Today, @BigHatBio and @AnthropicAI announced their historic therapeutics partnership. For the pilot project, an Anthropic researcher + Claude collaborated with BigHat’s automated lab to optimize two novel antibodies—with inspiring results. As frontier AI moves into bio, data gen platforms are more important than ever. Under the leadership of @peytongreenside and team, BigHat has built the Ferrari. A big step for Anthropic and BigHat, and an even bigger step for AI biodiscovery. Learn more in our latest blog by @fjeg1987 and @sussan_liu (below).

NEW 🚨 Inside Anthropic — AGI, AI Optimism, Regulatory Capture, Open Source & More!  @JTLonsdale sits down with Anthropic's @_sholtodouglas and @the_marwell - Have we reached AGI — and what does it mean? - What are the most exciting breakthroughs at Anthropic right now? - What are the biggest risks and concerns? - What is their stance on open source? - Is Anthropic angling for regulatory capture? - Do AI researchers share same values as the heartland?  Episode 164 out now!  (00:00) Episode intro (02:00) Self-taught ML; Unlikely paths to Anthropic (04:30) What are the most exciting things you're working on? (08:04) Have we reached AGI — and what comes next?  (10:10) How to measure AI progress (14:20) Is biology the next great frontier? (16:50) Career advice & the decade of the generalists (19:30) What could go wrong? What are the biggest AI risks? (23:11) Offense vs defense; how technology evolves (25:08) Answering the regulatory capture critique (30:45) Anthropic's stance on open source (31:50) Would you slow down and let China go ahead? (33:05) Anthropic's view on distillation (40:12) Is a post-scarcity world achievable? (48:54) How do we preserve values, tradition & community? (55:00) Predictions for 2028

One of the hardest parts of enterprise automation is understanding how work actually gets done. @worktrace_ai maps real workflows to identify where automation can have the most impact. Now they’re closing the loop, going from discovery to executing the work itself. Already deployed across 20+ enterprises, with 75,000+ hours of automation opportunities surfaced. Proud to back this team!

AI has collapsed the timeline from vulnerability discovery to exploitation. @ArmadinSecurity is building for that reality with autonomous swarms of agents that continuously test customers’ own systems, simulating sophisticated adversaries to uncover exploitable paths and help security teams eliminate them before real attackers can. Just seven months out of stealth, Armadin is already in production with Fortune 500 companies and government customers. We’re proud to double down as the team announces its $255.5M Series B.

"What this enables is the return of American shipbuilding... We're going to create 10,000 jobs... and Port Alpha is going to be the most advanced, most efficient, highest throughput shipyard, not just in the country but really in the world." @Saronic CEO @MavrookasD on the significance of today's announcement. We launched Saronic out of 8VC Build in the fall of '22 with a small, incredibly talented team united around a bold idea: America could rebuild its shipbuilding capacity. Only four years later, they're building the ships and shipyards to restore American maritime strength. We are incredibly proud of this team. They're just getting started.

Earlier today, Vice President JD Vance helped break ground on Saronic's new Port Alpha, where a $3 billion investment is expected to generate up to 10,000 jobs and lead to an economic impact of $264.5 billion for the great state of Texas! American shipbuilding is BACK!

Saronic has been awarded a production OT under the U.S. Navy's MUSV Marketplace for Marauder, our 180-foot dual-use autonomous ship — following a rigorous at-sea evaluation that tested the vessel’s capabilities. The trials included autonomously tracking and maneuvering around a variety of vessels, plus a battery of passive-perception tests — requiring Marauder to detect, track, and follow other vessels using only its passive onboard sensors, with no active radar. It's one of the hardest problems in autonomous maritime systems, and Marauder ran every scenario back-to-back on the same platform now under contract with the Navy. "Marauder went from initial design to on-the-water in under a year, and this evaluation demonstrated that the platform holds up under the Navy's own toughest testing," said Dino Mavrookas, CEO and Co-Founder. "We've now flipped our third Marauder hull, with several more moving through the production line behind it. We can build at that pace because we designed the autonomy, software, hardware, and the production process side-by-side, as one system, rather than bolting them together after the fact. That's what it means to be a vertically integrated shipbuilder, and it's why we've been able to build and deliver at speed and scale."

We first started investing in @cognition six financing rounds ago, in 2024; roughly eight years ago in AI years! What excited us then wasn’t simply the prospect of automating software engineering with some of the top talent we knew. @DevinAI pointed toward something much bigger, a true man-machine symbiosis where humans provide direction and judgment while agents execute and coordinate increasingly complex work across a jagged frontier of model capabilities. Two actual years later, the models improved remarkably quickly and the scope of what Cognition can do has expanded with them. We’ve continued to double down in every round as @ScottWu46, @russelljkaplan and the team have scaled with extraordinary urgency and ambition. Proud and honored to be along for the ride! Below is from @8vc on where we see machine intelligence heading:

Our case for Cognition (full piece with my colleagues @JTLonsdale @AlexKolicich @bela_becerra below) starts with two ideas from economics: Ricardo's comparative advantage and Coase's theory of the firm. Ricardo: gains from trade come from difference. Even a party that is better at everything comes out ahead by trading, because what matters is opportunity cost. A frontier model can be the best at every task and still be the wrong choice for most of the volume (every GPU-hour spent on routine edits is one not spent on work only it can do). The more jagged the frontier, the larger the gains, and the more it pays to be a good trading partner to every lab. A lab's own agent is protectionist by design (it buys domestic). Cognition trades with everyone and builds its own models where it has a comparative advantage. Coase: firms exist because markets are costly to use (finding a counterparty, negotiating, checking the work), so activity moves inside a firm wherever internal coordination is cheaper. As intelligence gets cheap, coordination becomes the scarce input. Ten thousand isolated copilots pay a transaction cost on every handoff, mostly in lost context. Devin brings those handoffs inside one system (shared context, planning, delegation, verification), with humans in the role Coase gave the entrepreneur: directing the work. Ricardo tells you where the surplus comes from, and Coase tells you what it costs to collect it. It goes to whomever can create the most intelligent system to solve this massive coordination problem and create true man-machine symbiosis (conclusion = build the best cloud agents). We heard a lot of skepticism when we invested at $2B and led the round at $4B. The labs were shipping their own agents, and the common view was that the layer in between would get squeezed. We thought better models would make it more valuable. Devin was a new kind of product, and it arrived early. It had an interface overhang (the mirror image of a capability overhang): you could delegate more to it than the models could yet deliver, and more than most teams were ready to hand off. The models grew into it, and cloud agents are now a category. The team built through that whole stretch like madmen. They are the most persistent group of people I've ever seen, and even by the standards of AI cycles, they move fast. Job not finished.

How do you quickly recognize top talent? Zack’s contests at MeritFirst are used by top AI teams - such as Ramp, below - to attract and hire talent based on ability, not resumes. Fascinating new tailored approach to talent and merit, only possible thanks to AI.

Yannis and I led forward deployed AI engineering at Palantir. Much of today’s boom is not true FDE work. Companies are filling a product gap with engineering headcount. What comes after that?

AI and robotics are advancing faster than most predicted, opening up categories of companies that would have sounded like science fiction only a few years ago. @vvkgopalan has pushed our team to believe the future will arrive faster than consensus expects, and he has repeatedly been right. I’m excited to announce that Vivek has been promoted to Partner, six years after joining @8VC from Yale. Vivek has earned the trust of exceptional researchers, engineers, and founders across AI and robotics, helping us partner with ambitious companies including @GeneralistAI, @BedrockRobotics, @edra_ai, @withsableai, @ValsAI, Bobyard and more. He genuinely loves the craft of company-building, helping founders pursue the maximally ambitious version of what they can build. Vivek and I recently sat down with @GeneralistAI founder @peteflorence to discuss recent breakthroughs in general-purpose robotics. Listen to our conversation below. Proud to call Vivek our Partner. To the frontier!

I'm proud to now be a Partner at @8VC, and I can't think of a better moment to be in this seat. Startups are beautiful things designed to fight the inertia of the world, but they die if the world stands still. The question we ask every founder, "why now?", is a second-derivative one (incumbents handle a low, predictable rate of change just fine), so four years ago most pitches didn't have a great answer, and today these answers are inspiring and plentiful. For years we argued for ideas outside consensus (that America needed to build again, that the legacy industries written off as bad software markets were exactly where AI would strip the tedium out of work). The Overton window has since moved our way, and more of the founders building in those markets now start with us. Ambition has changed too. You used to wait for scaled PMF before shipping products 2 and 3, but software is now infinitely iterable (thanks @DevinAI), so the best teams chase the maximally ambitious version of their company far earlier, and the intensity of the work right now is contagious. I'm an ambitious person myself, and I feel privileged to do this work at this moment. Thank you @JTLonsdale @AlexKolicich @andrewoetting and the whole 8VC team for the trust. Never been more optimistic.

America is pouring trillions into the data centers and power infrastructure required to win the AI race. But the finance teams supporting this buildout are drowning in complex, manual work. @kos_ai_inc is building the AI finance team for this new industrial era -- reconciling invoices, contracts and accounts, with a complete audit trail behind every decision. Billions of dollars of data center invoices already run through Kos. If America is going to build at the pace this moment demands, the systems supporting that buildout need to scale with it. Proud to back @tanujt and @kos_ai_inc 🇺🇸🇺🇸🇺🇸

Joined @EdLudlow to talk about AI progress, independent evaluation, and why I’m optimistic. Some quick takes: - We’re better at building AI than understanding it. Attention towards testing/evaluation matters more than slowing down. - Our RSI Index projects models could match human researchers on the tasks we test by August 2027. Embedded evaluators can produce more accurate estimates based on internal systems. - Public conflict masks cooperation. The labs, policymakers, and enterprises we work with want better evidence. I’ve seen enough to believe coordination is possible. - Independence comes at a cost. We’ve rejected contracts that would compromise ours. The same group doing the testing shouldn’t also sell the solution. - Evaluation should scale through better technology. If it becomes a bureaucratic moat for incumbent labs, we’ve failed. - Market-based evaluation has a role with or without regulation. Competition pushes us to build better technology and keep up with the frontier.

Introducing SWE-2, our closest model yet to the frontier. On leading evals, it scores on par with recent frontier models – at up to 70% lower cost. We scaled RL to multiple trillions of parameters, with a refined recipe that pushes the Pareto curve on both capabilities & cost.

The free world has seriously underinvested in the weapons required to deter serious adversaries. Covenant is fixing that. I’m in Dallas, TX today to help open our new factory. They are building the long-range missile arsenal of the United States: deep-strike weapons produced at scale in Texas. Better missiles at scale, for a fraction of the cost. This is how America restores deterrence 🇺🇸.