. Top News.
David Silver built AlphaGo at DeepMind. The system didn't study human games to beat the world champion. It played itself millions of times until it found moves no human had considered. Silver was the architect of that.
Last year, he left and started Ineffable Intelligence in London. This week, the company raised $1.1 billion in seed funding at a $5.1 billion valuation. Sequoia and Lightspeed co-led the round, with Nvidia, Google, DST Global, Index, and the U.K.'s Sovereign AI Fund participating.
The bet is on reinforcement learning at scale. Instead of training on human-labeled data, Ineffable wants AI that learns through trial, error, and simulation. Systems that improve without waiting for humans to label the next dataset.
NVIDIA also announced an engineering partnership to co-develop the infrastructure, starting on Grace Blackwell chips and extending to the Vera Rubin platform. The goal is pipelines that continuously feed large-scale RL systems with training data and simulations.
The dominant AI paradigm still depends heavily on human-generated data. That's becoming a bottleneck. The AI labs that figure out how to learn without human-generated data will have a compounding advantage that's hard to close. If you're making infrastructure or AI investment decisions, reinforcement learning at scale is the thesis to watch in 2026.
. Signals.
Tools
Links
. Poll.
Why would a large enterprise put $100 million into an external AI research fund?
. Market.
Funding
Graphon AI raised $8.3M (Seed) to build a pre-model intelligence layer for AI systems.
Iceotope raised $26M to scale liquid cooling infrastructure for AI data centers.
Roles In AI
Member of Technical Staff at Ineffable Intelligence
Senior Product Manager, ML Signals at Reddit
. Prompt of the Day.
The Adoption Fix Framework
When to use this?
When something has been launched, but usage, engagement, or compliance is lower than expected.
I’ll describe an initiative that has already launched but isn’t being adopted as expected.
Build an Adoption Fix Framework covering:
Friction Analysis
Where users are dropping off
What feels hard or unnecessary
Incentive Analysis
Why teams may not care
Misaligned KPIs or rewards
Leadership Signals
Where leadership behavior contradicts the initiative
Immediate Interventions
3 actions that can increase adoption in 30 days
Keep it practical and enterprise-ready.
Initiative + current reality: [describe clearly]P.S. Get more such prompts in the Prompting Playbook (free for you)
Stay curious, {{first_name | leaders}}
PS. If you missed yesterday’s issue, you can find it here.