forgotpwagain

And also their electricity bills rising because of datacenter power demands.

The cynical part of me wonders: if this has been a promising approach for 10+ years, why weren't they able to secure VC funding years ago (or nonprofit biomedical research funding from places like the Gates Foundation that care a lot about infectious disease)?

Thanks for the response! This is very cool and sounds like a reasonable plan. Best of luck!

I am totally onboard with the premise (as a TechBio-adjacent person), and some of the approaches you're taking (focused domain-specific models like Orthrus, rather than massive foundation models like Evo2).

I'm curious about what your strategy is for data collection to fuel improved algorithmic design. Are you building out experimental capacity to generate datasets in house, or is that largely farmed out to partners?

DeepMind/Google does a lot more than the other places that most HN readers would think about first (Amazon, Meta, etc). But there is a lot of excellent work with equal ambition and scale happening in pharma and biotech, that is less visible to the average HN reader. There is also excellent work happening in academic science as well (frequently as a collaboration with industry for compute). NVIDIA partners with whoever they can to get you committed to their tech stack.

For instance, Evo2 by the Arc Institute is a DNA Foundation Model that can do some really remarkable things to understand/interpret/design DNA sequences, and there are now multiple open weight models for working with biomolecules at a structural level that are equivalent to AlphaFold 3.

thought this was about tracing neural circuits in the brain and was disappointed.

It is possible that they are licensing technology that was developed in academic science and are raising money to scale it up and get it ultra-standarized for commercial scale.

I agree that the modern Silicon Valley model of VC funding has been spoiled by SaaS startups, where the capital expense is smaller, the timeline to exit is shorter, and pivots are easier. It is not great for deeptech innovation because those require more capital, time, and are more technology-constrained than software. Ironically, modern VC was developed to support semiconductor startups (1970s-90s), but has drifted from that technology-heavy origin.

Indeed, now is the moment to step on the gas in biotech. The past 15 years have been nothing short of extraordinary in the field. We finally have the tools needed to effectively measure biology, manipulate biology, and increasingly predict biology. More recently, we have been able to turn more and more problems into computational problems.

With all of this coming together, we should be accelerating both public and private investment in biotechnology because we're getting closer and closer to transformative therapies. But...we're failing to rise to the occasion and meet the moment.