Tech & Startups
Technology and startups: funding rounds and exits, product launches, AI companies, growth and SaaS, European startups, founder stories and technology trends.
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What Happens When AI Agents Can Spend Your Money?
What happens when AI agents start spending your money? VGS' Terry Angelos argues that agentic commerce will need new payment infrastructure that gives AI the ability to transact without sacrificing the protections consumers expect. Terry joins a16z General Partner Alex Rampell to discuss the emerging wallet wars, how agentic tokens could enable secure purchases, and what happens when an AI buys the wrong thing. They also explore why merchants may resist agentic commerce, how AI could change credit card rewards and subscriptions, and who controls the customer relationship when agents make purchasing decisions on our behalf. Timestamps: 00:00 Intro 00:56 What a payment credential actually is 09:48 When agentic commerce changed the game 13:43 The wallet wars 19:42 Stripe as the AOL of agentic commerce 23:51 The new agentic token standard 29:45 The three checks behind every transaction 35:43 Why Amazon blocked Muse 39:47 Over-hyped vs under-hyped 47:09 Where VGS goes next Resources: Follow Terry Angelos on X: https://x.com/terryangelos Follow Alex Rampell on X: https://x.com/arampell Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711 Follow our host: https://x.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see http://a16z.com/disclosures.
Bro built an app that won't let you fake your workouts
Bro built an app that won’t let you fake your workouts 🤯 It’s an alarm clock. To turn it off, you have to do push-ups and the app uses AI to actually verify you did them. No cheating. Jake (24) launched Early at the end of 2025. Four months later it’s doing $50K/month with 200K+ downloads.
I built this app for my dad. Now it makes $18K/month
Kourosh built an app in a very crowded market... and yet still managed to grow it to over $18K/month. His secret is insanely simple: everyone else is chasing tech-savvy power users, but Kourosh built this app for a type of custer everyone is ignoring (his dad). This video is the story of how to build a successful product in a "saturated" market. *50+ AI app ideas making millions:* https://clickhubspot.com/q8ij Say hi to Kourash: https://x.com/BuildingWaves Check out Voicy: https://usevoicy.com/ 🔔 Follow the Second Channel: @StarterStoryBuild 🏁 We're hiring: starterstory.com/jobs Chapters: 0:00 - He built this for his dad! 1:09 - What's your story? 1:59 - What's the app? 3:24 - Founder background 4:14 - More AI app ideas 4:56 - Finding the idea for Voicy 5:50 - Build process 7:28 - Tech stack 8:21 - Growth story 9:51 - Unique customers 11:22 - Building in a crowded market 13:06 - Final advice 14:28 - Reflections 15:17 - FREE app ideas This video is an educational case study of this founder’s experience. It is not financial advice and does not guarantee any income or results. Every business is different and your results may vary.
AlphaGo Took 2 Years. AlphaZero Beat It in Hours | David Silver, Ineffable Intelligence
David Silver on what happened when his team gave up human data, and why he says LLM labs are "human-in-the-loop learning algorithms with thousands of people in the loop." #AlphaZero #AlphaGo #AI #ReinforcementLearning
Stop Looking for a Unicorn, Hire 3 People Instead
Reminder to founders: To remove yourself from the business, sometimes you need more then one person. Sometimes, more like five. (And that’s okay)
$50k/month founder nearly killed his app by spending thousands on UGC 😊
Bro spent thousands on UGC with zero winning format and nearly killed the business. Lino went straight to big fitness influencers before he had any proof of what actually converts. Thousands per video, margins at 5-10%, barely scraping $3-4K in revenue. The fix: he partnered with one big fitness creator for equity instead of chasing deal after deal. Suddenly the content was free, the creator had skin in the game, and AbMaxx took off. From there he cut every other UGC deal, doubled down on Meta ads at 2-2.5X ROAS, then moved the same creatives to TikTok and hit 4-5X ROAS targeting a younger audience.
Recursive Self-Improvement and the Future of AI
a16z General Partner Martin Casado sits down with MakerMaker.AI co-founders Dhaval Adjodah and Owen He to unpack recursive self-improvement, or RSI, and separate the increasingly popular term from what it would actually mean for an AI system to improve itself. Dhaval and Owen distinguish true RSI from simply using AI to automate pieces of AI development. Their goal is a closed loop where the system being improved is also doing the improving, across components including models, harnesses, and eventually more of the AI stack. They explain why closing that loop still doesn’t guarantee runaway “takeoff,” and why measuring real progress requires accounting for both capability gains and the resources required to achieve them. They also share what they’re seeing in their own research, including early signs that self-improving systems can increase performance while improving efficiency. And they dig into the practical challenges of proving those gains are real, from variance and reward hacking to the enormous difficulty of distinguishing genuine improvement from noise. Timestamps: 00:00 - Intro 00:58 - What actually counts as RSI 05:10 - Capability as a numerator and denominator 06:58 - Closed loop vs takeoff 08:32 - Humans as agents in the system 13:39 - Why most RSI papers don't scale 17:41 - Why they chose inference 24:00 - "It's not a belief, it's happening" 25:42 - The Viceroy of Variance 29:39 - The garden hose company in Wisconsin Resources: Follow Dhaval Adjodah on LinkedIn: https://www.linkedin.com/in/rand0m Follow Owen He on LinkedIn:https://www.linkedin.com/in/owen-he-b7b064279/ Follow Martin Casado on X: https://x.com/martin_casado Stay Updated: If you enjoyed this episode, be sure to like, subscribe, and share with your friends! Find a16z on X: https://twitter.com/a16z Find a16z on LinkedIn: https://www.linkedin.com/company/a16z Listen to the a16z Show on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX Listen to the a16z Show on Apple Pod
OpenAI has more than 120 ex-YC founders
Typing fast is trending down. Great taste is trending up. Tibo Sottiaux, Head of ChatGPT & Codex, on the skills that matter in hiring now and why OpenAI has more than 120 ex-YC founders.
The AI Work Transformation Isn't Over
OpenAI's Tibo Sottiaux explains why he thinks that AI will keep radically transforming how we work.
The Best AI Gets Out of the Way
Setting up and fiddling with agent loops isn't the future. Tibo Sottiaux, Head of ChatGPT & Codex, on a permanent, active intelligence that's available on any screen and gets out of the way when you don't need it.
OpenAI DevDay: Dots, Agents & $100B Opportunities
I watched almost 60 minutes of Sam Altman on stage at OpenAI Dev Day 2026. Out of 20-plus launches, I pick the three or four that I think can make people billions of dollars in aggregate. I break down Dots, OpenAI's personal agent platform, plus the Decisions API, the Agents API with computer use, and Sign in with ChatGPT. I also share my four-step framework for building in this world and two business ideas I hope someone takes. If you want to build a business and make money around AI, this episode is for you. Timestamps 00:00 – Intro 01:48 – Dots, the Personal Agent Platform 03:29 – OpenAI Doubles Down on Plugins 05:02 – Decisions API 06:43 – Agents API With Computer Use 08:01 – Sign In With ChatGPT 12:36 – Where should you build? 13:46 – Idea: Real-World Work APIs 15:04 – Idea: Analytics for Agent Discovery 17:05 –Closing Thoughts Key Points • Dots gives plugin builders a front door to 1.2 billion weekly active users. • OpenAI is doubling down on plugins to make ChatGPT the app ecosystem for AI. • Sign in with ChatGPT lets users bring their current plan, so a free core app with paid upsells now works. • Workflows too niche for OpenAI to build become viable businesses. • The strongest businesses in this world own the trigger, the action, and the feedback. • Two open opportunities: real-world work APIs and an analytics layer for agent discovery Numbered Section Summaries 1. The Launches Worth Your Attention OpenAI shipped 20-plus features at Dev Day 2026, and I focus on four: Dots, the Decisions API, the Agents API, and Sign in with ChatGPT. I cover GPT 6.1 Soul, Ultrafast, Private Intelligence, and Spaces briefly at the end. 2. Dots and the Plugin Comeback Dots is OpenAI's answer to Muse by Meta and Instinct. A user asks for help, like fixing a permit or finding a restaurant, ChatGPT understands the job, and it selects a plugin, which is essentially a mini app. By my paraphrase, Sam admitted plugins got mixed reviews before, and now he plans to make them a
Why Claude can’t be your PM (yet) | Anthropic CPO Panel
What does product management look like when AI changes what teams can build every few months? At the Lenny and Friends Summit, Anthropic’s Ami Vora and Mike Krieger join Dan Shipper to discuss why understanding users, making good decisions, and adapting quickly remain essential to the PM role. They also explore software designed for agents, running experiments in parallel, and bringing the ideas that work into a product people can use. Recorded live at Lenny and Friends Summit on September 10, 2026, in San Francisco.
Stop planning for 2027: how OpenAI builds product 90 days at a time | Tara Sesha and Nan Yu (OpenAI)
How do you build AI products when the technology and users’ expectations are changing so quickly? At the Lenny and Friends Summit, OpenAI’s Tara Seshan and Nan Yu join Claire Vo to discuss shipping imperfect features, designing agents people can understand, and deciding how those agents should work with other tools. They explain why rapid testing, direct conversations with users, and completing the last mile of a task matter more than ever. Recorded live at Lenny and Friends Summit on September 10, 2026, in San Francisco.
Meta Muse AI Connectors: The App Store for AI?
In this solo episode, I break down the huge business opportunity because Meta has opened Muse, its personal AI agent, to developers. You can now submit a connector, which lets Muse use your service when someone asks it for help, and I think this could be the app store moment for AI. I explain how a connector works, share four startup ideas you can build on one, and cover how to get customers beyond Meta's directory. I also show how I'd build a first version with a coding agent like Claude Code or Codex, and what I'd test before submitting it to Meta for review. Timestamps 00:00 – Intro 01:35 – The App Store Parallel 04:55 – How a Muse Connector Works 07:29 – Startup Idea 1: Lead Gen for Business Suppliers 09:28 – Startup Idea 2: Home Repair Dispatch 11:07 – Startup Idea 3: Paddle Match and Court Finder 12:47 – Startup Idea 4: Family Dinner Planning 14:10 – How to pick an idea? 14:56 – How People Find Your Connector 15:32 – Growth Idea 1: Partner With Creators 16:17 – Growth Idea 2: Product-Led Sharing 17:46 – Growth Idea 3: Connected Marketplaces and Directory Placement 19:34 – Building the First Version 21:38 – Custom Connectors and Meta Approval 23:56 – Where to Start This Week 25:31 – Closing thoughts Muse Connector Prompt: https://startup-ideas-pod.link/muse-connector-prompt Key Points • Meta has opened Muse to developers, and a connector lets Muse use your service when someone asks it for help. • I look closely at the step in a request where someone needs a business that can deliver and money changes hands. • With a small budget, I'd start with lead generation, because I can show a customer a sample before writing much software. • I plan distribution around channels I can reach, such as creators, product-led sharing, and connected marketplaces. • A coding agent can build the first version, and I still inspect the results and test the awkward requests myself. • To start this week, I'd talk to one type of customer about the last time they dealt with the task
Take Upwork jobs, let AI do them (Astra etc). It's absurd
Build your own AI native creative agency with Higgsfield : https://startup-ideas-pod.link/higgsfield In this solo episode, I explain why I expect a wave of one-person businesses built as AI-native service firms, and I show you how I built one. Three things matured at the same time: specialist creative models, Astra as an orchestrator that plans and repairs work, and the Higgsfield API, which puts 50+ creative models behind one key. I demo Studio Operator, the MVP I built to show you how I’d run an AI-native creative Agency, and I walk through the six prompts I used to build it. I finish with a model-by-model primer, three startup ideas, and four filters for choosing which firm to build. Timestamps 00:00 – Intro 01:46 – 3 Shifts, All at Once 03:35 – The AI-Native Service Firm 04:22 – Demand on Upwork and Fiverr 05:16 – Human Agency vs. AI-Native Firm 11:14 – Studio Operator Tour 20:08 – The Six Build Prompts 26:42 – Model Primer by Job Type 32:42 – Idea 1: Always-On Ad Studio 33:52 – Idea 2: Franchise Localization 41:00 – Idea 3: Industrial Catalog Sales Assets 43:15 – Four Filters for Picking an Idea 45:28 – The Self Improvement Loop 46:59 – How to Start 46:11 – Closing Thoughts Studio Operator 6 Prompts: https://startup-ideas-pod.link/Creative_prompt_pack Key Points - Creative models, Astra, and one production API matured together, and that makes one-person creative firms possible now. - Upwork, Fiverr, and Contra show what buyers purchase again and again, so I start from validated demand. - I estimate AI-native firms can reach 60%+ net margins, compared with 10–15% for a typical human agency. - I built Studio Operator with six prompts, one layer of the firm per prompt, in a deliberate order. - I match each job to a model: people, products, text, repairs, long scenes, speech, motion, or finishing. - The strongest ideas pair a creative surface with a repetitive, checkable operational core. Numbered Section Summaries 1. Three Shifts at Once Creative mode
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