We’re spending $200B+ a year on data centers to power AI. One company raised $11M, grew human brain cells on a chip, and the cells taught themselves to play a 3D shooter in a week.
Cortical Labs grew 200,000 human neurons on a silicon chip and taught them to play Doom. The cells navigate, target enemies, and fire weapons in real time. Their previous game, Pong, took 18 months on older hardware. Doom took a week. An independent developer with zero biotech experience built the integration using a Python API. The neurons did the rest.
That compression from 18 months to one week tells you everything about where this is going.
Here’s what the “can it run Doom” crowd is missing: each CL1 unit costs $35,000. A full 30-unit server rack draws 850 to 1,000 watts total. Your brain runs on 20 watts. A single GPU cluster training an LLM can draw megawatts. The energy economics of biological compute are orders of magnitude better than silicon, and that gap scales.
The investor list tells you who’s paying attention. Horizons Ventures, Blackbird, and In-Q-Tel, the CIA’s venture arm. In-Q-Tel doesn’t fund science projects. They fund intelligence infrastructure. 115 units started shipping in 2025.
Cortical Labs is now selling “Wetware-as-a-Service” through the Cortical Cloud. Developers can deploy code to living neurons remotely without touching a lab. They’re pricing access at the level of a software subscription while the hardware runs on real human brain cells derived from adult skin and blood samples.
The Doom demo is marketing. The platform play is a bet that biological neurons will eventually outperform silicon at exactly the tasks AI struggles with most: real-time adaptation under uncertainty, learning from minimal data, and processing ambiguity without brute-force compute.
The question was never “can it run Doom.” The question is what happens when it can run everything else.
It doesn't surprise me that OpenAI is under immense pressure. They seem to be losing on most fronts:
- AI Mode on Google Search is actually pretty good for AI-enabled search, and it's free and super fast
- Sonnet/Opus 4.5 models are much better and faster at coding than OpenAI's Codex models
- Gemini's Nano Banana image editing and creation capabilities are super impressive
- Models from Google and Anthropic are winning in most benchmarks
- Gemini 2.5 Pro Deep Research is much faster and cheaper than ChatGPT Pro Research, and it covers a lot more sources
- Unlike Google, they don't have their own datacenters or custom silicon yet; they rely heavily on others like Microsoft and Nvidia for infrastructure
- On the Enterprise front, Anthropic, Google, Microsoft, etc. are advancing heavily
- On the API front, they don't have any moat either, since most companies use something like Google Cloud, Azure, or AWS, which gives you more control and access to many more models
They have an impressive team, and I'm sure they'll figure out a way to make a comeback, but as it stands, it's not looking good.
That was quickest and deepest alt coin crash crypto has ever seen.
Solana worked flawlessly, handling extreme load.
Hyperliquid performed better than CEXs, and all other perp DEXs.
All major DeFi protocols were robust (AAVE, Fluid, Kamino etc.) with no real issues for their users.
Ethena proved itself again (continuing to function with no downtime, and processed mints and burns), and has come out of this event stronger with even more learnings.
Onchain finance is the future of finance.
8/ It forces front-end providers to KYC users.
It explicitly says control of funds is irrelevant. All front-ends, including non-custodial wallets, must collect sensitive personal information and conduct warrantless surveillance to “prevent illicit finance.”
Spoiler: it won't.
23/ which then creates a cascading effect on Bitcoin prices. As more supply sits in treasury companies, and say @saylor knows another large DAT is winding up or selling their Bitcoin, does he (or other DAT holders) now have the fiduciary duty to sell down his own Bitcoin...