Podcasts about Cursor

AI has successfully solved the blank page problem for developers, but it has created a massive new bottleneck downstream in the SDLC. LinearB CEO Ori Keren joins us to explain why 2026 will be a year of norming as organizations struggle to digest the flood of AI-generated code. In this annual prediction episode, he details why upstream velocity gains are being lost to chaos in reviews and testing. We also discuss why enterprises aren't ready to hand over the keys to autonomous agents and how to build dynamic pipelines based on risk.LinearB Access the AI code review metrics dashboardUnify your Copilot and Cursor impact metricsFollow the show:Subscribe to our Substack Follow us on LinkedInSubscribe to our YouTube ChannelLeave us a ReviewFollow the hosts:Follow AndrewFollow BenFollow DanFollow today's guest:Follow Ori on LinkedInOFFERS Start Free Trial: Get started with LinearB's AI productivity platform for free. Book a Demo: Learn how you can ship faster, improve DevEx, and lead with confidence in the AI era. LEARN ABOUT LINEARB AI Code Reviews: Automate reviews to catch bugs, security risks, and performance issues before they hit production. AI & Productivity Insights: Go beyond DORA with AI-powered recommendations and dashboards to measure and improve performance. AI-Powered Workflow Automations: Use AI-generated PR descriptions, smart routing, and other automations to reduce developer toil. MCP Server: Interact with your engineering data using natural language to build custom reports and get answers on the fly.

In this episode of Run the Numbers, CJ sits down with Dan Griggs, CFO of Intercom, to break down how finance leaders should think about pricing, forecasting, and resource allocation in the AI era. Dan explains why “it's not zero” is his guiding forecasting principle, how Intercom landed on 99 cents per AI resolution for Fin, and what it means to build an AI product that could eventually cannibalize a successful SaaS core. A candid look at managing uncertainty while still making bold bets.—SPONSORS:Brex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.ai—LINKS:Dan on LinkedIn: https://www.linkedin.com/in/dan-griggs-0970181/Intercom: https://www.intercom.com/CJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:Inside Rocket Companies: M&A, Metrics, and Mortgage Moats | Brian Brownhttps://youtu.be/ttedn4AULt8—TIMESTAMPS:00:00:00 Cold Open00:01:03 Intro to Dan Griggs and Intercom's AI Pivot00:02:45 From Ice Cream to SaaS: Early Finance Lessons00:04:19 Learning the Business by Living the Operations00:06:26 Why Operational Reality Shapes Better Forecasts00:08:00 “It's Not Zero”: Forecasting the Unknowable00:10:09 Scenario Planning, Ambiguity, and Psychological Safety00:11:23 Sponsors — Brex | Metronome | RightRev00:14:43 Keeping a Mental Model of Key Business Metrics00:16:15 Using Mental Math to Sanity-Check Forecasts00:17:28 Core Ratios Every CFO Uses to Vet Decisions00:19:13 The Burn-the-Boats Moment for Intercom's AI Pivot00:20:53 Why AI Was an Existential, Not Incremental, Bet00:22:21 Which SaaS Categories AI Can Fully Replace Work00:23:04 Why Finance Hasn't Had Its AI Moment Yet00:23:39 Sponsors — Rillet | Tabs | Abacum00:27:05 Why Fin Needed Outcome-Based Pricing00:28:59 The Tradeoff Behind $0.99 Per Resolution00:30:46 Why Support Conversations Vary in Complexity00:32:01 What Drives the Unit Economics of AI Resolutions00:33:08 How Intercom Chooses Models as Costs Fall00:35:19 Replacing Generic LLMs With Domain-Specific Models00:36:08 Selling an AI Product That Could Cannibalize the Core00:38:50 Founder CEOs Versus Professional CEOs00:41:47 Hiring Mistakes and Acting on Instincts00:44:28 Intercom's Finance Software Stack00:45:49 The Craziest Expense Request#RunTheNumbersPodcast #Intercom #AICustomerSupport #OutcomeBasedPricing #CFOInsights This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cjgustafson.substack.com

If you're building software in the AI era, speed is everywhere—and that's exactly why discipline matters more than ever. In Part 2 of our interview with Angelo Zanetti, one strategy keeps coming up as the smartest path for founders and product teams: go web first. You validate demand faster, avoid app-store friction, and you get a clearer signal before you spend real money on the mobile "tax."  About Angelo Zanetti Angelo Zanetti is the co-founder and CEO of Elemental, a South African-based software development agency helping startups and scaleups worldwide bring digital products to life. Since 2005, his team has specialized in building scalable, high-performance web apps and software platforms that solve complex business problems. With deep technical knowledge and strategic thinking, Angelo has helped founders launch bespoke software products that are lean, user-focused, and future-ready. He's served on boards including BISA and Entrepreneurs' Organisation Cape Town, and he's a proud member of the global founder community OPUS. Go web first in the AI era AI is changing how teams build, but it doesn't change what makes a product succeed. Angelo's take is balanced: AI can absolutely make developers faster—but it can also make mistakes bigger if you don't have the experience to catch what's wrong.  He shares a story that captures the risk perfectly: a developer using Cursor accidentally had the database dropped and recreated. The tool didn't intend harm—it simply took a destructive shortcut with confidence.  Go web first and use AI like an amplifier. In the hands of an experienced developer, AI accelerates delivery. In the hands of someone guessing, it accelerates failure.  Go web first when you're still validating demand If the goal is traction, the fastest route is often not a mobile app. Angelo points out that mobile adds overhead: submissions take time, changes can slow down release cycles, and testing requires compiles plus device/emulator workflows that can drag early iterations.  When you go web first, you can ship faster, adjust faster, and learn faster. That matters when you're still figuring out what users actually value. Avoid app-store friction App stores introduce delays and rules. Even when you do everything right, you're waiting on review cycles and dealing with policies that can change. By starting on the web, you keep your feedback loop tight and your roadmap in your control. Shorten the feedback loop This is the hidden advantage: going web first makes iteration feel like steering instead of guessing. You can test onboarding, pricing pages, feature positioning, and workflows in days—not weeks—then respond to what real users do, not what you hope they do. Go web first, but use AI safely AI doesn't remove the need for senior judgment. Angelo's point is that experienced developers still matter because the hard part is translation—turning vision into structure, edge cases, and maintainable architecture.  AI can accelerate progress—go web first with guardrails Go web first and set guardrails early: backups, version control, review practices, and clear boundaries for what AI can touch. Tools can generate code quickly, but your team still owns security, data safety, and reliability. Mistakes are cheaper to fix When you're validating, mistakes are inevitable. The goal is to make them inexpensive. A web-first approach keeps the cost of change lower, so you don't "lock in" bad assumptions behind a costly mobile release cycle. Go web first by planning like an architect Angelo uses a metaphor that founders immediately get: building software is like building a house—you don't start by putting up walls. You start with an architect.  Planning is a real deliverable: scope, user journeys, exceptions, and specifications. It's often undervalued because it's not as tangible as code, but Angelo calls it key to success—especially if you want to scale later without rebuilding from scratch.  Start with a clear scope and user journeys Go web first with a simple, documented path: who the user is, what outcome they want, and what steps they take. When the journey is clear, the MVP stays focused—and your team can defend scope when feature requests start creeping in. Define a foundation you can scale You don't need to over-engineer. But you do need a foundation that won't collapse if adoption spikes. A web-first product can still be built with smart architecture that supports growth—without pretending you already have millions of users. Go web first, then go mobile when users pull you there Angelo shares a practical signal for mobile timing: when people keep asking for it—repeatedly—through engagement, social channels, and real usage patterns, the decision becomes obvious. That's when "it makes sense," not when it's a personal preference.  When mobile adds real value If the web product is solving the problem and users are happy, mobile isn't automatically better. Go web first until mobile improves retention, engagement, or access in a way the web can't. When hardware features make going mobile necessary Mobile becomes the right answer when you truly need what mobile devices offer—hardware-level capabilities that a web app can't reliably provide.  Closing: Go web first, then expand with confidence Part 2 is a reminder that modern tools don't replace fundamentals—they raise the stakes. Use AI to accelerate, but respect planning and safety. And when you're still proving demand, go web first. You'll learn faster, waste less, and you'll earn your way into mobile when the market makes the call.   Stay Connected: Join the Developreneur Community We invite you to join our community and share your coding journey with us. Whether you're a seasoned developer or just starting, there's always room to learn and grow together. Contact us at info@develpreneur.com with your questions, feedback, or suggestions for future episodes. Together, let's continue exploring the exciting world of software development. Additional Resources Why Build A Mobile Application? Defining An MVP Properly for Your Goals How to Build a Minimal Viable Product Without Blowing Your Budget Building Better Foundations Podcast Videos – With Bonus Content

Software engineering is being transformed by AI faster than any other domain. Unpacking how this transformation is playing out may offer a glimpse into the future Greg Foster's journey into software engineering began in an unlikely place: a Nevada high school where he couldn't land a job at Starbucks. Instead of serving coffee, he taught himself software development. Foster developed what he calls "a lifelong obsession with the craft of software engineering". That obsession evolved from building apps to building for other builders. From Airbnb cofounding Graphite, where he now serves as CTO running what he calls "a dev tools team for the entire industry". Foster is now building the future of software development with Cursor. Foster doesn't just have a front seat to watch how AI is changing software engineering - he gets to shape the change. We caught up and talked about the past, present and future of software engineering. The takeaway? There is a world of difference between vibes and solid foundations for software engineering at scale. Article published on Orchestrate all the Things: https://linkeddataorchestration.com/2026/01/29/foundations-or-vibes-lessons-learned-from-using-ai-in-software-engineering/

Jason William Johnson, PhD, Founder of SoundStrategist, is driven by two lifelong passions: creating and teaching. Through SoundStrategist, Jason designs AI-powered learning experiences and intelligent coaching systems that blend music, gamification, and experiential learning to drive real skill development and engagement for enterprises and entrepreneur support organizations. We explore Jason's journey as a musician, educator, and business coach, and how he fused those disciplines into an AI-first company. Jason shares his AI for Deep Experts Framework, showing how subject-matter experts can identify an industry pain point, envision a solution, brainstorm with AI, leverage AI tools to build it, and go after high-value impact—turning deep expertise into scalable products and platforms without needing to be technical. He also explains how AI accelerates research and product design, how “vibe coding” enables rapid MVP development, and why focusing on high-value B2B impact creates faster traction with less complexity. — Turn Your Expertise Into Software with Jason W. Johnson Good day, dear listeners. Steve Preda here, the Founder of the Summit OS Group, developing the Summit OS Business Operating System. And my guest today is Jason William Johnson, PhD, the Founder of SoundStrategist. His team designs AI-powered learning experiences and deploys intelligent coaching systems for enterprises and entrepreneur support organizations blending music, gamification, and experiential learning to drive real skill development and engagement. Jason, welcome to the show.  Thanks for having me, Steve.  I’m excited to have you and to learn about how you blend music and learning and all that together. But to start with, I’d like to ask you my favorite question. What is your personal ‘Why’ and how are you manifesting it in your business?  I would say my personal ‘Why’ is creating and teaching. Those are my two passions. So when I was younger, I was always a creative. I did music, writing, and a variety of other things. So I was always been passionate about creating, but I’ve also been passionate about teaching. I've been informally a teacher for my entire adult life—coaching, training. I've also been an actual professor. So through  SoundStrategist, I’m kind of combining those two passions: the passion for teaching and imparting wisdom, along with the passion for creating through music, AI-powered experiences, gamification, and all of those different things. So I'm really in my happy place.Share on X  Yeah, sounds like it. It sounds like you're very excited talking about this. So this is quite an unusual type of business, and I wonder how do you stumbled upon this kind of combination, this portfolio of activities and put them all into a business. How did that come about? So Liam Neeson says, “I have a unique combination of skills,” like in Taken. I guess that's kind of how I came up with SoundStrategist. I've pretty much been in music forever. I've been a musician, songwriter, producer, and rapper since I was a child. My father was a musician, so it was kind of like a genetic skill that I kind of adopted and was cultivated at an early age. So I was always passionate about music. Then got older, grew up, got into business, and really became passionate about training and educating. So I pretty much started off running entrepreneurship centers. My whole career has been in small business and economic development. SoundStrategist was a happy marriage of the two when I realized, oh, I can actually use rap to teach entrepreneurship, to teach leadership skills, and now to teach AI and a variety of other things.Share on X So pretty much it was just that fusion of things. And then when we launched the company, it was around the time ChatGPT came out. So we really wanted to make sure we were building it to be AI-first. At first, we were just using AI in our business operations, but then we started experimenting  with it for client work—like integrating AI-powered coaches in some of the training programs we were running and things like that. And that really proved to be really valuable, because one of the things I learned when I was running programs throughout my career was you always wanted to have the learning side and the coaching side. Because the learning side generalizes the knowledge for everybody and kind of level-sets everybody.Share on X But everybody’s business, or everybody’s situation, is extremely unique, so you need to have that personalized support and assistance. And when we were running programs in the entrepreneurship centers I were running and things like that, we would always have human coaches. AI enabled us to kind of scale coaching for some of the programs we’re building at SoundStrategist through AI. So with me having been a business coach for over 15 years, I knew how to train the AI chatbots. It started off as simple chatbots, and now it's evolved into full agents that use voice and all those other capabilities. But it really started as, let's put some chatbots into some of our courses and some of our programs to kind of reinforce the learning, personalize it, and then it just developed from there. Okay, so there's a lot in there, and I'd like to unpack some of it. When you say use rap to teach, I’m thinking about rap is kind of a form of poetry. So how do you use poetry, or how do you use rap to teach people? Is it more catchy if it is delivered in the form of a rap song? How does it work? So you kind of want to make it catchy. Our philosophy is this: when you listen to it, it should sound like a good song.Share on X Because there’s this real risk of it sounding corny if it's done wrong, right? So we always focus on creating good music first and foremost when we’re creating a music-based lesson. So it should be a good song. It should be something you hear and think, oh, between the chorus and the music, this actually sounds good. But then, the value of music is that once you learn the song, you learn the concept, right? Because once you memorize the song, you memorize the lyrics, which means you memorize the concept. One of the things we also make sure to do is introduce concepts. The best way I could describe this is this, and this might be funny, but I grew up in the nineties, and a lot of rappers talked about selling dr*gs and things like that. I never sold dr*gs in my life. But just by listening to rap music and hearing them introduce those concepts, if I ever decided to go bad, I would have a working theory, right? So the same thing with entrepreneurship, and the same thing with business principles. You can create songs that introduce the concepts in a way where if a person's never done it, they're introduced to the vocabulary.Share on X They’re introduced to the lived experiences. They’re introduced to the core principles. And then they can take that, and then they can go apply it and have a working theory on how to execute in their business. So that’s kind of the philosophy that we took, let’s make it memorable music, but also introduce key vocabulary. Let’s introduce lived experiences. Let’s introduce key concepts so that when people are done listening to the song, they memorize it, they embody it, and they connect with it. Now they have a working theory for whatever the song is about.  And are you using AI to actually write the song?  No, we're not. That’s one of the things we haven’t really integrated on the AI front, because the AI is not good enough to take what’s exactly in my head and turn it into a song. It’s good for somebody who doesn’t have any songwriting capability or musical capability to create something that’s cool. But as a musician, as somebody who writes, you have a vision in your head on how something should sound sonically, and the AI is not good enough to take what’s in my head and put it into a song. Now, what we are using are some of the AI tools like Suno for background music. So at first, we used that to create all our background music for our courses from scratch. We are using some of the AI to help with some of the background music and everything and all of that so that we can have original stuffShare on X as opposed to having to use licensed music from places like Epidemic Sound. So we are using it for like the background music. But for the actual music-based lessons, we're still doing those old school.  Okay, that's pretty good. We are going to dive in a little bit deeper here, but before we go there, I’d like to talk about the framework that you’re bringing to the show. I think we called it the AI for Deep Experts Framework. That's the working title right now, but yeah, we're still finalizing it. But that’s the working title. Yeah.  But the idea—at least the way I'm understanding it—is that if someone has deep domain expertise, AI can be a real accelerator and amplifier of that expertise. Yep.  So people who are listening to this and they have domain expertise and they want to do AI so that they can deliver it to more people, reach more people, create more value, what is the framework? What is the five-step framework to get them there?  Number one: provided that you have deep expertise, you should be able to identify a core pain point in your respective industry that needs solving.Share on X Maybe it’s something that, throughout your career, you wanted to solve, but you weren’t able to get the resources allocated to get it done in your job. Or maybe it required some technical talent and you weren’t a developer, or whatever, right? But you should be able to identify what’s the pain point, a sticking pain point that needs to be solved—and if it's solved, it could really create value for customers. That's just old-school opportunity recognition. Number two: now, the great thing about AI is that you can leverage AI to do a lot of deep research on the problem. So obviously, you're still going to have conversations to better understand the pain point further. You're going to look at your own lived experiences and things like that. But now you can also leverage AI tools—using Perplexity or Claude—to do deep research on a market opportunity. So whether or not you have experience in market research, you can use an AI tool to help identify the total addressable market. You can brainstorm with it to uncover additional pain points, and it help you flesh out your value proposition, your concept statement, and all of those things that are critical to communicating the offering. Because before we transact in money, we always transact in language, right? So pretty much, AI can help you articulate the value proposition, understand the pain point, all of those different things. And then also if you have like deep expertise and you haven't really turned it into a framework, the AI can help you framework it and then develop a workflow to deliver value.Share on X So now you have the framework, you have the market understanding, and all of those different things. AI can even help you think through what the product would look like—the user experience, the workflow, things like that. Now you can use the AI-powered tool to help you build that. You can use something like Lovable. You can use something like Bolt. You could use something like Cursor, all different AI-powered tools. For people who are newer to development and have never done development before, I would recommend something like Lovable or maybe Bolt. But once you get more comfortable and want to make sure you're building production-ready software, then you move to something like Cursor.  Cursor has a large enough context window—the context window is basically the memory of an AI tool. It has a large enough context window to deal with complex codebases. A lot of engineers are using it to build real, production-ready platforms. But for an MVP, Bolt and Lovable are more than good enough. So one of the things I recommend when building with one of these tools is to do what's called a PRD prompt. PRD stands for Product Requirements Document.Share on X For those who aren’t familiar with software development, typically, and this is not even really happening anymore, but traditionally with software development, you would have the product manager create a Product Requirements Document. So this basically outlines the goals of the platform, target audience, core features, database, architecture, technology stack, all of the different things that engineers would need to do in order to build the platform. So you can go to something like Claude, or ChatGPT, and you can say: “Create a PRD prompt for this app idea,” and then give as much detail as possible—the features, how it works, brand colors, all of those different things. Then the AI tool—whether you're using ChatGPT, Claude, or Gemini—will generate your PRD prompt. So it’s going to be like this really, really long prompt. But it’s going to have all of the things that the AI tool, web-building or app-building tool needs to know in order to build the platform. It’s going to have all the specifications. So you copy and paste.  Is this what people call vibe coding?  Yeah, this is vibe coding. But the PRD prompt helps you become more effective at vibe coding because it gives the AI the specifications it needs and the language that it understands to increase the likelihood that you build your platform correctly. Because once you build the PRD prompt, the AI is going to know, okay, this is the database structure. It's going to know whether this is a React app versus a Next.js app. It's going to know, okay, we're building a frontend with Netlify. The stuff that you may not know, the AI will know, and it will build the platform for that. So then you take that prompt, you paste it into Lovable, paste it into Cursor, and then you can kind of get into your vibe coding flow. Don't let the hype fool you, though, because a lot of people will say, “Oh, I built this app in 15 minutes using Lovable.” No—it still requires time. But if you can build a full-stack application in two weeks when it typically takes several months, that’s still like super fast. So pretty much, on average, you can build something in a couple of weeks—especially once you get familiar with the process, you can build something in a couple of weeks. But if this is your first time ever doing this, pay attention to things like when the app debugs and some of the other issues that come up.  Start paying attention because you’re going to learn certain things by doing. As you go through the process, you'll begin to understand things like, okay, this is what an edge function is, this is what a backend is. You’ll start learning these different things as you’re going through the process, right? So you get the platform built. Now the next step is you want to distribute the platform. So obviously, if you’ve been in your industry for a while and you have some expertise, you should have some distribution. You should have some folks in your space who are your ICP that you can kind of start having some customer conversations with and start trying to sell the platform. One of the things that I always recommend is going B2B and selling something for significant valueShare on X as opposed to going B2C and selling a bunch of $19.99 subscriptions. And the reason for that is a couple of different things. Number one, when you have to do a lot of volume, your business model becomes more complicated. And then you have to introduce things to manage that volume. Whereas if you’re selling a solution that’s a five-figure to six-figure offering, like 10 clients, 15 clients, the amount of money that you can get to with less complexity in your business model. So I always say go B2B, at least a five-figure annual offering, because I know most of the offerings that we offer are at least high five figures, low six figures—subscriptions, SaaS licensing, or whatever. And that way it just introduces less complexity to your business model, and it allows you to get as much revenue as possible. And then as you go to market, you’re going to learn. So the learning aspect, you’re going to learn maybe customers want this or this feature. We thought the people were going to use the platform this way, but they’re actually using it this way. So you’re always learning, always evolving, and adjusting the offering. Okay, so let's say I have deep expertise in some area—maybe investment banking or whatever. I want to use AI. I identify an industry pain point that I've addressed or maybe I personally experienced. I visualize a solution, then I brainstorm with ChatGPT or Claude or whatever, figure out what to do, and then I leverage AI tools like Cursor, Lovable, or Bolt. I set the price point. I go B2B. Is this something that, as a subject-matter expert, is efficient for me to do myself because I have the expertise and the vision? Or is it better for me to hire someone to do this?  It depends on what your bandwidth is. I mean, pretty much I’m of the firm belief that like these are skills that you probably want to unlock anyway. So it might be worth going through the process of learning the tools, leveraging them, and everything, and all of that. And that’s kind of how you future-proof yourself. Now, obviously, if you have bandwidth limitations, there are firms and organizations that you could hire, et cetera, et cetera, that can do it for you. Obviously, developers and things like that. But the funny thing about a lot of developers is, even though they're using AI, they're still charging the prices they charged before AI, right? They’re just getting it done faster, and their margins are a lot lower. So you're still going to pay, in a lot of instances, developer pricing for a platform. Those are the things that you have to consider as far as your own personal situation. But me personally, I believe these are skills worth unlocking.Share on X Because one of the things is, if you get very senior in your career—let's say you've been there 15, 16 years, 20 years—we all know there's this point where you either move up to the C-suite or you get caught in upper-middle-management purgatory, where you're kind of in that VP, senior director space, et cetera, et cetera, and you just kind of hover there. At that point, your career moves tend to be lateral—going from one VP role to another VP role, one senior director role to another senior director role, right? At that point, your income potential starts to get limited. So unlocking one of these skills and becoming more entrepreneurial is something I genuinely believe is worth developing personally. And what would you say is the time requirement for someone to get competent in vibe coding?  Three months minimal. You could be pretty solid in three months.  But three months full-time or three months part-time?  Three months part-time.  So three months. That's about 143 working hours in a regular month. So that's basically around 420–430 hours if you were full-time.  If you spend weekends working on your project, learning how to build it, taking notes, and actually going through the process, you can get pretty decent in a couple of months. Now, obviously, there are still levels as you continue and to progress and things like that, but you can get pretty solid in a couple months. Another thing you want to consider is who you're selling to. You obviously wanna make sure that your platform security is really well, is really done. So even if you build it yourself and then you have an engineer do code review, that’s cheaper than having them build it. I think if you spend three months, you can get really good at building solutions for what you need to get done. And then from there, you just get better and better and better and better.  How do I know that, let's say I hire someone in Serbia to do a code review for me? Let's say I learn the vibe coding thing and create the prototype, then I have someone to clean the code. How do I know that they did a good job or not?  You really don’t. You really don’t know until the platform’s in the wild, and it’s like, okay, it’s secure. So there are some things that you can do to check behind people. Let's say you don't have the money to do a full security audit or hire someone specifically for a security review, a developer for security review. One of the things that you can do is you can do multi-agent review. Like you take your codebase, have Claude review it, have OpenAI Codex review it, have a Cursor agent review it. You have multiple agents do a review. Then they kind of check each other’s work, if you will.  They kinda identify things that others may not have identified, so you can get the collective wisdom of those three to be able to be like, “Okay, I need to shore this up. I need to fix this. I need to address that.” That gives you more confidence. It still doesn’t replace a person who has deep expertise and making sure they build secure code, but it will catch common issues, like hard-coding API keys, which is a risk, right? It’ll catch those type of things that typically happen. But let’s say you do have a security, a code review, you could just kind of take that same approach also to check their work. Because they shouldn’t find any major vulnerabilities. The AI agents that come in after it shouldn’t really find any major vulnerabilities if it was like done securely securely. Another thing to consider is that a lot of these tools use Supabase for the backend and database. Supabase also has a built-in security advisor, including an AI security advisor, that points out security issues, performance problems, and configuration errors. So like you do have some AI-powered check and balances to check behind people.Share on X  Interesting. So basically, I can audit their applications, and the AI will check the code and tell me what needs to be improved?  Yeah. And they can make the fixes for you.  Yeah. Wow, that’s amazing. It still sounds a little bit overwhelming. It’s basically a language, a new language to learn, isn’t it?  It’s not really — it’s English. That’s the amazing thing about it—it’s English. I mean, you literally talk to AI in natural language, and it builds stuff for you, which is, if somebody is like, had a idea for a minute, because I mean, pretty much running entrepreneurship centers, I’ve known so many people who’ve had ideas that they were never able to launch or build, and then they see somebody build it later. If you learn these skills, you get to the point where anything that's in your head, you can kind of start bringing it to life in reality.Share on X And even if you've got to bring somebody in to make sure it's secure and production-ready, it's way cheaper than having them build it from scratch. And then another thing that you’ll find also is if you’re able to build something, let’s say you want to turn it into a startup or something, right? It’s a lot easier to bring in a technical co-founder when they don’t got to build the thing from scratch, and then they also see that you were able to build something, they’re able to see your product vision, et cetera, et cetera. It becomes a lot more easier to recruit people who actually have that expertise into the company because you’ve already handled the hard part. You got something and it works. And all they got to do is just come in, make it safe, and make it work better.  Yeah, that is very interesting. It feels analogous to writing a book yourself or having a ghostwriter. Because essentially, you are vibe coding with a ghostwriter, right? You tell the stories, and then the ghostwriter writes the book for you. Probably now you can use  AI to do that. Yep.  But that's a skill. Not everyone has the skill to write it themselves, and then they need to go to the ghostwriter, but still is their book, right?  Yep.  So it sounds a little bit similar. That’s fascinating. So what’s the path to launching an MVP? So let’s say I’m a subject matter expert, and I want to launch an MVP within a few weeks. Is there a path for me to go there?  Once you get good with the platform, once you get comfortable with the tools, yeah. So for example, we're launching an AI platform. It's an AI coaching platform, but it's also a data analytics platform. Basically, it's targeted to entrepreneur support organizations and municipalities supporting small businesses. So on the front end, it's an AI-powered advisor — it's a hotline that people can call 24/7. But on the back end, the municipalities and entrepreneur support organizations get access to analytics from each of those calls. We built this in two weeks. We’re already talking to customers, we’re already having conversations, and all of those things. We literally brought it to market in two weeks. So the thing is, once you kind of get caught up with the tools—and I'm not a developer, I'm not a developer by trade at all. I had a tech startup before, but I was a non-technical founder. I just know how to put together a product. But once you get good with the tools, that's very conceivable. And then you just go out there, and you go in the market, you start having conversations with your ideal customer profile.Share on X As you’re going through that process, you’re learning, okay, maybe this isn’t my ideal customer profile, this is their pain point. Or maybe instead of this being the feature they want, this is the feature they want. And the crazy thing about it is in the past you had to really get that ICP real tight and the feature set real tight because it cost so much money to go back and have to make tweaks and changes and to get it to market in the first place. Now, you can get a new feature added in the afternoon. It allows you to go to market a little bit faster. You don’t have to have the ideal feature set. You don’t have to have the ICP figured out. You get out there, you learn, and then you’re able to iterate a lot faster because the cost of development is super cheap now, and the speed in which like new features can be added or deprecated is a lot faster. So it allows you to go to market a lot faster than in the past.  Okay, I got it. You can do this, you can code. What do you recommend for someone who’s starting out? You mentioned Lovable, Bolt, and then Cursor. Is Cursor like an advanced product?  Cursor’s a little bit more advanced, but if you want to build production-ready software, it's something you're going to eventually have to use. But can you convert from Lovable to Cursor?  Yes, you can. Yep. So what you typically do — and I still do this to this day — is every time I launch a product, I build it in Bolt first. You could use Bolt or Lovable, either one's fine. I use Bolt because Bolt came out first, and that's what I started using. Then Lovable came out like a month later. But I use Bolt. I’ll spin up the idea in Bolt. And the reason I like doing it in Bolt or Lovable is that it's really good at doing two things. It's really good at quickly launching your initial feature set, and then spinning up your backend. Your database — it's really good at that. So I start off in Bolt, then I connect it to a repository.  For those who aren't familiar with GitHub, there's a button in Bolt or Lovable where you can easily connect it to a GitHub repository. So then once I kind of get the app to a point where the basic skeleton is set, then I go into Cursor. Then I pull the repository into Cursor and do the heavy work. The reason Cursor has a learning curve is because there are still some traditional developer things you need to know to spin up a project. Your initial database — it's a lot harder to spin up your initial database and backend in Cursor. It's also harder to identify your initial libraries and all of those things. If you're a developer, it's not difficult. But if you're new, it is. Bolt and Lovable abstract those things out for you. So you start it off in Bolt or Lovable. Basically, since they're limited in their context windows, when you're trying to build something complex, eventually they start making a whole bunch of errors. They basically start getting stup*d. That's when you know it's time to move to Cursor, because Cursor can handle the heavy lifting. So if you build in Bolt or Lovable until it gets stup*d, then you move to Cursor for the heavy lifting.  And then is there a point where Cursor gets stup*d as well? No. Cursor has a couple of different things that allow it to extend its context window, which is his memory. You can put documentation into Cursor. For example, whatever your PRD prompt was, you can save that as a document in Cursor. You can also set rules. One of my rules in Cursor is: I'm not technical, so explain everything in layman's terms. And then as you’re starting to build code, you can save that code or you can point it to that repository. So there's some more flexibility with Cursor as far as managing your context window.Share on X But with Bolt and Lovable, the context window is more limited right now. So I start off in those, and then once I kind of get the skeleton up, then I move to Cursor. And at that point, a lot of the complicated things like spinning up your dev environment and all those things are kind of abstracted out. Then you can just jump in and use it the same way you use Bolt and Lovable. Fantastic. Fantastic. So, Jason, super helpful information for domain experts who want to build an application that will help them promote their product or manifest their ideas in product form. I think that’s super powerful. So if someone would like to learn about SoundStrategist and what SoundStrategist can do for them in terms of learning and experiential products, incorporating music, or building curriculum, or they would just like to connect with you to learn more about what you can do for them, where should they go?  Jason William Johnson, PhD, on LinkedIn, or www.getsoundstrategies.com.  Okay. Well, Jason William Johnson, you are really ahead of the curve, especially connecting this whole idea of vibe coding to people who are subject matter experts and not technical. And you know it because you don't come from a technical background, yet you've mastered it. I’m living it. Everything I’m sharing—this is not like a theoretical framework. I'm living all of this. So everything I’m saying. Super authentic. And especially coming from you—you understand what it's like to not be technical person, learning this, applying this.  So if you'd like to do this, learn more, or maybe have Jason guide you, reach out to him. You can find him on LinkedIn at Jason William Johnson, PhD, or visit www.getsoundstrategies.com. And if you enjoyed this episode, make sure you follow us and subscribe on YouTube, follow us on LinkedIn, and on Apple Podcasts. Because every week I bring a super interesting entrepreneur, subject matter expert, or a combination of the two—like Jason—to the show, who will help you accelerate your journey with frameworks and AI frameworks in that gear. So thank you for coming, Jason, and thank you for listening. Important Links: Jason's LinkedIn Jason's website

In this episode of Run the Numbers, CJ sits down with Maria Izurieta, CFO of Huntress, to unpack what it really means to lead finance as a connective tissue across the organization. Drawing on experience across VC-backed, PE-owned, and public companies, Maria shares how she balances impact versus perfection, builds trust through small wins, and helps teams move from transactional finance to insight-driven decision making. They dig into data transparency, centralized BI, partnering with sales and marketing on revenue, and why the best CFOs unblock friction instead of becoming the “no” department — all while bringing a deeply people-first lens to scale.—SPONSORS:Abacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/run—LINKS:Maria on LinkedIn: https://www.linkedin.com/in/maria-izurieta-909a3b/Company: https://www.huntress.com/CJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:How the Best CFOs Lead Without Being the CEO | Ken Stillwellhttps://youtu.be/O4cx9NBqQso—TIMESTAMPS:00:00:00 Preview and Intro00:01:01 Maria's Background00:03:09 People-First Team Building00:05:16 People, Process, Systems at Scale00:07:13 Removing Friction Outside Finance00:09:15 Data Transparency & Decision-Making00:11:06 Sponsors — Abacum | Brex | Metronome00:14:22 Forward-Deployed Data00:16:21 Centralized Data vs. Silos00:19:23 Finance as Data Steward00:21:08 Cost-to-Price Feedback Loop00:22:35 Curiosity Builds Credibility00:23:43 Sponsors — RightRev | Rillet | Tabs00:27:12 Trust First, Then Impact00:30:27 Celebrating Small Wins00:31:21 From Transactions to Insights00:33:00 CFO at the Revenue Table00:34:32 Educating the Org on Metrics00:36:21 Customer-Level Margin Reality00:37:13 Using Facts to Change Decisions00:38:27 Ownership Mindset in Growth Companies00:39:10 VC vs. PE vs. Public CFO Tradeoffs00:41:02 Operating Inside Constraints00:42:18 Finding Your Stage Fit00:44:17 Building a Personal Advisor Network00:46:43 Visibility and Women in Leadership00:47:44 Work–Life Integration, Not Balance00:48:45 Lightning Round: Biggest Mistake00:50:10 Advice to Younger Self00:51:36 Finance Tech Stack00:52:01 Craziest Expense Story00:52:44 Credits#RunTheNumbersPodcast #CFOLeadership #ScalingCompanies #DataDrivenDecisions #ExecutiveLeadership This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cjgustafson.substack.com

In this episode of Tank Talks, Matt Cohen sits down with Timothy Chen, the sole General Partner at Essence VC. Tim shares his remarkable journey from being a “nerdy, geeky kid” who hacked open-source projects to becoming one of the most respected early-stage infrastructure investors, backing breakout companies like Tabular (acquired by Databricks for $2.2 billion). A former engineer at Microsoft and VMware, co-founder of Hyperpilot (acquired by Cloudera), and now a solo GP who quietly raised over $41 million for his latest fund, Tim offers a unique, no-BS perspective on spotting technical founders, navigating the idea maze, and rethinking sales and traction in the world of AI and infrastructure.We dive deep into his unconventional path into VC, rejected by traditional Sand Hill Road firms, only to build a powerhouse reputation through sheer technical credibility and founder empathy. Tim reveals the patterns behind disruptive infra companies, why most VCs can't help with product-market fit, and how he leverages his engineering background to win competitive deals.Whether you're a founder building the next foundational layer or an investor trying to understand the infra and AI boom, this conversation is packed with hard-won insights.The Open Source Resume (00:03:44)* How contributing to Apache projects (Drill, Cloud Foundry) built his career when a CS degree couldn't.* The moment he realized open source was a path to industry influence, not just a hobby.* Why the open source model is more “vertical than horizontal”, allowing deep contribution without corporate red tape.From Engineer to Founder: The Hyperpilot Journey (00:13:24)* Leaving Docker to start Hyperpilot and raising seed funding from NEA and Bessemer.* The harsh reality of founder responsibility: “It's not about the effort hard, it's about all the other things that has to go right.”* Learning from being “way too early to market” and the acquisition by Cloudera.The Unlikely Path into Venture Capital (00:26:07)* Rejected by top-tier VC firms for a job, then prompted to start his own fund via AngelList.* Starting with a $1M “Tim Chen Angel Fund” focused solely on infrastructure.* How Bain Capital's small anchor investment gave him the initial credibility.Building a Brand Through Focus & Reputation (00:30:42)* Why focusing exclusively on infrastructure was his “best blessing” creating a standout identity in a sparse field.* The reputation flywheel: Founders praising his help led to introductions from top-tier GPs and LPs.* StepStone reaching out for a commitment before he even had fund documents ready.The Essence VC Investment Philosophy (00:44:34)* Pattern Recognition: What he learned from witnessing the early days of Confluent, Databricks, and Docker.* Seeking Disruptors, Not Incrementalists: Backing founders who have a “non-common belief” that leads to a 10x better product (e.g., Modal Labs, Cursor, Warp).* Rethinking Sales & Traction: Why revenue-first playbooks don't apply in early-stage infra; comfort comes from technical co-building and roadmap planning.* The “Superpower”: Using his engineering background to pressure-test technical assumptions and timelines with founders.The Future of Infra & AI (00:52:09)* Infrastructure as an “enabler” for new application paradigms (real-time video, multimodal apps).* The coming democratization of building complex systems (the “next Netflix” built by smaller teams).* The shift from generalist backend engineers to specialists, enabled by new stacks and AI.Solo GP Life & Staying Relevant (00:54:55)* Why being a solo GP doesn't mean being a lone wolf; 20-30% of his time is spent syncing with other investors to learn.* The importance of continuous learning and adaptation in a fast-moving tech landscape.* His toolkit: Using portfolio company Clerky (a CRM) to manage workflow.About Timothy ChenFounder and Sole General Partner, Essence VCTimothy Chen is the Sole General Partner at Essence VC, a fund focused on early-stage infrastructure, AI, and open-source innovation. A three-time founder with an exit, his journey from Microsoft engineer to sought-after investor is a masterclass in building credibility through technical depth and founder-centric support. He has backed companies like Tabular, Iteratively, and Warp, and his insights are shaped by hundreds of conversations at the bleeding edge of infrastructure.Connect with Timothy Chen on LinkedIn: linkedin.com/in/timchenVisit the Essence VC Website: https://www.essencevc.fund/Connect with Matt Cohen on LinkedIn: https://ca.linkedin.com/in/matt-cohen1Visit the Ripple Ventures website: https://www.rippleventures.com/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tanktalks.substack.com

In this episode of Run the Numbers, CJ sits down with Ken Stillwell, CFO and COO of Pegasystems, to explore the realities of leading from the second seat. Ken shares hard-earned lessons from guiding Pega through the shift from term licenses to ARR and ACV, including how to rework sales compensation without losing trust or momentum. They discuss the limits of KPI obsession, the importance of directional clarity over false precision, and why private equity often drives sharper execution than public markets—and how to apply that discipline while still playing the long game.—SPONSORS:Tabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cj—LINKS:Ken on LinkedIn: https://www.linkedin.com/in/ken-stillwell-83a499a/Pegasystems: https://www.pega.com/CJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:How Finance Becomes a GTM Partner, Not a Bottleneck | Chris Brubakerhttps://youtu.be/T2YjdoiJtFA—TIMESTAMPS:00:00:00 Preview and Intro00:02:57 Sponsors — Tabs | Abacum | Brex00:07:26 The Strategic Value of Being Number Two00:08:46 Earnings Calls, Messaging, and Real-Time Judgment00:10:41 Using Feedback to Sharpen Executive Communication00:11:38 CFOs as Storytellers & Message Repetition00:12:31 Managing Up: Reading the Room00:13:59 Learning the Hard Way: Misreading Dynamics00:15:18 Confidence, Aggression, and Early CFO Mistakes00:15:58 Sponsors — Metronome | RightRev | Rillet00:19:45 When to Email vs Pick Up the Phone00:22:48 Tailoring Communication to Different Functions00:23:23 Audience-Specific Messaging: “Why Me?”00:25:24 Values vs Behaviors in Leadership00:28:13 Why Big Changes Need Anchoring00:31:14 Moving Pega to the Cloud00:32:43 Rewiring Sales Comp for ARR & ACV00:34:56 Sales Credibility Breakdowns with Customers00:36:20 Economics vs Trust in Sales Teams00:37:48 Balancing Field Feedback with Company Goals00:39:17 De-Emphasizing New Logos to Fix the Sales Model00:41:12 The Danger of Over-Obsessing on KPIs00:42:51 Public vs Private: Incentives and Operating Discipline00:45:57 Why Companies Go Private: Motivation Over Patience00:47:29 The Shrinking Public Markets00:47:57 Private vs Public CFO Mindsets00:49:39 Meeting Investors Where They Are00:50:16 A Risky Decision That Paid Off: Going All-In on the Cloud00:51:29 Long-Ass Lightning Round00:53:24 Ken's Finance Tech Stack & Craziest Expense00:54:39 Credits#RunTheNumbersPodcast #CFOLeadership #ExecutiveCommunication #SalesStrategy #PublicVsPrivate This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cjgustafson.substack.com

In this episode of This New Way, Aydin sits down with Hai Nghiem from AGI Ventures Canada to explore how Claude Code is changing the way teams build software, automate workflows, and even run go-to-market operations—without requiring everyone to be a developer.Hai walks through real, hands-on examples of using Claude Code as a terminal-based AI agent to qualify inbound leads, generate follow-up emails and statements of work, manage internal context with skills and sub-agents, and even automate browser-based tasks like filling out applications. The conversation dives deep into go-to-market engineering, context engineering, and why skills are becoming one of the most powerful primitives for scaling AI across an organization.If you're curious how non-technical teams can start using agents today—or how technical teams can dramatically compress GTM and sales workflows—this episode is a must-listen.Key Timestamps00:00 - Intro00:08.334 – “What's the killer AI product everyone should be using?”00:25.582 – Hai introduces Claude Code and why it's blowing up01:10.900 – Claude Code as an agent running in your terminal01:45.600 – Go-to-market engineering and reducing SDR teams02:10.222 – Industry trend: shrinking sales teams with AI agents03:45.976 – Claude Code vs Cursor for coding workflows04:32.100 – Writing 90% of production code with AI (safely)05:45.300 – Non-coding automation with Claude Code, Zapier, and n8n06:01.645 – What AGI Ventures Canada does06:45.900 – AI Tinkers community and the origins of AGI Ventures07:38.958 – Automating inbound lead qualification08:50.839 – Live role play: discovery call walkthrough09:12.607 – Using Notion as a live note-taker and context store10:03.350 – Example GTM automation use cases at Fellow11:52.973 – Running Claude Code with “dangerously skip permissions”13:07.050 – Sub-agents vs skills explained16:40.851 – What Claude “skills” actually are17:15.359 – Email writer skill walkthrough20:19.750 – Auto-updating skills from real GTM learnings22:19.592 – How Claude pulls context from Notion automatically25:42.632 – Generating follow-up emails using skills30:08.595 – Generating Statements of Work with scripts31:35.478 – Browser automation with the Claude Chrome extension32:16.870 – Auto-filling applications using personal skills34:56.562 – AI-powered Discord bot for community support37:18.114 – Live fact-checking inside Discord38:09.159 – How to contact AGI VenturesTools & Technologies MentionedClaude (Anthropic)An AI assistant positioned as a business-focused alternative to ChatGPT.Claude CodeA terminal-based AI agent that can write code, automate workflows, manage files, and interact with browsers—used heavily for GTM and internal automation.Claude SkillsLightweight, reusable instruction sets that teach Claude how to perform specific tasks (e.g., writing sales emails) without permanently consuming context.Claude Sub-agentsDelegated agents used to manage context and offload complex tasks without bloating the main agent's context window.NotionUsed as a lightweight CRM, document store, and central source of truth for agent context.DiscordPrimary internal and community communication platform, integrated with AI bots for automated responses.Chrome Automation (Claude Extension)Allows Claude Code to control the browser and complete web-based tasks like filling out forms.ZapierNo-code automation tool for connecting apps and workflows.n8nOpen-source workflow automation tool often used for advanced AI and agent pipelines.GPT Models (OpenAI)Currently used in AGI Ventures' Discord bot, with plans to migrate to Claude models.Contact Hai:agiventures.cahai@agiventures.cahttps://ca.linkedin.com/in/haiphunghiemSubscribe at⁠ thisnewway.com⁠ to get the step-by-step playbooks, tools, and workflows.

From graduate engineer to CTO, Andrew Phillips' 16-year journey at Skyscanner is a story of continuous reinvention. He didn't chase titles—he chased growth, deliberately stepping out of his comfort zone and unlearning the habits that no longer served him. What's kept him at the company for over a decade isn't status, but challenge: new teams, unfamiliar problems, and the chance to stay close to the work, even as his scope of leadership expanded.In this episode, we explore how Andrew is now applying that same mindset to leading in the AI era—personally and professionally. He shares how he's built a personal AI stack to stay more present, how Skyscanner is blurring traditional team roles to unlock speed, and why “directed autonomy” is more important than ever. For leaders navigating scale, technology, and the desire to make meaningful impact without burning out, Andrew offers a powerful perspective.Key TakeawaysGrowth through discomfort: Andrew's biggest accelerations came from switching roles and leaving his comfort zone—not climbing a predefined ladder.AI as a leadership enabler: He uses AI tools to be more present, thoughtful, and effective—especially during high-stakes meetings.From feature factory to outcome focus: Leaders must reconnect people to impact, not just output.Directed autonomy: Empowering teams with AI means giving clear goals—not micromanaging the execution.Unlearning process overreach: Traditional roles, ticketing systems, and rigid handoffs are ripe for reinvention in AI-native organizations.Additional InsightsThe personal AI stack Andrew uses includes ChatGPT, Otter, Cursor, and SpecKit—enabling him to ideate on walks, build apps during board meetings, and maintain strategic presence.Skyscanner's senior engineers are back coding, using AI to close the gap between architectural thinking and execution.AI-driven productivity unlocks don't just mean faster work—they mean better work-life balance, deeper engagement, and more human leadership.Episode Highlights00:00 – Episode RecapAndrew Phillips shares how stepping into uncertainty—and building his own AI stack—transformed his leadership at Skyscanner. From personal growth to organizational reinvention, he's leading the charge on what modern technology leadership looks like.01:35 – Guest Introduction: Andrew PhillipsBarry introduces Andrew Phillips, CTO of Skyscanner, reflecting on their 15-year relationship and Andrew's rise from graduate engineer to technology leader.05:45 – The One Trick Pony MomentAndrew recalls the pivotal moment when a CEO challenged him to move teams and stop playing it safe—triggering his real leadership evolution.12:33 – Starting with Yourself in AIBefore transforming your company with AI, Andrew urges leaders to start by experimenting personally and learning from the ground up.15:15 – Writing Better Prompts, Building Better SpecsAI tools thrive on clear direction. Andrew realized that better prompting and crisp product requirements accelerated his results dramatically.20:01 – Directed Autonomy in the AI EraGiving AI tools (and people) the “why” rather than micromanaging the “how” builds trust, speed, and better outcomes.24:56 – Parallel Productivity and Boardroom AppsHow Andrew built an entire app—during a board meeting—by offloading work to AI and staying fully present in the room.27:13 – Reclaiming Work-Life BalanceAI allows Andrew to unload his mental backlog—using...

“I don't worry about being replaced by AI. I worry about being replaced by someone who's really good at using AI.”Atlassian has 10,000+ engineers currently split-testing the world's top AI coding tools, from GitHub Copilot and Cursor to Claude Code. In this episode, Co-Founder & CEO Mike Cannon-Brookes joins Lukas Biewald to share what their data reveals about the world's best AI tools today.Hear how 24 years of building a tech giant and a massive internal study on AI productivity have shaped Mike's vision for the future of dev jobs.Connect with us here:Mike Cannon-Brookes: https://www.linkedin.com/in/mcannonbrookes/?originalSubdomain=auAtlassian: https://www.linkedin.com/company/atlassian/?viewAsMember=trueLukas Biewald: https://www.linkedin.com/in/lbiewald/ Weights & Biases: https://www.linkedin.com/company/wandb/00:00 Trailer01:08 Introduction03:11 Connecting Technology and Business Teams07:22 The Impact of AI on Business Workflows13:26 Developer Productivity and AI21:03 Measuring Developer Efficiency25:41 Future of AI in Development34:59 Legacy Technology and Code Changes39:29 AI's Role in Developer Productivity47:40 AI and Junior Developers52:30 Product-Led Growth and Business Strategy01:00:29 Core Metrics for Sustainable Growth01:06:56 Staying Creative in the Tech Industry

In this episode of Run the Numbers, CJ sits down with Chris Brubaker, SVP of Finance at Postscript, who's helped build the finance function from the ground up. Chris shares how he partners with sales through deal desks, sets pricing guardrails, and makes sure finance helps close deals instead of slowing them down. They dig into his hands-on approach to automation using AI with limited engineering resources, how Postscript's metrics evolved as the company scaled, when to trust internal data over benchmarks, and where teams get tripped up. Plus, a private jet accounting story—because of course.—SPONSORS:Rillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.com—LINKS:Chris on LinkedIn: https://www.linkedin.com/in/wchrisbrubaker/Postscript: https://postscript.io/CJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:So You're Looking for a “Strategic” CFO? Bloomerang's Steve Isom on What That Really Meanshttps://youtu.be/cgHOtvG1CesThe IPO Playbook: Expert Advice from Lee Kirkpatrick, Twilio's Former CFOhttps://youtu.be/PTKAUD7PSWUThe CFO Case for Probabilistic Forecasting With AI | Bruno Annicqhttps://youtu.be/Dl8nDZPJMpE—TIMESTAMPS:00:00:00 Preview and Intro00:02:22 Sponsors — Rillet | Tabs | Abacum00:06:55 Interview Begins00:07:36 First Finance Hire and Early Scale at Postscript00:09:02 Usage-Based Margins, COGS, and the Twilio Parallel00:10:31 Partnering With Sales and Building Deal Desk00:13:16 Pricing Guardrails, Payback, and Deal Economics00:15:35 How Deal Desk Evolves Over Time00:16:01 Sponsors — Brex | Metronome | RightRev00:19:44 Making Finance a Deal-Closing Partner00:20:44 Automating Deal Desk With a Slack Bot00:23:48 How Technical Finance Leaders Need to Be00:25:17 Automating Without Engineering Help00:27:12 Why Human Touch Still Matters in SaaS00:27:53 Postscript's Finance Tech Stack00:28:30 ERP Migration and Month-End Efficiency00:29:42 The Reality of Continuous Close00:30:34 First Real AI Wins in Accounting00:31:18 Experimenting With AI Forecasting00:33:32 Metrics That Matter: Usage as a Leading Indicator00:35:49 How Metrics Evolve as the Company Scales00:37:41 Understanding the Product in a Usage-Based Model00:39:27 Micro-Seasonality and Forecasting Volatility00:42:21 How to Use Benchmarks Without Misusing Them00:43:50 Long-Ass Lightning Round: A Costly Modeling Mistake00:45:45 Advice to a Younger Finance Leader00:47:05 The Private Jet Accounting Story00:49:11 Credits#RunTheNumbersPodcast #FinanceLeadership #DealDesk #UsageBasedSaaS #AIinFinance This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cjgustafson.substack.com

Loïc Houssier (CTO, Superhuman) joins VC.fm to unpack the Grammarly acquisition of Superhuman and what it signals about the future of AI-native productivity tools.We talk AI in the workflow vs standalone AI tools (ChatGPT/Gemini), voice-first computing, vibe coding vs production engineering, AI's impact on hiring, and why UX taste and product design may be the real moat in an era where everyone has access to the same LLMs.Keywords: Grammarly acquires Superhuman, Superhuman email, Loïc Houssier, AI productivity, AI-native software, email AI, workflow AI, OpenAI, Anthropic, LLMs, vibe coding, Cursor, UX design moat, product-led growth, startup defensibility, AI hiring.Follow the PodcastInstagram: https://www.instagram.com/venturecapitalfm/Twitter: https://twitter.com/vcpodcastfmLinkedIn: https://www.linkedin.com/company/venturecapitalfm/Spotify: https://open.spotify.com/show/7BQimY8NJ6cr617lqtRr7N?si=ftylo2qHQiCgmT9dfloD_g&nd=1&dlsi=7b868f1b72094351Apple: https://podcasts.apple.com/us/podcast/venture-capital/id1575351789Website: https://www.venturecapital.fm/Follow Jon BradshawLinkedIn: https://www.linkedin.com/in/mrbradshaw/Instagram: https://www.instagram.com/mrjonbradshaw/Twitter: https://twitter.com/mrjonbradshawFollow Peter HarrisLinkedIn: https://www.linkedin.com/in/peterharris1Twitter: https://twitter.com/thevcstudentInstagram: https://instagram.com/shodanpeteYoutube: https://www.youtube.com/@peterharris2812#Superhuman #Grammarly #AI #Productivity #Startups #VentureCapital #Email #LLM #OpenAI #Anthropic #VibeCoding #UXDesign #ProductManagement #Engineering

Zevi Arnovitz is a product manager at Meta with no technical background who has figured out how to build and ship real products using AI. His engineering team at Meta asks him to teach them how he does what he does. In this episode, Zevi breaks down his complete AI workflow that allows non-technical people to build sophisticated products with Cursor.We discuss:1. The complete AI workflow that lets non-technical people build real products in Cursor2. How to use multiple AI models for different tasks (Claude for planning, Gemini for UI)3. Using slash commands to automate prompts4. Zevi's “peer review” technique, which uses different AI models to review each other's code5. Why this might be the best time to be a junior in tech, despite the challenging job market6. How Zevi used AI to prepare for his Meta PM interviews—Brought to you by:10Web—Vibe coding platform as an APIDX—The developer intelligence platform designed by leading researchersFramer—Build better websites faster—Episode transcript: https://www.lennysnewsletter.com/p/the-non-technical-pms-guide-to-building-with-cursor—Archive of all Lenny's Podcast transcripts:https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Zevi Arnovitz• X: https://x.com/ArnovitzZevi• LinkedIn: https://www.linkedin.com/in/zev-arnovitz• Website: https://zeviarnovitz.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Zevi Arnovitz(04:48) Zevi's background and journey into AI(07:41) Overview of Zevi's AI workflow(14:41) Screenshare: Exploring Zevi's workflow in detail(17:18) Building a feature live: StudyMate app(30:52) Executing the plan with Cursor(38:32) Using multiple AI models for code review(40:40) Personifying AI models(43:37) Peer review process(45:40) The importance of postmortems(51:05) Integrating AI in large companies(53:42) How AI has impacted the PM role(57:02) How to improve AI outputs(58:15) AI-assisted job interviews(01:02:57) Failure corner(01:06:20) Lightning round and final thoughts—Referenced:• Becoming a super IC: Lessons from 12 years as a PM individual contributor | Tal Raviv (Product Lead at Riverside): https://www.lennysnewsletter.com/p/the-super-ic-pm-tal-raviv• Wix: https://www.wix.com• Building AI Apps: From Idea to Viral in 30 Days: https://www.youtube.com/watch?v=j2w4y7pDi8w• Riley Brown on YouTube: https://www.youtube.com/channel/UCMcoud_ZW7cfxeIugBflSBw• Greg Isenberg on YouTube: https://www.youtube.com/@GregIsenberg• Bolt: https://bolt.new• Inside Bolt: From near-death to ~$40m ARR in 5 months—one of the fastest-growing products in history | Eric Simons (founder and CEO of StackBlitz): https://www.lennysnewsletter.com/p/inside-bolt-eric-simons• Lovable: https://lovable.dev• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• StudyMate: https://studymate.live• Dibur2text: https://dibur2text.app• Claude: https://claude.ai• Everyone should be using Claude Code more: https://www.lennysnewsletter.com/p/everyone-should-be-using-claude-code• Bun: https://bun.com• Zustand: https://zustand.docs.pmnd.rs/getting-started/introduction• Cursor: https://cursor.com• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Wispr Flow: https://wisprflow.ai• Linear: https://linear.app• Linear's secret to building beloved B2B products | Nan Yu (Head of Product): https://www.lennysnewsletter.com/p/linears-secret-to-building-beloved-b2b-products-nan-yu• Cursor Composer: https://cursor.com/blog/composer• Replit: https://replit.com• Behind the product: Replit | Amjad Masad (co-founder and CEO): https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad• Base44: https://base44.com• Solo founder, $80M exit, 6 months: The Base44 bootstrapped startup success story | Maor Shlomo: https://www.lennysnewsletter.com/p/the-base44-bootstrapped-startup-success-story-maor-shlomo• v0: https://v0.app• Everyone's an engineer now: Inside v0's mission to create a hundred million builders | Guillermo Rauch (founder & CEO of Vercel, creators of v0 and Next.js): https://www.lennysnewsletter.com/p/everyones-an-engineer-now-guillermo-rauch• Cursor Browser mode: https://cursor.com/docs/agent/browser• Google Antigravity: https://antigravity.google• Grok: https://grok.com• Zapier: https://zapier.com• Airtable: https://www.airtable.com• Build Your Personal PM Productivity System & AI Copilot: https://maven.com/tal-raviv/product-manager-productivity-system• The definitive guide to mastering analytical thinking interviews: https://www.lennysnewsletter.com/p/the-definitive-guide-to-mastering-f81• AI tools are overdelivering: results from our large-scale AI productivity survey: https://www.lennysnewsletter.com/p/ai-tools-are-overdelivering-results-c08• Yaara Asaf on LinkedIn: https://www.linkedin.com/in/yaarasaf• The Pitt on Prime Video: https://www.amazon.com/The-Pitt-Season-1/dp/B0DNRR8QWD• Severance on AppleTV+: https://tv.apple.com/us/show/severance/umc.cmc.1srk2goyh2q2zdxcx605w8vtx• Loom: https://www.loom.com• Cap: https://cap.so• Supercut: https://supercut.ai...References continued at: https://www.lennysnewsletter.com/p/the-non-technical-pms-guide-to-building-with-cursor—Recommended books:• The Fountainhead: https://www.amazon.com/Fountainhead-Ayn-Rand/dp/0451191153• Shoe Dog: A Memoir by the Creator of Nike: https://www.amazon.com/Shoe-Dog-Memoir-Creator-Nike/dp/1501135910• Mindset: The New Psychology of Success: https://www.amazon.com/Mindset-Psychology-Carol-S-Dweck/dp/0345472322—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

From building internal AI labs to becoming CTO of Brex, James Reggio has helped lead one of the most disciplined AI transformations inside a real financial institution where compliance, auditability, and customer trust actually matter. We sat down with Reggio to unpack Brex's three-pillar AI strategy (corporate, operational, and product AI) [https://www.brex.com/journal/brex-ai-native-operations], how SOP-driven agents beat overengineered RL in ops, why Brex lets employees “build their own AI stack” instead of picking winners [https://www.conductorone.com/customers/brex/], and how a small, founder-heavy AI team is shipping production agents to 40,000+ companies. Reggio also goes deep on Brex's multi-agent “network” architecture, evals for multi-turn systems, agentic coding's second-order effects on codebase understanding, and why the future of finance software looks less like dashboards and more like executive assistants coordinating specialist agents behind the scenes. We discuss: Brex's three-pillar AI strategy: corporate AI for 10x employee workflows, operational AI for cost and compliance leverage, and product AI that lets customers justify Brex as part of their AI strategy to the board Why SOP-driven agents beat overengineered RL in finance ops, and how breaking work into auditable, repeatable steps unlocked faster automation in KYC, underwriting, fraud, and disputes Building an internal AI platform early: LLM gateways, prompt/version management, evals, cost observability, and why platform work quietly became the force multiplier behind everything else Multi-agent “networks” vs single-agent tools: why Brex's EA-style assistant coordinates specialist agents (policy, travel, reimbursements) through multi-turn conversations instead of one-shot tool calls The audit agent pattern: separating detection, judgment, and follow-up into different agents to reduce false negatives without overwhelming finance teams Centralized AI teams without resentment: how Brex avoided “AI envy” by tying work to business impact and letting anyone transfer in if they cared deeply enough Letting employees build their own AI stack: ChatGPT vs Claude vs Gemini, Cursor vs Windsurf, and why Brex refuses to pick winners in fast-moving tool races Measuring adoption without vanity metrics: why “% of code written by AI” is the wrong KPI and what second-order effects (slop, drift, code ownership) actually matter Evals in the real world: regression tests from ops QA, LLM-as-judge for multi-turn agents, and why integration-style evals break faster than you expect Teaching AI fluency at scale: the user → advocate → builder → native framework, ops-led training, spot bonuses, and avoiding fear-based adoption Re-interviewing the entire engineering org: using agentic coding interviews internally to force hands-on skill upgrades without formal performance scoring Headcount in the age of agents: why Brex grew the business without growing engineering, and why AI amplifies bad architecture as fast as good decisions The future of finance software: why dashboards fade, assistants take over, and agent-to-agent collaboration becomes the real UI — James Reggio X: https://x.com/jamesreggio LinkedIn: https://www.linkedin.com/in/jamesreggio/ Where to find Latent Space X: https://x.com/latentspacepod Substack: https://www.latent.space/ Chapters 00:00:00 Introduction 00:01:24 From Mobile Engineer to CTO: The Founder's Path 00:03:00 Quitters Welcome: Building a Founder-Friendly Culture 00:05:13 The AI Team Structure: 10-Person Startup Within Brex 00:11:55 Building the Brex Agent Platform: Multi-Agent Networks 00:13:45 Tech Stack Decisions: TypeScript, Mastra, and MCP 00:24:32 Operational AI: Automating Underwriting, KYC, and Fraud 00:16:40 The Brex Assistant: Executive Assistant for Every Employee 00:40:26 Evaluation Strategy: From Simple SOPs to Multi-Turn Evals 00:37:11 Agentic Coding Adoption: Cursor, Windsurf, and the Engineering Interview 00:58:51 AI Fluency Levels: From User to Native 01:09:14 The Audit Agent Network: Finance Team Agents in Action 01:03:33 The Future of Engineering Headcount and AI Leverage

This is a recap of the top 10 posts on Hacker News on January 16, 2026. This podcast was generated by wondercraft.ai (00:30): Cloudflare acquires AstroOriginal post: https://news.ycombinator.com/item?id=46646645&utm_source=wondercraft_ai(01:56): STFUOriginal post: https://news.ycombinator.com/item?id=46649142&utm_source=wondercraft_ai(03:23): Just the BrowserOriginal post: https://news.ycombinator.com/item?id=46645615&utm_source=wondercraft_ai(04:49): Cursor's latest “browser experiment” implied success without evidenceOriginal post: https://news.ycombinator.com/item?id=46646777&utm_source=wondercraft_ai(06:16): Canada slashes 100% tariffs on Chinese EVs to 6%Original post: https://news.ycombinator.com/item?id=46648778&utm_source=wondercraft_ai(07:42): OpenBSD-current now runs as guest under Apple HypervisorOriginal post: https://news.ycombinator.com/item?id=46642560&utm_source=wondercraft_ai(09:09): East Germany balloon escapeOriginal post: https://news.ycombinator.com/item?id=46648916&utm_source=wondercraft_ai(10:35): 6-Day and IP Address Certificates Are Generally AvailableOriginal post: https://news.ycombinator.com/item?id=46647491&utm_source=wondercraft_ai(12:02): List of individual treesOriginal post: https://news.ycombinator.com/item?id=46641284&utm_source=wondercraft_ai(13:29): Michelangelo's first painting, created when he was 12 or 13Original post: https://news.ycombinator.com/item?id=46646263&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Sam Lessin is a partner at Slow Ventures, a former VP of Product at Facebook, and a two-time founder who's now teaching etiquette to Silicon Valley's founders. In this unconventional episode, Sam explains why proper etiquette has become a vital skill for founders in 2026—especially as technology becomes more central to society and trust becomes harder to build. His etiquette book and courses have become surprisingly popular, teaching founders how to “show up in a room with a low heart rate” and quickly build trust.We discuss:1. Why etiquette matters2. Sam's framework for showing up confidently, with a low heart rate, in any room3. How to navigate introductions, small talk, meetings, and meals like a pro4. Simple hacks for remembering names and handling awkward social situations5. 30+ specific etiquette tips—Brought to you by:10Web—Vibe-coding platform as an APIDX—The developer intelligence platform designed by leading researchersWorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUs—Episode transcript: https://www.lennysnewsletter.com/p/silicon-valleys-missing-etiquette-playbook—Archive of all Lenny's Podcast transcripts:https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0—Where to find Sam Lessin:• X: https://x.com/lessin• LinkedIn: https://www.linkedin.com/in/wlessin• Website: https://www.wlessin.com• Podcast: https://moreorlesspod.com• Lettermeme: https://lettermeme.com/lessin—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Sam's background(04:18) The role of etiquette in business success(09:30) Introductions and entering a room(16:20) Engaging conversations and building relationships(23:55) Hygiene and dress code essentials(33:42) Dining etiquette(37:15) Tipping etiquette(41:36) The “B&D trick”(43:05) Humor in social settings(45:18) Self-deprecating humor(47:42) Winding down conversations(49:20) Scheduling etiquette(55:23) Communication and email etiquette(01:02:28) Meeting etiquette tips(01:04:03) Virtual meeting best practices(01:05:15) The importance of cleaning up after yourself(01:05:58) Exiting and follow-up etiquette(01:07:24) Final thoughts(01:09:20) AI corner(01:11:13) Contrarian corner(01:16:25) Lightning round—Referenced:• Y Combinator: https://www.ycombinator.com• Kleiner Perkins: https://www.kleinerperkins.com• “Lose Yourself” by Eminem on Spotify: https://open.spotify.com/track/7MJQ9Nfxzh8LPZ9e9u68Fq• Alison Gopnik on Childhood Learning, AI as a Cultural Technology, and Rethinking Nature vs. Nurture: https://conversationswithtyler.com/episodes/alison-gopnik• Garry Tan on LinkedIn: https://www.linkedin.com/in/garrytan• Bain & Company: https://www.bain.com• Evernote: https://evernote.com• Calendly: https://calendly.com• Morning Brew: https://www.morningbrew.com• Cursor: https://cursor.com• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• DigitalOcean: https://www.digitalocean.com• Cloudflare: https://www.cloudflare.com• SpaceX: https://www.spacex.com• Marc Andreessen on X: https://x.com/pmarca• Landman on Prime Video: https://www.amazon.com/Landman-Season-1/dp/B0D4D8RTMD• Dave Morin on X: https://x.com/davemorin—Recommended books:• Modern Etiquette in Technology, Finance, Society, and at Home: A Slow Ventures Handbook: https://www.amazon.com/Modern-Etiquette-Technology-Finance-Society-ebook/dp/B0G4HSKSY5• Life, the Universe and Everything: https://www.amazon.com/Universe-Everything-Hitchhikers-Guide-Galaxy-ebook/dp/B001ODEQ7A• The Ancient City: A Study on the Religion, Laws, and Institutions of Greece and Rome: https://www.amazon.com/Ancient-City-Religion-Institutions-Greece/dp/0801823048• Man's Search for Meaning: https://www.amazon.com/Mans-Search-Meaning-Viktor-Frankl-ebook/dp/B009U9S6FI• Area 51: An Uncensored History of America's Top Secret Military Base: https://www.amazon.com/Area-51-Uncensored-Americas-Military-ebook/dp/B004THU68Q• The Lessons of History: https://www.amazon.com/Lessons-History-Will-Durant/dp/143914995X• The Fish That Ate the Whale: The Life and Times of America's Banana King: https://www.amazon.com/Fish-That-Ate-Whale-Americas/dp/1250033314• The Last Kings of Shanghai: The Rival Jewish Dynasties That Helped Create Modern China: https://www.amazon.com/Last-Kings-Shanghai-Jewish-Dynasties/dp/0735224439—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

AGENDA: 05:02 Anthropic's $10 Billion Fundraise 07:54 Has Claude Code Beaten Cursor Already 15:54 OpenAI Could Still Go to Zero 26:33 Andreessen Horowitz's $15 Billion Fundraise 45:16 The Middle is Dead: Boutique vs. Large Platforms in Venture 50:01 The Future of Venture Capital 01:08:06 The Impact of Wealth Taxes on the Industry      

In this episode of Run the Numbers, CJ sits down with Bruno Annicq, CFO of Wellhub (formerly Gympass), to unpack a practical finance playbook built around cash discipline, sustainable growth, and simplicity. Bruno explains how he rebuilt forecasting using an AI-driven, probabilistic ensemble model, moving teams beyond single-scenario planning. They also dig into his EMPOWER planning framework, usable OKRs, and why tighter alignment between finance, HR, and wellbeing is becoming a durable lever for long-term performance.—SPONSORS:RightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.com—LINKS:Bruno on LinkedIn: https://www.linkedin.com/in/bannicq/Wellhub: https://wellhub.com/CJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:“Run Toward a Tough Market” — Developing the Hard and Soft Skills To Be a Great Finance Leaderhttps://youtu.be/iNHbkcG7YEo—TIMESTAMPS:00:00:00 Preview and Intro00:02:19 Sponsors — RightRev, Rillet, Tabs00:06:43 Accidental CFO Origin Story00:07:34 Consulting to Operations Pivot00:08:12 Why Finance Clicked for Bruno00:09:28 McKinsey Prioritization in Real World00:10:02 Eisenhower Matrix and Prioritization00:11:08 Investing in Non-Urgent Work00:13:30 Lessons From AOL Reinvention00:16:10 Sponsors — Abacum, Brex, Metronome00:20:01 Career Growth Through Hard Problems00:20:52 Broadening Skills Through Change00:23:12 Five Core Finance Principles00:24:02 Cash Is King00:25:14 Driving Sustainable Growth00:26:01 No Surprises and Forecasting00:26:07 Finance as Business Enabler00:27:22 Less Is More Philosophy00:28:47 Hardest Principle: Less Is More00:29:46 Deterministic vs Probabilistic Forecasting00:31:11 Marketplace Volatility and Forecast Error00:32:10 Ensemble Models Explained00:33:37 Forecast Accuracy Gains00:34:53 Building Models In-House00:36:46 Why Explainability Matters00:37:48 Empower Framework Introduction00:47:47 Urgency, Compounding, Long-Term Thinking00:48:10 Advice to Younger Self00:50:06 Finance Stack and Expense Stories00:52:51 Credits#RunTheNumbersPodcast #CFO #FinanceLeadership #Forecasting #AIinFinance This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cjgustafson.substack.com

Amir (Co-Founder at Humblytics) shares how he builds an “AI-native” company by focusing less on shiny tools and more on change management: assessing AI fluency across roles, setting the right success metrics, and creating shared context so AI can reliably ship work. The big theme is convergence—engineering, product, and design are collapsing into tighter loops thanks to tools like Cursor, MCP connectors, and Figma Make. Amir demos workflows like: AI-generated context files + auto-updated documentation, scraping customer domains to infer ICPs, turning screenshots into layered Figma designs, then converting Figma to working React code in minutes, and even running an “AI co-founder” Slack bot that files Linear tickets and can hand work to agents.Timestamps0:00 Introduction0:06 Amir's stance: “no AI experts” — it's constant learning in a fast-changing field.1:59 Cursor as the unlock: not just coding, but PM/strategy/design work via MCPs.4:17 The real problem: AI adoption is mostly change management + fluency assessment.5:18 The AI fluency rubric (helper → automator → augmentor → agentic) and why it matters.8:13 Cursor analytics: measuring AI-generated code and usage across the team.9:24 “New code is ~99% AI-generated” + how they keep quality via tight review + incremental changes.10:58 Docs workflow: GitBook connected to repo → AI edits docs and pushes live fast.14:02 ICP building: export Stripe customers → scrape domains with Firecrawl → cluster personas.17:45 Hallucination in the wild: AI misclassifies a company; human correction loop matters.34:43 Wild move: they often design in code and use an AI-generated style guide to stay consistent.38:10 Best demo: screenshot → Figma Make → layered design → Figma MCP → React code in minutes.45:29 “AI co-founder” Slack bot (Pixel): turns a bug report into a Linear ticket and can hand off to agents.48:46 Amir's wish list: we “solved dev”; now we need Cursor for marketing/sales → path to $1M ARR.Tools & technologies mentionedCursor — AI-first IDE used for coding and product/design/strategy workflows; includes team analytics.MCP (Model Context Protocol) — “connector” layer (Anthropic-origin) that lets LLMs interface with external tools/services.ChatGPT — used as a common baseline tool; discussed in the context of prompting practices and workflows.Microsoft Copilot — referenced via the law firm incentive story; used as an example of “usage metrics” gone wrong.Anthropic (AI fluency framework) — inspiration source for the helper/automator/augmentor/agentic rubric.GitBook — documentation platform connected to the repo so docs can be updated and published quickly.Firecrawl (MCP) — agentic web scraper used to analyze customer domains and infer ICP/personas.Stripe — source of customer export data (domains) to build ICP clustering.Figma — design collaboration tool; used here with Make + MCP to move from design → code.Figma Make — feature to recreate UI from an image/screenshot into editable, layered designs.Figma MCP — connector that allows Cursor/LLMs to pull Figma components/designs and generate code.React — front-end framework used in the demo for generating functional UI components.Supabase — mentioned as part of a sample stack when generating a PRD.React Router — mentioned as part of the sample stack in PRD generation.Slack — where Amir runs internal agents (including the “AI co-founder” bot).Linear — project management tool used for creating tickets from Slack/agent workflows.CI/CD — their deployment/review pipeline; emphasized as the human accountability layer.Subscribe at⁠ thisnewway.com⁠ to get the step-by-step playbooks, tools, and workflows.

Greg Foster, Co-founder and CTO of Graphite (recently acquired by Cursor), joins the podcast to discuss the massive shift occurring in software engineering: the move from maximizing "Inner Loop" speed (writing code) to solving "Outer Loop" bottlenecks (reviewing, testing, merging). With AI generating code faster than humans can review it, the traditional Pull Request model is under pressure. Greg explains how "Stacked PRs" and agentic review workflows are essential for high-performing teams, and why he believes the role of the software engineer is evolving into an "architect of agents." We also cover the strategic rationale behind the Graphite/Cursor merger, the controversial "PRs per engineer" metric, and why he predicts that by 2029, manual code writing will be near zero—but demand for engineers will be higher than ever.

ProductLed 100 - The Solo-Founder Playbook: How to Run a $1M ARR SaaS with 1 person Most founders believe scaling requires a massive headcount, co-founders, and VC funding. They think success is measured by the size of the team, not the efficiency of the revenue. In this episode of the ProductLed 100 series, Wes Bush sits down with Vincent Jong (Founder of Poolside Ventures) and Esben Friis-Jensen (Co-Founder of Userflow) to discuss the emerging era of the "One-Person Company" - businesses designed to generate millions in revenue with just a single operator. Vincent reveals his strategy for building a portfolio of lean, highly profitable SaaS companies like MeetBot. Together with Esben, they break down how AI tools like Lovable and Cursor have removed the technical barrier to entry, why "speed" is the new competitive moat against incumbents like Calendly, and the exact skill sets required to thrive as a solo builder. Whether you are a developer looking to launch your own venture or a founder trying to maximize efficiency, this episode offers a blueprint for building high-revenue, low-headcount businesses that are built to last forever. Key Highlights: 01:36: Why Vincent stopped looking for co-founders and started building alone03:09: The AI Tech Stack: How tools like Lovable and Cursor replace engineering teams06:07: Why building the product is the easy part (and selling is the hard part)13:17: Disrupting a Red Ocean: Why MeetBot entered the crowded scheduling market16:53: The Economics of Infinite Runway: Operating a SaaS for a few hundred dollars a month20:31: Speed vs. Scale: How one-person teams outmaneuver incumbents27:21: The "Launch Early" myth vs. the new bar for MVP quality37:44: Vincent's advice: Don't quit your job. Build on weekends Resources:

In this episode of Run the Numbers, CJ sits down with Jason Kong, General Partner at Base10 Ventures, to unpack the firm's focus on “automation for the real economy” — software built for industries most tech investors overlook, but the world depends on. Jason breaks down what makes Series B investing uniquely hard, how he evaluates back-office and vertical SaaS opportunities, and where markets tip from niche to overcrowded. They also discuss Base10's decision to donate 50% of profits to fund scholarships, plus a lightning round spanning fantasy football, shorting SaaS in 2022, and a venture take that might spark debate.—SPONSORS:Metronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metrics—LINKS:Jason on LinkedIn: https://www.linkedin.com/in/jasonykong/Base10 Partners: https://base10.vc/CJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:Scaling to $1B+ Revenue: From ServiceNow to Samsara | Dominic Phillipshttps://youtu.be/vBY6WZBMljw—TIMESTAMPS:00:00:00 Preview and Intro00:02:20 Sponsors — Metronome, RightRev, Rillet00:06:02 Base10 Background00:06:41 Automation for the Real Economy00:09:27 Vertical vs. Horizontal Software00:10:38 Cash Flow and Durability00:11:19 Product-Market Fit and ROI00:12:56 Growth Limits Selling to Tech00:13:19 The Size of the Real Economy00:14:16 Sponsors — Tabs, Abacum, Brex00:18:50 Base10's Giving Model00:20:30 Access, Education, and Tech00:21:53 Purpose and Founder Alignment00:22:51 Radical Transparency00:23:56 Portfolio Focus and Strategy00:24:05 Investing Ahead of Consensus00:26:29 ERP Adjacency as Alpha00:28:58 Lessons From Hedge Funds00:32:29 Public Markets Reality00:34:05 Public vs. Private Investing00:34:48 The Series B Sweet Spot00:36:49 A Bifurcated Series B Market00:38:56 Fast Series Bs and 2021 Vibes00:42:16 What Series B Looks Like Now00:44:36 Back Office Automation00:46:02 ERP-Centric Workflows00:48:33 Long-Ass Lightning Round00:49:36 Shorting SaaS in 202200:50:16 Fantasy Football and Investing00:52:57 Career Advice That Surprises00:55:03 A Contrarian Venture Take00:56:22 Credits#RunTheNumbersPodcast #SeriesB #RealEconomy #VerticalSaaS #BackOfficeAutomation This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cjgustafson.substack.com

In this episode of Run the Numbers, CJ sits down with Dominic Phillips, CFO of Samsara, to unpack what it takes to scale a capital-intensive SaaS business from startup to public company in under a decade. Dominic reflects on his six-plus years at Samsara through hypergrowth, COVID disruption, supply chain constraints, a down-round survival raise, and an IPO at the very end of the 2021 tech window. Drawing on his earlier career at ServiceNow under Mike Scarpelli, he shares how experience across FP&A, IR, corp dev, and treasury shaped his approach to capital allocation, investor education, and analyst management. The conversation dives into asset-based pricing, selling into non-discretionary operations budgets, balancing hardware and software economics, and building credibility with a broad analyst base while scaling past $1B in ARR.—SPONSORS:Brex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/runAbacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.ai—LINKS:Dominic on LinkedIn: https://www.linkedin.com/in/dominicphillips/Company: https://www.samsara.com/CJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:“Steal Your Boss's Job”: Calendly CFO John McCauley on Leadership, Ownership & Growthhttps://youtu.be/VRpTNDIfzPYFrom SMB to Enterprise: The CFO Scaling Playbook With Andrew Casey | Mostly Classicshttps://youtu.be/kMuJ6gAuEpgDriving revenue without selling | Greg Henry of 1Passwordhttps://youtu.be/f5FsNoG8A3E—TIMESTAMPS:00:00:00 Preview & Intro00:02:40 Sponsors — Brex | Metronome | RightRev00:06:18 Interview Begins00:06:46 Dominic's Early Career00:08:47 From ServiceNow to CFO00:09:47 Joining Samsara00:10:54 COVID, Burn, and a Down Round00:12:40 IPO Messaging and Investor Education00:15:50 Sponsors — Rillet | Tabs | Abacum00:20:27 Hardware + Software Story00:21:29 What Samsara Does00:22:30 Data, AI, and ROI00:23:23 Horizontal Platform and Verticals00:24:27 Growth Drivers at Scale00:26:09 Selling Into Operations00:28:19 Change Management in Legacy Orgs00:29:46 Non-Discretionary Budgets00:33:02 Storytelling Lessons from Scarpelli00:36:14 Managing Analysts00:39:23 Earnings Timing Strategy00:41:06 Metrics and Investor Trust00:42:38 Investor Communication Channels00:44:22 Investor Days and Long-Term Vision00:45:37 Annual Planning Maturity00:48:24 Forecast Accuracy and Cadence00:49:28 The 1000-Day Strategy00:50:40 Top-Line and Margin Targets00:51:59 Capital Allocation by Function00:54:46 Becoming a CFO00:58:21 Lightning Round and a CFO Mistake01:02:41 End Credits#RunTheNumbersPodcast #CFO #ScalingCompanies #B2BSaaS #PublicMarkets This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cjgustafson.substack.com

In this episode, we separate the AI hype from the reality of the 2025 job market and look at why the "AGI" promises of tech founders haven't yet materialized. From "AI washing" in corporate layoffs to critical privacy alerts for Gmail users, here is what you need to know:The "AI Washing" Trend: Ryan explores why companies are using AI as an excuse for layoffs, arguing that replacing human customer service and coders with AI is often a move for headlines rather than actual efficiency.The Innovation Plateau: We discuss whether AI development has hit a wall; while early progress was lightning-fast, current updates feel like minor adjustments rather than revolutionary leaps.Coding vs. Vibe Coding: While tools like Claude Code are making development easier for non-coders, the CEO of Cursor warns that "vibe coding" can lead to shaky foundations and crumbling infrastructure without human oversight.Privacy Red Alert: A crucial breakdown on why Google has automatically opted Gmail users into AI training and the specific steps you must take to opt-out and protect your private data.AI Failures & Market Shifts: From a lawyer losing his career over fake AI citations to ChatGPT's recent 22% traffic drop following the Gemini 3 launch, we look at the growing skepticism surrounding LLM reliability.Claude coding - https://x.com/emollick/status/2008253907701821650@emollick

Our 230th episode with a summary and discussion of last week's big AI news!Recorded on 01/02/2026Hosted by Andrey Kurenkov and Jeremie HarrisFeel free to email us your questions and feedback at contact@lastweekinai.com and/or hello@gladstone.aiRead out our text newsletter and comment on the podcast at https://lastweekin.ai/In this episode:Nvidia's acquisition of AI chip startup Groq for $20 billion highlights a strategic move for enhanced inference technology in GPUs.New York's RAISE Act legislation aims to regulate AI safety, marking the second major AI safety bill in the US.The launch of GLM 4.7 by Zhipu AI marks a significant advancement in open-source AI models for coding.Evaluation of long-horizon AI agents raises concerns about the rising costs and efficiency of AI in performing extended tasks.Timestamps:(00:00:10) Intro / Banter(00:01:58) 2025 RetrospectiveTools & Apps(00:24:39) OpenAI bets big on audio as Silicon Valley declares war on screens | TechCrunchApplications & Business(00:26:39) Nvidia buying AI chip startup Groq for about $20 billion, biggest deal(00:34:28) Exclusive | Meta Buys AI Startup Manus, Adding Millions of Paying Users - WSJ(00:38:05) Cursor continues acquisition spree with Graphite deal | TechCrunch(00:39:15) Micron Hikes CapEx to $20B with 2026 HBM Supply Fully Booked; HBM4 Ramps 2Q26(00:42:06) Chinese fabs are reportedly upgrading older ASML DUV lithography chipmaking machines — secondary channels and independent engineers used to soup up Twinscan NXT seriesProjects & Open Source(00:47:52) Z.AI launches GLM-4.7, new SOTA open-source model for coding(00:50:11) Evaluating AI's ability to perform scientific research tasksResearch & Advancements(00:54:32) Large Causal Models from Large Language Models(00:57:33) Universally Converging Representations of Matter Across Scientific Foundation Models(01:02:11) META-RL INDUCES EXPLORATION IN LANGUAGE AGENTS(01:07:16) Are the Costs of AI Agents Also Rising Exponentially?(01:11:17) METR eval for Opus 4.5(01:16:19) How to game the METR plotPolicy & Safety(01:17:24) New York governor Kathy Hochul signs RAISE Act to regulate AI safety | TechCrunch(01:20:40) Activation Oracles: Training and Evaluating LLMs as General-Purpose Activation Explainers(01:26:46) Monitoring Monitorability(01:32:07) Sam Altman is hiring someone to worry about the dangers of AI | The Verge(01:33:38) X users asking Grok to put this girl in bikini, Grok is happy obliging - India TodaySee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

It's been a travel-heavy hiatus—Mark's been living in Spain and Shashank's been bouncing across Asia (including a month in China)—but they're back to unpack a packed week of AI news. They start with the headline hardware story: the Groq (GROQ) deal/partnership dynamics and why ultra-fast inference is becoming the next battleground, plus how this could reshape access to cutting-edge serving across the ecosystem. From there, they pivot to NVIDIA's CES announcements and what “Vera Rubin” implies for data center upgrades, cost-per-token curves, and the messy real-world math of rolling hardware generations. Shashank then brings the future to life with on-the-ground stories from China: a Huawei “everything store” that feels like an Apple Store meets a luxury dealership, folding devices that look straight out of sci-fi, and a parade of robots—from coffee bots to delivery robots that can ride elevators and deliver to your hotel room. They also touch on companion-style consumer robots and why “cute” might be a serious product strategy. Finally, Mark announces the launch of Novacut, a long-form AI video editor built to turn hours of travel footage into a coherent vlog draft—plus export workflows for Premiere, DaVinci Resolve, and Final Cut. They close by talking about the 2026 shift from single model calls to “agentic” systems, including a fun (and slightly alarming) lesson from LLM outcome bias using poker hand reviews. Topics include: Groq inference, NVIDIA + CES, Vera Rubin GPUs, GPU depreciation math, China robotics, Huawei ecosystem, hotel delivery bots, companion robots, Novacut launch, Cursor vs agent workflows, and why agents still struggle with sparse feedback loops. Link mentioned: Novacut — https://novacut.ai

In this episode of Run the Numbers, CJ Gustafson sits down with Gordon Coyle, a 40-year commercial insurance veteran, to demystify one of the most anxiety-inducing topics for founders and CFOs: business insurance. Drawing on decades of experience with startups, scaleups, and regulated industries, Gordon breaks down what leaders need to know about D&O, E&O, cyber, and general liability, why investor pressure is rising, and where “cheap and easy” online policies fail when real risk hits. Through real-world examples, they explore how claims arise, how defense costs erode limits, why cyber insurance is as much about response as reimbursement, and how to balance budget, risk tolerance, and peer benchmarks—treating insurance as a critical layer of protection, not a box-checking exercise.—SPONSORS:Abacum is a modern FP&A platform built by former CFOs to replace slow, consultant-heavy planning tools. With self-service integrations and AI-powered workflows for forecasting, variance analysis, and scenario modeling, Abacum helps finance teams scale without becoming software admins. Trusted by teams at Strava, Replit, and JG Wentworth—learn more at https://www.abacum.aiBrex is an intelligent finance platform that combines corporate cards, built-in expense management, and AI agents to eliminate manual finance work. By automating expense reviews and reconciliations, Brex gives CFOs more time for the high-impact work that drives growth. Join 35,000+ companies like Anthropic, Coinbase, and DoorDash at https://www.brex.com/metricsMetronome is real-time billing built for modern software companies. Metronome turns raw usage events into accurate invoices, gives customers bills they actually understand, and keeps finance, product, and engineering perfectly in sync. That's why category-defining companies like OpenAI and Anthropic trust Metronome to power usage-based pricing and enterprise contracts at scale. Focus on your product — not your billing. Learn more and get started at https://www.metronome.comRightRev is an automated revenue recognition platform built for modern pricing models like usage-based pricing, bundles, and mid-cycle upgrades. RightRev lets companies scale monetization without slowing down close or compliance. For RevRec that keeps growth moving, visit https://www.rightrev.comRillet is an AI-native ERP built for modern finance teams that want to close faster without fighting legacy systems. Designed to support complex revenue recognition, multi-entity operations, and real-time reporting, Rillet helps teams achieve a true zero-day close—with some customers closing in hours, not days. If you're scaling on an ERP that wasn't built in the 90s, book a demo at https://www.rillet.com/cjTabs is an AI-native revenue platform that unifies billing, collections, and revenue recognition for companies running usage-based or complex contracts. By bringing together ERP, CRM, and real product usage data into a single system of record, Tabs eliminates manual reconciliations and speeds up close and cash collection. Companies like Cortex, Statsig, and Cursor trust Tabs to scale revenue efficiently. Learn more at https://www.tabs.com/run—LINKS:Gordon on LinkedIn: https://www.linkedin.com/in/gordoncoyle/The Coyle Group: https://thecoylegroup.com/CJ on LinkedIn: https://www.linkedin.com/in/cj-gustafson-13140948/Mostly metrics: https://www.mostlymetrics.com—RELATED EPISODES:The Coyle Group - Business Insurancehttps://www.youtube.com/@TheCoyleGroupNY—TIMESTAMPS:00:00:00 Preview and Intro00:01:53 Sponsors — Abacum | Brex | Metronome00:05:39 Interview Begins with Gordon Coyle00:06:23 Gordon Coyle & The Coyle Group00:07:21 Explaining Insurance on YouTube00:08:40 Turning Education into Inbound Leads00:09:40 Content as a Pull Strategy00:10:53 Insurance Complexity for Tech Founders00:13:28 Why Investors Require D&O Insurance00:14:09 What D&O Covers and Why It Matters00:15:50 Sponsors — RightRev | Rillet | Tabs00:20:19 Who D&O Covers and Rising Investor Pressure00:22:37 D&O Limits and Cost Tradeoffs00:23:21 Panic Calls and Late D&O Purchases00:24:39 How Defense Costs Erode Coverage00:25:31 Common D&O Claims and Employment Risk00:27:08 D&O vs E&O Explained00:29:12 Cyber Insurance and Social Engineering00:31:59 AI's Impact on Cyber Risk00:33:50 Real-World Ransomware Stories00:34:17 Cyber Insurance as Money and Response00:35:29 Business Email Compromise Scams00:39:43 Why Tech Still Needs General Liability00:41:16 What a BOP Covers00:42:32 Convenience vs Proper Coverage00:44:29 Surprising General Liability Claims00:46:45 Insurance Costs for Startups00:47:36 Higher Costs in High-Risk Industries00:48:26 Balancing Budget, Risk, and Coverage00:50:39 PEOs, Workers' Comp, and EPLI00:54:39 Choosing the Right Insurance Partner00:56:42 End Credits#RunTheNumbersPodcast #StartupFinance #BusinessInsurance #RiskManagement #CyberRisk This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit cjgustafson.substack.com

SaaStr 835: AI + B2B in 2026: Find the Tailwinds or Get Left Behind with SaaStr CEO and Founder Jason Lemkin Software spend is set to hit record levels in 2026, but you're not getting any of it unless you change. SaaStr CEO and Founder Jason Lemkin breaks down the paradox facing B2B companies right now: It's never been easier to scale to $100M (for a select few), while everyone else struggles. Half of all VC dollars are going into just 4 deals. IPOs ended the year with a whimper. And that AI copilot you built? It doesn't count. In this session, Jason shares the data on what's actually happening and what you need to do to capture your share of the hundreds of billions flowing into software. Key insights: Why "seed is for suckers" in today's VC environment The 3 types of AI products that unlock budget (and the one that doesn't) Why 30% of new IT budget is going to AI and how to steal it The TAM expansion math behind Cursor, Gamma, and AI SDR tools Why copilots and AI features alone won't save you The efficiency metrics every founder needs to track in 2026 If you didn't reaccelerate growth in 2025, you get a D. You can't get a D in 2026.

Jason Lemkin is the founder of SaaStr, the world's largest community for software founders, and a veteran SaaS investor who has deployed over $200 million into B2B startups. After his last salesperson quit, Jason made a radical decision: replace his entire go-to-market team with AI agents. What started as an experiment has transformed into a new operating model, where 20 AI agents managed by just 1.2 humans now do the work previously handled by a team of 10 SDRs and AEs. In this conversation, Jason shares his hands-on experience implementing AI to run his sales org, including what works, what doesn't, and how the GTM landscape is quickly being transformed.We discuss:1. How AI is fundamentally changing the sales function2. Why most SDRs and BDRs will be “extinct” within a year3. What Jason is observing across his portfolio about AI adoption in GTM4. How to become “hyper-employable” in the age of AI5. The specific AI tools and tactics he's using that have been working best6. Practical frameworks for integrating AI into your sales motion without losing what works7. Jason's 2026 predictions on where SaaS and GTM are heading next—Brought to you by:DX—The developer intelligence platform designed by leading researchersVercel—Your collaborative AI assistant to design, iterate, and scale full-stack applications for the webDatadog—Now home to Eppo, the leading experimentation and feature flagging platform—Transcript: https://www.lennysnewsletter.com/p/we-replaced-our-sales-team-with-20-ai-agents—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/182902716/my-biggest-takeaways-from-this-conversation—Where to find Jason Lemkin:• X: https://x.com/jasonlk• LinkedIn: https://www.linkedin.com/in/jasonmlemkin• Website: https://www.saastr.com• Substack: https://substack.com/@cloud—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Jason Lemkin(04:36) What SaaStr does(07:13) AI's impact on sales teams(10:11) How SaaStr's AI agents work and their performance(14:18) How go-to-market is changing in the AI era(19:19) The future of SDRs, BDRs, and AEs in sales(22:03) Why leadership roles are safe(23:43) How to be in the 20% who thrive in the AI sales future(28:40) Why you shouldn't build your own AI tools(30:10) Specific AI agents and their applications(36:40) Challenges and learnings in AI deployment(42:11) Making AI-generated emails good (not just acceptable)(47:31) When humans still beat AI in sales(52:39) An overview of SaaStr's org(53:50) The role of human oversight in AI operations(58:37) Advice for salespeople and founders in the AI era(01:05:40) Forward-deployed engineers(01:08:08) What's changing and what's staying the same in sales(01:16:21) Why AI is creating more work, not less(01:19:32) Why Jason says these are magical times(01:25:25) The "incognito mode test" for finding AI opportunities(01:27:19) The impact of AI on jobs(01:30:18) Lightning round and final thoughts—Referenced:• Building a world-class sales org | Jason Lemkin (SaaStr): https://www.lennysnewsletter.com/p/building-a-world-class-sales-org• SaaStr Annual: https://www.saastrannual.com• Delphi: https://www.delphi.ai/saastr/talk• Amelia Lerutte on LinkedIn: https://www.linkedin.com/in/amelialerutte/• Vercel: https://vercel.com• What world-class GTM looks like in 2026 | Jeanne DeWitt Grosser (Vercel, Stripe, Google): https://www.lennysnewsletter.com/p/what-the-best-gtm-teams-do-differently• Everyone's an engineer now: Inside v0's mission to create a hundred million builders | Guillermo Rauch (founder and CEO of Vercel, creators of v0 and Next.js): https://www.lennysnewsletter.com/p/everyones-an-engineer-now-guillermo-rauch• Replit: https://replit.com• Behind the product: Replit | Amjad Masad (co-founder and CEO): https://www.lennysnewsletter.com/p/behind-the-product-replit-amjad-masad• ElevenLabs: https://elevenlabs.io• The exact AI playbook (using MCPs, custom GPTs, Granola) that saved ElevenLabs $100k+ and helps them ship daily | Luke Harries (Head of Growth): https://www.lennysnewsletter.com/p/the-ai-marketing-stack• Bolt: https://bolt.new• Lovable: https://lovable.dev• Harvey: https://www.harvey.ai• Samsara: https://www.samsara.com/products/platform/ai-samsara-intelligence• UiPath: https://www.uipath.com• Denise Dresser on LinkedIn: https://www.linkedin.com/in/denisedresser• Agentforce: https://www.salesforce.com/form/agentforce• SaaStr's AI Agent Playbook: https://saastr.ai/agents• Brian Halligan on LinkedIn: https://www.linkedin.com/in/brianhalligan• Brian Halligan's AI: https://www.delphi.ai/minds/bhalligan• Sierra: https://sierra.ai• Fin: https://fin.ai• Deccan: https://www.deccan.ai• Artisan: https://www.artisan.co• Qualified: https://www.qualified.com• Claude: https://claude.ai• HubSpot: https://www.hubspot.com• Gamma: https://gamma.app• Sam Blond on LinkedIn: https://www.linkedin.com/in/sam-blond-791026b• Brex: https://www.brex.com• Outreach: https://www.outreach.io• Gong: https://www.gong.io• Salesloft: https://www.salesloft.com• Mixmax: https://www.mixmax.com• “Sell the alpha, not the feature”: The enterprise sales playbook for $1M to $10M ARR | Jen Abel: https://www.lennysnewsletter.com/p/the-enterprise-sales-playbook-1m-to-10m-arr• Clay: https://www.clay.com• Owner: https://www.owner.com• Momentum: https://www.momentum.io• Attention: https://www.attention.com• Granola: https://www.granola.ai• Behind the founder: Marc Benioff: https://www.lennysnewsletter.com/p/behind-the-founder-marc-benioff• Palantir: https://www.palantir.com• Databricks: https://www.databricks.com• Garry Tan on LinkedIn: https://www.linkedin.com/in/garrytan• Rippling: https://www.rippling.com• Cursor: https://cursor.com• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• The new AI growth playbook for 2026: How Lovable hit $200M ARR in one year | Elena Verna (Head of Growth): https://www.lennysnewsletter.com/p/the-new-ai-growth-playbook-for-2026-elena-verna• Pluribus on AppleTV+: https://tv.apple.com/us/show/pluribus/umc.cmc.37axgovs2yozlyh3c2cmwzlza• Sora: https://openai.com/sora• Reve: https://app.reve.com• Everything That Breaks on the Way to $1B ARR, with Mailchimp Co-Founder Ben Chestnut: https://www.saastr.com/everything-that-breaks-on-the-way-to-1b-arr-with-mailchimp-co-founder-ben-chestnut/• The Revenue Playbook: Rippling's Top 3 Growth Tactics at Scale, with Rippling CRO Matt Plank: https://www.youtube.com/watch?v=h3eYtzBpjRw• 10 contrarian leadership truths every leader needs to hear | Matt MacInnis (Rippling): https://www.lennysnewsletter.com/p/10-contrarian-leadership-truths—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

From creating SWE-bench in a Princeton basement to shipping CodeClash, SWE-bench Multimodal, and SWE-bench Multilingual, John Yang has spent the last year and a half watching his benchmark become the de facto standard for evaluating AI coding agents—trusted by Cognition (Devin), OpenAI, Anthropic, and every major lab racing to solve software engineering at scale. We caught up with John live at NeurIPS 2025 to dig into the state of code evals heading into 2026: why SWE-bench went from ignored (October 2023) to the industry standard after Devin's launch (and how Walden emailed him two weeks before the big reveal), how the benchmark evolved from Django-heavy to nine languages across 40 repos (JavaScript, Rust, Java, C, Ruby), why unit tests as verification are limiting and long-running agent tournaments might be the future (CodeClash: agents maintain codebases, compete in arenas, and iterate over multiple rounds), the proliferation of SWE-bench variants (SWE-bench Pro, SWE-bench Live, SWE-Efficiency, AlgoTune, SciCode) and how benchmark authors are now justifying their splits with curation techniques instead of just "more repos," why Tau-bench's "impossible tasks" controversy is actually a feature not a bug (intentionally including impossible tasks flags cheating), the tension between long autonomy (5-hour runs) vs. interactivity (Cognition's emphasis on fast back-and-forth), how Terminal-bench unlocked creativity by letting PhD students and non-coders design environments beyond GitHub issues and PRs, the academic data problem (companies like Cognition and Cursor have rich user interaction data, academics need user simulators or compelling products like LMArena to get similar signal), and his vision for CodeClash as a testbed for human-AI collaboration—freeze model capability, vary the collaboration setup (solo agent, multi-agent, human+agent), and measure how interaction patterns change as models climb the ladder from code completion to full codebase reasoning. We discuss: John's path: Princeton → SWE-bench (October 2023) → Stanford PhD with Diyi Yang and the Iris Group, focusing on code evals, human-AI collaboration, and long-running agent benchmarks The SWE-bench origin story: released October 2023, mostly ignored until Cognition's Devin launch kicked off the arms race (Walden emailed John two weeks before: "we have a good number") SWE-bench Verified: the curated, high-quality split that became the standard for serious evals SWE-bench Multimodal and Multilingual: nine languages (JavaScript, Rust, Java, C, Ruby) across 40 repos, moving beyond the Django-heavy original distribution The SWE-bench Pro controversy: independent authors used the "SWE-bench" name without John's blessing, but he's okay with it ("congrats to them, it's a great benchmark") CodeClash: John's new benchmark for long-horizon development—agents maintain their own codebases, edit and improve them each round, then compete in arenas (programming games like Halite, economic tasks like GDP optimization) SWE-Efficiency (Jeffrey Maugh, John's high school classmate): optimize code for speed without changing behavior (parallelization, SIMD operations) AlgoTune, SciCode, Terminal-bench, Tau-bench, SecBench, SRE-bench: the Cambrian explosion of code evals, each diving into different domains (security, SRE, science, user simulation) The Tau-bench "impossible tasks" debate: some tasks are underspecified or impossible, but John thinks that's actually a feature (flags cheating if you score above 75%) Cognition's research focus: codebase understanding (retrieval++), helping humans understand their own codebases, and automatic context engineering for LLMs (research sub-agents) The vision: CodeClash as a testbed for human-AI collaboration—vary the setup (solo agent, multi-agent, human+agent), freeze model capability, and measure how interaction changes as models improve — John Yang SWE-bench: https://www.swebench.com X: https://x.com/jyangballin Chapters 00:00:00 Introduction: John Yang on SWE-bench and Code Evaluations 00:00:31 SWE-bench Origins and Devon's Impact on the Coding Agent Arms Race 00:01:09 SWE-bench Ecosystem: Verified, Pro, Multimodal, and Multilingual Variants 00:02:17 Moving Beyond Django: Diversifying Code Evaluation Repositories 00:03:08 Code Clash: Long-Horizon Development Through Programming Tournaments 00:04:41 From Halite to Economic Value: Designing Competitive Coding Arenas 00:06:04 Ofir's Lab: SWE-ficiency, AlgoTune, and SciCode for Scientific Computing 00:07:52 The Benchmark Landscape: TAU-bench, Terminal-bench, and User Simulation 00:09:20 The Impossible Task Debate: Refusals, Ambiguity, and Benchmark Integrity 00:12:32 The Future of Code Evals: Long Autonomy vs Human-AI Collaboration 00:14:37 Call to Action: User Interaction Data and Codebase Understanding Research

From investing through the modern data stack era (DBT, Fivetran, and the analytics explosion) to now investing at the frontier of AI infrastructure and applications at Amplify Partners, Sarah Catanzaro has spent years at the intersection of data, compute, and intelligence—watching categories emerge, merge, and occasionally disappoint. We caught up with Sarah live at NeurIPS 2025 to dig into the state of AI startups heading into 2026: why $100M+ seed rounds with no near-term roadmap are now the norm (and why that terrifies her), what the DBT-Fivetran merger really signals about the modern data stack (spoiler: it's not dead, just ready for IPO), how frontier labs are using DBT and Fivetran to manage training data and agent analytics at scale, why data catalogs failed as standalone products but might succeed as metadata services for agents, the consumerization of AI and why personalization (memory, continual learning, K-factor) is the 2026 unlock for retention and growth, why she thinks RL environments are a fad and real-world logs beat synthetic clones every time, and her thesis for the most exciting AI startups: companies that marry hard research problems (RAG, rule-following, continual learning) with killer applications that were simply impossible before. We discuss: The DBT-Fivetran merger: not the death of the modern data stack, but a path to IPO scale (targeting $600M+ combined revenue) and a signal that both companies were already winning their categories How frontier labs use data infrastructure: DBT and Fivetran for training data curation, agent analytics, and managing increasingly complex interactions—plus the rise of transactional databases (RocksDB) and efficient data loading (Vortex) for GPU-bound workloads Why data catalogs failed: built for humans when they should have been built for machines, focused on discoverability when the real opportunity was governance, and ultimately subsumed as features inside Snowflake, DBT, and Fivetran The $100M+ seed phenomenon: raising massive rounds at billion-dollar valuations with no 6-month roadmap, seven-day decision windows, and founders optimizing for signal ("we're a unicorn") over partnership or dilution discipline Why world models are overhyped but underspecified: three competing definitions, unclear generalization across use cases (video games ≠ robotics ≠ autonomous driving), and a research problem masquerading as a product category The 2026 theme: consumerization of AI via personalization—memory management, continual learning, and solving retention/churn by making products learn skills, preferences, and adapt as the world changes (not just storing facts in cursor rules) Why RL environments are a fad: labs are paying 7–8 figures for synthetic clones when real-world logs, traces, and user activity (à la Cursor) are richer, cheaper, and more generalizable Sarah's investment thesis: research-driven applications that solve hard technical problems (RAG for Harvey, rule-following for Sierra, continual learning for the next killer app) and unlock experiences that were impossible before Infrastructure bets: memory, continual learning, stateful inference, and the systems challenges of loading/unloading personalized weights at scale Why K-factor and growth fundamentals matter again: AI felt magical in 2023–2024, but as the magic fades, retention and virality are back—and most AI founders have never heard of K-factor — Sarah Catanzaro X: https://x.com/sarahcat21 Amplify Partners: https://amplifypartners.com/ Where to find Latent Space X: https://x.com/latentspacepod Substack: https://www.latent.space/ Chapters 00:00:00 Introduction: Sarah Catanzaro's Journey from Data to AI 00:01:02 The DBT-Fivetran Merger: Not the End of the Modern Data Stack 00:05:26 Data Catalogs and What Went Wrong 00:08:16 Data Infrastructure at AI Labs: Surprising Insights 00:10:13 The Crazy Funding Environment of 2024-2025 00:17:18 World Models: Hype, Confusion, and Market Potential 00:18:59 Memory Management and Continual Learning: The Next Frontier 00:23:27 Agent Environments: Just a Fad? 00:25:48 The Perfect AI Startup: Research Meets Application 00:28:02 Closing Thoughts and Where to Find Sarah

From Berkeley robotics and OpenAI's 2017 Dota-era internship to shipping RL breakthroughs on GPT-4o, o1, and o3, and now leading model development at Cursor, Ashvin Nair has done it all. We caught up with Ashvin at NeurIPS 2025 to dig into the inside story of OpenAI's reasoning team (spoiler: it went from a dozen people to 300+), why IOI Gold felt reachable in 2022 but somehow didn't change the world when o1 actually achieved it, how RL doesn't generalize beyond the training distribution (and why that means you need to bring economically useful tasks into distribution by co-designing products and models), the deeper lessons from the RL research era (2017–2022) and why most of it didn't pan out because the community overfitted to benchmarks, how Cursor is uniquely positioned to do continual learning at scale with policy updates every two hours and product-model co-design that keeps engineers in the loop instead of context-switching into ADHD hell, and his bet that the next paradigm shift is continual learning with infinite memory—where models experience something once (a bug, a mistake, a user pattern) and never forget it, storing millions of deployment tokens in weights without overloading capacity. We discuss: Ashvin's path: Berkeley robotics PhD → OpenAI 2017 intern (Dota era) → o1/o3 reasoning team → Cursor ML lead in three months Why robotics people are the most grounded at NeurIPS (they work with the real world) and simulation people are the most unhinged (Lex Fridman's take) The IOI Gold paradox: "If you told me we'd achieve IOI Gold in 2022, I'd assume we could all go on vacation—AI solved, no point working anymore. But life is still the same." The RL research era (2017–2022) and why most of it didn't pan out: overfitting to benchmarks, too many implicit knobs to tune, and the community rewarding complex ideas over simple ones that generalize Inside the o1 origin story: a dozen people, conviction from Ilya and Jakob Pachocki that RL would work, small-scale prototypes producing "surprisingly accurate reasoning traces" on math, and first-principles belief that scaled The reasoning team grew from ~12 to 300+ people as o1 became a product and safety, tooling, and deployment scaled up Why Cursor is uniquely positioned for continual learning: policy updates every two hours (online RL on tab), product and ML sitting next to each other, and the entire software engineering workflow (code, logs, debugging, DataDog) living in the product Composer as the start of product-model co-design: smart enough to use, fast enough to stay in the loop, and built by a 20–25 person ML team with high-taste co-founders who code daily The next paradigm shift: continual learning with infinite memory—models that experience something once (a bug, a user mistake) and store it in weights forever, learning from millions of deployment tokens without overloading capacity (trillions of pretraining tokens = plenty of room) Why off-policy RL is unstable (Ashvin's favorite interview question) and why Cursor does two-day work trials instead of whiteboard interviews The vision: automate software engineering as a process (not just answering prompts), co-design products so the entire workflow (write code, check logs, debug, iterate) is in-distribution for RL, and make models that never make the same mistake twice — Ashvin Nair Cursor: https://cursor.com X: https://x.com/ashvinnair_ Chapters 00:00:00 Introduction: From Robotics to Cursor via OpenAI 00:01:58 The Robotics to LLM Agent Transition: Why Code Won 00:09:11 RL Research Winter and Academic Overfitting 00:11:45 The Scaling Era and Moving Goalposts: IOI Gold Doesn't Mean AGI 00:21:30 OpenAI's Reasoning Journey: From Codex to O1 00:20:03 The Blip: Thanksgiving 2023 and OpenAI Governance 00:22:39 RL for Reasoning: The O-Series Conviction and Scaling 00:25:47 O1 to O3: Smooth Internal Progress vs External Hype Cycles 00:33:07 Why Cursor: Co-Designing Products and Models for Real Work 00:34:14 Composer and the Future: Online Learning Every Two Hours 00:35:15 Continual Learning: The Missing Paradigm Shift 00:44:00 Hiring at Cursor and Why Off-Policy RL is Unstable

En 2025, une nouvelle expression s'est imposée dans le vocabulaire de la tech : le « vibe coding ». Derrière ce terme intrigant se cache une pratique qui transforme en profondeur la manière de développer des logiciels.Le vibe coding, que l'on peut traduire par « programmation intuitive », désigne une approche où le développeur ne code plus ligne par ligne, mais décrit simplement ce qu'il souhaite obtenir à une intelligence artificielle. Popularisé par Andrei Karpathy, ancien responsable de l'IA chez Tesla et cofondateur d'OpenAI, ce concept est né dans les communautés de développeurs avant de se diffuser largement dans l'écosystème numérique.Concrètement, il suffit désormais de formuler une demande en langage naturel : créer un script Python, concevoir une page web avec un formulaire, modifier l'interface d'une application ou même développer un jeu ou une application mobile complète. Cette méthode permet un gain de temps spectaculaire et ouvre la création logicielle à des non-développeurs, capables de produire des outils fonctionnels pour le web, le mobile ou des usages métiers comme des CMS ou des ERP.De nombreux outils incarnent cette tendance, à commencer par GitHub Copilot, mais aussi Cursor, Windsurf ou des assistants généralistes comme ChatGPT, Claude ou Gemini, qui génèrent du code à intégrer ensuite de manière classique. D'autres solutions vont plus loin encore, en produisant directement des applications prêtes à l'emploi, comme le propose la startup suédoise Lovable.Dans cet épisode, Sébastien Stormacq, responsable des relations développeurs chez AWS, partage une expérience concrète : la création, en une heure et sans écrire une seule ligne de code, d'un jeu inspiré de Pac-Man grâce au vibe coding. Un exemple révélateur de la puissance, mais aussi des limites de cette approche.Le phénomène soulève des questions cruciales : qualité et sécurité du code généré, risques de bugs majeurs, mais aussi impact sur l'emploi. Si le vibe coding accélère le travail des équipes et augmente la productivité des développeurs expérimentés, il fragilise davantage les profils juniors. Une chose est sûre : plus qu'un simple outil, le vibe coding redéfinit en profondeur le métier de développeur.-----------♥️ Soutien : https://mondenumerique.info/don

Want to build your own website but don't know how to code? This episode is for you.Join JJ and Bubble expert Gio as they show how beginners can use AI-powered tools to design, build, and launch a personal website from scratch — for free.You'll learn how Vibe Coding works, how AI can help you write and edit code, and how tools like Cursor make building websites feel approachable, even if you've never coded before. JJ also shares his own journey from no-code tools to AI-assisted development, showing how anyone can level up their skills.By the end, you'll understand how to preview your site locally, save your work with GitHub, and deploy a live website on the internet — all with AI helping every step of the way.Perfect for students, creators, and curious beginners who want to build real projects using AI.What you'll learn:• What “Vibe Coding” is and why it's beginner-friendly• How AI helps you write, edit, and understand code • How to preview your website before publishing• How to host a personal website for free• How no-code and AI tools work togetherTimestamps:00:00 What is Vibe Coding?00:33 Gio's experience getting started03:59 Intro to GitHub (no stress)06:04 Creating and managing projects13:34 Using Cursor to build locally16:00 Editing and previewing with AI26:18 Deploying with Cloud tools28:30 Publishing your site live32:16 No-code vs AI-assisted building41:50 Where AI and no-code are headed48:10 Final thoughts + course update

Note: Steve and Gene's talk on Vibe Coding and the post IDE world was one of the top talks of AIE CODE: https://www.youtube.com/watch?v=7Dtu2bilcFs&t=1019s&pp=0gcJCU0KAYcqIYzv From building legendary platforms at Google and Amazon to authoring one of the most influential essays on AI-powered development (Revenge of the Junior Developer, quoted by Dario Amodei himself), Steve Yegge has spent decades at the frontier of software engineering—and now he's leading the charge into what he calls the "factory farming" era of code. After stints at SourceGraph and building Beads (a purely vibe-coded issue tracker with tens of thousands of users), Steve co-authored The Vibe Coding Book and is now building VC (VibeCoder), an agent orchestration dashboard designed to move developers from writing code to managing fleets of AI agents that coordinate, parallelize, and ship features while you sleep. We sat down with Steve at AI Engineer Summit to dig into why Claude Code, Cursor, and the entire 2024 stack are already obsolete, what it actually takes to trust an agent after 2,000 hours of practice (hint: they will delete your production database if you anthropomorphize them), why the real skill is no longer writing code but orchestrating agents like a NASCAR pit crew, how merging has become the new wall that every 10x-productive team is hitting (and why one company's solution is literally "one engineer per repo"), the rise of multi-agent workflows where agents reserve files, message each other via MCP, and coordinate like a little village, why Steve believes if you're still using an IDE to write code by January 1st, you're a bad engineer, how the 12–15 year experience bracket is the most resistant demographic (and why their identity is tied to obsolete workflows), the hidden chaos inside OpenAI, Anthropic, and Google as they scale at breakneck speed, why rewriting from scratch is now faster than refactoring for a growing class of codebases, and his 2025 prediction: we're moving from subsistence agriculture to John Deere-scale factory farming of code, and the Luddite backlash is only just beginning. We discuss: Why Claude Code, Cursor, and agentic coding tools are already last year's tech—and what comes next: agent orchestration dashboards where you manage fleets, not write lines The 2,000-hour rule: why it takes a full year of daily use before you can predict what an LLM will do, and why trust = predictability, not capability Steve's hot take: if you're still using an IDE to develop code by January 1st, 2025, you're a bad engineer—because the abstraction layer has moved from models to full-stack agents The demographic most resistant to vibe coding: 12–15 years of experience, senior engineers whose identity is tied to the way they work today, and why they're about to become the interns Why anthropomorphizing LLMs is the biggest mistake: the "hot hand" fallacy, agent amnesia, and how Steve's agent once locked him out of prod by changing his password to "fix" a problem Should kids learn to code? Steve's take: learn to vibe code—understand functions, classes, architecture, and capabilities in a language-neutral way, but skip the syntax The 2025 vision: "factory farming of code" where orchestrators run Cloud Code, scrub output, plan-implement-review-test in loops, and unlock programming for non-programmers at scale — Steve Yegge X: https://x.com/steve_yegge Substack (Stevie's Tech Talks): https://steve-yegge.medium.com/ GitHub (VC / VibeCoder): https://github.com/yegge-labs Where to find Latent Space X: https://x.com/latentspacepod Substack: https://www.latent.space/ Chapters 00:00:00 Introduction: Steve Yegge on Vibe Coding and AI Engineering 00:00:59 The Backlash: Who Resists Vibe Coding and Why 00:04:26 The 2000 Hour Rule: Building Trust with AI Coding Tools 00:03:31 The January 1st Deadline: IDEs Are Becoming Obsolete 00:02:55 10X Productivity at OpenAI: The Performance Review Problem 00:07:49 The Hot Hand Fallacy: When AI Agents Betray Your Trust 00:11:12 Claude Code Isn't It: The Need for Agent Orchestration 00:15:20 The Orchestrator Revolution: From Cloud Code to Agent Villages 00:18:46 The Merge Wall: The Biggest Unsolved Problem in AI Coding 00:26:33 Never Rewrite Your Code - Until Now: Joel Spolsky Was Wrong 00:22:43 Factory Farming Code: The John Deere Era of Software 00:29:27 Google's Gemini Turnaround and the AI Lab Chaos 00:33:20 Should Your Kids Learn to Code? The New Answer 00:34:59 Code MCP and the Gossip Rate: Latest Vibe Coding Discoveries

Aaron and Brian review the Year in AI, hand out AI awards, and discuss the biggest AI trends from 2025. Maybe a few predictions will be made as well.SHOW: 987SHOW TRANSCRIPT: The Cloudcast #987 TranscriptSHOW VIDEO: https://youtube.com/@TheCloudcastNET CLOUD NEWS OF THE WEEK: http://bit.ly/cloudcast-cnotwCHECK OUT OUR NEW PODCAST: "CLOUDCAST BASICS"SHOW SPONSORS:SHOW NOTESCLOUD & AI NEWS OF THE MONTH - NOV 2025 (show)CLOUD & AI NEWS OF THE MONTH - OCT 2025 (show)CLOUD & AI NEWS OF THE MONTH - SEPT 2025 (show)CLOUD & AI NEWS OF THE MONTH - AUG 2025 (show)CLOUD & AI NEWS OF THE MONTH - JUL 2025 (show)CLOUD & AI NEWS OF THE MONTH - JUN 2025 (show)CLOUD & AI NEWS OF THE MONTH - MAY 2025 (show)CLOUD & AI NEWS OF THE MONTH - APR 2025 (show)CLOUD & AI NEWS OF THE MONTH - MAR 2025 (show)CLOUD & AI NEWS OF THE MONTH - FEB 2025 (show)CLOUD & AI NEWS OF THE MONTH - JAN 2025 (show)2025 AI YEAR IN REVIEWThe Year of OpenAIThe Year of NVIDIAThe Year of MicrosoftThe Year of GoogleThe Year of OracleThe Year of China AIThe Year of AppleThe Year of Coding Agents (Anthropic, Cursor, Windsurf, CLIs, etc..)The Year of Data CentersAI Highlights and Lowlights (Corporate Layoffs, Acquihires, Funding, etc..)2026 AI DraftFEEDBACK?Email: show at the cloudcast dot netTwitter/X: @cloudcastpodBlueSky: @cloudcastpod.bsky.socialInstagram: @cloudcastpodTikTok: @cloudcastpod

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The Information's Aaron Holmes talks with TITV Host Akash Pasricha about Satya Nadella's deep-dive into Microsoft's product management to fix Copilot. We also talk with Graphite CEO Merrill Lutsky about selling his startup to Cursor, and Madrona Ventures' Matt McIlwain about the future of software investing in 2026. AI Reporter Rocket Drew speaks about the safety risks of humanoid robots, and EV reporter Steve LeVine about Ford's decision to ditch EV production for AI data centers.Articles discussed on this episode: https://www.theinformation.com/articles/microsofts-nadella-pressures-deputies-accelerate-copilot-improvementshttps://www.theinformation.com/articles/electric-fords-leap-powering-ai-data-centers-reflects-industry-adrifthttps://www.theinformation.com/briefings/waymo-suspends-san-francisco-service-city-outageTITV airs on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Subscribe to: - The Information on YouTube: https://www.youtube.com/@theinformation- The Information: https://www.theinformation.com/subscribe_hSign up for the AI Agenda newsletter: https://www.theinformation.com/features/ai-agenda

Send us a textStefan Georgie made $30M by age 23 and has scaled multiple 8-figure businesses. In this conversation, he reveals why AI is creating the biggest wealth transfer opportunity of our generation and why most people are missing it.The marketing world is being rewritten in real time. AI is eliminating old skill sets while creating unprecedented opportunities for those who act fast. Stefan breaks down exactly how young entrepreneurs can leverage AI tools like vibe coding, Cursor, and Claude to build valuable solutions in days, not months.What You'll Learn:• Why AI makes it easier than ever to build profitable businesses from scratch• The exact AI skills that are in highest demand right now (and how to learn them fast)• How to use AI to solve real problems for established businesses willing to pay• Why aging "boomer businesses" are goldmines for AI-savvy entrepreneurs• The hiring story: How a 22-year-old with $40 got a $3K/month job using AI• Why teams are desperate for people who can think AND execute with AI• The fastest path to making your first $10K using AI toolsStefan doesn't hold back, he shares why 98% of your competition can't think critically, why the bar is lower than you think, and how curiosity + AI skills = unlimited opportunity.Connect with Stefan! https://www.stefanpaulgeorgi.com/Connect with Us!https://www.instagram.com/alchemists.library/https://twitter.com/RyanJAyala

Elena Verna is the head of growth at Lovable, the leading AI-powered app builder that hit $200 million in annual recurring revenue in under a year with just 100 employees. In this record fourth appearance on the podcast, Elena shares how the traditional growth playbook has been completely rewritten for AI companies. She explains why Lovable focuses on innovation over optimization, how they've shifted from activation to building new features, and why giving away their product for free has become their most powerful growth strategy.We discuss:1. Why 60% to 70% of traditional growth tactics no longer apply in AI2. Why you have to re-find product-market fit every 3 months3. The specific growth tactics driving Lovable's unprecedented growth4. Why giving away product is a growth strategy that beats paid ads5. “Minimum lovable product” as the new standard (not minimum viable product)6. Why activation now belongs to product teams, not growth teams7. Whether you should join an AI startup (honest tradeoffs)—Brought to you by:WorkOS—Modern identity platform for B2B SaaS, free up to 1 million MAUsVercel—Your collaborative AI assistant to design, iterate, and scale full-stack applications for the webPersona—A global leader in digital identity verification—Transcript: ⁠https://www.lennysnewsletter.com/p/the-new-ai-growth-playbook-for-2026-elena-verna⁠—My biggest takeaways (for paid newsletter subscribers): ⁠https://www.lennysnewsletter.com/i/181207556/my-biggest-takeaways-from-this-conversation⁠—Where to find Elena Verna:• X: https://x.com/elenaverna• LinkedIn: https://www.linkedin.com/in/elenaverna• Newsletter: https://www.elenaverna.com—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Elena Verna(05:19) The scale and growth of Lovable(08:55) Confidence in Lovable as a business(12:17) Retention at Lovable(15:02) Lovable's unique growth levers(28:13) The role of marketing in Lovable's success(38:09) Launching new features(40:59) Hiring and team dynamics(43:17) The value of vibe coding(49:46) The importance of community(51:47) Giving away your product for free(56:26) Tripling their company size(01:00:23) Product-market-fit challenges(01:08:50) Advice for joining AI companies(01:12:00) Work-life balance(01:15:20) What it's like to work at Lovable(01:19:45) Women in tech(01:25:29) Final thoughts and lightning round—Referenced:• Elena Verna on how B2B growth is changing, product-led growth, product-led sales, why you should go freemium not trial, what features to make free, and much more: https://www.lennysnewsletter.com/p/elena-verna-on-why-every-company• The ultimate guide to product-led sales | Elena Verna: https://www.lennysnewsletter.com/p/the-ultimate-guide-to-product-led• 10 growth tactics that never work | Elena Verna (Amplitude, Miro, Dropbox, SurveyMonkey): https://www.lennysnewsletter.com/p/10-growth-tactics-that-never-work-elena-verna• Lovable: https://lovable.dev• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• Stripe: https://stripe.com• What differentiates the highest-performing product teams | John Cutler (Amplitude, The Beautiful Mess): https://www.lennysnewsletter.com/p/what-differentiates-the-highest-performing• How to win in the AI era: Ship a feature every week, embrace technical debt, ruthlessly cut scope, and create magic your competitors can't copy | Gaurav Misra (CEO and co-founder of Captions): https://www.lennysnewsletter.com/p/how-to-win-in-the-ai-era-gaurav-misra• “Dumbest idea I've heard” to $100M ARR: Inside the rise of Gamma | Grant Lee (CEO): https://www.lennysnewsletter.com/p/how-50-people-built-a-profitable-ai-unicorn• Eric Ries on LinkedIn: https://www.linkedin.com/in/eries• Elena's post on LinkedIn about Lovable Missions: https://www.linkedin.com/posts/elenaverna_everythingispossible-lovableway-activity-7401627519646474242-hn6e• SheBuilds: https://shebuilds.lovable.app• Shopify + Lovable: https://lovable.dev/shopify• The Product-Market Fit Treadmill: Why every AI company is sprinting just to stay in place: https://www.elenaverna.com/p/the-product-market-fit-treadmill• Cursor: https://cursor.com• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Unorthodox frameworks for growing your product, career, and impact | Bangaly Kaba (YouTube, Instagram, Facebook, Instacart): https://www.lennysnewsletter.com/p/frameworks-for-growing-your-career-bangaly-kaba• The adjacent user: https://brianbalfour.com/quick-takes/the-adjacent-user• Granola: https://www.granola.ai• Wispr Flow: https://wisprflow.ai• I'm worried about women in tech: https://www.elenaverna.com/p/im-worried-about-women-in-tech• Slack founder: Mental models for building products people love ft. Stewart Butterfield: https://www.lennysnewsletter.com/p/slack-founder-stewart-butterfield—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

Did AI end up being a political force this year?

AGENDA: 03:32 Lightspeed's $9 Billion Fundraise 05:20 The Impact of Mega Funds on Seed VCs 10:09 The Supercycle of Growth and Late-Stage Investments 13:06 Disney Invests $1BN into OpenAI and What It Means 23:19 Oracle Hit Hard: Is Now the Time to Buy 28:34 Broadcom's Market Cap Drop and Anthropic's AI Chip Orders 35:04 Cursor Competes with Figma: The Convergence of Design and Coding Tools 46:20 The Biggest Danger for Incumbents: Being Maimed by AI 55:28 Boom Supersonic Raising $300M to… Power Data Centres… WTF 01:00:24 Will SpaceX IPO at $1.5TRN and The Elon Option Value    

Ryo Lu spent years watching his designs die in meetings. Then he discovered the tool that lets designers ship code at the speed of thought: Cursor, the company where Ryo is now Head of Design. In this episode, a16z General Partner Jennifer Li sits down with Ryo to discuss why "taste" is the wrong framework for understanding the future, why purposeful apps are "selfish," how System 7 holds secrets about AI interfaces, and the radical bet that one codebase can serve everyone if you design the concepts right instead of the buttons. Timecodes:00:01:45 - Design Becomes Approachable to Everyone00:02:36 - From Years to Minutes: Product Feedback Loops Collapse00:07:54 - "Each role used their own tool...their own lingo"00:13:15 - "If you don't have an opinion, you'll get AI slop"00:17:18 - The Lost Art of Being a Complete Builder00:21:42 - Design Is Not About Aesthetics00:28:57 - User-Centric vs System-Centric Philosophy00:34:00 - AI as Universal Interface, Not Chat Box00:38:42 - "Simplicity is the Biggest Constraint"00:43:42 - "I Don't Sit in Figma All Day Making Mocks"00:46:33 - RyoOS: Building A Personal Operating System00:48:45 - "We've been doing the same thing since 1984" Resources:Follow Ryo Lu on X: https://x.com/ryolu_Follow Jennifer Li on X: https://x.com/JenniferHliFollow Erik Torenberg on X: https://x.com/eriktorenberg Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!  Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease 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 a16z.com/disclosures. Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.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 a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

A deadbolt clicks. This email, that voice--they sound all right. Then things go sideways. This week, 911 Cyber CEO Marc Raphael joins the pod to explore how AI makes scams faster, smoother, and harder to spot, and what you can do to stay hard to hit in the new threatscape.  Learn more about your ad choices. Visit megaphone.fm/adchoices

David George is a General Partner at Andreessen Horowitz, where he leads the firm's Growth investing team. His team has backed many of the defining companies of this era, including Databricks, Figma, Stripe, SpaceX, Anduril, and OpenAI, and is now investing behind a new generation of AI startups like Cursor, Harvey, and Abridge. AGENDA: 03:05 – Why Everyone is Wrong: Mega Funds Does Not Reduce Returns 10:40 – Is Public Market Capital Actually Cheaper Than Private Capital? 18:55 – The Biggest Advantage of Staying Private for Longer 23:30 – The #1 Investing Rule for a16z: Always Invest in the Founder's Strength of Strengths 31:20 – Why Fear of Theoretical Competition Makes Investors Miss Great Companies 35:10 – Does Revenue Matter as Much in a World of AI? 44:10 – Does Kingmaking Still Exist in Venture Capital Today? 49:20 – Do Margins Matter Less Than Ever in an AI-First World? 53:50 – My Biggest Miss: Anthropic and What I Learn From it?  56:30 – Has OpenAI Won Consumer AI? Will Anthropic Win Enterprise? 59:45 – The Most Controversial Decision in Andreessen Horowitz History 1:01:30 – Why Did You Invest $300M into Adam Neumann and Flow?    

What if understanding your codebase was no longer a blocker for great testing? Most testers were trained to work around the code — clicking through UIs, guessing selectors, and relying on outdated docs or developer explanations. In this episode, Playwright expert Ben Fellows flip that model on its head. Using AI tools like Cursor, testers can now explore the codebase directly — asking questions, uncovering APIs, understanding data relationships, and spotting risk before a single test is written. This isn't about becoming a developer. It's about using AI to finally see how the system really works — and using that insight to test smarter, earlier, and with far more confidence. If you've ever joined a new team, inherited a legacy app, or struggled to understand what really changed in a release, this episode is for you. Registration for Automation Guild 2026 Now: https://testguild.me/podag26

Tomer Cohen is the longtime chief product officer at LinkedIn, where he's pioneering the Full Stack Builder program, a radical new approach to product development that fully embraces what AI makes possible. Under his leadership, LinkedIn has scrapped its traditional Associate Product Manager program and replaced it with an Associate Product Builder program that teaches coding, design, and PM skills together. He's also introduced a formal “Full Stack Builder” title and career ladder, enabling anyone from any function to take products from idea to launch. In this conversation, Tomer explains why product development has become too complex at most companies and how LinkedIn is building an AI-powered product team that can move faster, adapt more quickly, and do more with less.We discuss:1. How 70% of the skills needed for jobs will change by 20302. The broken traditional model: organizational bloat slows features to a six-month cycle3. The Full Stack Builder model4. Three pillars of making FSB work: platform, agents, and culture (culture matters most)5. Building specialized agents that critique ideas and find vulnerabilities6. Why off-the-shelf AI tools never work on enterprise code without customization7. Top performers adopt AI tools fastest, contrary to expectations about leveling effects8. Change management tactics: celebrating wins, making tools exclusive, updating performance reviews—Brought to you by:Vanta—Automate compliance. Simplify security: https://vanta.com/lennyFigma Make—A prompt-to-code tool for making ideas real: https://www.figma.com/lenny/Miro—The AI Innovation Workspace where teams discover, plan, and ship breakthrough products: https://miro.com/lenny—Transcript: https://www.lennysnewsletter.com/p/why-linkedin-is-replacing-pms—My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/180042347/my-takeaways-from-this-conversation—Where to find Tomer Cohen:• LinkedIn: https://www.linkedin.com/in/tomercohen• Podcast: https://podcasts.apple.com/us/podcast/building-one-with-tomer-cohen/id1726672498—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to Tomer Cohen(04:42) The need for change in product development(11:52) The full-stack builder model explained(16:03) Implementing AI and automation in product development(19:17) Building and customizing AI tools(27:51) The timeline to launch(31:46) Pilot program and early results(37:04) Feedback from top talent(39:48) Change management and adoption(46:53) Encouraging people to play with AI tools(41:21) Performance reviews and full-stack builders(48:00) Challenges and specialization(50:05) Finding talent(52:46) Tips for implementing in your own company(56:43) Lightning round and final thoughts—Referenced:• How LinkedIn became interesting: The inside story | Tomer Cohen (CPO at LinkedIn): https://www.lennysnewsletter.com/p/how-linkedin-became-interesting-tomer-cohen• LinkedIn: https://www.linkedin.com• Cursor: https://cursor.com• The rise of Cursor: The $300M ARR AI tool that engineers can't stop using | Michael Truell (co-founder and CEO): https://www.lennysnewsletter.com/p/the-rise-of-cursor-michael-truell• Devin: https://devin.ai• Figma: https://www.figma.com• Microsoft Copilot: https://copilot.microsoft.com• Windsurf: https://windsurf.com• Building a magical AI code editor used by over 1 million developers in four months: The untold story of Windsurf | Varun Mohan (co-founder and CEO): https://www.lennysnewsletter.com/p/the-untold-story-of-windsurf-varun-mohan• Lovable: https://lovable.dev• Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (co-founder and CEO): https://www.lennysnewsletter.com/p/building-lovable-anton-osika• APB program at LinkedIn: https://careers.linkedin.com/pathways-programs/entry-level/apb• Naval Ravikant on X: https://x.com/naval• One Song podcast: https://podcasts.apple.com/us/podcast/%D7%A9%D7%99%D7%A8-%D7%90%D7%97%D7%93-one-song/id1201883177• Song Exploder podcast: https://songexploder.net• Grok on Tesla: https://www.tesla.com/support/grok• Reid Hoffman on X: https://x.com/reidhoffman—Recommended books:• Why Nations Fail: The Origins of Power, Prosperity, and Poverty: https://www.amazon.com/Why-Nations-Fail-Origins-Prosperity/dp/0307719227• Outlive: The Science and Art of Longevity: https://www.amazon.com/Outlive-Longevity-Peter-Attia-MD/dp/0593236599• The Beginning of Infinity: Explanations That Transform the World: https://www.amazon.com/Beginning-Infinity-Explanations-Transform-World/dp/0143121359—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. To hear more, visit www.lennysnewsletter.com

This Week In Startups is made possible by:LinkedIn Ads - http://linkedin.com/thisweekinstartupsVanta - https://www.vanta.com/twistPilot - https://pilot.com/twistToday's show: Did you know there's actually a shortage of US bricklayers? It's TRUE! So feel free to marvel at Monumental's brick-laying robots. They're not putting anyone out of work, but filling a much-needed gap.Join Alex and Monumental founder/CEO Salar al Khafaji for a deep-dive on how the startup is making construction robots play nice together by maintaining separate “zones” of operation, why Salar thinks startups need to focus on truly complex, real-world problems to truly blossom, and the secrets of fundraising in Europe.PLUS Alex chats with Seasats CEO Mike Flanigan about designing the next generation of autonomous marine crafts. (That is to say, ocean drones.) From their home base in San Diego, the company is trying to get completely independent of all Chinese parts. Find out how it's going, how they're overcoming the “wildly negative” ROI on maritime tech, and why we have so few defenses against tiny, agile drones.All that AND Jason takes some of YOUR Founder Questions.Timestamps:(03:23) How Monumental determined what kinds of robots construction sites need the most(06:49) How maintaining “zones” ensure that the robots all play nice with one another(07:52) There's a shortage of bricklayers, so Monumental's NOT taking anyone's job(9:16) LinkedIn Ads: Start converting your B2B audience into high quality leads today. Launch your first campaign and get $250 FREE when you spend at least $250. Go to http://linkedin.com/thisweekinstartups to claim your credit.(13:21) Why startups need to tackle large-scale, complex, real-world problems to really grow(15:44) Why Monumental is building in The Netherlands, and running pilots in the UK(19:07) Vanta - Get $1000 off your SOC 2 at https://www.vanta.com/twist(20:44) Why construction is unique among applications for automation and robots(26:01) Salar argues that fundraising in Europe is not as hard as you may have heard(27:55) We don't just need housing, we need BEAUTIFUL housing(31:11) Pilot - Visit https://www.pilot.com/twist and get $1,200 off your first year. (33:25) How the Scout autonomous boat challenge inspired Seasats(35:28) Trying to make drones into an “iPhone Style” project(37:39) Why Seasats is focused on endurance and staying power more than launches(39:15) The complexities of working with fuel cells(42:27) The importance of beautiful design even when working on government technology(45:51) Why they're building Seasats in beautiful San Diego, CA(47:29) The challenge of getting entirely free from Chinese components(53:52) “The Power of Small Things Has Changed”(55:18) The “wildly negative” ROI on most humanoid robotics companies also applies to maritime tech(59:09) Why there are so few defense nets against people with tiny but agile drones(01:02:32) FOUNDER Q's: Is a founder working 24/7 a red flag?(01:10:11) How bad is it to use VC money to pay off credit cards?(01:12:49) A look at Cursor's unique recruitment strategy.(01:19:57) Should young VCs go to startup conferences?Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com/Check out the TWIST500: https://twist500.comSubscribe to This Week in Startups on Apple: https://rb.gy/v19fcp*Follow Lon:X: https://x.com/lons*Follow Alex:X: https://x.com/alexLinkedIn: https://www.linkedin.com/in/alexwilhelm/*Thank you to our partners:(9:16) LinkedIn Ads: Start converting your B2B audience into high quality leads today. Launch your first campaign and get $250 FREE when you spend at least $250. Go to http://linkedin.com/thisweekinstartups to claim your credit.(19:07) Vanta - Get $1000 off your SOC 2 at https://www.vanta.com/twist(31:11) Pilot - Visit https://www.pilot.com/twist and get $1,200 off your first year.

My guest today is David George. David is a General Partner at Andreessen Horowitz, where he leads the firm's growth investing business. His team has backed many of the defining companies of this era – including Databricks, Figma, Stripe, SpaceX, Anduril, and OpenAI – and is now investing behind a new generation of AI startups like Cursor, Harvey, and Abridge. This conversation is a detailed look at how David built and runs the a16z growth practice. He shares how he recruits and builds his team a “Yankees-level” culture, how his team makes investment decisions without traditional committees, and how they work with founders years before investing to win the most competitive deals. Much of our conversation centers on AI and how his team is investing across the stack, from foundational models to applications. David draws parallels to past platform shifts – from SaaS to mobile – and explains why he believes this period will produce some of the largest companies ever built. David also outlines the models that guide his approach – why markets often misprice consistent growth, what makes “pull” businesses so powerful, and why most great tech markets end up winner-take-all. David reflects on what he's learned from studying exceptional founders and why he's drawn to a particular type, the “technical terminator.” Please enjoy my conversation with David George. For the full show notes, transcript, and links to mentioned content, check out the episode page ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠.⁠⁠⁠⁠⁠⁠⁠⁠ ----- This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Ramp⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Ramp's mission is to help companies manage their spend in a way that reduces expenses and frees up time for teams to work on more valuable projects. Go to⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ramp.com/invest to sign up for free and get a $250 welcome bonus. ----- This episode is brought to you by⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Ridgeline⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. Ridgeline has built a complete, real-time, modern operating system for investment managers. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Head to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgelineapps.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to learn more about the platform. ----- This episode is brought to you by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AlphaSense⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠. AlphaSense has completely transformed the research process with cutting-edge AI technology and a vast collection of top-tier, reliable business content. Invest Like the Best listeners can get a free trial now at⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Alpha-Sense.com/Invest⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and experience firsthand how AlphaSense and Tegus help you make smarter decisions faster. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Show Notes: (00:00:00) Welcome to Invest Like The Best (00:04:00) Meet David George (00:03:04) Understanding the Impact of AI on Consumers and Enterprises (00:05:56) Monetizing AI: What is AI's Business Model (00:11:04) Investing in Robotics and American Dynamism (00:13:31) Lessons from Investing in Waymo (00:15:55) Investment Philosophy and Strategy (00:17:15) Investing in Technical Terminators (00:20:18) Market Leaders Capture All of the Value Creation (00:24:56) The Maturation of VC and Competitive Landscape (00:28:18) What a16z Does to Win Deals (00:33:06) David's Daily Routine: Meetings Structure and Blocking Time to Think (00:36:34) Why David Invests: Curiosity and Competition (00:40:12) The Unique Culture at Andreessen Horowitz (00:42:46) The Perfect Conditions for Growth Investing (00:47:04) Push v. Pull Businesses (00:49:19) The Three Metrics a16z Uses to Evaluate AI Companies (00:52:15) Unique Products and Unique Distribution (00:54:55) Tradeoffs of the a16z Firm Structure (00:59:04) a16z's Semi-Algorithmic Approach to Selling (01:00:54) Three Ways Startups can Beat Incumbents in AI (01:03:44) The Kindest Thing