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Top 10 Startups, Founders & Venture Capital Podcasts of 2026
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EquityTechCrunch, Rebecca Bellan, Kirsten Korosec, Anthony Ha, Sean O'Kane, Theresa LoconsoloA researched podcast for Startups, Founders & Venture Capital.
TechSurge: Deep Tech PodcastCelesta Capital | Deep Tech Venture Capital FirmA researched podcast for Startups, Founders & Venture Capital.
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All latest episodesEquity33 min
What’s next for cybersecurity, according to Index Ventures’ Shardul Shah
As concern over AI safety and rogue agents continue to make headlines, it’s no surprise that cybersecurity stocks are rising, or that investors are pouring massive amounts of capital into startups trying to build the next generation of security for an AI-native world. We’re even seeing companies like Instinct and Simile bring in nine-figure checks and valuations that wouldn't have made sense a few years ago. With a front row seat to it all is Shardul Shah , a partner at Index Ventures who’s spent nearly two decades investing in cybersecurity and enterprise software — including six consecutive rounds in cloud security startup Wiz, which Google acquired for $32 billion earlier this year in one of its largest acquisitions ever. On this episode of TechCrunch's Equity podcast, Shah joins Rebecca Bellan to break down why he thinks periodic, human-in-the-loop security can’t keep up anymore, and why Index continues investing in AI-native security companies at stages that once required much more proof. Listen to the full episode to hear more about: Why Shah thinks cyber insurance should be most companies’ first move in managing AI-era risk What happens when a single attacker can deploy a 10,000-agent ransomware swarm, and why that could force cybersecurity to become more machine-to-machine How Wiz’s founders paved the way for a new generation of startups, including Frame and Enigma Shah’s take on “pacing the frontier,” and why he thinks even a slowdown in model capability wouldn’t cool off cybersecurity investment Subscribe to Equity on YouTube , Apple Podcasts , Overcast , Spotify and all the casts. You also can follow Equity on X and Threads , at @EquityPod. Chapters: 00:00 Intro 02:02 Why AI is creating a new cybersecurity category 05:16 The cyber insurance layer most companies overlook 09:15 Why Index is betting on AI-native security platforms 12:40 Will Wiz become a talent factory for new startups? 14:46 What actually makes a founder worth betting on 16:11 The hard part of knowing when to double down 18:53 Why Shardul says he’s not a visionary investor 21:16 How AI could reshape the entire security stack 24:18 What makes an AI security startup defensible 25:14 Why he doesn’t want to find “the next Wiz” 26:48 Why investors are writing huge checks so early 29:39 What happens to AI valuations if the frontier gets paced 31:01 Outro Learn more about your ad choices.
SaaStr 879: Stripe's CRO of AI on The New AI GTM Playbook
175% Growth, 120 Countries, Agents as Buyers: Stripe's CRO of AI on The New AI GTM Playbook The fastest-growing AI companies are rewriting every rule in the GTM playbook, and Stripe has a front-row seat to all of it. In this episode, Maia Josebachvili, Chief Revenue Officer of AI at Stripe, breaks down the four patterns she's seeing across the world's top AI companies, and what they mean for how you build, price, sell, and scale. The numbers alone are staggering: top AI companies grew 120% in 2025 and 175% in 2026. Lovable went from $100M to $400M in eight months. Cursor hit a $2B run rate in under two years. And 48% of revenue at top AI companies now comes from outside their home market. Maia covers: Why the fastest AI companies are in 42 countries on day one and 120 by year three How pricing is evolving from seats to usage to hybrid models, and why getting it wrong kills retention Why enterprise sales is now a year-one problem, not a year-five one What it actually means that agent traffic to Stripe's docs 10x'd in a single year How to build systems that move with your customer across PLG, enterprise, and agent-led motions If you're building an AI company or trying to compete with one, this is the data you need. Starting a business can get expensive fast. Website here. Email somewhere else. Business phone with another provider… Northwest Registered Agent gives you a complete Business Identity in one place — with free tools, resources, and built-in privacy from day one.
Masters of Scale27 min
Rebranding in a war zone, with Prosper Global (formerly Mercy Corps)
The world has never faced so many humanitarian crises at once, and the systems built to respond to them are fraying. Tjada McKenna, CEO of Prosper Global — the organization long known as Mercy Corps — joins Rapid Response during UN General Assembly week to share what she's seeing firsthand from Syria to Sudan to Ukraine to Gaza, and why she rebranded a 40-year-old institution in the middle of a funding crisis rather than in spite of one. She explains how the Strait of Hormuz conflict is quietly writing next year's food shortages into fragile countries right now, why Russia's playbook of attacking civilian infrastructure has spread to conflicts around the world, and what she's asking AI companies to do that no one has figured out yet.
Invest Like the Best1 hr 4 min
Gabe Stengel - Building Investing Superintelligence - [Invest Like the Best, EP.492]
Gabe Stengel is the co-founder and CEO of Rogo, the AI platform for finance. He believes the best investors will spend the next several years reinventing their firms around AI, and Rogo is trying to build the infrastructure that allows them to do it. We discuss how Rogo evolved alongside the frontier models, why the last mile and the harness around the models matter so much, what happens when every portfolio manager can deploy thousands of agents against a problem, and how AI could transform the way capital is raised, assets are priced, and deals get done. We also cover which investing skills become more valuable as AI improves, the move from seat-based to outcome-based pricing, Rogo’s internal company brain called Shrek, the 40 investor rejections Gabe received before his Series A, why building in applied AI requires extraordinary aggression, and what it takes to become a black hole for talent and capital. Please enjoy my conversation with Gabe Stengel. For the full show notes, transcript, and links to mentioned content, check out the episode page here . ----- Become a Colossus member to get our quarterly print magazine and private audio experience, including exclusive profiles and early access to select episodes. It covers everything enterprise security requires: SSO, SCIM, RBAC, Audit Logs, AI governance, and more. Trusted by 2,000+ fast-growing companies, including OpenAI, Anthropic, Cursor, and Vercel. ----- Rogo is the AI platform for finance. They're building agents for Wall Street that are trained to understand how bankers and investors actually do work: from diligence and modeling, to turning analysis into deliverables. It handles trading, portfolio management, compliance, customer reporting, and much more through an all-in-one real-time cloud platform. Timestamps: (00:00:00) Welcome to Invest Like The Best (00:01:24) The Model Eras of Rogo (00:04:15) Why the Last Mile Mattered (00:06:14) Who’s Using Rogo Right Now (00:10:55) Which Investor Skills Still Matter (00:12:23) Inside Rogo's Data Stack (00:14:33) Competing With the Frontier Labs (00:16:57) Why the Harness Matters Most (00:18:05) What Makes a Vertical AI Winner (00:22:00) How Firms Buy AI Software (00:23:43) From Seats to Outcome Pricing (00:31:19) Auditability Beats Accuracy (00:32:55) Scaling Enterprise Sales Fast (00:34:48) Meet Shrek, Rogo's Company Brain (00:35:49) The Pitch to Great Talent (00:39:37) What Chewing Glass Feels Like (00:42:18) Forty Investor Rejections (00:47:41) Finance's Innovator's Dilemma (00:50:22) Questions Every Firm Should Ask (00:53:09) What Remains Most Uncertain (00:56:46) Becoming a Black Hole for Talent (00:57:56) The Kindest Thing
20VC1 hr 11 min
20VC: Why AI Cannot Replace Humans in Enterprise | Why Work Processes Not Models Will Be The Most Valuable Asset in AI | Why Europe Has Lost and Building in the US vs EU with Daniel Dines, UiPath
Daniel Dines is one of the greatest European founders of the last decade. As the Co-Founder of UiPath, he has scaled the business to a market cap high of $ 44BN in 2021, with the company now generating $1.72BN in revenue, growing 15% year-on-year. The company raised $2BN before its IPO, backed by Sequoia, Accel, CapitalG, Coatue and Kleiner Perkins. AGENDA: 05:00 Why Dario is Wrong About Millions of AI Einsteins? 13:00 Is AI Safety Becoming an Excuse to Kill Open Source? 21:00 Does UiPath Really Need 4,000 Employees? 28:00 Would You Help Train the AI That Could Replace You? 32:00 What Percent of Salary Spend Does Daniel Spend on Inference? 34:00 Can You Really Vibe Code Your Way Out of Paying for Software? 37:00 Why Would Anyone Take Their Company Public Today? 39:00 Could an OpenAI–Anthropic Duopoly Break Nvidia's Business? 45:00 Will AI Models Capture the Value—or Will the Apps? 49:00 Is Fireworks Still Undervalued at $15 Billion? 56:00 Has Europe Already Lost—and Should Founders Leave? 1:03:00 What Could Kill UiPath—and How Is AI Changing the CEO's Job? 1:06:00 60 Supplements a Day: How Far Would You Go to Live Longer?
Founders49 min
#434 Sam Walton
What I learned from reading Sam Walton: The Inside Story of America's Richest Man by Vance Trimble. 1:35 — "Sam's idea was absurdly simple: buy cheap, sell low, every day, and while doing it with a smile." 2:15 — "If he adopts a business course that doesn't work out, he's neither too vain nor too blind to see his mistake, to say so, and to change his heading one hundred and eighty degrees." 3:34 — "The secret is work, work, work. I taught the boys how to do it." — Sam's father 4:34 — "Sam Walton plunged into this new world of merchandising with the keen and furious dedication of a quarterback who was one touchdown behind with two minutes to go." 5:55 — "Boys, you know we don't make a dime out of the merchandise we sell. We only make our profit out of the paper and string that we save." — J.C. Penney 11:10 — "No, I'm not whipped. I found Newport, and I found the store. I can find another good town and another Ben Franklin. Just wait and see." — Sam Walton, after losing his first store 11:35 — "I insist on buying the building that the store is in. I need control over my own destiny." — Sam Walton, arriving in Bentonville 11:45 — "My store will be number one. It's important for me to be the best. Not one of the best. I must be the best. I want to be the leader in the category I compete in." — Sam Walton 13:05 — "Sometimes hardship can enlighten and inspire." 13:15 — "That same boredom and frustration triggered ideas that eventually bought him billions of dollars." 18:55 — "Many of our best opportunities were created out of necessity, the things that we were forced to learn and do because we started out under financed and under capitalized." — Sam Walton 19:30 — "If they had something good, we copied it." — Sam Walton 24:35 — "This is just part of the education process. I'm still learning." — Sam Walton, on his hands and knees examining a competitor's display 25:00 — "You can make a lot of different mistakes and still recover if you run an efficient operation, or you can be brilliant and still go out of business if you're too inefficient." — Sam Walton 30:55 — "Remember Walmart's golden rule. Number one, the customer is always right. Number two, if the customer isn't right, refer to rule number one." — Sam Walton 31:35 — "Move from Bentonville? That would be the last thing we'd do unless they run us out of here. The best thing we ever did was to hide back there in the hills and eventually build a company that makes folks want to find us." — Sam Walton 32:05 — "I had no vision of the scope of what I would start, but I always had confidence that as long as we did our work well and were good to our customers, there would be no limit to us." — Sam Walton 32:55 — "He also had a very interesting competitive strategy in the early days. He was like a prizefighter who wanted a great record so he could be in the finals. So what did he do? He went out and fought 42 palookas. And the result was knockout, knockout, knockout—42 times. Walton, being as shrewd as he was, basically broke other small-town merchants in the early days. With his more efficient system, he might not have been able to tackle some titan head-on at the time. But with his better system, he could sure as hell destroy those small-town merchants. And he went around doing it time after time after time. Then, as he got bigger, he started destroying the big boys. Well, that was a very, very shrewd strategy. It’s an interesting model of how the scale of things and fanaticism combine to be very powerful." — Charlie Munger on Sam Walton's strategy 37:30 — "We have a low resistance to change. We call it our RC factor." — Sam Walton 39:45 — "Control your expenses better than your competition. This is where you can always find your competitive advantage. We rank number one in our industry for the lowest ratio of expenses to sales." — Sam Walton 48:20 — The day the market dropped 500 points and knocked a billion dollars off the value of his stock holdings in Walmart, reporters asked Sam what his reaction to the disaster on Wall Street was. He hadn't heard about it.
TechSurge: Deep Tech Podcast1 hr 12 min
The Race to Build the Next Trillion-Dollar AI Chip Company
Almost 2% of U.S. GDP will be spent on AI infrastructure this year, nearly double 2025's figure. But beneath those headline numbers, the composition of that spending has quietly flipped: for the first time, dollars spent on running models in production now outweigh dollars spent training them. In this episode of TechSurge, host David Goldman speaks with Austin Lyons, a semiconductor analyst at Creative Strategies, co-host of the Semi Doped podcast, and author of the Chipstrat newsletter. Lyons previously worked as a hardware engineer at Intel and as a product manager on John Deere's autonomous tractor and Blue River Technology teams before turning to full-time chip industry analysis. The conversation opens with why AI buyers have moved from assembling commoditized parts to buying entire pre-integrated systems, tracing how Nvidia's rack-scale approach, exemplified by its 72-GPU Grace Blackwell racks, made turnkey deployment the default, and why that raises the bar for any chip startup trying to compete. Lyons and Goldman then unpack how inference workloads have split into two distinct problems, prefill and decode, and how that split created an opening for SRAM-based challengers to outperform general-purpose GPUs on decode speed. From there, the discussion turns to the rise of neoclouds, the GPU-rental companies that grew into public businesses worth well over $100 billion combined, and why so many traditional investors missed them. Lyons and Goldman work through the circular financing debate head-on: the mechanics of Nvidia's equity stakes, GPU-backed debt, and hyperscaler off-take agreements that critics compare to dot-com-era vendor financing, and the counterargument that demand is simply outrunning fixed supply. The episode closes on Lyons's own framework for identifying the next trillion-dollar chip company, built on four conditions including the ability to run trillion-parameter models at rack scale, beat an incumbent on a key performance metric, and land a frontier anchor customer, along with a look at how AI-assisted chip design is lowering the barrier for more companies, from OpenAI to electric vehicle makers, to design their own custom silicon. 14:12 — Prefill vs. Decode: Splitting the Inference Workload 24:51 — Fragmentation vs. Consolidation in AI Silicon 28:22 — Why Investors Missed the First Wave of Neoclouds 38:18 — The Circular Financing Debate 48:29 — Lyons's Four Conditions for the Next Trillion-Dollar Chip Company About TechSurge: TechSurge Podcast shares the latest insights directly from legendary Silicon Valley leaders, daring new founders, and visionary technologists. #AISilicon #LLMHardware #Nvidia #AIInference #TechPodcasts #AIInfrastructure
This Week in Startups19 min
90% of AI prototypes never reach production (w/ Temporal's Samar Abbas) | AI Basics
The TWiST AI Basics series is made possible by Google for Startups! Build context-aware AI workflows using DeepMind models and orchestration tools with Google for Startups' new Startup technical guide on generative media. On today's AI Basics, Temporal co-founder and CEO Samar Abbas says the problem isn't with the model at all. You're missing a harness, the layer that keeps an AI agent's work durable, secure, and recoverable if/when something breaks. Temporal's durable execution platform is used by major companies from OpenAI to Stripe to Netflix, and helps to make their long-running software reliable and interruption-free. On AI Basics, he explains why so many AI-coded prototypes die in the imagineering stage, before ever hitting production. Plus he walks Jason through Temporal's live dashboard, to show how it gives developers full visibility into what their agents are doing at every step. Build context-aware AI workflows using DeepMind models and orchestration tools with Google for Startups' new Startup technical guide on generative media.
SaaS Club49 min
Founder-Led Sales to $1M ARR With Just 10 Customers
He needed a big retailer's data to build the product. No big retailer gives data to a company with no product. Felix Hoffmann solved it sideways: 7Learnings sold a paid consulting project, kept the right to use the data, and built its predictive pricing product on top of it. Ten customers later it was at $1M ARR, and he had closed every one himself. Felix explains why a demand forecasting product cannot start with a small customer, how he structured the first pilot as an A/B test so a retailer could hand over half its prices without betting the business, and what happened when the first run came back far too expensive. Plus: how a pricing optimization company prices itself, and why he refuses success-based fees even though he can prove the uplift. 7Learnings is a Berlin company whose software forecasts demand for each product at each price, then sets the price that hits a retailer's goal. It is now at multiple seven figures in ARR with around 40 customers. Felix spent six years as a pricing consultant at Kearney and two years running price optimization at Zalando before founding it. Take one. 🔑 Key Lessons 🎯 Solve the data cold start by selling something else first: 7Learnings could not train a forecasting model without a large retailer's sales history, so it sold a paid consulting project and kept the right to use that dataset. 🤝 Shrink a scary ask into a reversible test: Retailers would not hand pricing to an algorithm outright, so 7Learnings ran an A/B test on half the assortment while the retailer's own team priced the rest. 📉 Pick an early customer who can survive a failure: The first live pricing run was badly wrong on high-priced products. It survived because the buyer had a big enough problem, no alternative, and understood they were working with a startup. 💰 Price high enough to lose some deals: His test is blunt. If nobody is walking away because you are too expensive, you are too cheap, especially for a complex product carrying real delivery cost. 🚀 Founder-led sales lasts longer than founders expect: Felix closed all ten customers behind the first $1M ARR himself, and stayed closely involved through the next forty, because handing off enterprise sales is genuinely hard. ⚡ Pick the technology after the problem, not before: Felix argues founders are all digging in the same technical space, and that decisions needing determinism, low cost and explainability should not be handed to an LLM.
In Depth1 hr 2 min
The fastest way to learn is to run two competing bets | Jay Parikh (EVP CoreAI, Microsoft)
In the latest episode of Executive Function, Brett sits down with Jay Parikh, Executive Vice President of CoreAI at Microsoft. Before joining Microsoft, Jay spent over a decade as Facebook's global head of engineering, scaling the company from a few hundred engineers to billions of users, then ran the cybersecurity company Lacework as CEO through its acquisition by Fortinet. In this conversation, he breaks down the five ingredients of great engineering leadership, discusses why he'd rather run two competing bets in parallel than forecast a winner, and explains why company culture remains more important than ever for teams to get right. 41:35 What actually counts as a "big bet" worth taking 45:01 The concrete big bets that reshaped Facebook's entire technology stack 47:35 Why vertical integration keeps being the right call as companies scale 51:46 How being a startup CEO reshaped Jay as a leader 1:01:26 Why Jay overruled Zuckerberg's instinct to skip efficiency