Lux Capital

Benchling’s "State of Tech in Biopharma" report is here 🧪 🧬 🔬 It’s a must-read for anyone tracking the intersection of technology and life sciences. AI models like AlphaFold and EvolutionaryScale’s ESM3 are transforming science. But the right tech infrastructure is just as critical. Without the tools to connect wet and dry labs, standardize data, and scale workflows, even the best AI models fall short. The report highlights that only 14% of large biopharma companies — and just 3% of small ones — are prepared for AI at scale. At Lux, we’re proud to support companies like Benchling and EvolutionaryScale as they drive the convergence of biotech and AI, laying the foundation for faster, smarter drug discovery.    Read the report to explore how AI is reshaping the future:https://lnkd.in/eAdXV9Ck

BIG NEWS: Introducing Lux Labs 🧪🔬 Brilliant scientists + proven ideas + freedom to build = turning sci-fi into sci-fact For too long, we've watched ideas get stuck in the limbo between research labs and the real world. Whether it's university IP red tape, corporate bureaucracy, or the challenge of building the right team - great innovations often stay trapped in the lab. Lux Labs is our way of giving transformative ideas the entrepreneurial scaffolding they need to thrive: ⚡ Navigating IP rights and incorporation ⚡ Strategic funding from seed to scale ⚡ Access to elite technical networks ⚡ Startup guidance from battle-tested experts We're formalizing what we've already done with 20+ successful companies including: 💡 Aeva (next-gen autonomous sensing) 💡 EvolutionaryScale (gen AI for bio) 💡 Kurion (nuclear waste cleanup) 💡 Osmo (digitizing scent via AI/ML) 💡 Variant Bio (genome based drug discovery) While we'll continue our traditional venture investing ($100K-$100M checks), Lux Labs doubles down on our commitment to turn the impossible into the inevitable. Let's build the future 🚀 More 👇 on how we're creating & building companies from scratch READ 📕 https://lnkd.in/eTcNUNHU WATCH 📺 https://lnkd.in/eteeT2vd

Science and politics don't always mix well.... Last week, in a thought provoking op-ed for RealClearPolitics, our Josh Wolfe argues that Scientific American presidential endorsement may alienate those we need to persuade rather than change minds. The real issue: both US parties neglect long-horizon scientific research investment, CRUCIAL for peace and economic power! We urgently need the US government to invest in cutting-edge scientific discovery. This maintains our national defense, deters aggression, spawns new industries, and gives America the advantage in Great Power competition. 🧪 🧬 🔬 🇺🇸 Read the full piece here: https://lnkd.in/ekCVCbBA

A decade into their journey, the world needs Maven Clinic more than ever before. We’re excited to support the Maven team as they continue to drive innovation for women and families in this next phase of growth.

Deena Shakir of Lux Capital says every company needs an answer to AI adoption, efficiency and application https://trib.al/Ng5RNN4

We so enjoyed sponsoring and participating in Lux Capital Second Annual AI Summit today. It was a tremendous gathering of incredible leaders and talented entrepreneurs driving innovation from “Sci-Fi to Sci-Fact.” Thank you Lux team for inviting Banc of California to be part of this engaging event! #LuxAISummit #bancofcal

Small molecule optimization is complex, difficult, and expensive. At the Lux Capital AI Summit, Josh Haimson shared how Inductive Bio is transforming this process by combining unique data with cutting-edge ML models to enhance the quality and effectiveness of small molecule drug discovery. Thank you to Lux Capital and all the attendees today!

Our final conversation today at the #LuxAISummit was on AI and the future of modern war, with former SOCOM commander Tony Thomas and West Point Dean Shane Reeves. Gen. Tony Thomas: “The conversation invariably tilts to Skynet right away. I would like to disabuse us of that — that's a long way off, but it is comprehensive. In fact, as the focus has been on lethal effects and maneuver, it's absolutely about lethal maneuver, but it's [also] about maintenance, it's about logistics, it's about medical, it's about, more importantly, information ops. … AI will both be a positive player and a negative player for that in the future.” Brig. Gen. Shane Reeves: “Pick your area. If you want to talk about trying to solve the contested logistics problem in the Pacific; if you want to talk about targeting in a really complicated urban environment in Gaza or southern Lebanon; if you want to talk about collecting all the ubiquitous data that's all over the battlefield in Ukraine and trying to process it to the into the drone revolution that's taking place; or you talk about what probably Israel is trying to do right now with the Iron Dome, with missiles coming into Tel Aviv. At this moment, it's all going to be reliant on artificial intelligence … and what we have known — and we know — is that technology always wins. Technology always finds its way into the into the battle space — that's never not happened. There's a long history of this. The one I always go to is famously Pope Urban II in the end of the 11th century, realized crossbows were very effective, and they were good at piercing armor, and it was upsetting the societal norms of the time. So what did he do? He's like, ‘We're going to ban crossbows,’ right? And crossbows, anyone who used them, are excommunicated. So what does everybody do? Well, let's get a bunch of crossbows, right? And this has happened repeatedly, aircraft, submarines, balloons — you pick it — the technology finds itself into into the battle space.” Brig. Gen. Shane Reeves: “We are also increasingly recognizing that [West Point cadets] have to be AI-enabled officers. If they aren't AI-enabled, then we're going to lose. And that's just not a viable solution for us. So what did we have done? We've really aggressively stepped into this AI revolution at the military academy in lots of different ways. As mentioned, we have an academic year theme called The Human and the Machine: Leadership on the Emerging Battlefield.” … “They can take these skills with them, and they have to be AI-enabled, not just AI-comfortable, not just know about it, but they have to be able to embrace it and employ it, though, without having critical thinking skills atrophy. We still need them to be able to think through problems when all the technology perhaps goes away.”

Our last panel at the #LuxAISummit focused on AI x Bio, and included Maruan Al-Shedivat, PhD of Genesis Therapeutics, Mohammed AlQuraishi of Columbia, Alex Rives of EvolutionaryScale and Rahul Satija of NYU. Some top quotes: Mohammed: “Let me start with the long-term vision, which is probably a 20-year horizon. And to me, what that would constitute is basically a system, a computational artifact, that essentially reproduces anything you could do experimentally, right? So there's an experimental question that you need to do in the real cell. You could pose that question to the system, and essentially it gives you the same answer.” … “What I suspect is going to be true in the next few years are essentially, kind of digital twins of cellular behavior in more constrained settings…” Rahul: “I like that the theme of this, which is on the back wall, is ‘Impossible to Inevitable,’ which I think really highlights the idea of this virtual AI cell.” … “What an incredible opportunity that would be: half of my lab at NYU in the New York Genome Center is focused on doing experiments. We take cells, we perturb them in very specific ways, and then we run these very intricate measurement technologies to figure out what's happening. This costs hundreds of thousands to millions of dollars. We have PhD students, postdocs feeding the cells, making these measurements, and we're doing individual experiments at a time. The idea that we could do this at scale on a computer is absolutely transformative and incredibly exciting at the same time.” Alex:  “Nature wasn't created to be comprehensible to the unaided human intellect. What we want to do [at Evolutionary Scale] is really build tools that can … make biology programmable from first principles.” “We started with proteins, because there's an incredible wealth of data there, there's incredible impact. If you can solve that problem, [you can span] the hierarchy of biology from molecule all the way to cell to organism.”

Infrastructure is critical for running AI models effectively and efficiently, and we had an excellent pair talking about the subject during the #LuxAISummit: Vipul Ved Prakash of Together AI and Clem Delangue 🤗 of Hugging Face in conversation with Brandon Duderstadt of Nomic AI. Here are some highlighted quotes: Vipul at Together: “We can create synthetic data with more entropy, with all these different synthetic data methods. So I do see that being another exciting area, which is you don't have yield problems there. You can run experiments really quickly, and we'll see more and more of that.” Vipul at Together: “I think we need more power and more data centers. That's pretty clear. Like right now, it's way [too] difficult to find anything above 15 megawatts in North America. All these data centers have already been reserved and will not provide enough capacity for building and serving models. So I think we need more power. I also think the GPU power envelope is quite off.” Hugging Face’s Clem: “I also think that we can do some things to make AI more energy efficient today. I think this movement of only using and focusing a lot of our efforts on large generalist models is a mistake in many aspects. You don't need to take a private jet to go to work. In a similar way when you're doing like a specialized, customized use case, as I mentioned, you don't need a model that is going to tell you about the meaning of life. You can actually use a smaller model that is going to take less energy to train, take less energy to run. The world is a bit like biased right now, and a lot of the investment goes towards large, very energy-intensive models and directions, I think as a field, we can take a different direction and focus on specialized, customized, smaller models that give us a more credible path to continuing to build AI capabilities without ruining the planet.” Hugging Face’s Clem: “Usually what we see is that companies start with like using a large, generalist model behind an API, because it's easier, it's in a way safer, because that's what others are using. And after a few months, especially when it's production with users, and you start to see more users and the cost is starting to pile up, they think, ‘Okay, can we build different systems where we have more control, where we can optimize the models to run cheaper, faster, more focused on our specific use case, specific constraints?’ And that's usually when they when they start experimenting with other approaches, with taking open-source-based models and fine-tuning them, optimizing them, training them, and I think ultimately it's going to pay off for them, because it's a learning curve, right? It takes more time, it takes more investment. But at the end of the day, if you want to be an AI company, you have to be able to build AI, right?”