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!