 min+ back-and-forth conversation. Biggest complaint: Stability and Scaling of underlying systems. Here is the best part: Not much of a difference between teams in India vs the Valley. The info for building voice stack has diffused quite well to Bangalore startups. Most commonly used Voice infra: Assembly AI, Deepgram, Elevenlabs, Twillio, Ultravox, Whisper, and OpenAI STS models.

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