It’s deeply disturbing that an AI system unavailable to the public solved NS in what seems like less than a week. It’s hard to overstate the power that a small number of people at OpenAI and Anthropic hold. The American dream rests on the belief that anyone can achieve greatness through hard work and intelligence. When intelligence and work themselves are hoarded, what does that dream become?

After an amazing ride at Ramp, I’ve joined Hone. I’m grateful to have helped build some of the first agents that could accomplish real financial tasks. As intelligence rapidly improved, it became evident that the next frontier is to build ergonomic systems where agents can graduate from finishing tasks to owning responsibilities. Our team today consists of early engineers at Cognition and Mercor as well as former founders. We’re rapidly growing, and if this sounds exciting to you, please DM me.

OWN YOUR INTELLIGENCE Last year, building on open-weight models was primarily a cost rationalization exercise. Slightly worse performance for a much cheaper price. Now, it is increasingly an existential and strategic topic for our portfolio. Intelligence is the product. Companies want to shape it and own it and let it compound within their own walls. Not your weights, not your product. Now, with frontier open-weight models and fantastic tooling/infrastructure, owning your intelligence at the frontier is finally becoming possible. The result: every application company we work with is embarking on the journey of doing their own research on post-training, evals, harnesses, etc. The hottest neolabs may just be @Harvey, @FactoryAI, @Ramp, etc. The list goes on. We held a summit @sequoia to convene our portfolio on this topic, together with @gabepereyra (@Harvey) on building Harvey Labs, @lqiao (@FireworksAI_HQ) on post-training, @hwchase17 (@LangChain) on harnesses + evals, @BrendanFoody (@mercor) on RL environments and synthetic data, @QuantumArjun (@trajectorylabs) on online continual learning. Opening talk below; rest to come this week! 00:00 What is sovereign AI (and what it isn't) 01:24 Centralized vs. decentralized intelligence 02:54 Four reasons companies own their models: cost, speed, performance, destiny 04:22 "Not your weights, not your product" 05:32 The application companies are the newest neo labs 07:05 Step 1: Deciding what to own vs. rent 09:51 Step 2: Build the team (and don't shoehorn your platform team) 11:17 Step 3: Legibility – why your research has to be visible 12:33 Step 4: The technical roadmap 13:56 The stack: production vs. development 15:16 Opening Pandora's box – base models, harnesses, context

We’re open-sourcing PorTAL, our framework for shared task representations and cross model LoRA adaptation. It now spans from hybrid attention models to multimodal systems including Gemma 4 E2B, Mistral 7B & @thinkymachines' Inkling. Code: ramp-public/portallib Models: @huggingface /RampPublic