Latent Space: The AI Engineer Podcast

Latest Episodes:

AI to AE's: Grit, Glean, and Kleiner Perkins' next Enterprise AI hit — Joubin Mirzadegan, Roadrunner Dec 12, 2025

Show notes

Glean started as a Kleiner Perkins incubation and is now a $7B, $200m ARR Enterprise AI leader. Now KP has tapped its own podcaster to lead it’s next big swing.

From building go-to-market the hard way in startups (and scaling Palo Alto Networks’ public cloud business) to joining Kleiner Perkins to help technical founders turn product edge into repeatable revenue, Joubin Mirzadegan has spent the last decade obsessing over one thing: distribution and how ideas actually spread, sell, and compound. That obsession took him from launching the CRO-only podcast Grit (https://www.youtube.com/playlist?list=PLRiWZFltuYPF8A6UGm74K2q29UwU-Kk9k) as a hiring wedge, to working alongside breakout companies like Glean and Windsurf, to now incubating Roadrunner which is an AI-native rethink of CPQ and quoting workflows as pricing models collapse from “seats” into consumption, bundles, renewals, and SKU sprawl.

We sat down with Joubin to dig into the real mechanics of making conversations feel human (rolling early, never sending questions, temperature + lighting hacks), what Windsurf got right about “Google-class product and Salesforce-class distribution,” how to hire early sales leaders without getting fooled by shiny logos, why CPQ is quietly breaking the back of modern revenue teams, and his thesis for his new company and KP incubation Roadrunner (https://www.roadrunner.ai/): rebuild the data model from the ground up, co-develop with the hairiest design partners, and eventually use LLMs to recommend deal structures the way the best reps do without the Slack-channel chaos of deal desk.

We discuss:

* How to make guests instantly comfortable: rolling early, no “are you ready?”, temperature, lighting, and room dynamics

* Why Joubin refuses to send questions in advance (and when you might have to anyway)

* The origin of the CRO-only podcast: using media as a hiring wedge and relationship engine

* The “commit to 100 episodes” mindset: why most shows die before they find their voice

* Founder vs exec interviews: why CEOs can speak more freely (and what it unlocks in conversation)

* What Glean taught him about enterprise AI: permissions, trust, and overcoming “category is dead” skepticism

* Design partners as the real unlock: why early believers matter and how co-development actually works

* Windsurf’s breakout: what it means to be serious about “Google-class product + Salesforce-class distribution”

* Why technical founders struggle with GTM and how KP built a team around sales, customer access, and demand gen

* Hiring early sales leaders: anti-patterns (logos), what to screen for (motivation), and why stage-fit is everything

* The CPQ problem & Roadrunner’s thesis: rebuilding CPQ/quoting from the data model up for modern complexity

* How “rules + SKUs + approvals” create a brittle graph and what it takes to model it without tipping over

* The two-year window: incumbents rebuilding slowly vs startups out-sprinting with AI-native architecture

* Where AI actually helps: quote generation, policy enforcement, approval routing, and deal recommendation loops

—

Joubin

* X: https://x.com/Joubinmir

* LinkedIn: https://www.linkedin.com/in/joubin-mirzadegan-66186854/

Where to find Latent Space

* X: https://x.com/latentspacepod

Full Video Episode

Timestamps

00:00:00 Introduction and the Zuck Interview Experience00:03:26 The Genesis of the Grit Podcast: Hiring CROs Through Content00:13:20 Podcast Philosophy: Creating Authentic Conversations00:15:44 Working with Arvind at Glean: The Enterprise Search Breakthrough00:26:20 Windsurf's Sales Machine: Google-Class Product Meets Salesforce-Class Distribution00:30:28 Hiring Sales Leaders: Anti-Patterns and First Principles00:39:02 The CPQ Problem: Why Salesforce and Legacy Tools Are Breaking00:43:40 Introducing Roadrunner: Solving Enterprise Pricing with AI00:49:19 Building Roadrunner: Team, Design Partners, and Data Model Challenges00:59:35 High Performance Philosophy: Working Out Every Day and Reducing Friction01:06:28 Defining Grit: Passion Plus Perseverance

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

The Future of Email: Superhuman CTO on Your Inbox As the Real AI Agent (Not ChatGPT) — Loïc Houssier Dec 11, 2025

Show notes

From applied cryptography and offensive security in France’s defense industry to optimizing nuclear submarine workflows, then selling his e-signature startup to Docusign (https://www.docusign.com/company/news-center/opentrust-joins-docusign-global-trust-network and now running AI as CTO of Superhuman Mail (Superhuman, recently acquired by Grammarly https://techcrunch.com/2025/07/01/grammarly-acquires-ai-email-client-superhuman/), Loïc Houssier has lived the full arc from deep infra and compliance hell to obsessing over 100ms product experiences and AI-native email. We sat down with Loïc to dig into how you actually put AI into an inbox without adding latency, why Superhuman leans so hard into agentic search and “Ask AI” over your entire email history, how they design tools vs. agents and fight agent laziness, what box-priced inference and local-first caching mean for cost and reliability, and his bet that your inbox will power your future AI EA while AI massively widens the gap between engineers with real fundamentals and those faking it.

We discuss:

* Loïc’s path from applied cryptography and offensive security in France’s defense industry to submarines, e-signatures, Docusign, and now Superhuman Mail

* What 3,000+ engineers actually do at a “simple” product like Docusign: regional compliance, on-prem appliances, and why global scale explodes complexity

* How Superhuman thinks about AI in email: auto-labels, smart summaries, follow-up nudges, “Ask AI” search, and the rule that AI must never add latency or friction

* Superhuman’s agentic framework: tools vs. agents, fighting “agent laziness,” deep semantic search over huge inboxes, and pagination strategies to find the real needle in the haystack

* How they evaluate OpenAI, Anthropic, Gemini, and open models: canonical queries, end-to-end evals, date reasoning, and Rahul’s infamous “what wood was my table?” test

* Infra and cost philosophy: local-first caching, vector search backends, Baseten “box” pricing vs. per-token pricing, and thinking in price-per-trillion-tokens instead of price-per-million

* The vision of Superhuman as your AI EA: auto-drafting replies in your voice, scheduling on your behalf, and using your inbox as the ultimate private data source

* How the Grammarly + Coda + Superhuman stack could power truly context-aware assistance across email, docs, calendars, contracts, and more

* Inside Superhuman’s AI-dev culture: free-for-all tool adoption, tracking AI usage on PRs, and going from ~4 to ~6 PRs per engineer per week

* Why Loïc believes everyone should still learn to code, and how AI will amplify great engineers with strong fundamentals while exposing shallow ones even faster

—

Loïc Houssier

* LinkedIn: https://www.linkedin.com/in/houssier/

Where to find Latent Space

* X: https://x.com/latentspacepod

Full Video Episode

Timestamps

00:00:00 Introduction and Loïc's Journey from Nuclear Submarines to Superhuman00:06:40 Docusign Acquisition and the Enterprise Email Stack00:10:26 Superhuman's AI Vision: Your Inbox as the Real AI Agent00:13:20 Ask AI: Agentic Search and the Quality Problem00:18:20 Infrastructure Choices: Model Selection, Base10, and Cost Management00:27:30 Local-First Architecture and the Database Stack00:30:50 Evals, Quality, and the Rahul Wood Table Test00:42:30 The Future EA: Auto-Drafting and Proactive Assistance00:46:40 Grammarly Acquisition and the Contextual Advantage00:38:40 Voice, Video, and the End of Writing00:51:40 Knowledge Graphs: The Hard Problem Nobody Has Solved00:56:40 Competing with OpenAI and the Browser Question01:02:30 AI Coding Tools: From 4 to 6 PRs Per Week01:08:00 Engineering Culture, Hiring, and the Future of Software Development

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

World Models & General Intuition: Khosla's largest bet since LLMs & OpenAI Dec 06, 2025

Show notes

From building Medal into a 12M-user game clipping platform with 3.8B highlight moments to turning down a reported $500M offer from OpenAI (https://www.theinformation.com/articles/openai-offered-pay-500-million-startup-videogame-data) and raising a $134M seed from Khosla (https://techcrunch.com/2025/10/16/general-intuition-lands-134m-seed-to-teach-agents-spatial-reasoning-using-video-game-clips/) to spin out General Intuition, Pim is betting that world models trained on peak human gameplay are the next frontier after LLMs.

We sat down with Pim to dig into why game highlights are “episodic memory for simulation” (and how Medal’s privacy-first action labels became a world-model goldmine https://medal.tv/blog/posts/enabling-state-of-the-art-security-and-protections-on-medals-new-apm-and-controller-overlay-features), what it takes to build fully vision-based agents that just see frames and output actions in real time, how General Intuition transfers from games to real-world video and then into robotics, why world models and LLMs are complementary rather than rivals, what founders with proprietary datasets should know before selling or licensing to labs, and his bet that spatial-temporal foundation models will power 80% of future atoms-to-atoms interactions in both simulation and the real world.

We discuss:

* How Medal’s 3.8B action-labeled highlight clips became a privacy-preserving goldmine for world models

* Building fully vision-based agents that only see frames and output actions yet play like (and sometimes better than) humans

* Transferring from arcade-style games to realistic games to real-world video using the same perception–action recipe

* Why world models need actions, memory, and partial observability (smoke, occlusion, camera shake) vs. “just” pretty video generation

* Distilling giant policies into tiny real-time models that still navigate, hide, and peek corners like real players

* Pim’s path from RuneScape private servers, Tourette’s, and reverse engineering to leading a frontier world-model lab

* How data-rich founders should think about valuing their datasets, negotiating with big labs, and deciding when to go independent

* GI’s first customers: replacing brittle behavior trees in games, engines, and controller-based robots with a “frames in, actions out” API

* Using Medal clips as “episodic memory of simulation” to move from imitation learning to RL via world models and negative events

* The 2030 vision: spatial–temporal foundation models that power the majority of atoms-to-atoms interactions in simulation and the real world

—

Pim

* X: https://x.com/PimDeWitte

* LinkedIn: https://www.linkedin.com/in/pimdw/

Where to find Latent Space

* X: https://x.com/latentspacepod

Full Video Episode

Timestamps

00:00:00 Introduction and Medal's Gaming Data Advantage00:02:08 Exclusive Demo: Vision-Based Gaming Agents00:06:17 Action Prediction and Real-World Video Transfer00:08:41 World Models: Interactive Video Generation00:13:42 From Runescape to AI: Pim's Founder Journey00:16:45 The Research Foundations: Diamond, Genie, and SEMA00:33:03 Vinod Khosla's Largest Seed Bet Since OpenAI00:35:04 Data Moats and Why GI Stayed Independent00:38:42 Self-Teaching AI Fundamentals: The Francois Fleuret Course00:40:28 Defining World Models vs Video Generation00:41:52 Why Simulation Complexity Favors World Models00:43:30 World Labs, Yann LeCun, and the Spatial Intelligence Race00:50:08 Business Model: APIs, Agents, and Game Developer Partnerships00:58:57 From Imitation Learning to RL: Making Clips Playable01:00:15 Open Research, Academic Partnerships, and Hiring01:02:09 2030 Vision: 80 Percent of Atoms-to-Atoms AI Interactions

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

After LLMs: Spatial Intelligence and World Models — Fei-Fei Li & Justin Johnson, World Labs Nov 25, 2025

Show notes

Fei-Fei Li and Justin Johnson are cofounders of World Labs, who have recently launched Marble (https://marble.worldlabs.ai/), a new kind of generative “world model” that can create editable 3D environments from text, images, and other spatial inputs. Marble lets creators generate persistent 3D worlds, precisely control cameras, and interactively edit scenes, making it a powerful tool for games, film, VR, robotics simulation, and more. In this episode, Fei-Fei and Justin share how their journey from ImageNet and Stanford research led to World Labs, why spatial intelligence is the next frontier after LLMs, and how world models could change how machines see, understand, and build in 3D.

We discuss:

* The massive compute scaling from AlexNet to today and why world models and spatial data are the most compelling way to “soak up” modern GPU clusters compared to language alone.

* What Marble actually is: a generative model of 3D worlds that turns text and images into editable scenes using Gaussian splats, supports precise camera control and recording, and runs interactively on phones, laptops, and VR headsets.

* Fei-fei’s essay:

on spatial intelligence as a distinct form of intelligence from language: from picking up a mug to inferring the 3D structure of DNA, and why language is a lossy, low-bandwidth channel for describing the rich 3D/4D world we live in.

* Whether current models “understand” physics or just fit patterns: the gap between predicting orbits and discovering F=ma, and how attaching physical properties to splats and distilling physics engines into neural networks could lead to genuine causal reasoning.

* The changing role of academia in AI, why Fei-Fei worries more about under-resourced universities than “open vs closed,” and how initiatives like national AI compute clouds and open benchmarks can rebalance the ecosystem.

* Why transformers are fundamentally set models, not sequence models, and how that perspective opens up new architectures for world models, especially as hardware shifts from single GPUs to massive distributed clusters.

* Real use cases for Marble today: previsualization and VFX, game environments, virtual production, interior and architectural design (including kitchen remodels), and generating synthetic simulation worlds for training embodied agents and robots.

* How spatial intelligence and language intelligence will work together in multimodal systems, and why the goal isn’t to throw away LLMs but to complement them with rich, embodied models of the world.

* Fei-Fei and Justin’s long-term vision for spatial intelligence: from creative tools for artists and game devs to broader applications in science, medicine, and real-world decision-making.

—

Fei-Fei Li

* X: https://x.com/drfeifei

* LinkedIn: https://www.linkedin.com/in/fei-fei-li-4541247

Justin Johnson

* X: https://x.com/jcjohnss

* LinkedIn: https://www.linkedin.com/in/justin-johnson-41b43664

Where to find Latent Space

* X: https://x.com/latentspacepod

Full Video Episode

Timestamps

00:00:00 Introduction and the Fei-Fei Li & Justin Johnson Partnership00:02:00 From ImageNet to World Models: The Evolution of Computer Vision00:12:42 Dense Captioning and Early Vision-Language Work00:19:57 Spatial Intelligence: Beyond Language Models00:28:46 Introducing Marble: World Labs' First Spatial Intelligence Model00:33:21 Gaussian Splats and the Technical Architecture of Marble00:22:10 Physics, Dynamics, and the Future of World Models00:41:09 Multimodality and the Interplay of Language and Space00:37:37 Use Cases: From Creative Industries to Robotics and Embodied AI00:56:58 Hiring, Research Directions, and the Future of World Labs

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

⚡️ 10x AI Engineers with $1m Salaries — Alex Lieberman & Arman Hezarkhani, Tenex Nov 19, 2025

Show notes

Alex Lieberman and Arman Hezarkani, co-founders of Tenex, reveal how they’re revolutionizing software consulting by compensating AI engineers for output rather than hours—enabling some engineers to earn over $1 million annually while delivering 10x productivity gains. Their company represents a fundamental rethinking of knowledge work compensation in the age of AI agents, where traditional hourly billing models perversely incentivize slower work even as AI tools enable unprecedented speed.

The Genesis: From 90% Downsizing to 10x Output The story behind 10X begins with Arman’s previous company, Parthian, where he was forced to downsize his engineering team by 90%. Rather than collapse, Arman re-architected the entire product and engineering process to be AI-first—and discovered that production-ready software output increased 10x despite the massive headcount reduction. This counterintuitive result exposed a fundamental misalignment: engineers compensated by the hour are disincentivized from leveraging AI to work faster, even when the technology enables dramatic productivity gains. Alex, who had invested in Parthian, initially didn’t believe the numbers until Arman walked him through why LLMs have made such a profound impact specifically on engineering as knowledge work.

The Economic Model: Story Points Over Hours 10X’s core innovation is compensating engineers based on story points—units of completed, quality output—rather than hours worked. This creates direct economic incentives for engineers to adopt every new AI tool, optimize their workflows, and maximize throughput. The company expects multiple engineers to earn over $1 million in cash compensation next year purely from story point earnings. To prevent gaming the system, they hire for two profiles: engineers who are “long-term selfish” (understanding that inflating story points will destroy client relationships) and those who genuinely love writing code and working with smart people. They also employ technical strategists incentivized on client retention (NRR) who serve as the final quality gate before any engineering plan reaches a client.

Impressive Builds: From Retail AI to App Store Hits The results speak for themselves. In one project, 10X built a computer vision system for retail cameras that provides heat maps, queue detection, shelf stocking analysis, and theft detection—creating early prototypes in just two weeks for work that previously took quarters. They built Snapback Sports’ mobile trivia app in one month, which hit 20th globally on the App Store. In a sales context, an engineer spent four hours building a working prototype of a fitness influencer’s AI health coach app after the prospect initially said no—immediately moving 10X to the top of their vendor list. These examples demonstrate how AI-enabled speed fundamentally changes sales motions and product development timelines.

The Interview Process: Unreasonably Difficult Take-Homes Despite concerns that AI would make take-home assessments obsolete, 10X still uses them—but makes them “unreasonably difficult.” About 50% of candidates don’t even respond, but those who complete the challenge demonstrate the caliber needed. The interview process is remarkably short: two calls before the take-home, review, then one or two final meetings—completable in as little as a week. A signature question: “If you had infinite resources to build an AI that could replace either of us on this call, what would be the first major bottleneck?” The sophisticated answer isn’t just “model intelligence” or “context length”—it’s controlling entropy, the accumulating error rate that derails autonomous agents over time.

The Limiting Factor: Human Capital, Not Technology Despite being an AI-first company, 10X’s primary constraint is human capital—finding and hiring enough exceptional engineers fast enough, then matching them with the right processes to maintain delivery quality as they scale. The company has ambitions beyond consulting to build their own technology, but for the foreseeable future, recruiting remains the bottleneck. This reveals an important insight about the AI era: even as technology enables unprecedented leverage, the constraint shifts to finding people who can harness that leverage effectively.

Full Video Episode

Timestamps

00:00:00 Introduction and Meeting the 10X Co-founders00:01:29 The 10X Moment: From Hourly Billing to Output-Based Compensation00:04:44 The Economic Model Behind 10X00:05:42 Story Points and Measuring Engineering Output00:08:41 Impressive Client Projects and Rapid Prototyping00:12:22 The 10X Tech Stack: TypeScript and High Structure00:13:21 AI Coding Tools: The Daily Evolution00:15:05 Human Capital as the Limiting Factor00:16:02 The Unreasonably Difficult Interview Process00:17:14 Entropy and Context Engineering: The Future of AI Agents00:23:28 The MCP Debate and AI Industry Sociology00:26:01 Consulting, Digital Transformation, and Conference Insights

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

Anthropic, Glean & OpenRouter: How AI Moats Are Built with Deedy Das of Menlo Ventures Nov 14, 2025

Show notes

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.

Full Video Episode

Timestamps

* 00:00:00 Introduction and Deedy’s Return to Latent Space

* 00:01:20 Glean’s Journey: From Boring Enterprise Search to Valuation

* 00:15:37 Anthropic’s Meteoric Rise and Market Share Dynamics

* 00:17:50 Claude Artifacts and Product Innovation

* 00:41:20 The Anthology Fund: Investing in the Anthropic Ecosystem

* 00:48:01 Goodfire and Mechanistic Interpretability

* 00:51:25 Prime Intellect and Distributed AI Training

* 00:53:40 OpenRouter: Building the AI Model Gateway

* 01:13:36 The Stargate Project and Infrastructure Arms Race

* 01:18:14 The Future of Software Engineering and AI Coding

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

⚡ Inside GitHub’s AI Revolution: Jared Palmer Reveals Agent HQ & The Future of Coding Agents Nov 10, 2025

Show notes

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.

Full Video Episode

Timestamps

00:00:00 Introduction and Jared's New Role at GitHub00:01:00 From V0 to Agent HQ: The Evolution of Coding Agents00:02:51 The V0 Origin Story: From ChatGPT to AI Playground00:05:40 Building the AI SDK and ShadCN Collaboration00:07:08 The Birth of V0: Prompt to UI Revolution00:09:18 V0's Growth Journey and Model Evolution00:11:05 Model Strategy: Composite Models vs User Choice00:13:16 GitHub's Agent HQ and Model Marketplace00:15:51 The Future of Agent Abstraction and Standards00:16:33 Microsoft Core AI Integration and Workflow Vision00:18:37 Dev Containers and Repo Setup Challenges00:24:10 Agent Quality and Infrastructure Reliability00:27:05 Using Coding Agents for Non-Coding Tasks00:29:11 GitHub Homepage Redesign and Community Feedback00:30:27 Stacked Diffs: GitHub's Most Requested Feature

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

⚡ [AIE CODE Preview] Inside Google Labs: Building The Gemini Coding Agent — Jed Borovik, Jules Nov 10, 2025

Show notes

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.

Full Video Episode

Timestamps

00:00:00 Introduction and GitHub Universe Recap00:00:57 New York Tech Scene and East Coast Hackathons00:02:19 From Google Search to AI Coding: Jed's Journey00:04:19 Google Labs Mission and DeepMind Collaboration00:06:41 Jules: Autonomous Coding Agents Explained00:09:39 The Evolution of Agent Scaffolding and Model Quality00:11:30 RAG vs Attention: The Shift in Code Understanding00:13:49 Jules' Journey from Preview to Production00:15:05 AI Engineer Summit: Community Building and Networking00:25:06 Context Management in Long-Running Agents00:29:02 The Future of Software Engineering with AI00:36:26 Beyond Vibe Coding: Spec Development and Verification00:40:20 Multimodal Input and Computer Use for Coding Agents

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

⚡️ Ship AI recap: Agents, Workflows, and Python — w/ Vercel CTO Malte Ubl Oct 31, 2025

Show notes

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

Full Video Episode

Timestamps

00:00 Introduction and Malte’s Background at Google

01:16 Vercel’s AI Engineering Philosophy and Ship AI Recap

03:19 Deep Dive: Workflows vs Agents Architecture

09:33 AI SDK Success Story: Staying Low-Level and Humble

16:35 Framework Design Principles and Open Source Strategy

19:20 Vercel Agent: AI-Powered DevOps and Anomaly Detection

27:06 Internal Agent Use Cases: Lead Qualification and Abuse Analysis

29:49 Agent on Every Desk Program and Enterprise Adoption

32:13 Python Support and Multi-Language Infrastructure

39:42 The Future of AI-Native Security and Development

This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.latent.space/subscribe

Why RL Won — Kyle Corbitt, OpenPipe (acq. CoreWeave) Oct 16, 2025

Show notes

In this deep dive with Kyle Corbitt, co-founder and CEO of OpenPipe (recently acquired by CoreWeave), we explore the evolution of fine-tuning in the age of AI agents and the critical shift from supervised fine-tuning to reinforcement learning. Kyle shares his journey from leading YC’s Startup School to building OpenPipe, initially focused on distilling expensive GPT-4 workflows into smaller, cheaper models before pivoting to RL-based agent training as frontier model prices plummeted. The conversation reveals why 90% of AI projects remain stuck in proof-of-concept purgatory - not due to capability limitations, but reliability issues that Kyle believes can be solved through continuous learning from real-world experience. He discusses the breakthrough of RULER (Relative Universal Reinforcement Learning Elicited Rewards), which uses LLMs as judges to rank agent behaviors relatively rather than absolutely, making RL training accessible without complex reward engineering. Kyle candidly assesses the challenges of building realistic training environments for agents, explaining why GRPO (despite its advantages) may be a dead end due to its requirement for perfectly reproducible parallel rollouts. He shares insights on why LoRAs remain underrated for production deployments, why GEPA and prompt optimization haven’t lived up to the hype in his testing, and why the hardest part of deploying agents isn’t the AI - it’s sandboxing real-world systems with all their bugs and edge cases intact. The discussion also covers OpenPipe’s acquisition by CoreWeave, the launch of their serverless reinforcement learning platform, and Kyle’s vision for a future where every deployed agent continuously learns from production experience. He predicts that solving the reliability problem through continuous RL could unlock 10x more AI inference demand from projects currently stuck in development, fundamentally changing how we think about agent deployment and maintenance.

Key Topics:

* The rise and fall of fine-tuning as a business model

* Why 90% of AI projects never reach production

* RULER: Making RL accessible through relative ranking

* The environment problem: Why sandboxing is harder than training

* GRPO vs PPO and the future of RL algorithms

* LoRAs: The underrated deployment optimization

* Why GEPA and prompt optimization disappointed in practice

* Building world models as synthetic training environments

* The $500B Stargate bet and OpenAI’s potential crypto play

* Continuous learning as the path to reliable agents

References