Dry Run

Eshita Nandini + Khushi Wadhwa
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Explaining ideas in frontier technology with experts.

  1. Oct 14

    What is an open weight model? | Shiv Shankar, Boundless

    Shiv Shanker, founder and CEO of Boundless, joins the Dry Run to explain what an open weight model actually is, and why he thinks most companies will try closed models first but eventually shift to open ones. We go from the difference between a model's code and its weights to why OpenAI and Anthropic going public could open the door for American open source models. 00:40 Understanding open weight models 01:57 What labs actually share when they publish weights 06:33 Cost considerations in AI adoption 08:47 Security and data privacy 13:08 You can't vibe code DocuSign 15:48 The MySQL playbook for open source profits 20:33 Why the top open weight models come from China 24:09 What changes after OpenAI and Anthropic go public 25:32 The case against open weight models 30:04 Don't give an agent your passwords 31:50 Regulatory frameworks for open weight models 36:23 Education and workforce development for AI 38:41 The sign that open weight models are winning

  2. Sep 30

    How is the AI buildout financed? | Austin Campbell, Zero Knowledge

    Austin Campbell, founder of Zero Knowledge and adjunct professor at NYU Stern, has spent his career in risk and structured finance. In this episode, he dives into the ins and outs of the AI buildout, especially how it is financed, who's doing the financing, the role of debt, and how the AI boom compares to past bubbles, from the dot-com era to 2008. 03:39 Components of the physical buildout 07:41 Capex spent so far vs. future commitments 10:13 Why Big Tech buybacks are dropping 13:18 How SPVs are structured and good vs. bad reasons to use them 20:13 AI makes money, but can it pay back the capex?20:59 Who takes the hit if demand falls short 24:33 How today's SPVs compare to 2008 32:25 Minsky's three stages of a bubble, applied to AI Catch Dry Run wherever you listen to podcasts: Spotify: https://open.spotify.com/show/57Re2SH8LhE99F5RnFHomZ Apple Podcasts: https://podcasts.apple.com/de/podcast/dry-run/id6814644273 YouTube: https://www.youtube.com/@TheDryRunPodX: https://x.com/TheDryRunPod Catch Austin & the hosts on X: Austin Campbell https://x.com/austincampbell Eshita https://x.com/eshita Khushi https://x.com/khushii_w

  3. Sep 9

    Why is it so hard to get data for robots? | Naman Kapasi, Nirvana

    Naman Kapasi, CEO and co-founder of Nirvana, joins us on the Dry Run to talk about why data quality, not intelligence, is what's holding general purpose robotics back. 02:05 LLMs to VLAs to world models 07:39 Playing air hockey with a robot 08:34 The bottleneck isn't perception, planning, or control 10:37 The tiers of robotics data quality 13:48 Scale AI vs Mercor 18:01 Nobody knows how long a humanoid lasts 23:52 Five fingered hands vs giving robots tools 27:24 At 80% nobody cares, at 99.9% it's useful 29:28 A robot can't tell if an avocado is ripe 30:09 The US just banned imported humanoids

  4. Aug 26

    How GTM Engineers Are Rebuilding Sales | Everett Berry, Clay

    Everett, Head of GTM Engineering at Clay, joins Dry Run for a conversation on the rise of GTM engineering and what it changes about sales. Timestamps: 06:00 - Why the old outbound motion broke 09:51 - GTM loops: data > agents > action > feedback 12:46 - The “golden list” every founder should build 14:04 - Good outbound means sending fewer emails 16:24 - The campaign that booked ~60% of targets 19:48 - How other industries leapfrog into modern GTM 22:17 - The GTM mistake founders make early 25:19 - What sales agents will automate first 29:06 - Why GTM engineering is an index on AI capabilities

  5. Aug 12

    How do agentic payments work? | Lincoln Murr, Coinbase

    The internet has had a status code reserved for payments since the 1990s. Nobody could implement it until AI agents needed wallets.Lincoln Murr, AI products at Coinbase, joins Eshita and Khushi to answer an important question: how do agentic payments work?We go from buying a VPN with Bitcoin at 12 years old to whether agents will run their own businesses.00:31 Trading a Target gift card for Bitcoin at age 1201:34 What x402 actually is05:23 How AWS, Vercel, and Cloudflare legitimized agentic payments06:45 Why every previous attempt at 402 failed08:36 Agents can't get bank accounts09:12 Chargebacks, fraud, and reputation systems11:40 Agentic payments as a revenue driver for AWS14:46 The 2020s shift from mobile to agents16:16 Solopreneurs running companies with fleets of agents17:45 Why every company will want its own settlement layer20:15 AI Advisors: regulated financial advice at scale24:41 Asking an AI for a delta neutral basis trade29:28 Agents buying premium data to make better trades

  6. Jul 29

    Why the best designers make the worst employees | Simon Corry (Basis, Ramp)

    Simon Corry has spent over two decades in product design, from the Royal Mail to WeTransfer to Ramp, and he's now at Basis. In this episode he joins us to talk about his design journey, how he hires for spiky people over polished portfolios, and what he learned building a multiplayer game solo with AI agents. We get into the collapse of design, engineering, and product into a single role, why one of his agents started making its own decisions (and named its own game engine), AI brain rot and untested code slipping into production, and how Ramp Labs became a blueprint for how companies should experiment. Follow Simon's game dev and agent-building journey here: https://x.com/simoncorry/status/2062229653746577770 00:53 From call center manager to accidental designer 03:39 Why he's the worst employee you've ever hired 10:21 How he hires for spiky, interesting people 13:51 Shipping a multiplayer game by passing infra to Claude 16:54 How one of his agents decided to build a game engine 20:06 AI brain rot and untested agent code in production 23:52 Design, engineering, and product collapsing into one 26:29 Ramp Labs as the blueprint for experimentation 31:08 Building out a neolab at Basis 32:19 New media and testing the bleeding edge

  7. Jul 16

    From Louis Vuitton to Stablecoin Orchestration: How Digital Assets Solve Card Payments | Gap, Suby

    Gap, founder and CEO of Suby, joins joins Khushi and Eshita on the Dry Run to talk about who really moves your money when you swipe a card, and where stablecoins fit into the stack.We go from a $40K NFT checkout problem at Louis Vuitton to why stablecoins transform global money movement.00:25 Reselling sneakers to running crypto at Louis Vuitton05:27 The $40K NFT chargeback problem08:47 The four companies behind every card swipe12:23 Why Stripe became a bank15:59 Uncapped US interchange vs Europe's 0.2% cap18:14 Where stablecoins actually matter21:59 Why Stripe struggles in Latam and India23:49 Tempo, agentic payments, and nano payments28:04 Neobanks don't hold your money36:07 Buy vs build: Bridge, Privy, BVNK39:28 Circle as fintech's plumbing40:43 What Gap is building at Suby

  8. Jul 1

    What AI Trading Agents Are Doing With Your Money

    Amy Zhao from Initiative for Cryptocurrencies and Contracts (IC3) to break down her team's research on AI trading platforms that hit $3B+ in combined market cap. We go from how users lost $191M to why most agents aren't trading autonomously yet, and what a maturity framework for investment-grade agents looks like. 0:40 The motivation behind studying AI trading agents5:36 Why Eliza OS and Virtuals captured billions10:22 Early market or something more predatory?14:49 Tradeoff between transparency and profitability18:10 The maturity framework for trading agents23:47 How does this apply beyond DeFi trading?26:08 The most unexpected finding from the research

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About

Explaining ideas in frontier technology with experts.

Information

  • Creator
    Eshita Nandini + Khushi Wadhwa
  • Years Active
    2026
  • Episodes
    20
  • Rating
    Explicit
  • Copyright
    © Eshita Nandini + Khushi Wadhwa
  • Show Website
    Dry Run