Infinite Curiosity Pod with Prateek Joshi

Prateek Joshi
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The best place to find out how AI builders build. The host Prateek Joshi interviews world-class AI founders and VCs on this podcast. You can visit prateekj.com to learn more about the host.

  1. Building a $4 Billion AI Infra Company | Benny Chen, cofounder of Fireworks AI

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    Building a $4 Billion AI Infra Company | Benny Chen, cofounder of Fireworks AI

    Benny Chen is the cofounder of Fireworks AI, an AI infrastructure platform. They have raised $327M in funding from Benchmark, Sequoia, Lightspeed, Index, and others.  Benny's favorite book: Principles (Author: Ray Dalio) (00:01) Intro and why AI infrastructure is having a moment (00:06) Training vs inference: what’s working and where the real bottlenecks are (01:25) Why inference is the hard problem in production (03:30) What breaks at scale when AI systems hit real users (05:29) GPUs, hardware constraints, and why power is now a first-class concern (06:02) What you’re actually paying for in inference (07:21) Reliability, compliance, and enterprise expectations (09:49) Training and inference capacity: when they blur together (11:06) How to make inference fast in practice (13:06) System design choices behind modern inference platforms (15:28) Inference economics and cost tradeoffs (18:02) When fine-tuning actually makes sense (21:58) What “best model” really means for real companies (24:25) Production LLM architectures that actually work (27:46) Building an AI infra company customers can trust (29:27) Shipping fast without breaking reliability (31:14) Go-to-market lessons for infra startups (34:17) Where inference platforms are heading next (36:32) Rapid fire round -------- Where to find Benny Chen:  LinkedIn: https://www.linkedin.com/in/benny-yufei-chen-2238575a/ -------- Where to find Prateek Joshi:  Website: https://prateekj.com  Research Column: https://www.infrastartups.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-infinite X: https://x.com/prateekj

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  2. Building AI Employees | Surojit Chatterjee, CEO of Ema

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    Building AI Employees | Surojit Chatterjee, CEO of Ema

    Surojit Chatterjee is CEO of Ema, an agent platform build AI employees. They have raised $61M in funding from Accel, Section 32, and others. Before Ema, he was the chief product officer at Coinbase. And before that, a VP at Google.  Surojit's favorite book: Man's Search for Meaning (Author: Viktor Frankl) (00:01) Welcome (00:07) Defining the “AI Employee” (02:23) Lessons from Google: Building for Scale (06:59) Coinbase CPO: Hypergrowth & Product Leadership (09:24) Market Framing: Why “AI Employee” vs Copilot (14:29) Platform Building Blocks (Agents, Orchestrator, Fusion, Governance) (19:26) Trust, Security, and On-Prem Deployment (23:11) Model of Models: How Fusion Picks & Combines LLMs (29:10) What Infra Is Still Missing (Eval at Scale, Speed) (32:10) Rapid Fire Round -------- Where to find Surojit Chatterjee:  LinkedIn: https://www.linkedin.com/in/surojitchatterjee/ -------- Where to find Prateek Joshi:  Website: https://prateekj.com  Research Column: https://www.infrastartups.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-infinite X: https://x.com/prateekj

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  3. Passwords Are Broken: AI Agents Need Identity | Rishi Bhargava, cofounder of Descope

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    Passwords Are Broken: AI Agents Need Identity | Rishi Bhargava, cofounder of Descope

    Rishi Bhargava is CEO of Descope, an identity management platform for customers and AI agents. They've raised $88M in funding from investors such as Notable Capital, Lightspeed, Unusual Ventures. The two previous he founded were acquired by Palo Alto Networks and McAfee.  (00:01) Introduction (00:08) Origin story: why identity and passwords needed a rethink (02:59) Passwords vs passkeys explained in plain English (05:06) Why logging in is still painful (and why passwords persist) (09:06) Account takeovers explained: how hacks actually happen (11:59) Building security products: philosophy vs regular software (14:24) The ideal login experience: from frustration to seamless access (16:40) What is an AI agent? Defining agent identity simply (21:54) Good bots vs bad bots: trust, access, and control in an agent world (25:03) Breaches and blast radius: security before vs after Descope (27:55) Company building lessons from Demisto to Descope (30:15) AI trends that matter most for enterprise products (32:40) Rapid Fire Round -------- Where to find Rishi Bhargava:  LinkedIn: https://www.linkedin.com/in/bhargavarishi/ -------- Where to find Prateek Joshi:  Website: https://prateekj.com  Research Column: https://www.infrastartups.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-infinite X: https://x.com/prateekj

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  4. AI Agents Are Taking Over Infra | Gou Rao, CEO of NeuBird

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    AI Agents Are Taking Over Infra | Gou Rao, CEO of NeuBird

    Gou Rao is CEO of NeuBird, an agentic AI Site Reliability Engineer for IT teams. They've raised $44.5 Million from Mayfield and M12. He was previously the CTO of Citrix and Portworx. (00:01) Introduction (01:07) What Does an SRE Do? (02:19) Inside a Typical Incident Flow (04:16) What Can Be Automated? (05:52) Deploying Hawkeye: Day 1 to Day 100 (11:59) Earning Trust for Autonomous Agents (14:57) Versioning Agent Behavior & Chain of Thought (17:02) Building Agentic Infra Products (18:38) Access Control for Agents (20:29) Company Building in the AI Era (23:53) Competitive Edge in AI + Infra (26:35) Model Choice & Agent Reasoning Quality (29:33) Biggest Product Bet (31:22) Exciting AI Advancements (33:04) Rapid Fire Round -------- Where to find Gou Rao:  LinkedIn: https://www.linkedin.com/in/gouthamrao/ -------- Where to find Prateek Joshi:  Research Column: https://www.infrastartups.com Newsletter: https://prateekjoshi.substack.com  Website: https://prateekj.com  LinkedIn: https://www.linkedin.com/in/prateek-joshi-infinite X: https://x.com/prateekj

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  5. Building a Visual AI Platform | Brian Moore, CEO of Voxel51

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    Building a Visual AI Platform | Brian Moore, CEO of Voxel51

    Brian Moore is CEO of Voxel51, a data infra platform for visual AI. They most recently raised a $30M Series B led by Bessemer.   Brian's favorite books: Trillion Dollar Coach (Author: Eric Schmidt, Jonathan Rosenberg, and Alan Eagle) (00:01) Introduction and setup (00:22) Defining visual AI — beyond traditional computer vision (02:14) Why visual data is so hard to manage (04:17) Common “gotchas” in image and video datasets (06:43) Is it a data problem or a model problem? (09:41) The importance of edge cases and scenario analysis (10:46) Coverage and handling rare events in datasets (13:35) Using synthetic data and foundation models to fill data gaps (14:25) The origin story of Voxel51 and the birth of FiftyOne (17:56) Open source strategy and community growth (19:31) Handling massive visual datasets — storage best practices (22:03) Cost vs. quality tradeoffs in video storage (23:54) Cleaning and indexing messy datasets (25:49) Measuring real progress — beyond simple metrics (27:40) Compute bottlenecks and faster iteration loops (30:05) The economics of data infrastructure (31:53) Labeling inefficiencies and smarter annotation workflows (33:56) Hidden costs of data wrangling and wasted engineering time (35:10) Positioning Voxel51 and lessons for founders (37:53) The future of visual AI and missing industry standards (40:36) Rapid Fire Round -------- Where to find Brian Moore:  LinkedIn: https://www.linkedin.com/in/brimoor/ -------- Where to find Prateek Joshi:  Research Column: https://www.infrastartups.com Newsletter: https://prateekjoshi.substack.com  Website: https://prateekj.com  LinkedIn: https://www.linkedin.com/in/prateek-joshi-infinite X: https://x.com/prateekvjoshi

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  6. Building an AI Mathematician | Carina Hong, CEO of Axiom Math

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    Building an AI Mathematician | Carina Hong, CEO of Axiom Math

    Carina Hong is CEO of Axiom Math, where they're building a self-improving superintelligent reasoner, starting with an AI mathematician. She's a Rhodes Scholar, first-gen college grad and mathematics prodigy who earned dual degrees in mathematics and physics from MIT in 3 years. And a joint JD/PhD at Stanford. They just raised a $64M seed round from B Capital, Greycroft, Madrona, and Menlo Ventures.  Carina's favorite books: Proofs from THE BOOK (Author: Martin Aigner, Günter M. Ziegler) (00:02) Intro (00:38) What self-improving mathematical superintelligence means (04:04) Proofs as programs: Lean and the data gap (06:36) How AI proves: human-style vs. Lean-style reasoning (10:43) Carina’s journey: from Olympiad problem-solver to theory-builder (14:47) The engine room: data, infra, and building a math knowledge graph (17:42) Verifying results: compile checks vs. LLM judges (18:56) Self-improvement loops: skills libraries, memory, and conjecture↔prover curricula (21:30) Synthetic data & auto-formalization strategy (24:00) Benchmarks that matter: miniF2F, CombiBench, miniCTX v2 (26:24) Why combinatorics is uniquely hard for AI (31:13) Compute footprint & scaling philosophy (32:20) In-house Lean tooling and productization path (33:57) Early use cases: formal verification in hardware/software (36:19) Team blueprint: AI, programming languages, and math (37:35) Scaling laws, efficiency, and bottlenecks (38:26) If Axiom works: what becomes cheaper/faster for the world (40:22) Rapid Fire Round -------- Where to find Carina Hong:  LinkedIn: https://www.linkedin.com/in/carina-hong/ -------- Where to find Prateek Joshi:  Research column: https://www.infrastartups.com Newsletter: https://prateekjoshi.substack.com  Website: https://prateekj.com  LinkedIn: https://www.linkedin.com/in/prateek-joshi-infinite X: https://x.com/prateekvjoshi

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    From 0 to $15M ARR in 3 months | Mukund Jha, CEO of Emergent

    Mukund Jha is CEO of Emergent, an agentic vibe-coding platform. They've raised $23M from Lightspeed, Y Combinator, Together Fund, and Prosus. He was previously the cofounder and CTO of Dunzo, a hugely popular ecommerce company in India. Mukund's favorite books: The Hard Thing About Hard Things (Author: Ben Horowitz) (00:01) Intro (00:07) State of vibe-coding and where we are today (01:42) Emergent in plain English: what the product delivers (03:07) From prototype to traction: the first 90 days (06:03) What changed in the last 24 months (models + infra) (08:13) Early infra bets that enabled speed (12:07) Precision vs. control: editing and debugging without code (14:21) One-click to production: the unglamorous infra behind it (15:55) Points of failure across prompt → plan → code → test → deploy (17:53) Models division of labor: planning, codegen, tests, commits (20:05) What “reasoning” means and how they evaluate it (22:13) Context & memory strategy (beyond naive RAG) (24:22) Representing large codebases so agents don’t hallucinate structure (27:03) Orchestration walkthrough: adding SSO end-to-end (29:40) Agent coordination protocols (how agents talk) (31:05) Debugging long-running agents and trace observability (32:37) Company-building lessons from Dunzo to Emergent (36:10) Philosophy: offloading decisions to models (36:57) Rapid Fire Round -------- Where to find Mukund Jha:  LinkedIn: https://www.linkedin.com/in/mukund-jha-a1596413/ -------- Where to find Prateek Joshi:  Newsletter: https://prateekjoshi.substack.com  Website: https://prateekj.com  LinkedIn: https://www.linkedin.com/in/prateek-joshi-infinite X: https://x.com/prateekvjoshi

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    Diffusion LLMs - The Fastest LLMs Ever Built | Stefano Ermon, cofounder of Inception Labs

    Stefano Ermon is the cofounder of Inception Labs and an associate professor at Stanford. Inception is developing a new type of AI models called Diffusion LLMs. Stefano's favorite book: If on a Winter's Night a Traveler (Author: Italo Calvino) (00:01) Introduction (00:38) What are autoregressive LLMs and how do they work (02:28) How diffusion LLMs rethink generation (04:02) The ceiling of autoregressive LLMs: cost, latency, reliability (06:19) Why diffusion LLMs are commercially viable now (09:12) Parallel refinement: how diffusion models generate text (12:05) Understanding diffusion steps and efficiency (13:49) Hardest engineering challenges at Inception (15:23) From research to production: the power of data (16:24) Where diffusion LLMs still lag behind (18:18) Evaluations and benchmarks for diffusion LLMs (20:20) Developer experience and OpenAI-compatible API (21:47) Economics and GPU efficiency (23:38) Hardware and runtime stack (24:58) Competition and the evolving diffusion LLM landscape (27:01) Where diffusion will win first — coding and agentic systems (30:13) How diffusion changes infra, serving, and hardware design (33:04) What’s next at Inception: reasoning and multimodality (35:20) Rapid Fire Round -------- Where to find Stefano Ermon:  LinkedIn: https://www.linkedin.com/in/ermon/ -------- Where to find Prateek Joshi:  Research column: https://www.infrastartups.com Newsletter: https://prateekjoshi.substack.com  Website: https://prateekj.com  LinkedIn: https://www.linkedin.com/in/prateek-joshi-infinite X: https://x.com/prateekvjoshi

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The best place to find out how AI builders build. The host Prateek Joshi interviews world-class AI founders and VCs on this podcast. You can visit prateekj.com to learn more about the host.

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