Infinite Curiosity Pod with Prateek Joshi

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

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Where to find Benny Chen: 

LinkedIn: https://www.linkedin.com/in/benny-yufei-chen-2238575a/

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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

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

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Where to find Surojit Chatterjee: 

LinkedIn: https://www.linkedin.com/in/surojitchatterjee/

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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

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

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Where to find Rishi Bhargava: 

LinkedIn: https://www.linkedin.com/in/bhargavarishi/

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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

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

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Where to find Gou Rao: 

LinkedIn: https://www.linkedin.com/in/gouthamrao/

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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

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

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Where to find Brian Moore: 

LinkedIn: https://www.linkedin.com/in/brimoor/

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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