Latent Space: The AI Engineer Podcast

• 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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• ⚡️ Ship AI recap: Agents, Workflows, and Python — w/ Vercel CTO Malte Ubl

  In this conversation with Malte Ubl, CTO of Vercel (http://x.com/cramforce), we explore how the company is pioneering the infrastructure for AI-powered development through their comprehensive suite of tools including workflows, AI SDK, and the newly announced agent ecosystem. Malte shares insights into Vercel's philosophy of "dogfooding" - never shipping abstractions they haven't battle-tested themselves - which led to extracting their AI SDK from v0 and building production agents that handle everything from anomaly detection to lead qualification. The discussion dives deep into Vercel's new Workflow Development Kit, which brings durable execution patterns to serverless functions, allowing developers to write code that can pause, resume, and wait indefinitely without cost. Malte explains how this enables complex agent orchestration with human-in-the-loop approvals through simple webhook patterns, making it dramatically easier to build reliable AI applications. We explore Vercel's strategic approach to AI agents, including their DevOps agent that automatically investigates production anomalies by querying observability data and analyzing logs - solving the recall-precision problem that plagues traditional alerting systems. Malte candidly discusses where agents excel today (meeting notes, UI changes, lead qualification) versus where they fall short, emphasizing the importance of finding the "sweet spot" by asking employees what they hate most about their jobs. The conversation also covers Vercel's significant investment in Python support, bringing zero-config deployment to Flask and FastAPI applications, and their vision for security in an AI-coded world where developers "cannot be trusted." Malte shares his perspective on how CTOs must transform their companies for the AI era while staying true to their core competencies, and why maintaining strong IC (individual contributor) career paths is crucial as AI changes the nature of software development. What was launched at Ship AI 2025: AI SDK 6.0 & Agent Architecture Agent Abstraction Philosophy: AI SDK 6 introduces an agent abstraction where you can "define once, deploy everywhere". How does this differ from existing agent frameworks like LangChain or AutoGPT? What specific pain points did you observe in production that led to this design? Human-in-the-Loop at Scale: The tool approval system with needsApproval: true gates actions until human confirmation. How do you envision this working at scale for companies with thousands of agent executions? What's the queue management and escalation strategy? Type Safety Across Models: AI SDK 6 promises "end-to-end type safety across models and UI". Given that different LLMs have varying capabilities and output formats, how do you maintain type guarantees when swapping between providers like OpenAI, Anthropic, or Mistral? Workflow Development Kit (WDK) Durability as Code: The use workflow primitive makes any TypeScript function durable with automatic retries, progress persistence, and observability. What's happening under the hood? Are you using event sourcing, checkpoint/restart, or a different pattern? Infrastructure Provisioning: Vercel automatically detects when a function is durable and dynamically provisions infrastructure in real-time. What signals are you detecting in the code, and how do you determine the optimal infrastructure configuration (queue sizes, retry policies, timeout values)? Vercel Agent (beta) Code Review Validation: The Agent reviews code and proposes "validated patches". What does "validated" mean in this context? Are you running automated tests, static analysis, or something more sophisticated? AI Investigations: Vercel Agent automatically opens AI investigations when it detects performance or error spikes using real production data. What data sources does it have access to? How does it distinguish between normal variance and actual anomalies? Python Support (For the first time, Vercel now supports Python backends natively.) Marketplace & Agent Ecosystem Agent Network Effects: The Marketplace now offers agents like CodeRabbit, Corridor, Sourcery, and integrations with Autonoma, Braintrust, Browser Use. How do you ensure these third-party agents can't access sensitive customer data? What's the security model? "An Agent on Every Desk" Program Vercel launched a new program to help companies identify high-value use cases and build their first production AI agents. It provides consultations, reference templates, and hands-on support to go from idea to deployed agent

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• ⚡️ Ship AI recap: Agents, Workflows, and Python — w/ Vercel CTO Malte Ubl

  In this conversation with Malte Ubl, CTO of Vercel (http://x.com/cramforce), we explore how the company is pioneering the infrastructure for AI-powered development through their comprehensive suite of tools including workflows, AI SDK, and the newly announced agent ecosystem. Malte shares insights into Vercel's philosophy of "dogfooding" - never shipping abstractions they haven't battle-tested themselves - which led to extracting their AI SDK from v0 and building production agents that handle everything from anomaly detection to lead qualification. The discussion dives deep into Vercel's new Workflow Development Kit, which brings durable execution patterns to serverless functions, allowing developers to write code that can pause, resume, and wait indefinitely without cost. Malte explains how this enables complex agent orchestration with human-in-the-loop approvals through simple webhook patterns, making it dramatically easier to build reliable AI applications. We explore Vercel's strategic approach to AI agents, including their DevOps agent that automatically investigates production anomalies by querying observability data and analyzing logs - solving the recall-precision problem that plagues traditional alerting systems. Malte candidly discusses where agents excel today (meeting notes, UI changes, lead qualification) versus where they fall short, emphasizing the importance of finding the "sweet spot" by asking employees what they hate most about their jobs. The conversation also covers Vercel's significant investment in Python support, bringing zero-config deployment to Flask and FastAPI applications, and their vision for security in an AI-coded world where developers "cannot be trusted." Malte shares his perspective on how CTOs must transform their companies for the AI era while staying true to their core competencies, and why maintaining strong IC (individual contributor) career paths is crucial as AI changes the nature of software development. What was launched at Ship AI 2025: AI SDK 6.0 & Agent Architecture Agent Abstraction Philosophy: AI SDK 6 introduces an agent abstraction where you can "define once, deploy everywhere". How does this differ from existing agent frameworks like LangChain or AutoGPT? What specific pain points did you observe in production that led to this design? Human-in-the-Loop at Scale: The tool approval system with needsApproval: true gates actions until human confirmation. How do you envision this working at scale for companies with thousands of agent executions? What's the queue management and escalation strategy? Type Safety Across Models: AI SDK 6 promises "end-to-end type safety across models and UI". Given that different LLMs have varying capabilities and output formats, how do you maintain type guarantees when swapping between providers like OpenAI, Anthropic, or Mistral? Workflow Development Kit (WDK) Durability as Code: The use workflow primitive makes any TypeScript function durable with automatic retries, progress persistence, and observability. What's happening under the hood? Are you using event sourcing, checkpoint/restart, or a different pattern? Infrastructure Provisioning: Vercel automatically detects when a function is durable and dynamically provisions infrastructure in real-time. What signals are you detecting in the code, and how do you determine the optimal infrastructure configuration (queue sizes, retry policies, timeout values)? Vercel Agent (beta) Code Review Validation: The Agent reviews code and proposes "validated patches". What does "validated" mean in this context? Are you running automated tests, static analysis, or something more sophisticated? AI Investigations: Vercel Agent automatically opens AI investigations when it detects performance or error spikes using real production data. What data sources does it have access to? How does it distinguish between normal variance and actual anomalies? Python Support (For the first time, Vercel now supports Python backends natively.) Marketplace & Agent Ecosystem Agent Network Effects: The Marketplace now offers agents like CodeRabbit, Corridor, Sourcery, and integrations with Autonoma, Braintrust, Browser Use. How do you ensure these third-party agents can't access sensitive customer data? What's the security model? "An Agent on Every Desk" Program Vercel launched a new program to help companies identify high-value use cases and build their first production AI agents. It provides consultations, reference templates, and hands-on support to go from idea to deployed agent Two open-source agent templates were shared: a Lead Qualification Agent (built with Next.js, Vercel AI SDK, Workflows, Slack) that scrapes lead data and prioritizes prospects, and a Data Analyst Agent that links Slack to SQL for natural-language data queries. By seeding these templates and guides, Vercel is strategically lowering the barrier for organizations to adopt agents internally.

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• The Agents Economy Backbone - with Emily Glassberg Sands, Head of Data & AI at Stripe

  Emily Glassberg Sands is the Head of Data & AI at Stripe where she leads the organization’s efforts to build financial infrastructure for the internet & leverage AI to power Stripe’s products. Stripe processes about $1.4 trillion in payments annually (~1.3% of global GDP), making it an exciting opportunity to apply AI & ML at scale. In this episode, Emily shares insights into how Stripe is using AI to solve complex problems like fraud detection, optimizing checkout experiences, & enabling new business models for AI companies. Emily also shares her economist perspective on market efficiency & how Stripe’s focus on building economic infrastructure for AI is driving growth across the ecosystem. We discuss: Stripe’s domain-specific foundation model and “payments embeddings” that run inline on the charge path to detect sophisticated card-testing at scale (improved detection rates at large users from ~59% to ~97%). The launch of the Agentic Commerce Protocol (ACP) with OpenAI, creating a shared standard for how businesses can expose products to AI agents which is used by Walmart and Sam’s Club. How Stripe is helping AI companies manage new fraud vectors, such as free trial and refund abuse, and the importance of real-time, outcome-based billing The impact of AI on Stripe’s internal operations, including the use of LLMs for code generation, merchant understanding, and internal tooling Why many AI companies are going global day-one how Stripe’s Link network (200M+ consumers) concentrates AI demand. Whether we're in an AI bubble, why GDP hasn't reflected AI productivity gains yet, and how agentic commerce could expand consumption by removing time constraints for high-income consumers Emily’s perspective on the changing social contract around AI, the importance of deep thinking, and the role of brand and design in AI-driven products — Where to find Emily Sands X: https://x.com/emilygsands LinkedIn: https://www.linkedin.com/in/egsands/ Where to find Shawn Wang X: https://x.com/swyx LinkedIn: https://www.linkedin.com/in/shawnswyxwang/ Where to find Alessio Fanelli X: https://x.com/FanaHOVA LinkedIn: https://www.linkedin.com/in/fanahova/ Where to find Latent Space X: https://x.com/latentspacepod Substack: https://www.latent.space/ Chapters 00:00:00 Introduction and Emily's Role at Stripe 00:09:55 AI Business Models and Fraud Challenges 00:13:49 Extending Radar for AI Economy 00:16:42 Payment Innovation: Token Billing and Stablecoins 00:23:09 Agentic Commerce Protocol Launch 00:29:40 Good Bots vs Bad Bots in AI 00:40:31 Designing the Agents Commerce Protocol 00:49:32 Internal AI Adoption at Stripe 01:04:53 Data Discovery and Text-to-SQL Challenges 01:21:00 AI Economy Analysis: Bubble or Boom?

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