Corsair™ is now in full production. Products are beginning to ship in volume to priority hyperscalers, neoclouds, and frontier AI labs. Thanks to our ecosystem partners TSMC, @AristaNetworks, @Broadcom, @Supermicro, @gimletlabs, and @AlchipTECH for helping make this milestone possible. Read more: d-matrix.ai/announcements/d-… #AIInference #AIInfrastructure #HeterogeneousCompute #DataCenterAI #Corsair

Last week, I was invited to present at @AnthropicAI'e Boston Tech Week event. Since the Claude Code moment at the end of last year, the way we do science at @ManifoldBio made a quantum leap overnight. But really, it just came full circle. Manifold was founded by “hybrid scientists” and most of our first few hires could and did both code and do wet lab experiments. This was critical to invent and build the foundational molecular barcoding tech that lets us do the million-scale experiments no one else in the world can do, including interrogating drug candidates directly in vivo. Soon, the form of most of the data generated at Manifold became next-gen DNA sequencing data. (At the Anthropic forum, I live vibe-analyzed a summary that showed we’ve now done 525 NGS runs generating over 70 tera-bases — 10,000 human genomes worth). Engineering biology through this NGS lens is both a feature and a challenge. A feature, because this is how Manifold unlocks massive parallelism (a.k.a. GPU-ification of biology). A challenge, because for many scientists this was the first time they couldn’t easily analyze their own data, and became bottlenecked by a dedicated computational counterpart to help turn around insights, which still took weeks. Over night, that bottleneck has evaporated, aided by a strong data integration layer and an agentic interface we’ve built on top. Once again, everyone at Manifold Bio has become a hybrid scientist, just in time as the in vivo engine has achieved both scale and richness that have not been possible before. Even before this, we had already started making immensely valuable discoveries, including shuttles exploiting novel portals to deliver medicines to the brain – a high value problem that led to our first landmark deal with Roche last fall. Now that the loop is closed for scientists (and agents) at Manifold Bio, the pace of these discoveries is accelerating and we’re about to sweep through many more of the grand challenges in medicine. Thanks again to the Anthropic team for the opportunity.

Thanks @jhuber In addition to advancing the core mission of improving human health, the type of biology we do at @ManifoldBio is uniquely iterative/perturbative in nature and fascinating substrate for connecting AI to the real world.

1/ Since February, 8 papers across algebraic geometry, representation theory, number theory, combinatorics have been quietly appearing on arXiv. Proofs by AxiomProver. 5 papers are now accepted at solid peer-reviewed math journals. To our knowledge, a first for the literature.

The best place to do it would be somewhere generating diverse data with high throughput, low latency (~week scale), and deep relevance to living biological systems. That place is @ManifoldBio. We recently opened up 4 new AI roles, and we are adding an RL-focused role soon: manifold.bio/careers

One of my favorite podcast appearances, with @l2k. There is a long history of AI overpromising and underdelivering in biotech and pharma -- why is this time different? Super fun episode.

Today, we dropped the price of enterprise AI by 80%. Same frontier AI. Same chat, cowork, and code experience. Just 5x more tokens for the same spend. Here’s how and why. 👇 Also, we made a video. Please enjoy.

We live in a golden age of biology. So why are people still dying from disease? Because discovery and development move slower than they should. Today, we’re partnering with Incyte to change that. Kosmos is now the first agent that can compress months of drug development into weeks, from the earliest stages of scientific discovery through to FDA approval. @Incyte will be the first company to deploy it across their pipeline. Work that used to take a team of scientists months now happens in weeks. Patients can't wait, and neither can we.