Latent Space: The AI Engineer Podcast

• Anthropic, Glean & OpenRouter: How AI Moats Are Built with Deedy Das of Menlo Ventures

  Deedy Das, Partner at Menlo Ventures, returns to Latent Space to discuss his journey from Glean to venture capital, the explosive rise of Anthropic, and how AI is reshaping enterprise software and coding. From investing in Anthropic early on when they had no revenue to managing the $100M Ontology Fund, Das shares insider perspectives on the fastest-growing software company in history and what's next for AI infrastructure, research investing, and the future of engineering. We cover Glean’s rise from “boring” enterprise search to a $7B AI-native company, Anthropic's meteoric rise, the strategic decisions behind products like Claude Code, and why market share in enterprise AI is shifting dramatically. Das explains his investment thesis on research companies like Goodfire, Prime Intellect, and OpenRouter and how the Anthology Fund is quietly seeding the next wave of AI infra, research, and devtools.

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• ⚡ Inside GitHub’s AI Revolution: Jared Palmer Reveals Agent HQ & The Future of Coding Agents

  Jared Palmer, SVP at GitHub and VP of CoreAI at Microsoft, joins Latent Space for an in-depth look at the evolution of coding agents and modern developer tools. Recently joining after leading AI initiatives at Vercel, Palmer shares firsthand insights from behind the scenes at GitHub Universe, including the launch of Agent HQ which is a new collaboration hub for coding agents and developers. This episode traces Palmer’s journey from building Copilot inspired tools to pioneering the focused Next.js coding agent, v0, and explores how platform constraints fostered rapid experimentation and a breakout success in AI-powered frontend development. Palmer explains the unique advantages of GitHub’s massive developer network, the challenges of scaling agent-based workflows, and why integrating seamless AI into developer experiences is now a top priority for both Microsoft and GitHub.

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• ⚡ [AIE CODE Preview] Inside Google Labs: Building The Gemini Coding Agent — Jed Borovik, Jules

  Jed Borovik, Product Lead at Google Labs, joins Latent Space to unpack how Google is building the future of AI-powered software development with Jules. From his journey discovering GenAI through Stable Diffusion to leading one of the most ambitious coding agent projects in tech, Borovik shares behind-the-scenes insights into how Google Labs operates at the intersection of DeepMind's model development and product innovation. We explore Jules' approach to autonomous coding agents and why they run on their own infrastructure, how Google simplified their agent scaffolding as models improved, and why embeddings-based RAG is giving way to attention-based search. Borovik reveals how developers are using Jules for hours or even days at a time, the challenges of managing context windows that push 2 million tokens, and why coding agents represent both the most important AI application and the clearest path to AGI. This conversation reveals Google's positioning in the coding agent race, the evolution from internal tools to public products, and what founders, developers, and AI engineers should understand about building for a future where AI becomes the new brush for software engineering. Chapters 00:00:00 Introduction and GitHub Universe Recap 00:00:57 New York Tech Scene and East Coast Hackathons 00:02:19 From Google Search to AI Coding: Jed's Journey 00:04:19 Google Labs Mission and DeepMind Collaboration 00:06:41 Jules: Autonomous Coding Agents Explained 00:09:39 The Evolution of Agent Scaffolding and Model Quality 00:11:30 RAG vs Attention: The Shift in Code Understanding 00:13:49 Jules' Journey from Preview to Production 00:15:05 AI Engineer Summit: Community Building and Networking 00:25:06 Context Management in Long-Running Agents 00:29:02 The Future of Software Engineering with AI 00:36:26 Beyond Vibe Coding: Spec Development and Verification 00:40:20 Multimodal Input and Computer Use for Coding Agents

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• Priscilla Chan and Mark Zuckerberg: Frontier AI + Virtual Biology To Solve All Diseases

  Today’s guests are Priscilla Chan and Mark Zuckerberg, co-founders of Biohub (fka Chan Zuckerberg Initiative). They are one of the leading institutes for AI x Bio and open science research with projects like CELLxGENE, rbio1, VariantFormer, and many more. We talked about the evolution from a broad philanthropic institute to specializing in frontier AI + bio, why they are building 12ft tall microscopes to gather better data, and how building a virtual cell model + virtual immune system could potentially help us cure all diseases. Chapters 00:00:00 Introduction and CZI's 10-Year Anniversary 00:00:56 Learning from Bill Gates 00:04:05 Science vs Translation 00:10:45 The Power of Physical Proximity in Science 00:13:55 Building the Virtual Cell: From Data to Models 00:15:51 Microscopes, Imaging, and Converting Atoms to Bits 00:23:18 AI Meets Biology: The Frontier Lab Concept 00:27:25 How Models Can Enable More Ambitious Research 00:30:15 Precision Medicine and Clinical Impact 00:45:17 The Virtual Immune System and Cellular Engineering 00:48:27 Accelerating the Timeline: What It Takes to Cure All Disease 00:28:45 Joining Forces with Evolutionary Scale

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• How Zyphra went all-in on AMD + Why Devs feel faster with AI but are slower — with Quentin Anthony

  OpenAI recently made waves by being the first big model lab to commit to a hyperscale AMD cluster (together with their own Titan XPUs), giving AMD the first biglab silicon win outside of Nvidia/Google. Returning guest Quentin Anthony, Head of Model Training at Zyphra and advisor at EleutherAI, has recently done this same transition. In part 1 of this pod, Quentin describes his journey from working on Oak Ridge National Lab's Frontier supercomputer to leading Zyphra's ambitious move to AMD MI300X GPUs, where they're achieving performance that beats NVIDIA H100s on certain workloads while dramatically reducing costs. The discussion dives deep into the technical challenges of kernel development, with Quentin explaining why he often bypasses high-level frameworks like Triton to write directly in ROCm or even GPU assembly when necessary. He reveals how Zyphra's hybrid transformer-Mamba models like Zamba 2 can match Llama 3 8B performance at 7B parameters, optimized specifically for edge deployment across a spectrum from 1.2B models for phones to 7B for desktops. In Part 2, Quentin then candidly discusses his experience in the controversial METR software engineering productivity study, which found that developers felt 20% faster while using AI coding tools, but were in fact 20% slower. Quentin was one of the few developers who showed measurable speedup from AI tools. He shares practical insights on avoiding the "slot machine effect" of endlessly prompting models, the importance of context rot awareness, and why he prefers direct API access over tools like Cursor to maintain complete control over model context. The conversation also covers the state of open source AI research, with Quentin arguing that siloed, focused teams with guaranteed funding produce better results than grand collaborative efforts. He explains why kernel datasets alone won't solve the GPU programming problem, the challenges of evaluating kernel quality, and why companies should invest more in ecosystem development rather than traditional marketing. https://www.linkedin.com/in/quentin-anthony/ https://www.zyphra.com/post/zamba2-7b Key Topics: • AMD MI300X advantages: 192GB VRAM, superior memory bandwidth • Writing kernels from PTX/AMD GCN assembly up through CUDA/ROCm • Hybrid attention-Mamba architectures and optimal sparsity ratios • The Menlo productivity study: achieving positive AI speedup • Context rot and why shorter conversations beat long threads • Why physicists make great ML engineers ("embryonic stem cells") • Edge deployment strategies from phones to local clusters • The future of on-device vs cloud inference routing • EleutherAI's focus on interpretability with fully open pipelines • Building velocity-focused teams over position-based hiring

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