6-12 months ago the large labs started buying bio data this spend is starting to ramp very aggressively. the labs are somewhat indiscriminate buyers with enormous budgets. the therapeutics market has long had buyers of data (large and small pharma) but they've historically been the exact opposite of huge-budget and indiscriminate this shift will distort the bio startup market in a bunch of ways. there will be a lot of short-termist behavior to try to get in front of this capital firehouse. but unlike the AI data labelers (mercor, et al) and the robotics labelers (mecka, et al), the bio startups serving up this data have a chance to emerge from this period with businesses that don't rely on selling data the best bio startups will use these sudden resources the labs are dumping on them to supercharge their novel assay and work towards independently powerful models and eventually directly produce therapies bio is the final frontier and its a very exciting time ahead for bio startups IMO

1/🌐 DAO 2.0: The Distributed Agentic Organization DAO 1.0 showed that coordination could live on‑chain: shared treasuries, automated rules, votes without a CEO. It was groundbreaking—but it had limits. It often drifted toward hidden hierarchies, brittle rules, and shallow participation. DAO 2.0 keeps the “O” (organization) but redefines the first two letters. “Decentralized” becomes Distributed; “Autonomous” becomes Agentic.

Update on NeuroLabs Ignition Sale We want to clarify what happened during the launch and what we’re changing going forward. 1. Based on past launches, we intentionally didn’t communicate a specific finalization time to allow a buffer for the launch to finalize and for us to verify that everything was correct before distributing allocations. However, an outdated message from a previous deployment was still displayed on the finalization page, stating it would happen within 30 minutes. A disruption to our subgraph infrastructure delayed finalization leaving about an hour between the sale ending and TGE. We’re sorry for the wait. 2. We saw concerns about a wallet receiving tokens without participating in the Ignition Sale and selling shortly after. This wallet belonged to a NeuroLabs advisor and had been funded just before the launch. The advisor sold tokens representing 2.15% of the total supply. NeuroLabs AI's tokenomics allocated 14.83% of the total supply to advisors, fully liquid at launch as communicated on the Bio launchpad sale page. We always recommend vesting for team, investor and advisor allocations but accepted the team's request to make the advisor allocation fully liquid for this launch. Going forward, we’ll enforce vesting for all team, investor and advisor allocations to better protect supporters. 3. From our side, we will refund all BioXP spent on the sale to all participants including those who purchased instant BioXP. We believe DeSci should give projects the funding and community support to deliver on their roadmaps beyond the TGE. We're building towards this through early community building and funding on OpenLabs, with permissionless launches and milestone-based funding in development for the Bio launchpad. The goal is to let projects focus on their scientific work, with supporters committed to that longer-term progress. Thank you for your patience and continued trust as we build an ecosystem of tools and communities to move science forward.

The PeptAI team has been selected for Track 2 of the @adaptyvbio x @AnthropicAI Protein Design Competition! The competition brings together researchers from around the world to use AI to design new potential drug candidates for diseases that affect millions of lives. Track 2 includes Claude Max 20x accounts for three months, plus $500 in @Modal compute credits for the team. Excited to join researchers exploring AI for protein design, with experimental testing and results shared openly on @Proteinbase so others can access the data and build on what we learn.

Former Scale AI founder and newly appointed @Meta Chief AI Officer @alexandr_wang dropped a bombshell during his YC conversation with @garrytan, and you could almost hear the tech leadership world go quiet. He said Meta has already seen this internally: Build the right agentic loop, give it an evaluation system and metrics that let it optimize itself, and a group of AI agents can complete more work than a team of 100 senior engineers. And they do it “very easily.” But the most interesting part wasn’t the 100-engineer comparison. It was how simple the system underneath it actually is. You’d expect some insanely complex, almost alien architecture powering a swarm like this. Instead, Wang described it with a few almost comically basic words: “Markdown files, cron jobs, goal, metrics, data.” Once you strip away the hype, the implications for traditional software engineering become pretty clear: 1️⃣ It’s not that the models are magically smarter. The eval loop is doing the heavy lifting. Traditional approach: humans write prompts, run the code, inspect the output, and hope nothing broke. Meta’s approach: turn the business goal into something a machine can score automatically. The agent submits its work. The system runs tests, calculates metrics, finds what’s wrong, and sends it back for another pass. Repeat until it passes. Nobody has to babysit every step. The metric becomes the supervisor. 2️⃣ The real alpha is burning 1,000x more tokens inside the feedback loop. A lot of people are still optimizing for the cost of a single AI call. The frontier labs are playing a different game: spend 1,000x or even 1,000,000x more tokens in the background so agents can constantly review, rerun, challenge, and verify each other’s work until they reach a reliable business outcome. Token cost is fixed. The payoff is a pipeline that keeps running. 3️⃣ Memory doesn’t need some fancy database. Persistent memory can live in Markdown files. Scheduling can be handled by the server’s built-in cron jobs, running overnight. The simpler the scaffolding, the more robust the system can be. Less infrastructure also means fewer ways for context to fall apart. This is a pretty brutal change in how technical organizations work. The ceiling for a tech lead used to be partly about how many people they could manage, how many meetings they could sit through, and how many teams they could coordinate. The leverage for the next generation of technical leaders may look very different: Can you turn a messy business objective into a rigorous set of metrics that an AI can evaluate automatically? If 100 people’s output can be replaced by a few cron jobs, Markdown files, and a well-designed eval loop, the era of “just take the ticket and write the code” is coming to an end. The people who can design the evals and orchestrate the swarm aren’t just holding a new tool. They’re effectively running a virtual company.