Radical AI's Joseph Krause argues the lab infrastructure is the moat in AI-driven materials science
Latent Space interviews Joseph Krause of Radical AI about their 'self-driving lab' approach to materials discovery, where automated physical experimentation is the core differentiator rather than the underlying AI model. Krause argues that in materials science, the data generation pipeline and lab automation create defensible advantages that model capabilities alone cannot replicate. The piece highlights a deployment pattern where AI is tightly coupled with physical-world feedback loops in scientific research.
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Physical AI that Moves the World — Applied Intuition CEO & CTO Interview
Latent Space interviews Applied Intuition CEO Qasar Younis and CTO Peter Ludwig about deploying AI in physical vehicles and machinery including mining rigs, drones, trucks, and warships. The discussion covers AI systems operating in adversarial real-world environments. Applied Intuition is a company focused on autonomous vehicle and physical AI tooling that has expanded into defense and industrial sectors.
Andrew Ng argues Anthropic's usage restrictions and U.S. export controls on frontier AI accelerate push for open alternatives
Andrew Ng's editorial in The Batch analyzes two recent events: Anthropic restricting use of its 'Fable 5' model for LLM research (including initially degrading outputs silently for detected researchers), and the U.S. Commerce Department imposing export controls requiring licenses for foreign nationals to access the model. Ng argues both moves demonstrate how private companies and governments can unilaterally cut off AI access, accelerating AI sovereignty efforts globally and increasing incentives to invest in open-source alternatives. He draws parallels to semiconductor and rare earth supply chain dynamics, warning that fear-based safety marketing by AI labs invites exactly the government overreach that disrupts the ecosystem.
Giving Agents Computers — Ivan Burazin, Daytona
Latent Space interviews Daytona CEO Ivan Burazin about the company's infrastructure for giving AI agents secure compute environments. The discussion covers Daytona's bare metal sandbox architecture, 850K daily runs, 74% month-over-month growth, and their approach to RL-based evaluations for agent workloads. The piece positions Daytona as part of an emerging 'agent cloud' category providing isolated execution environments for autonomous AI systems.
AINews: Open Models, Model Labs vs Agent Labs, and What's Untrainable — Sarah Guo
A Latent Space AINews digest covers open model developments, the emerging distinction between model labs and agent labs, and a featured essay by Sarah Guo on what capabilities remain untrainable. The piece appears to be a reflective commentary day with a focus on strategic framing of the AI ecosystem. The 'model labs vs agent labs' framing and 'what's untrainable' angle suggest substantive industry analysis worth indexing.
Real AI Agents and Real Work
A commentary piece from One Useful Thing examining the practical deployment of AI agents in real work contexts, framing the tension between human-centered work and AI-generated productivity outputs. The piece appears to analyze how autonomous AI agents are changing knowledge work workflows. Published by a Tier 2 source known for applied AI analysis aimed at practitioners and researchers.
Latent Space profiles Axiom Math on verified generation and compounding intelligence
Latent Space interviews Carina Hong of Axiom Math, a company focused on formal verification applied to AI-generated mathematics. The discussion centers on 'verified generation' and 'compounding intelligence' as frameworks for scaling AI reasoning beyond informal, unverified outputs. The piece is relevant to the growing intersection of formal methods, mathematical reasoning, and AI capability development.
Advancing Red Teaming with People and AI
OpenAI published a blog post describing advances in their red teaming methodology, combining human red teamers with AI-assisted approaches. The post outlines how AI tools are being integrated into the red teaming pipeline to improve coverage and efficiency of safety evaluations. This represents an evolution in OpenAI's pre-deployment safety testing practices.
Measuring AI's capability to accelerate biological research
OpenAI introduces a real-world evaluation framework designed to measure how AI systems can accelerate biological research in wet lab settings. The work uses GPT-5 to optimize a molecular cloning protocol as a concrete demonstration case. The framework explicitly addresses both the potential benefits and biosecurity risks of AI-assisted experimentation, positioning this as a dual-use capability assessment.