Podcast
Infinite Curiosity Pod with Prateek Joshi
Hosted by Prateek Joshi · EN
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.
11episodes
Episodes
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Building a $4 Billion AI Infra Company | Benny Chen, cofounder of Fireworks AI
Feb 600:41:45Tap to summarizeBenny 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.comLinkedIn: https://www.linkedin.com/in/prateek-joshi-infiniteX: https://x.com/prateekj
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Building AI Employees | Surojit Chatterjee, CEO of Ema
Dec 2200:40:51Tap to summarizeSurojit 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.comLinkedIn: https://www.linkedin.com/in/prateek-joshi-infiniteX: https://x.com/prateekj
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Passwords Are Broken: AI Agents Need Identity | Rishi Bhargava, cofounder of Descope
Dec 1200:37:40Tap to summarizeRishi 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.comLinkedIn: https://www.linkedin.com/in/prateek-joshi-infiniteX: https://x.com/prateekj
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AI Agents Are Taking Over Infra | Gou Rao, CEO of NeuBird
Nov 2600:34:55Tap to summarizeGou 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.comNewsletter: https://prateekjoshi.substack.com Website: https://prateekj.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-infiniteX: https://x.com/prateekj
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Building a Visual AI Platform | Brian Moore, CEO of Voxel51
Nov 600:47:38Tap to summarizeBrian 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.comNewsletter: https://prateekjoshi.substack.com Website: https://prateekj.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-infiniteX: https://x.com/prateekvjoshi
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Building an AI Mathematician | Carina Hong, CEO of Axiom Math
Oct 3000:45:01Tap to summarizeCarina 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.comNewsletter: https://prateekjoshi.substack.com Website: https://prateekj.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-infiniteX: https://x.com/prateekvjoshi
Transcribe →From 0 to $15M ARR in 3 months | Mukund Jha, CEO of Emergent
Oct 2400:42:05Tap to summarizeMukund 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-infiniteX: https://x.com/prateekvjoshi
Transcribe →Diffusion LLMs - The Fastest LLMs Ever Built | Stefano Ermon, cofounder of Inception Labs
Oct 900:39:09Tap to summarizeStefano 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.comNewsletter: https://prateekjoshi.substack.com Website: https://prateekj.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-infiniteX: https://x.com/prateekvjoshi
Transcribe →LLMs, Vibe Coding, and Security | Idan Plotnik, CEO of Apiiro
Sep 3000:37:36Tap to summarizeIdan Plotnik is the CEO of Apiiro, an application security platform built for the AI era. They've raised $135M in funding from investors like Greylock, Kleiner Perkins, and General Catalyst.Idan's favorite books: Zero to IPO (Author: Frederic Kerrest)(00:01) Introduction (00:07) How LLMs Generate Code(02:11) Rise of Vibe Coding: Opportunities and Risks(05:24) Debugging and Security in Vibe Coding(09:13) Vulnerabilities Introduced by AI Code Assistants(12:20) Security Basics for Builders Using AI and Cloud Platforms(15:44) Security by Design and Organizational Standards(18:08) Making Security Dead Simple: The Appiro Approach(22:28) Winning Developer Trust Through UX and Integration(26:59) Biggest Technical and GTM Challenges in Building Appiro(33:55) Rapid Fire Round--------Where to find Idan Plotnik: LinkedIn: https://www.linkedin.com/in/idanplotnik/--------Where to find Prateek Joshi: Newsletter: https://prateekjoshi.substack.com Website: https://prateekj.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-infiniteX: https://x.com/prateekvjoshi
Transcribe →Infra Investing | Astasia Myers, GP at Felicis
Sep 800:52:09Tap to summarizeAstasia Myers is a GP at Felicis, an iconic VC firm with investments in companies like Shopify, Canva, Adyen, Notion, Mercor, Plaid, Supabase, Flexport, and more. Astasia's favorite books: God's Bankers (Author: Gerald Posner)(00:01) Introduction(00:26) Astasia’s Infra Thesis(03:59) Golden Age of Infra & Innovators Network(06:22) RL Environments & AI Agents(08:57) Disruption Opportunities: Data & Observability(11:31) Where to Find Infra Founders(16:31) Early Signals & Thesis-Driven Investing(18:01) Picking & Decision-Making Process(20:11) Red Flags in Infra Investing(22:20) References & Diligence(24:35) Proof of Usage & Production Signals(26:24) Building Edge as an Investor(28:01) How Felicis Helps Founders Post-Investment(30:05) Consensus vs. Contrarian Views in Infra(32:09) Tourist Traps in Infra Investing(34:43) GTM & Sales Motion in Infra(37:25) Pricing Strategies for Infra Startups(40:09) Ecosystem vs. Core Product Focus(42:15) Lessons from Outlier vs. Good Companies(44:30) Infra Wedges to Fund Today(45:23) Commoditized but Promising Categories(47:06) Exciting AI Advancements(48:21) Rapid Fire Round--------Where to find Astasia Myers: LinkedIn: https://www.linkedin.com/in/astasiamyers/--------Where to find Prateek Joshi: Website: https://prateekj.com LinkedIn: https://www.linkedin.com/in/prateek-joshi-infiniteX: https://x.com/prateekvjoshiResearch column: https://infrastartups.com
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