What happens when you combine world-class mathematicians with cutting-edge AI systems?

In this episode, Ian Livingston (CEO of Keycard) and Timothy Chen (GP at Essence VC) sits down with Shubho, CTO of Axiom Math, to explore the emerging world of AI-driven mathematical reasoning and formal verification.

From proving theorems in Lean to scaling software verification for the agentic coding era, Shubo lays out a compelling vision for why mathematical infrastructure matters more than ever. The conversation also ventures into homomorphic encryption, multi-party computation, Navier-Stokes, and why the next frontier of computing might just be running on math we haven't discovered yet.

[00:00] Guest introductions and backgrounds

[00:52] Company founding story

[03:01] Axiom Math mission explained

[05:15] Software verification applications

[16:02] MPC and encryption challenges

[24:01] Business model and products

[33:49] Spicy Future hot takes

In this episode of The Infra Pod, hosts Tim Chen (Essence VC) and Ian Livingstone (Keycard) sit down with Benny Chen, co-founder of Fireworks AI, to explore the evolving world of AI inference infrastructure.

Benny shares his journey from Meta — where capacity planning meetings made it clear GPUs were heading "up and to the right" — to co-founding Fireworks AI before ChatGPT even launched. The conversation dives deep into why the team bet early on inference over training, how they approached model optimization from horizontal compiler techniques to per-model kernel tuning, and why model customization is the key to unlocking better-than-frontier performance for vertical use cases.

Benny discusses the reality of open source vs. closed models, the rise of agentic workloads, and why the real question isn't which model to use — it's which tasks have already been saturated. This episode is packed with technical insights on inference infrastructure, reinforcement learning for model customization, and what it means to truly adopt an AI-native engineering culture.

0:24 Benny's journey and founding Fireworks AI3:23 Early conviction: betting on inference before ChatGPT8:29 Pivoting from PyTorch training to text inference15:42 Horizontal vs. per-model optimization strategies11:14 Open source vs. frontier models: the real gap32:35 How customers engage: PLG to hands-on customization17:37 When to move off frontier models33:42 The future of agentic memory and data sovereignty32:35 Fireworks' differentiation in a crowded market33:53 Spicy Future: AI doomers, bot management, and going fully out of loop

Tim (Essence VC) and Ian (Keycard) interviewed Louis Vichy, co-founder of OpenRouter, about why he built OpenRouter to de-risk AI app development (end-user pays LLM costs), how it scaled to processing ~5–6T tokens/week, and what OpenRouter is today: a reliable inference routing/control layer across ~60 providers with consolidated billing and reduced vendor lock-in. Louis explains why teams adopt OpenRouter (constant new model integrations, pricing/billing, differing API shapes), how routing focuses on practical heuristics (fallbacks, cost, throughput, latency), and how reliability is achieved via provider failover (e.g., alternate endpoints like Vertex/Bedrock). They discuss agent trends (longer-running agents, small models for routing/classification with specialized downstream models), possible memory support, developer conveniences (e.g., PDF parsing), and enterprise features (security/compliance guardrails, presets). The episode ends with links to OpenRouter chat/rankings pages and hiring for high-agency TypeScript-focused engineers.00:00 Welcome & Meet Louis (OpenRouter Co‑Founder)00:27 Origin Story: De‑Risking AI App Costs (Hackathon Lessons)01:35 First Big Feature: End‑User Pays for Tokens (Sign in with OpenRouter)02:34 From Routing to Rankings: Scaling to Trillions of Tokens03:42 What OpenRouter Is Today: Reliable Inference Across 60+ Providers05:55 Why Teams Adopt It: Avoiding Model API Churn, Billing, and Vendor Lock‑In08:37 Winning Strategy: Don’t Build a “Magic Router”—Optimize Cost/Latency/Throughput18:58 From Chat to RAG + Memory: Building Persistent Agent Context20:37 Developer Bells & Whistles: Auto PDF Parsing and More21:11 Enterprise Readiness: Compliance, Security Guardrails & Model Presets22:22 Customer Growth at Warp Speed in the AI Era23:03 Spicy Future!

In this episode of The Infra Pod, hosts Tim Chen (Essence VC) and Ian Livingstone (Keycard) sat down with Catherine Jue, co-founder and CEO of Kernel, to explore the cutting-edge world of browser infrastructure for AI agents.

Catherine shares her journey from Cash App to founding Kernel, explaining how she discovered the critical need for scalable browser automation when AI agents need to interact with the web. The conversation dives deep into the technical innovations behind Kernel's use of unikernels and micro VMs, which enable blazingly fast browser startup times (20ms vs 30+ seconds) and unique snapshot/restore capabilities.

Catherine discusses the evolution from deterministic browser automation to truly agentic behavior, the challenges of optimizing for variable web workloads, and her optimistic vision for an AI-powered future where the pie expands rather than consolidates. This episode is packed with technical insights about infrastructure, agent tooling, and the future of how software interfaces will evolve in an agent-native world.

0:24 Catherine's startup journey and founding Kernel
1:30 Cash App's OpenAI experiment sparks the idea
3:56 Why browser infrastructure for AI agents?
6:36 Unikernels: 20ms startup vs 30+ seconds
15:02 Optimizing for variable web workloads
23:25 Future of agent-native software
32:05 Hot takes!

Tim (Essence VC) and Ian (Keycard) sat down with Tejas Bhakta (CEO of Morph) to chat about building infrastructure for the fastest file edit APIs for coding agents. He shares how Morph delivers 10,000 tokens/second through speculative decoding, why cursor removed fast apply, and his vision for autonomous software that updates without prompts. The conversation covers subagent architecture, code search optimization, and the path to reliable AI coding at scale.

Timestamps:

0:00 - Introduction
0:29 - Why start Morph and pivoting through YC
1:23 - The fast apply insight from Cursor
3:42 - How fast apply works and speculative decoding
6:09 - Use cases: when and where fast apply matters
8:19 - Why Cursor removed fast apply
9:22 - Morph's value prop beyond speed
11:58 - Subagent architecture and SDK approach
14:45 - Semantic search and code-specific tooling
19:52 - Building custom coding agents vs platforms
22:42 - Adoption inhibitors and the future of codegen
23:26 - Spicy take: Autonomous software and reliability

In this episode of The Infra Pod, hosts Tim and Ian sit down with Dexter Horthy, CEO of Human Layer, to explore the evolution of AI coding agents and the future of software development. Dexter shares his journey from building data tools to discovering the real problem: making AI coding agents actually productive for senior engineers, not just juniors.

The conversation dives deep into the research-plan-implement workflow that enables engineers to ship 99% of their code with AI assistance, the challenges of getting staff engineers to adopt AI tools, and why most AI coding ecosystems don't actually help you sell to enterprises. Dexter also shares his spicy take on how Ralph-style agents can be even further enhanced.

Whether you're a skeptical senior engineer or an AI-curious developer, this episode offers practical insights into what actually works in production AI coding today.

[0:00] Introduction & Dexter's Journey
Why Dexter finally started a company, the failed data catalog pivot, and building an AI janitor for data warehouses

[8:00] The Hard Lessons of AI Ecosystem Hype
Why there's no "SAML for AI agents" and what enterprises actually need versus what the hype machine promises

[13:00] The Research-Plan-Implement Breakthrough
How to make senior engineers productive with AI, staying objective during research, and making decisions at the top of the context window

[26:00] The Vibe Shift & Where We Are Today
When respected engineers started believing, the role of Ralph and spec-driven development, and what's working in production

[37:00] Spicy Take: Ralph Goes to the Supreme

In this episode of the Infra Pod, hosts Ian Livingston (Keycard) and Tim Chen (Essence VC) interviewed the Field CTO Akshay Shah of Antithesis, diving deep into the world of distributed systems, reliability, and the future of software testing. The conversation covers the challenges of building bug-free distributed systems, the story behind Antithesis, lessons from major outages, and the evolving landscape of infrastructure and AI-driven operations.

Timeline with Timestamps:

• 00:00 – Introduction & guest background
• 02:00 – What Antithesis does and why it matters
• 06:00 – Real-world impact: Testing distributed systems (etcd, Kubernetes)
• 09:00 – Major outages & lessons learned (AWS, Knight Capital)
• 12:00 – The origins and philosophy behind Antithesis
• 16:00 – The future of reliability, testing, and AI in infrastructure
• 28:00 – Closing thoughts & where to learn more

Links:

• Learn more about Antithesis: https://antithesis.com
• Antithesis on YouTube: @AntithesisHQ

Join Tim from Essence VC and Ian Livingston from Keycard for the year-end 2025 recap of Infra Pod! In this special episode, Tim and Ian reflect on their favorite moments, hottest takes, and biggest lessons from a year of rapid change in infrastructure, AI, and agent technology.

They revisit standout episodes—like deep dives into browser automation, the evolving role of memory in LLMs, and the disruptive potential of agent sandboxes. The hosts discuss how companies are pivoting in the AI era, the importance of adapting quickly, and the surprising ways hardware choices are shaping the future of compute.

Looking ahead, Tim and Ian share bold predictions for 2026, debate the next big abstractions in infrastructure, and invite listeners to share their own hot takes and favorite episodes. Whether you’re an engineer, founder, or just passionate about the future of tech, this episode is packed with insights, energy, and a look at what’s next for the Infra Pod community.