Latent Space: The AI Engineer Podcast

Synthesis Superintelligence: from Semiconductors to Superconductors — Periodic Labs’ Liam Fedus and Ekin Dogus Cubuk

Academia is for Ambition — Alex Zhang, MIT

Why Dwarkesh is Wrong about Computer Use + How OpenAI shipped its Jev competitor in 1 Week

Claude Code’s Next Era — Thariq Shihipar, Anthropic

OpenRouter: from Seed to Stripe — with OpenRouter’s Alex Atallah & AMP’s Anjney Midha

OpenRouter functions as a critical distribution layer for the AI ecosystem, bridging the gap between model labs and developers by providing a unified, neutral marketplace for inference. By abstracting the complexities of endpoint management, versioning, and cost-efficiency, the platform enables rapid adoption of fronti...

Runway’s WorldPrompt and the Engineering of Real-Time Worlds

🔬Bio-security is an AI Arms Race - Eric Nguyen (CEO, Radical Numerics)

🔬 An Oscar, Two Asteroids, and the Algorithm in Your sklearn: John Platt on AI for Science

Jev: System One models for Prod, not God — with Diogo Almeida, CEO, TypeSafe AI

Underwriting Superintelligence: Backing Agents you can Sue — Rune Kvist, AIUC

Frontier AI adoption faces a critical bottleneck: the lack of trust and liability frameworks. Rune Kvist, founder of AIUC, addresses this by building "confidence infrastructure" through standardized technical testing and insurance. AIUC1, an agent certification framework, requires quarterly simulations to verify safety...

Humanity’s Last Invention — Richard Socher of Recursive

The "Eureka Machine" represents a vision for superintelligence capable of automating scientific and technological discovery, shifting AI development toward recursive self-improvement. Richard Socher, founder of Recursive, contends that superintelligence should be marketed for its transformative potential in fields like...

🔬“We have foundation models for language, not for physics” — Anima Anandkumar, Bren Professor of Computing

Simulation: the new Scaling Law — Joon Sung Park, Simile AI

🔬The BioAI Phase Shift - Matthew McPartlon & Neil Patil, Chai Discovery

The Inference Engineering Masterclass — Philip Kiely & Ali Taha, Baseten

Inference engineering has evolved from simple model deployment into a complex, integrated discipline where training and inference are increasingly unified. Optimizing large language model performance requires a multi-layered approach, including cache-aware routing, speculative decoding, and precise quantization strateg...

Codex from 0 to 10M Users: Building ChatGPT Work — Akshay Nathan, OpenAI

Inside the Model Factory — Eiso Kant, Poolside AI

Building foundation models requires an industrialized "model factory" approach that prioritizes engineering rigor, data immutability, and rapid experimentation cycles. Poolside, led by Eiso Kant, emphasizes that model building is primarily an engineering challenge rather than purely theoretical research. By treating da...

🔬Causal Models Need Causal Data - Xaira’s X-Cell model for Drug Discovery (Bo Wang & Ci Chu, Chief Discovery Officer & Chief AI Scientist)

🔬 The Lab of the Future Should Feel Like a Data Center — Andy Beam & Rafa Gómez-Bombarelli, Lila Sciences

Why AI Infrastructure must evolve for Agent Experience — Akshat Bubna, Modal CTO

Modal functions as a specialized cloud platform for AI applications, evolving from a general-purpose serverless runtime into a high-performance infrastructure provider for inference, training, and agentic workflows. By co-locating infrastructure requirements directly within code via decorators, the platform eliminates ...