AI Malaise
When intelligence is abundant, scarcity moves to atoms and deployment.
Each week, we share a small collection of ideas that shaped our internal thinking. Inspired by experiments like USV’s Librarian, this series is powered by an AI assistant that helps synthesize recurring themes from our discussions, alongside our own reflections.
AI abundance creates builder paralysis. As agent tooling gets absurdly powerful, more builders feel stuck on what’s worth building, because so much software starts to feel commoditized. This matters because as intelligence becomes abundant, differentiation shifts to scarce constraints, real-world deployment, and distribution. We think venture-scale winners will be the platforms and infrastructure that unlock “atoms at scale” (robot deployments, energy, compute, regulated workflows, logistics) and the coordination primitives (including crypto networks) that turn these ecosystems into modern utilities, not another software wrapper.
MIT launched NeuroSkill, a brain-computer interface integrated with foundation models. It uses brain signals to infer cognitive state, then runs an offline agent loop that can respond to your state, not just prompts. This matters because “state” is a new input channel for agents, enabling interfaces that adapt in real time and opening new possibilities for human-AI interaction.
Physical Intelligence built a memory system for robot models, giving them short-term and long-term memory so they can complete long-horizon tasks, like cleaning a kitchen or making a sandwich. When the robot makes a mistake (e.g., opens the fridge door the wrong way), it remembers and tries a different strategy. We think memory will become core robotics infrastructure, because reliable robots must remember, recover, and adapt, not just act.
Humanoid robotics are marketing and hype. In most deployments, wheels beat legs because legs add safety and compliance friction, plus cost, for marginal gains. This matters because as bodies commoditize, the bottleneck shifts to trust, integration depth, uptime, and scaling from one site to many. We think winners will be the teams with a repeatable deployment playbook (EHS/OSHA cleared) and software that embeds robots into workflows so they are hard to rip out.
REMem pushes “agent memory” beyond RAG, storing past events with context (who/what/when/where) so agents can reason across a timeline, not just retrieve documents. This matters because long-running agents will fail without event memory, they lose continuity and repeat mistakes. We think memory becomes a first-class layer in the agent stack, and winners will treat it like structured state, not a bigger context window.
We’ll share another edition next week.