Podcasts about Saas

In this episode, Varun, co-founder of Yoodli, shares insights into how his startup leverages AI to enhance communication skills, from public speaking to enterprise sales training. Tune in to understand how AI can empower humans rather than replace them, and the strategic evolution from consumer to enterprise products.Key Topics:The origin story of Yoodli and its focus on helping people find their voiceTransition from B2C to B2B: What was learned along the wayThe role of storytelling as a meta-skill in a world dominated by AIUsing AI to make communication more authentic and humanHow large organizations like Google and Snowflake are integrating YoodliThe evolution of AI capabilities, from role plays to experiential learningBuilding modular, customizable AI products that adapt to customer needsThe importance of deep integrations and the challenge of SaaS vendor proliferationReal-world growth stats: 900% revenue increase and millions of usersInsights into leadership, authenticity on social media, and the value of vulnerabilityPersonal stories from Sergey Brin's projects and leadership lessons learnedTimestamps: 00:00 – Introduction to Varun and Yoodli's journey 02:01 – Early days of Yoodli: Founding thesis and initial challenges 04:19 – Key lessons about public speaking skills 05:45 – The importance of recording and reviewing oneself 06:25 – Describing Yoodli as “Duolingo for public speaking” 07:25 – The role of storytelling in high-performance communication 08:21 – Building AI to enhance, not replace, human authenticity 09:07 – Judgment as a differentiator in AI-enabled work 10:01 – How Yoodli expanded into enterprise with Google & others 11:24 – Social media as a branding tool for founders 12:38 – The impact of authenticity on LinkedIn and lead generation 14:09 – The Google GTM training case study: How it started 15:07 – Product features for enterprise sales training 16:05 – Impact on sales onboarding and role play automation 17:32 – The future of experiential learning and AI role plays 20:17 – The broader vision for AI in education and training 21:26 – Impressive growth stats and customer insights 22:01 – The technological foundation: Modular AI architectures 23:52 – The influence of LLM improvements on product features 24:46 – The commoditization of AI role plays and experiential learning 25:12 – Building deep, customizable, scalable AI solutions 26:36 – The importance of scale and deep integrations 30:03 – Product differentiation through vertical focus and deep specialization 33:07 – Market challenges: Demand, consolidation, and customer expectations 34:42 – How to find and connect with Varun 35:30 – Sergey Brin's projects, leadership lessons, and human insights 37:36 – Overcoming imposter syndrome: Everyone's learning curve39:01 – Final reflections and looking aheadResources & Links:Varun on LinkedinNataraj on LinkedinTry Yoodli

Sumeet Arora, Chief Product Officer at Teradata, joins The Tech Trek for a sharp conversation on the shift from human driven SaaS to agentic software. This episode digs into what changes when software stops just supporting human workflows and starts driving outcomes alongside people, why trust and governance matter more as AI systems take on more responsibility, and what serious companies need to do now to prepare.This is a practical discussion about where the market actually is, what gets overhyped, and what leaders should focus on beneath the noise. Sumeet lays out a clear view of the emerging enterprise stack, from knowledge and context to agents, governance, and outcomes. He also explains why the winners may not be the loudest companies in AI, but the ones that get their data, knowledge, and operating model right.In this episode• Why agentic software is a real shift, but still in its early stages• What trust, governance, and explainability need to look like in an AI first enterprise• How software companies should rethink product strategy for agents as well as humans• Why every employee may need to become a manager of AI agents• Why knowledge infrastructure could matter more than the agent layer itselfTimestamped highlights• 00:45 Teradata's role in helping enterprises become autonomous• 02:34 Where we really are in the agentic AI maturity curve• 10:16 How software shifts from workflow centric to outcome centric• 16:17 Why every employee may need an AI workforce• 21:57 The skill gap between enterprise users and agentic adoption• 24:48 Why knowledge, not just agents, will define the winnersStandout line“The fundamental winners will be ones who get the knowledge fabric correct.”Practical takeawayIf you are building for an AI driven future, do not start with agents alone. Start with trusted knowledge, usable context, clear policies, and systems that can explain decisions. The companies that treat agentic AI as a stack, not a feature, will be in a much stronger position.Follow The Tech Trek for more conversations with leaders shaping the future of technology, product, AI, and enterprise transformation.

AI is everywhere right now. In IPO markets, in SaaS business models and in your day to day workflow. On this week's episode of The Liquidity Event, Shane and AJ break down why mega IPOs like OpenAI and SpaceX are creating a gravity well in the public markets, what that means for smaller companies waiting to go public and whether the IPO pipeline is actually reopening in 2026. They also dig into the growing tension inside software companies as AI agents begin to threaten seat based pricing models and what recent disclosures are quietly admitting about the competitive risks. Finally, they unpack new research showing that AI may not be lightening workloads at all. Instead, it may be increasing email volume, decision fatigue and what researchers are calling AI brain fry. Is AI making us more productive or just more overwhelmed? Key Timestamps 01:45 – Vibe coding, Claude inside PowerPoint and AI workflow shifts 06:10 – Is the IPO winter actually over? 08:25 – The gravity well effect of mega IPOs like OpenAI and SpaceX 11:40 – Why smaller IPOs are stuck in the pipeline 14:05 – SaaS companies quietly disclosing AI as a material risk 17:20 – Will AI agents break the seat based pricing model? 21:10 – AI is not reducing workload, it is increasing intensity 24:35 – Email volume, messaging overload and deep work decline 27:50 – AI brain fry and the rising intent to quit 31:15 – Mastery, autonomy and purpose in an AI driven workplace

From grassroots soccer parks to $600 million exits, Marty M. Fahncke reveals why every dollar of EBITDA sacrificed for tax savings costs you seven on a multiple, how the build versus buy decision needs a reality check, and why a business fully prepared to sell is the best business to own. In this episode of the DealQuest Podcast, host Corey Kupfer sits down with Marty M. Fahncke, CMAA, who has helped hundreds of businesses scale to over $1 billion in combined revenue and executed nearly $500 million in M&A deals. He is the founder of Westbound Road, an M&A advisory firm specializing in digital businesses in the $5-50 million range, and author of Boomer Sells the Business: A Step-by-Step Guide to Cashing Out and Living Large. WHAT YOU'LL LEARN: In this episode, you'll discover why the build versus buy analysis fails when founders underestimate timelines and costs, and why opportunity cost is often the biggest expense that never appears in spreadsheets. Marty explains how combining marketing expertise with M&A strategy creates advantages most advisors lack, the costly trade-off between profit maximization and tax mitigation that saves twenty cents but costs seven dollars on a multiple, and why operational decisions like CRM selection or staffing structure can kill deals worth millions. You'll also learn how the Who Not How philosophy transforms into a powerful acquisition playbook, why SaaS founders who turned down $50 million in 2021 are accepting those valuations were an anomaly, and how authority marketing through podcasts generates clients who arrive ready to sign without sales conversations. MARTY'S JOURNEY: Marty grew up in the mountains of Utah wanting to be either a forest ranger or join the military. Neither path worked out, and he ended up on the entrepreneur path instead. Even as a teenager, he showed entrepreneurial instincts, selling water purifiers and vacuums and running a bicycle rehab business at age twelve. M&A was completely off his radar until he and some friends started a soccer training product company. They took a truly grassroots approach, setting up canopies at local parks every weekend where kids played soccer. Marty had his children demonstrate the product while he sold to parents. Those park sessions taught them exactly what messaging resonated. Marty used those insights to create a marketing campaign that got the product onto QVC in the United States and Japan. Just eighteen months in, they received an unsolicited $1.5 million offer from a private equity firm buying their proven QVC sales channel. His next deal flipped the approach. Instead of building from scratch, Marty and a partner combined two competing businesses, each doing $1.5-2 million in revenue. By eliminating competition and consolidating operations, they scaled from under $4 million to $30 million in two years. That company eventually became part of a $600 million exit through a reverse merger. After that exit, Marty built a personal portfolio of businesses. In 2019, he focused on M&A full-time. When 2020 hit, he saw opportunity in the chaos. He reached out to companies about selling, and economic uncertainty generated many yes responses. When businesses weren't right for his portfolio, sellers asked if he knew other buyers. He started triangulating deals, brought in partner Becky, and launched Westbound Road in 2020. They focus exclusively on digital businesses between $5 and $50 million, including e-commerce, SaaS, publishing, marketing agencies, and virtual professional services. The firm is intentionally small at five people but highly specialized. THE MARKETER'S EDGE: Marty brings a rare combination of world-class marketing expertise and deep M&A experience. Most advisors excel at one or the other, rarely both. He is a marketer at heart and applies marketing principles to M&A strategy. This matters because organic growth drives valuation multiples. Buyers pay premiums for demonstrated growth momentum, often adding an extra turn or two on exit multiples. Marty sees both sides of the equation, knowing how to build marketing systems that drive organic growth and how to structure deals that accelerate inorganic expansion. KEY INSIGHTS: The build versus buy decision requires brutal honesty. Marty sees unreasonable optimism every time founders analyze whether to build or acquire. His rule: double the timeline, triple the costs. Even then, most analyses miss opportunity cost. What revenue will you lose spending years building? What market share will competitors capture while you're distracted? These costs rarely appear in spreadsheets but are often the most expensive part of the build decision. The Who Not How philosophy becomes an acquisition playbook. When something needs to be done, don't ask how you can learn it yourself. Find someone already better at it and acquire them. Marty applied this when a bookkeeping firm asked for growth help. Instead of consulting fees, he negotiated equity, brought marketing expertise and clients, they tripled revenue, and everyone won when they sold. Profit maximization beats tax mitigation. Every dollar of EBITDA sacrificed to save taxes saves twenty cents but costs seven dollars on a multiple when you sell. When Marty shows clients this math, they immediately shift strategies. This insight often represents millions in additional exit value. Begin with the buyer in mind. Westbound Road identifies upfront who will buy your business and why, then builds the business to suit those buyers. Every decision from CRM selection to staffing structure gets evaluated through one lens: how will this impact deal viability at exit? Minor operational choices can kill deals worth millions. A client built operations entirely on offshore VAs with impressive margins, but many buyers walked away. To command their target multiple, they needed to onshore roles and add W-2 employees. Another business chose a non-standard CRM. An acquirer walked away because integrating it created too much friction. That preference cost millions. The SaaS valuation bubble is being accepted as an anomaly. In 2021-2022, founders got $50 million offers for businesses worth $12 million today. Many refused, expecting the market to stay hot. Marty shows them the charts: the market returned to historical norms, and eighteen months was a bubble. Those founders are finally accepting realistic valuations. Authority marketing creates pre-sold clients. When prospects ask questions, Marty's team sends time-coded podcast links. Prospects listen to full episodes, then more episodes, and arrive ready to sign without sales calls. His first ChatGPT client found Westbound Road via AI recommendation, watched three episodes, and asked where to sign. A business fully prepared to sell is the best business to own. The best practices for creating enterprise value that commands premium multiples are the same practices that make a business pleasurable to operate. Clean financials, reduced owner dependency, autonomous systems, and strategic structure benefit owners whether they sell or not. Perfect for business owners in the $5-50 million range planning exits, entrepreneurs considering M&A advisory relationships, and anyone interested in combining marketing expertise with deal-making to build and sell businesses. FOR MORE ON THIS EPISODE: https://www.coreykupfer.com/blog/martyfahncke FOR MORE ON MARTY FAHNCKE: Website: https://westboundroad.com LinkedIn: https://www.linkedin.com/in/martyfahncke/ FOR MORE ON COREY KUPFER https://www.linkedin.com/in/coreykupfer/ https://www.coreykupfer.com/ Corey Kupfer is an expert strategist, negotiator, and dealmaker with more than 35 years of professional deal-making and negotiating experience. Corey is a successful entrepreneur, attorney, consultant, author, and professional speaker deeply passionate about deal-driven growth. He is the creator and host of the DealQuest Podcast. Episode Highlights with Timestamps[00:06:48] - Introduction: Marty Fahncke's credentials and experience [00:08:38] - Childhood dream of being a forest ranger in the mountains of Utah [00:11:12] - Early entrepreneurial ventures: bike rehab business at age twelve [00:11:30] - First major deal: grassroots soccer product to QVC and $1.5 million exit[00:23:36] - Eliminating competition through collaboration instead of competing [00:23:36] - Who Not How philosophy applied to M&A and acquisition strategy [00:24:09] - Build versus buy analysis and unreasonable optimism trap [00:28:52] - Combining marketing expertise with M&A strategy as unique advantage [00:31:08] - Starting Westbound Road advisory firm during 2020 disruption [00:33:37] - Focus areas: $5-50M digital businesses, e-commerce, and SaaS [00:34:09] - Exit planning gap: helping founders understand what they have [00:47:13] - Beginning with the end in mind: identifying buyers before building [00:48:01] - Offshore VA staffing structure as deal-killer example [00:48:30] - Non-standard CRM selection costing millions in lost deal value [00:50:36] - A business fully prepared to sell is the best business to own [00:53:23] - Freedom defined: making impact on entrepreneurs and participating in exits[00:57:32] - Authority marketing: podcast content creating pre-sold clients [01:01:00] - First ChatGPT-originated client story Guest Bio:Marty Fahncke, CMAA is a seasoned marketer and dealmaker with over 35 years of business experience and over 25 years in M&A. He has helped hundreds of businesses scale to over $1 billion in combined revenue and executed nearly $500 million in M&A deals. His first deal was selling a grassroots soccer product business for $1.5 million. His second deal combined a $2 million company with a $1.5 million company and built it to $30 million in revenue in two years. Since then, he has been involved in deals from $5 million to $600 million. He is the founder of Westbound Road, an M&A advisory firm specializing in digital businesses between $5 and $50 million, including e-commerce, SaaS, publishing, marketing agencies, and virtual professional services. Marty is a top-ranked podcast guest, international speaker, and author of Boomer Sells the Business: A Step-by-Step Guide to Cashing Out and Living Large. He owns five motorcycles and over thirty businesses. Host Bio:Corey Kupfer is an expert strategist, negotiator, and dealmaker with more than 35 years of professional deal-making and negotiating experience. Corey is a successful entrepreneur, attorney, consultant, author, and professional speaker deeply passionate about deal-driven growth. He is the creator and host of the DealQuest Podcast. Show Description:Do you want your business to grow faster? The DealQuest Podcast with Corey Kupfer reveals how successful entrepreneurs and business leaders use strategic deals to accelerate growth. From large mergers and acquisitions to capital raising, joint ventures, strategic alliances, real estate deals, and more, this show discusses the full spectrum of deal-driven growth strategies. Get the confidence to pursue deals that will help your company scale faster. Related Episodes:Episode 332 - John Martinka: Exit Planning and Value Drivers for Business Sales Episode 331 - Solocast 72: 2025 M&A Market Outlook and Deal Activity PredictionsEpisode 328 - Richard Manders: Growing Through Acquisition and the Deal-Maker MindsetEpisode 327 - Solocast 71: Authority Marketing and Content Strategy for Business Growth Social Media:Follow DealQuest Podcast: LinkedIn: https://www.linkedin.com/in/coreykupfer/ Website: https://www.coreykupfer.com/ Follow Marty Fahncke: Website: https://westboundroad.com LinkedIn: https://www.linkedin.com/in/martyfahncke/ Keywords/Tags:M&A advisory, exit planning, business valuation, lower middle market, build versus buy, organic growth, inorganic growth, SaaS valuation, e-commerce acquisitions, profit maximization, tax mitigation, enterprise value, Who Not How, business exit strategy, exit readiness, authority marketing, content marketing, podcast marketing, deal-driven growth, strategic acquisitions, business combination, marketing expertise, digital businesses, operational decisions, CRM selection, staffing structure, business sale preparation

If buyers don't care about your product story, how do you meet them where they are and still drive revenue growth? In this episode of Sharkpreneur, Seth Greene interviews Brent Keltner, Ph.D., Founder and President of Winalytics LLC, who leverages his experience leading marketing and sales teams and achieving multiple growth results to explain why most go-to-market efforts fail: they begin with the seller, not the buyer. He explains how to establish a “journey-first” approach that allows buying committees to self-educate, aligns internal teams around a shared value proposition, and turns discovery into the engine that drives real revenue growth. Key Takeaways:→ Most teams talk about themselves first, but buyers care more about what is in it for them.→ A strong value proposition starts with the outcome the buyer wants.→ The best value propositions connect product value, business value, and enterprise value. → Buyers prefer to educate themselves, so companies should give them clear ways to learn at their own pace. → Discovery should be a major part of the sales process because it helps build support across the buying committee. Brent Keltner, Ph.D., is President of Winalytics LLC and the creator of Winalytics' Journey First Growth methodology. Winalytics helps mid-market and enterprise clients accelerate account-based B2B growth. The team has expertise in various industries, including education, human capital, healthcare, and SaaS. Before starting Winalytics, Brent expanded growth as a revenue leader at four different companies. He began his career as a Ph.D. social scientist at Stanford University and the RAND Corporation. His first book was the Revenue Acceleration Playbook. He has published articles on marketing and sales strategy in MarketingProfs, CEOWorld, the Sloan Management Review, the California Management Review, and Sales and Marketing Magazine. Connect With Brent:Website: http://winalytics.com/LinkedIn: https://www.linkedin.com/company/winalytics-llc/

In this sponsored episode, FluidCloud co-founders Sharad Kumar and Harshit Omar sit down with William and Eyvonne to discuss how FluidCloud tackles multi-cloud portability. They detail how FluidCloud acts as a cloning platform that scans an existing cloud or VMware environment, extracts complex infrastructure configurations (including compute and storage, as well as firewall rules and... Read more »

Most businesses struggle with growth not because they lack effort — but because they lack focus.When you try to serve everyone, your messaging becomes generic, your positioning weakens, and your sales conversations lose impact.In this episode, we break down why defining a clear ideal customer profile (ICP) is one of the most important decisions you can make, and how narrowing your focus actually creates more opportunities — not less.We also explore:Why broad targeting leads to weak messagingHow to think about your ideal client in a practical wayThe difference between activity and real progress in salesWhy focus improves both confidence and conversionThis is Episode 2 of the How To Get More Clients mini-series.If you work in B2B sales, SaaS, professional services, or revenue leadership, this episode will challenge common sales assumptions and help you build a more effective sales process.⭐ Unlock free resources (templates, frameworks & prompts):⁠⁠⁠⁠⁠⁠https://coachpilot.beehiiv.com/⁠⁠⁠⁠⁠⁠Join the community & access 157+ templates, frameworks and mega AI prompts used by top revenue teams.Watch Full Episode on YouTube:⁠⁠⁠⁠⁠⁠⁠https://www.youtube.com/@revenueleaders⁠⁠⁠⁠⁠⁠⁠Follow us:⁠⁠⁠⁠⁠⁠⁠https://www.instagram.com/davidfastuca/⁠⁠⁠⁠⁠⁠

The RSAC Conference, a major cybersecurity gathering in the spring, is coming up and the impacts of agents will be on full display. Scott Crawford, Brenon Daly, and Dan Kennedy join host Eric Hanselman to explore their expectations and look at what's been taking place in both the marketplace, investments and M&A activity. Agents are automating tasks, not jobs, and there are a great set of use cases, but they're not a panacea. There will be disruption, but it will be in specific areas, rather than a universal replacement of existing tooling. Are we industrializing the automated creation of software? Will agents really replace SaaS applications? We're clearly in the early days, but these questions are causing massive market shifts. A better question is how agentic interactions will change how we interact with the applications that drive businesses today. Join the team at RSAC and get all the details we didn't have time to cover. The annual 451 Research breakfast will be on, as always, so you can meet the team in person.    More S&P Global Content: 451 Research RSAC Breakfast 2026: Beyond the shine of AI, a new cyber reality is unfolding Next in Tech | Ep. 222: FinOps – Managing Cloud and AI Costs Next in Tech | Ep. 205: Agentic AI Impacts       RSAC Conference 2025: Breaking records at the threshold of uncertainty   For S&P Global subscribers: An ominous opening for RSA AI, automation enhance SecOps by reducing alert burdens, boosting efficiency Software's bloodless evolution turns bloody Big Picture 2026 AI Outlook: Unleashing agentic potential   Credits: Host/Author: Eric Hanselman Guests: Scott Crawford, Brenon Daly, Daniel Kennedy Producer/Editor: Feranmi Adeoshun Published With Assistance From: Sophie Carr, Kyra Smith  

David Gaylord is the cofounder and CEO of BushBalm, a $30 million, bootstrapped, profitable hair removal brand that puts skin first. Follow David on LinkedIn at https://www.linkedin.com/in/davidagaylord.FOLLOW UP WITH ANDREW X: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://x.com/andrewjfaris⁠⁠⁠⁠⁠ Email: ⁠⁠⁠⁠⁠podcast@ajfgrowth.com⁠⁠⁠⁠⁠Work with Andrew: ⁠⁠⁠⁠⁠https://ajfgrowth.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠INTELLIGEMSIntelligems brings A/B testing to business decisions beyond copy and design. Test your pricing, shipping charges, free shipping thresholds, offers, SaaS tools, and more by clicking here: https://bit.ly/42DcmFl. Get 20% off the first 3 months with code FARIS20.RICHPANELCut your support costs by 30% and reduce tickets by 30%—guaranteed—with Richpanel's AI-first Customer Service Platform that will reduce costs, improve agent productivity & delight customers at http://www.richpanel.com/partners/ajf?utm_source=spotify.

Are finance teams implementing AI the wrong way? In episode #359, Ben Murray argues that many CFOs and finance leaders are approaching AI backward—focusing too much on prompts and quick wins rather than building the foundational data infrastructure required for meaningful, repeatable insights. Drawing from recent AI webinars and his experience building softwaremetrics.ai, Ben explains why SaaS metrics, retention, and cohort analysis should not rely on AI. Instead, these should be computed through structured, deterministic systems first—then enhanced with AI for deeper analysis and pattern recognition. Resources Mentioned My new metrics engine: https://softwaremetrics.ai/ My SaaSpocalypse post: https://www.thesaascfo.com/the-saaspocalypse-ai-agents-vibe-coding-and-the-changing-economics-of-saas/ What You'll Learn Why prompt-driven AI workflows are not scalable in finance The difference between deterministic systems and AI-driven analysis Why you don't need AI to calculate core SaaS metrics like retention or CAC payback The importance of structured data and clean data pipelines How AI should be layered on top of computed financial data—not raw inputs Why context windows and token usage matter when working with large datasets How AI can uncover insights (like expansion opportunities) that FP&A teams may miss Why It Matters Prompt-based workflows create inconsistency and lack of auditability Without structured data, AI outputs are unreliable and not repeatable Finance teams risk “prompt fatigue” without building scalable systems Deterministic calculations ensure accuracy for critical SaaS metrics and reporting AI delivers the most value when used for analysis—not basic computation Efficient data handling reduces token costs and improves performance

Kate Tarling — consultant, trainer, and author of The Service Organization — joins Lily and Randy to discuss what it takes to deliver great services inside large, complex organizations. The conversation covers the distinction between products and services, why transformation so often stalls, how to make the business case for change using existing investment, and how product people can contribute to, and benefit from, a more service-oriented way of working.Chapters00:01:30 — Introduction and Kate's background00:04:00 — Defining services vs. products00:07:00 — Product organizations vs. service organizations00:09:00 — Why service delivery is hard00:11:30 — Transformation in practice: there is no magic process00:13:30 — Starting with one area and cutting across silos00:15:30 — Common mistakes organizations make00:19:30 — Measuring progress and making the business case00:22:30 — Redirecting existing investment: a UK government example00:25:00 — Triage functions and portfolio management00:26:00 — How product people can contribute in service organizations00:30:30 — Kate's 12 principles00:34:00 — Summary00:37:00 — Examples of good service organizationsOur HostsLily Smith enjoys working as a consultant product manager with early-stage and growing startups and as a mentor to other product managers. She's currently Chief Product Officer at BBC Maestro, and has spent 13 years in the tech industry working with startups in the SaaS and mobile space. She's worked on a diverse range of products – leading the product teams through discovery, prototyping, testing and delivery. Lily also founded ProductTank Bristol and runs ProductCamp in Bristol and Bath.Randy Silver is a Leadership & Product Coach and Consultant. He gets teams unstuck, helping you to supercharge your results. Randy's held interim CPO and Leadership roles at scale-ups and SMEs, advised start-ups, and been Head of Product at HSBC and Sainsbury's. He participated in Silicon Valley Product Group's Coaching the Coaches forum, and speaks frequently at conferences and events. You can join one of communities he runs for CPOs (CPO Circles), Product Managers (Product In the {A}ether) and Product Coaches. He's the author of What Do We Do Now? A Product Manager's Guide to Strategy in the Time of COVID-19. A recovering music journalist and editor, Randy also launched Amazon's music stores in the US & UK.

Most security breaches don't begin with sophisticated code or elaborate technical exploits. They begin with a phone call, a convincing email, or someone at a help desk who just wanted to be helpful. The human layer is often the weakest link, and the criminals who understand that are the ones causing the most damage. My guest today is May Chen-Contino. She's the CEO of Unit 221B, a threat disruption company that delivers actionable intelligence to enterprises, law enforcement, and government agencies. Her background spans cybersecurity, fintech, and SaaS leadership at companies like PayPal and eBay, and she brings a distinctly mission-driven lens to the work, shaped equally by a career in business and a background as a Krav Maga instructor. Unit 221B operates less like a typical security vendor and more like a specialized investigative unit, with a team that includes tenured ransomware experts, incident responders, and former law enforcement, all focused on one outcome: criminal arrest. May has seen firsthand how ransomware gangs operate with their own codes of conduct, how a younger generation of cybercriminals is throwing those rules out entirely, and why paying a ransom is increasingly a bet that doesn't pay off. We talk about why social engineering has overtaken technical hacking as the dominant attack vector, what organizations and individuals should never do in the aftermath of a breach, and how crimes against children online often go unreported for the worst possible reasons. May also shares a story from her own experience being scammed on eBay, and what she did about it, which tells you everything you need to know about how she approaches this work. Show Notes: [1:28] May shares her background and how she came to lead Unit 221B, a threat disruption company serving enterprises, law enforcement, and government. [1:41] May traces her path into cybersecurity, explaining how a lifelong sense of justice and a friendship built through Krav Maga training led her to a team of investigators doing real criminal work. [5:55] May recounts being scammed while selling luxury shoes on eBay, describing how a fraudulent PayPal email convinced her the sale had failed after she had already shipped the item. [8:22] Rather than accepting the loss, May engaged the scammer directly, intercepted her own shipment through FedEx, and used a photoshopped payment screenshot to flip the situation on him. [11:36] The story ends with May recovering her shoes, followed by a candid note that this approach carries real risk and is not something she would recommend to others. [12:57] May outlines Unit 221B's core work, including criminal investigations, threat intelligence, pen testing, and incident response, all oriented toward federal prosecution and criminal arrest. [16:52] The evolving threat landscape, contrasting professional ransomware organizations that tend to honor agreements with a younger generation of cybercriminals who operate without limits. [18:44] May describes this younger criminal group in detail, noting members are predominantly 14 to 26 years old, English-speaking, and motivated as much by social status as financial gain. [21:49] May explains why wiping systems and restoring backups after a breach is one of the most damaging mistakes an organization can make, eliminating evidence and removing any path to prosecution. [23:04] She walks through Unit 221B's incident response process, covering digital forensics, insider threat identification, and determining who is behind an attack before advising on next steps. [26:32] May addresses the ransom payment question directly, recommending against paying as a default while acknowledging that knowing your adversary is essential to making the right call. [28:04] The discussion covers the legal and PR dimensions of a breach, including notification obligations and why some organizations choose to go public about what happened. [31:08] May pushes back on the perception that law enforcement doesn't help, explaining that federal agencies are understaffed and must prioritize cases, but are genuinely committed to the work. [34:08] The issue of victims deleting evidence before reporting, and how frequently this forecloses any possibility of investigation or prosecution. [34:55] The conversation turns to crimes targeting children, including sextortion, and why open dialogue between parents and kids is critical to getting victims to come forward before lasting harm is done. [37:18] May reflects on a keynote she gave at Harvard's Bold Conference for young women, describing the tension between advice to build an online presence and the real safety risks that come with it. [38:51] May shares practical security guidance for young people online, including being mindful of what appears in video backgrounds, using strong passwords, and enabling two-factor authentication. [40:35] May identifies AI-assisted attacks and social engineering as the two most significant forces reshaping the threat landscape, with technology now available to both attackers and defenders equally. [43:45] May describes Unit 221B's invite-only intelligence platform, which brings together top investigators, law enforcement, and private sector experts to collaborate and move cases forward. [45:10]Listeners can find Unit 221B at unit221b.com and on LinkedIn, and anyone facing a threat or needing guidance can reach out. Thanks for joining us on Easy Prey. Be sure to subscribe to our podcast on iTunes and leave a nice review.  Links and Resources: Podcast Web Page Facebook Page whatismyipaddress.com Easy Prey on Instagram Easy Prey on Twitter Easy Prey on LinkedIn Easy Prey on YouTube Easy Prey on Pinterest May Chen-Contino - LinkedIn Unit 221B - LinkedIn Unit 221B

William Hockey is the co-founder of Plaid and the founder and CEO of Column, a software company that owns a bank and powers Ramp, Wise, Bilt, Mercury, and others. He funded Column by borrowing against his Plaid shares and has never raised outside capital.  William talks about what owning 100% of his company allows him to do that other venture-backed founder cannot and the personal risk he took to do so. He shares how Silicon Valley's consensus culture produces consensus founders, and why becoming a founder has become too safe. He believes the best builders are specialists and explains with unusual clarity what it takes to become the best in the world at one specific thing. William also spends a lot of time in emerging markets which has given him a unique perspective of the power of the US dollar. 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. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠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. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Visit ⁠vanta.com/invest⁠.  ----- ⁠WorkOS⁠ is a developer platform that enables SaaS companies to quickly add enterprise features to their applications. Visit⁠⁠ ⁠WorkOS.com⁠⁠⁠ to transform your application into an enterprise-ready solution in minutes, not months. ----- ⁠Rogo⁠ is an AI-powered platform that automates accounts payable workflows, enabling finance teams to process invoices faster and with greater accuracy. Learn more at ⁠Rogo.ai/invest⁠. ----- ⁠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. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps: (00:00:00) Welcome to Invest Like the Best (00:02:43) Intro: William Hockey (00:03:49) Column: A Software Company That Owns a Bank (00:06:46) Finding Ideas in Emerging Markets (00:11:58) Why Constrained Societies Are More Innovative (00:16:02) What's Wrong With Silicon Valley (00:19:28) Building a Business Without Raising Money (00:22:48) What Venture-Backed Companies Can't Do (00:28:39) Getting Margin Called (00:31:41) Starting Companies Has Become Too Safe (00:34:23) Why Employees Take More Risks Than Founders (00:37:09) A Maniacal Commitment to Research (00:39:09) Finding Boring Problems to Solve (00:41:45) Why Building a Second Company is Easier (00:42:36) Missionary vs. Mercenary (00:45:49) Funding a Company with Cash Flows (00:50:04) Perspective on the Venture Ecosystem (00:52:48) The Dominance of the US Dollar (00:58:37) The Future of Financial Services (01:02:06) Why Big, Inefficient Brands Win From AI (01:06:29) The Opportunity for Non-Consensus Founders (01:08:03) The Kindest Thing

This week we chat with Kipp Bodnar!Kipp is the Chief Marketing Officer of HubSpot, the #1 CRM platform for scaling companies, where he leads the company's global marketing strategy—driving awareness, demand, and growth across one of the most influential software brands in the world.Before stepping into the CMO role, Kipp served as Vice President of Marketing at HubSpot, overseeing worldwide demand generation, building out the EMEA and APAC marketing teams, and managing field marketing, localization, strategic partnerships, and social media. He's helped shape how modern SaaS companies think about growth at scale.Beyond HubSpot, Kipp is a trusted advisor to leading SaaS companies like SimplyMeasured, InsightSquared, and Guidebook. He's also the co-author of The B2B Social Media Book, a playbook for marketers looking to generate real results through digital channels.An industry-leading speaker, blogger, and marketing strategist, Kipp combines storytelling with data-driven execution—and has been at the forefront of how B2B marketing has evolved over the past decade.This is going to be a masterclass in modern marketing.✨ This episode is presented by Brex.Brex: brex.com/trailblazerspodThis episode is supported by RocketReach, Gusto, OpenPhone & Athena.RocketReach: rocketreach.co/trailblazersGusto: gusto.com/trailblazersQuo: Quo.com/trailblazersAthena: athenago.me/Erica-WengerFollow Us!Kipp Bodnar: @kippbodnar@thetrailblazerspod: Instagram, YouTube, TikTokErica Wenger: @erica_wenger

Robin Thurston raised $150 million to turn Outside into more than a magazine. He explains how the company married media brands with mapping apps, SaaS platforms, and a festival to reach profitability at $125 million in revenue.

המשפט המפורסם של ג'ף בזוס, "המניה היא לא החברה והחברה היא לא המניה", מעולם לא היה רלוונטי יותר עבור סקטור התוכנה. אחרי שנים שבהן חברות ה-SaaS נחשבו לאי של יציבות וצמיחה בטוחה, מהפכת ה-AI הזרימה לשוק חוסר וודאות שחתך שוויי שוק בעשרות אחוזים "אובר-נייט" עבור לא מעט חברות. בפרק הזה של ״עדכון גרסה״ דריה ורטהיים מארחת את ערן זינמן, Co-CEO במאנדיי, ואת אלירן גלזר, ה-CFO של החברה, לשיחה על הדיסוננס שבין הביצועים העסקיים לבין הסנטימנט השלילי של המשקיעים. בפרק נדבר על פרספקטיבה על המשבר הנוכחי מתוך משברי עבר, ממשבר הדוט-קום והסאב פריים לתקופת הפוסט-קורונה ונפילת מנייה של מאנדיי מ-400$ ל-70$ - ומה למדנו מהסייקלים האלה, נדבר גם על למה מאנדיי בחרה לפתוח תוכנית Buyback ואיך זה משפיע על הערך למשקיעים ולעובדים, ועל איך מסתכלים במאנדיי על השוק הציבורי, השוק הפרטי, ואיפה הם נפגשים בזמן מהפכת ה-AI. See omnystudio.com/listener for privacy information.

What happens when a bored teenager starts a Discord trading group and accidentally discovers the power of video? For Elijah Khasabo, co-founder of Vidovo, it became the foundation for a bootstrapped UGC and influencer platform now serving over 200 brands and 20,000 creators.In this episode, Elijah shares the unfiltered origin story of Vidovo — from running negative for the first six months to crossing 20,000 organic creators without spending a dollar on paid acquisition. He breaks down why building for creators (not brands) is the real flywheel, how AI is actually strengthening the case for real human content, and what it means to stay gritty when the Stripe dashboard shows zero day after day.This is a masterclass in marketplace strategy, relationship-driven growth, and the kind of founder mindset that turns dark days into fuel.Key Takeaways3:52 — **The Origin Story:** Elijah explains how a Discord trading community led to TikTok affiliates generating 100M+ views, sparking his obsession with video and UGC.5:25 — **First Big Win (That Was Really an L):** The Life Fuel cold email that landed after a month of silence — they lost money on the deal but it taught Elijah how to brief, strategize, and actually create content that converts.7:05 — **Going All In:** Why December 2023/January 2024 was the turning point — when brands started buying in and creators began leaving full-time jobs for UGC income.8:22 — **The Creator-First Flywheel:** Why most platforms build for brands (and why that's wrong). Vidovo built for creators first, which indirectly built for brands — because brands go where the best creators are.10:09 — **Growth Without Paid Ads:** Relationship-building and showing up hungry at New York events — how sweating through the city and connecting person-to-person fueled 50–100 new creators per day organically.11:31 — **Bootstrapping Philosophy:** Why going net negative in the early months actually built the right muscles — and why having no investors means entering future fundraising from a position of power.14:02 — **SaaS is Humbling:** Launching at 19, learning to drop the ego, spending months alone building, and understanding that success requires working for it — nobody is just handed a software company.16:04 — **Dark Days:** How Elijah nearly quit multiple times in the first six to eight months when the Stripe dashboard showed zero — and why "I have nothing to lose" became his survival mindset.19:10 — **What Brands Get Wrong with UGC:** Volume is the real issue. Brands come in wanting 2–3 videos when they need 10 minimum to test, iterate, and find what actually converts.20:52 — **AI's Surprising Impact on UGC:** AI content is actually driving more brands *toward* real creators — because consumers don't connect with AI ads the same way, and brands are noticing.24:27 — **Building Creator Community:** Why quality beats quantity in community building — taking negative feedback seriously, building features from creator input, and making people feel heard.31:13 — **Advice for Bootstrapped Founders:** Network relentlessly. Send 5–10 connection requests a day. Ask questions. Be the person willing to help, connect, and listen — doors open through people, not platforms.33:48 — **Final Mindset Principle:** "You can really do anything you put your mind to" — when your goals are all you think about every day, you naturally become the person who achieves them.Tweetable Quotes"When you build for the creator, you're indirectly building for the brand. Brands wanna be where the best creators are." — Elijah Khasabo"I have nothing to lose. I'm 19. Where would I go if I quit? That's the mindset that kept me going through the dark days." — Elijah Khasabo"Entrepreneurship is a game of who. Build the right relationships and doors will open that no budget could buy." — Elijah Khasabo"If you give me a million dollars on day one, it would all be gone. Now I know exactly what to do with it — that's the value of bootstrapping." — Elijah Khasabo"Volume testing is everything in UGC. Don't launch 3 ads and call it a failure. Launch 10, find what works, and iterate." — Elijah Khasabo"AI UGC actually made our industry better. Brands are realizing consumers want real people — and they're coming to us because of it." — Elijah Khasabo"Put your mind toward the right things. If it's all you think about every single day, you're just naturally going to become that person." — Elijah KhasaboSaaS Leadership Lessons1. Build for the underserved side of your marketplace. Vidovo chose creators over brands — the side that doesn't pay. That counterintuitive decision created loyalty, word-of-mouth, and a quality flywheel that now attracts the paying side (brands) naturally. In any two-sided market, ask: who is underserved? That's your moat.2. Losses that teach you are wins in disguise. The Life Fuel campaign cost Elijah money. But it forced him to learn strategy, briefing, and how to create content that converts. In SaaS, early customers who expose your weaknesses are more valuable than easy wins that mask them.3. Bootstrapping builds judgment that money can't buy. Going net negative for six months taught Elijah exactly where dollars should go. When you bootstrap through adversity, you develop operational discipline that funded founders often skip — and that discipline becomes leverage when you do have capital.4. Relationships are your most scalable growth channel. Vidovo scaled to 20,000 creators and 200+ brands without paid acquisition. The engine? Showing up to events, following up, being genuinely helpful, and playing the long game. In a world of funnels and paid media, personal relationships remain the highest-ROI growth lever.5. Volume and iteration beat perfection. Brands that win with UGC don't launch one great video. They launch 10, find 3 winners, iterate on those 3, and test 7 new concepts. This is exactly how product-led SaaS should work too — ship fast, measure, iterate, and let data drive the roadmap.6. Your mindset is your product roadmap. Every dark day Elijah survived made the next one lighter. The founders who push through are the ones who refuse to let the fire go out — not because it's easy, but because they've tied their identity to the mission. Grit isn't a strategy; it's the prerequisite.Guest Resourceselijah@vidovo.comvidovo.comhttps://www.linkedin.com/in/elijah-khasabo/Episode SponsorThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

In this episode, I sit down with Demetrios Brinkmann (godfather of the MLOps Community) to talk about the absolute Wild West of AI right now. We cover how fast coding agents are changing the game, the reality of "vibe coding" your own CRM , and how Demetrios's community saved $20,000 just by ditching bloated enterprise tools.But we don't just talk tech. We get into the weeds on the content creation pipeline, from the bizarre rise of AI OnlyFans to the "Doorman Paradox" of automated content. Finally, we spill some serious inside baseball on the tech sponsorship game, calling out the sheer audacity of heavily-funded startups expecting free labor from communities , and why protecting your reputation is worth more than any quick paycheck.

Futurum Group's Nick Patience and Hydra Host's Aaron Ginn talk with TITV Host Akash Pasricha about Nvidia's $1 trillion revenue projection and the new Groq-based chip system. We also talk with Reporter Sri Muppidi about OpenAI's new AWS deal for government contracts and Editor Ken Brown about Mastercard's $1.8 billion acquisition of BVNK. Lastly, we get into Asana's AI agent strategy and the "SaaS apocalypse" with CEO Dan Rogers.Articles discussed on this episode: https://www.theinformation.com/articles/openai-clinches-aws-deal-bid-win-government-contractshttps://www.theinformation.com/newsletters/ai-agenda/nvidia-needed-groqhttps://www.theinformation.com/briefings/mastercard-buy-stablecoin-startup-bvnk-1-8-billionSubscribe: 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-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/

How many engineers does it take to run the ecommerce site for a retail company that does over a billion dollars in revenue per year? Well, if you're Rainbow Shops, the answer is just 2. Most ecommerce teams assume scale requires more engineers, more tools, more complexity. Chief Digital Officer David Cost has built something many people in ecommerce would say isn't possible — a lean, fast-moving digital operation that runs on vendor partnerships instead of a massive internal team. Two engineers, hundreds of programmers' worth of output, and none of the overhead that comes with scaling the traditional way. In this episode, David shares: A detailed, under-the-hood look at the specific vendors they use to stay so lean His playbook for using strategic partnerships with vendors as an external dev team How being a testbed for new tech gives them a competitive edge And why their choice of ecommerce platform was vital in enabling Rainbow's digital strategy Links David's LinkedIn: https://www.linkedin.com/in/davidcost/ Rainbow Shops: https://www.linkedin.com/company/rainbow-apparel-co/ Chapters 00:00 Introduction 00:42 David's product journey 02:36 How Rainbow runs with only two engineers 03:07 Rainbow's decision to migrate from  Salesforce Commerce Cloud to Shopify 07:14 How Rainbow uses AI to support a lean team 11:34 Rainbow's partnership with Lica for AI-generated product images 17:25 The future of personalization in ecommerce 23:01 Shop Pay and Rainbow's checkout features 26:15 Conclusion Resoures Lica: https://lica.world/ Fuego: https://fuego.io/ Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: David Cost.

Dean Mathews is a founder and CEO with over 20 years of experience in the Software as a Service (SaaS) industry. Throughout his career, he has focused on building innovative products that solve real-world problems while fostering a culture where people can thrive and reach their full potential. A self-taught doer driven by curiosity rather than profit, Dean believes that the foundation of any successful business lies in its people—their talent, passion, and potential.Dean's leadership philosophy emphasizes transparency, a people-first mindset, and a relentless pursuit of excellence. By encouraging experimentation, learning from failure, and adapting quickly, he ensures both the organization and its employees grow meaningfully. He often reminds teams that the job of leadership is to chart the course while nurturing a sense of purpose and belonging.Passionate about staying ahead in the ever-evolving SaaS landscape, Dean combines visionary thinking with practical execution to differentiate products through technology and exceptional user experiences. By engaging directly with customers and prioritizing their needs, he drives innovation, strengthens long-term relationships, and ensures sustained value creation.Beyond professional accomplishments, Dean is a dedicated mentor and advocate for lifelong learning, sharing insights to inspire the next generation of leaders and innovators. He remains committed to empowering others, making a positive impact, and connecting with curious minds eager to grow. Connect with Dean Mathews:Website: ontheclock.com LinkedIn: https://www.linkedin.com/in/dean-mathews-yes/ YouTube: https://www.youtube.com/user/OnTheClockTimeClock LinkedIn: https://www.linkedin.com/company/ontheclock-com Facebook: https://www.facebook.com/ontheclocktimeclock TurnKey Podcast Productions Important Links:Guest to Gold Video Series: www.TurnkeyPodcast.com/gold The Ultimate Podcast Launch Formula- www.TurnkeyPodcast.com/UPLFplusFREE workshop on how to "Be A Great Guest."Free E-Book 5 Ways to Make Money Podcasting at www.Turnkeypodcast.com/gift Ready to earn 6-figures with your podcast? See if you've got what it takes at TurnkeyPodcast.com/quizSales Training for Podcasters: https://podcasts.apple.com/us/podcast/sales-training-for-podcasters/id1540644376Nice Guys on Business: http://www.niceguysonbusiness.com/subscribe/The Turnkey Podcast: https://podcasts.apple.com/us/podcast/turnkey-podcast/id1485077152

GuestZak Calisto CEO and Founder Karooooo's (KARO) Company InfoKarooooo Ltd.Ticker:$KAROWebsitehttps://karooooo.com/Zak's BioMr. Calisto is Karooooo's (KARO) Chief Executive Officer and has been a member of our board of directors since May 2018. He has been the Chief Executive Officer of the Group since its founding in 2001. Before founding the Company, Mr. Calisto was a Member of Vehicle Tracking Services, a company specializing in the distribution of telematics services, from 1994 through 2001. Prior to that, Mr. Calisto was a Member of Cell Communications, a company specializing in the distribution of telecommunication services, from 1994 to 1996. Mr. Calisto also completed an accelerated training program at Standard Bank, Africa's largest lender by assets, from 1986 through 1991. Mr. Calisto studied at the University of South Africa and University of the Witwatersrand.Company BioKarooooo digitally transforms physical operations by simplifying decision making. Through its cloud platform, Karooooo empowers businesses to conquer operations including fleet maintenance, fuel management and asset utilization, workforce management, logistics, safety, compliance, risk and environmental impact. Karooooo's differentiated insights and analytics simplify day-to-day operations and enable businesses to decrease costs, increase efficiency, improve safety and strengthen workforce and customer satisfaction. Karooooo operates through two business segments: Cartrack - Cartrack operates a smart mobility SaaS cloud that improves customer's physical operations through real-time data analytics. Cartrack empowers systems integration, fleet management and administration, field worker management, video-based safety, risk mitigation and compliance, delivery management and reporting. Karooooo Logistics - Karooooo Logistics provides a software application that enables the management of last-mile delivery for our retail customers. By empowering drivers, automating processes, boosting efficiency and scaling operations, we enable customers to offer fast, efficient and affordable eCommerce delivery. Cartrack has a unique financial profile with ~20% subscription revenue growth and a ~30% operating profit margin. Cartrack is the primary driver of Karooooo's financial performance. Karooooo is headquartered in Singapore and services more than 125,000 commercial customers and more than 2,600,000 active subscribers in more than 20 countries globally.

The Information's Wayne Ma talks with TITV Host Akash Pasricha about Nvidia's GTC keynote and the company's new inference chip technology. We also talk with Khosla Ventures' Ethan Choi about the U.S.-China AI race and the rise of AI agents in super apps, AI Reporter Laura Bratton about why SaaS companies are quietly flagging AI as a major business risk in regulatory filings, and we get into industrial-scale data center hacks with Columnist Ann Davis Vaughan.Articles discussed on this episode: https://www.theinformation.com/newsletters/the-briefing/expect-gtc-nvidias-groq-chiphttps://www.theinformation.com/articles/figma-hubspot-ceos-say-fazed-risks-ai-agents-disclosures-say-otherwisehttps://www.theinformation.com/newsletters/ai-infrastructure/5-ingenious-hacks-boosting-ai-data-centersSubscribe: 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-agendaTITV airs weekdays on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.Follow us:X: https://x.com/theinformationIG: https://www.instagram.com/theinformation/TikTok: https://www.tiktok.com/@titv.theinformationLinkedIn: https://www.linkedin.com/company/theinformation/

Would you like access to our advanced agency training for FREE? https://www.agencymastery360.com/training How are you protecting yourself from the real risk of owner burnout? Agency owners often burn out because they built a business that depends entirely on them. Today's featured guest is a former agency owner turned AI SaaS founder. He'll unpack what really caused his agency collapse, what he learned from it, and how he rebuilt from a completely different role. Austin Armstrong is the owner of Syllaby, a tool for social media marketing that helps users create their very own realistic digital clone to personalize their marketing efforts, allowing them to forge a deeper connection with their audience. Austin spent over a decade in the agency world, working his way up from intern to running an agency before launching his own. For a while, it worked, until the cracks appeared. His agency was built around organic marketing and heavily centered on his personal brand. High months meant hiring fast. Low months meant wondering if payroll would clear. When a few large clients (that accounted for about 60% of monthly revenue) churned, the instability became unbearable. So Austin made his tech pivot and moved to starting Syllaby, which also came with a role pivot. More recently, he just released his first book Virality and is the co-founder of the upcoming AI marketing World conference. In this episode, we'll discuss: From agency failure to early AI adopter Why the founder bottleneck is emotional The founder evolution model AI exposes weaknesses Subscribe Apple | Spotify | iHeart Radio Sponsors and Resources This episode is brought to you by Wix Studio: If you're leveling up your team and your client experience, your site builder should keep up too. That's why successful agencies use Wix Studio — built to adapt the way your agency does: AI-powered site mapping, responsive design, flexible workflows, and scalable CMS tools so you spend less on plugins and more on growth. Ready to design faster and smarter? Go to wix.com/studio to get started. Making the Decision to Be an Early Adopter When he started Syllaby, Austin could already see the writing on the wall with AI. He was already not happy navigating the agency world, so the question was, "Do I want to place a bet as an early adopter of this technology? Potentially cannibalizing my own agency?" He spoke with several clients and business owners and came to the conclusion that most people hire an agency because they know they need to create content to be relevant, but didn't know how to pick the right topics, and in many cases didn't want to be on camera. They needed help staying consistent and accountable. Some of them don't even have the money to hire an agency, but still have a message and an expertise to share. So Austin started to look for ways to automate those processes using AI. The Founder Bottleneck Is Emotional Before It's Operational The emotional weight of the unraveling of Austin's agency was real. Nightmares about client complaints. Constant vigilance. Inability to disconnect. Eventually, he decided to make a bet on AI and launched Syllaby, an AI-powered content platform designed to automate much of what agencies manually execute, from topic discovery to scripting to publishing. Now, looking back, he sees his agency's failure came from several mistakes. It wasn't bad marketing or lack of demand. It was structural dependency. The agency relied on: His personal brand His client relationships His decision-making His emotional capacity When large clients churned, revenue collapsed because concentration risk hadn't been designed out of the model. When delivery required nuance, he couldn't step away because "he stirred the pot." This is the Operator trap. The Founder Evolution Model Most founders believe they own an agency. In reality, the agency owns them. What is supposed to happen as your agency evolves is that your role in it evolves as follows: Operator → Manager → Architect → CEO → Owner At the Operator level: Sales depends on you. Delivery depends on you. Escalations go to you. Pricing goes through you. And when you focus on one area, another suffers. Systems Create Freedom But They Also Create Identity Shifts As the owner, being needed feels good and letting go feels disorienting. Austin acknowledged this tension. In his agency, clients wanted him. Even with SOPs, some work required nuance. Some of it was ego. Some of it was positioning. Some of it was hiring the wrong people in the wrong seats. Having learned his lesson, things look very different in his SaaS company, where he can rely on strong partners, defined ownership, AI-supported workflows, and clear decision rights. Now he can disappear for two weeks, go skiing with family, speak at events, and the business doesn't break. AI Exposes Weakness All over the industry owners agree that AI isn't replacing strong agencies. It's exposing weak ones. At Syllaby, Austin has integrated AI so much is hard to think where he DOESN'T use it. He automates what many agencies sell manually: SEO-based topic discovery Script generation Video creation Scheduling and publishing For smaller businesses, this lowers the barrier to entry. For agencies, it creates leverage. Which tool are owners using? This varies from time to time. What you should be doing is testing them all out to see which ones work better for you, as well as creating a brief with all the information you'll need in case you decide to migrate to a different tool. Jason calls this his "AI Operating Brief", a master document loaded with: Company positioning Customer data Success stories CRM insights Transcripts Strategic principles Once embedded into AI tools, it eliminates repetitive context-setting and removes founder bottlenecks. Austin does something similar with what he calls his "Austin Codex", years of content, frameworks, and intellectual property housed inside AI models. The result is institutional memory without constant founder involvement. Time Audits Reveal the Hidden Ceiling Austin is a big fan of the full-time audit exercise: For one to two weeks, document: Every task Start and end times Whether it's mandatory or optional Your enjoyment level The dollar value of your time The outcome is uncomfortable. Once you're done, you'll see which $10 tasks eating $1,000/hour time, the emotional drain disguised as "important work", and the distractions masquerading as urgency. He outsourced email management, calendar coordination, travel booking — all consolidated into a daily executive summary delivered where he actually spends time. Not because he can't do it, but because he shouldn't. The bigger lesson: you don't scale an agency… you outgrow your role. Do You Want to Transform Your Agency from a Liability to an Asset? Looking to dig deeper into your agency's potential? Check out our Agency Blueprint. Designed for agency owners like you, our Agency Blueprint helps you uncover growth opportunities, tackle obstacles, and craft a customized blueprint for your agency's success.

Sam Jacobs (CEO, Pavilion), AJ Bruno (CEO, QuotaPath), and Asad Zaman (CEO, Sales Talent Agency) debate exactly how to handle team members resisting AI adoption. When to leave them, when to nudge them, and when to fire them. The discussion highlights real-world data, including how leading companies reach the top decile of AI adoption and the mechanics of running a 24-hour, four-squad AI hackathon to force experimentation. We also cover a critical performance heuristic from the past CPO of LaunchDarkly: if your team cannot execute simple tasks in a single day, you are falling behind. The conversation covers change management for revenue leaders, how to integrate AI into your daily enterprise pipeline generation, and why optimizing your GTM strategy means making hard decisions about personnel who refuse to adapt. Key Takeaways: >Driving AI adoption requires clear communication and rewarding good behavior, but AJ Bruno warns that leaders will ultimately have to "leave behind a handful of folks that are just not going to get on the bus, that aren't getting on board." >When implementing new AI tools across your teams, Asad Zaman notes that expectations must scale with seniority, stating "I have more tolerance as I move lower in the org and less tolerance at the higher levels." >AI should be treated as a creative partner for deeper analysis rather than a shortcut for unedited output, a reality Sam Jacobs emphasizes by warning "If you are just the pass through, you will be fired." Connect with the Hosts Host: Sam Jacobs - https://www.linkedin.com/in/samfjacobs/  Host: AJ Bruno - https://www.linkedin.com/in/ajbruno3/  Host: Asad Zaman - https://www.linkedin.com/in/azaman1/   Topline is more than a YouTube Channel:  Subscribe to Topline Newsletter: https://www.joinpavilion.com/topline-newsletter  Tune into Topline Podcast, the #1 podcast for founders, operators, and investors in B2B tech: https://www.joinpavilion.com/topline-podcast  Join the free Topline Slack channel to connect with 600+ revenue leaders to keep the conversation going beyond the podcast: https://www.joinpavilion.com/topline-slack Chapters: 00:00 Intro 00:35 The Question: Employees resisting AI 01:39 Convert them or fire them? 02:07 Running internal AI hackathons 03:54 How CEOs drive adoption 05:08 Mapping tasks to AI agents 06:27 The "Robot Layer" in emails 07:40 Claire Vo's anti-dinosaur framework 08:07 The One-Day Execution Heuristic 12:52 Why you should be scared 14:30 Elevating junior AI talent 16:35 Reducing 3 hours of work to 45 mins 18:54 Summary: How to uplevel the org 21:09 The tension between speed and depth 21:52 Pass-through? Fired! FIRED!!!  

Michael Perica had been discussing the implications of AI with investors for more than a year, but the market didn't fully react—until one particular moment. In late January and early February, a wave of announcements around enterprise-focused AI models and workflow plugins triggered what Perica refers to as the “SaaS-pocalypse.” In a single day, roughly $258 billion in SaaS market value disappeared, he tells us.For Perica, the episode confirmed something he had already been sensing in conversations with investors and clients. The traditional path to enterprise modernization—committing to large, monolithic software platforms—was no longer the only option. AI, particularly emerging agentic AI technologies, was beginning to offer organizations a new route: modernizing workflows and processes without necessarily replacing entire systems.The sudden market reaction accelerated those conversations. Investors and executives began reaching out to Rimini Street asking whether this moment validated the alternative technology path the company had been discussing. For Perica, the answer was clear. The event underscored that organizations now had the ability to tailor AI models directly to specific business processes rather than conforming their operations to a rigid software roadmap.That shift has shaped how Perica thinks about strategy going forward. Instead of viewing AI purely as a tool for efficiency, he sees it as a catalyst for enterprise-wide transformation. Finance leaders, he argues, now have an opportunity to work closely with CIOs to rethink workflows, eliminate operational bottlenecks, and deploy targeted AI solutions that create quick wins across the organization.In Perica's view, the SaaS-pocalypse wasn't just a market correction. It was a signal that a new technology paradigm had arrived—and that forward-looking CFOs must be ready to lead the change.

As RSAC 2026 approaches, Daniel Bardenstein, CEO and Co-Founder of Manifest, joins hosts Sean Martin and Marco Ciappelli to unpack the growing disconnect between how security leaders perceive their AI and software supply chain posture and what practitioners on the ground actually experience. Drawing from Manifest's new research report — Beyond the Black Box — Bardenstein connects the dots between shadow AI, SBOM adoption gaps, and a dangerous pattern: history is repeating itself as organizations rush to adopt AI with the same disregard for security that characterized the early cloud era.   In a wide-ranging pre-event conversation ahead of RSAC 2026, Daniel Bardenstein, CEO and Co-Founder of Manifest, explores what it means to truly secure the software and AI supply chain — not just check the compliance box. Manifest's new research report, Beyond the Black Box, surveyed more than 300 security and AI leaders globally to understand the reality of AI adoption and software supply chain risk. One of the most striking findings was not a statistic, but a structural problem: a significant perception gap exists between how confident executive security leadership feels about their AI security posture and how unprepared frontline practitioners actually are. Where there is misalignment, Bardenstein notes, there is risk.   The conversation draws a vivid parallel to the cloud adoption wave of a decade ago, when organizations rushed to SaaS and cloud infrastructure without thinking through security implications — and gave birth to entire new industries to clean up the mess. Today, the same dynamic is playing out with AI. Nearly two-thirds of the survey respondents reported encountering shadow AI within their organizations, as employees freely use tools like ChatGPT, DeepSeek, or locally downloaded models without centralized governance. When that AI eventually gets embedded into software that organizations build, deploy, and sell, the blind spots compound.   SBOMs — software bills of materials — represent a promising step toward supply chain transparency, and Bardenstein credits the US government's regulatory nudging for driving adoption. Manifest's research shows that roughly 60% of organizations are now generating SBOMs, a meaningful milestone. But generation is not governance. Too many organizations treat an SBOM as a compliance artifact — a JSON file on a hard drive — rather than an operational tool that could dramatically accelerate vulnerability response, regulatory compliance, and incident management. The prescription has been filled; it's just not being taken.   To reframe the urgency, Bardenstein introduces the concept of the "transparency tax" — the hidden cost organizations pay in time, money, and risk when they build or buy opaque technology. Just as consumers demand ingredient labels on food, Carfax reports on used cars, and active ingredient disclosures on prescriptions, the technology sector needs to normalize the same transparency for software and AI. For organizations willing to do the math, the case for investing in supply chain visibility becomes not just a security argument, but a business one.   Heading into RSAC 2026, Manifest will not have a booth but will be active across the conference floor, meeting with customers, partners, and prospects. Bardenstein will appear on an invite-only panel alongside leadership from Corridor Dev, 1Password, and Google to discuss secure software and secure AI. The team is also planning to announce new platform capabilities designed to close the governance gaps their research surfaced — helping organizations move fast without creating the kind of blind spots that make AI adoption a liability rather than an advantage.   Tune in for this sharp, candid pre-event conversation — and look for the full on-location Brand Spotlight recorded live at RSAC 2026 in San Francisco.  

What happens when a stager with a tech background gets tired of duct-taping together Trello, QuickBooks, and a CRM that wasn't built for her? She builds something better. In this episode, I sit down with Kate, co-founder of Hutch — a purpose-built project management and inventory platform designed specifically for home stagers. Kate shares her journey from SaaS startup life to staging side hustler to software founder, and walks us through exactly what Hutch does, why the staging industry has been underserved for so long, and how better data can finally help stagers price their services for what they're actually worth. WHAT YOU'LL LEARN FROM THIS EPISODE: How Kate went from staging side hustler to software co-founder Why so many stagers are stitching together tools that were never built for them and what that's costing them What Hutch does as an all in one staging business tailored platform Why your average inventory investment per stage might be the most important number you're not tracking RESOURCES: Apply for Private Coaching: www.rethinkhomeinteriors.com/privatecoachingapp Enroll in Staging Business School Accelerate Track: www.rethinkhomeinteriors.com/accelerate Join the Staging Business School Growth Track Waitlist: www.rethinkhomeinteriors.com/growth Follow the Staging Business School on Instagram: www.instagram.com/stagingbusinessschool Follow Lori on Instagram: www.instagram.com/rethinkhome Hutch Staging Website: hutchstaging.com Hutch Staging on Instagram: @hutchstaging If you want to learn how to market and grow your staging business, enrollment is open for Rethink You Accelerate. This is a year-long mentorship program, where I help you and other staging business owners plan, grow, flow, and thrive with the results that you've always wanted. The doors are open and I would love to see you in the classroom! ENJOY THE SHOW? Leave a 5-star review on Apple Podcasts so that more Staging CEOs find it. Follow over on Spotify, Stitcher, Amazon Music, or Audible.

How do you turn trillions of user interactions into meaningful decisions without drowning in data? In this episode of Tech Talks Daily, I sit down with Todd Olson, co-founder and CEO of Pendo, to talk about the future of product-led organizations and why AI is reshaping how software companies grow, build, and compete. Pendo tracks trillions of product usage events to help organizations understand how customers actually interact with their software. That level of data sounds powerful, but it also raises a challenge many teams face today. How do you turn massive data sets into clear signals that teams can act on without falling into analysis paralysis? Todd explains how Pendo approaches this problem by organizing product data around real user journeys, feature adoption, and areas where people drop off. Instead of leaving teams buried in dashboards, the goal is to surface insights that matter. Increasingly, AI is helping by acting as a kind of embedded analyst that highlights the patterns product teams should focus on. Our conversation also revisits the idea behind Todd's book, The Product-Led Organization. When it was published around the time of the pandemic, it argued that great products should do much of the heavy lifting traditionally done by sales or support teams. Looking back now, Todd believes the core idea remains intact. AI simply accelerates the model by allowing companies to experiment faster and scale product-driven experiences with far fewer people. But that shift is also creating tension in the software industry. We talk about the so-called reckoning in SaaS economics and the growing debate around whether AI will make traditional software companies obsolete. Todd offers a more measured perspective. While AI allows anyone to prototype software quickly, the companies that survive will still be the ones solving difficult problems, navigating compliance requirements, and building products that customers trust. Another theme we explore is geography and innovation. Pendo is headquartered in Raleigh, North Carolina, far from the usual coastal tech hubs. Todd shares how building outside Silicon Valley has shaped the company's culture, talent strategy, and mindset. There are advantages to being close to the center of the AI boom, but there is also value in building away from the echo chamber. We also spend time unpacking the rise of AI-assisted development and the trend many people call "vibe coding." Todd believes AI will dramatically reshape product teams, but he also pushes back against the idea that humans will disappear from the development process. Engineers will still need to review code, teach AI systems best practices, and ensure security and reliability. One of the most interesting moments in our conversation comes near the end when Todd shares a belief that originality will become one of the most valuable assets in the age of AI. As automated content and automated code become easier to generate, he believes people will increasingly value craft, taste, and original thinking. So in a world where AI can generate almost anything with a prompt, the real question becomes far more human. What problems are actually worth solving? If you care about the future of software, product strategy, and how AI is reshaping the economics of building companies, this is a conversation that offers plenty to think about. And after listening, I would love to hear your perspective. As AI becomes embedded in every product and workflow, do you believe originality and craft will become the true differentiators in the software industry?

In this episode of Tank Talks, Matt Cohen and John Ruffolo unpack a volatile moment across software, capital markets, AI, and Canadian industrial policy. The conversation opens with Constellation Software's AI-era challenge, as new president Mark Miller faces investor skepticism around whether legacy vertical market software can maintain its moat in a world increasingly shaped by AI-driven productivity, automation, and code generation.From there, Matt and John examine Salesforce's decision to raise billions in debt to fund share buybacks, questioning whether this is smart balance-sheet engineering or a red flag that large software companies are running out of offensive growth options. The episode then turns to the private credit market, where redemption gates, liquidity pressure, and fears around AI infrastructure lending raise deeper concerns about leverage, accounting, and systemic fragility.Back in Canada, the discussion shifts to the country's defence industrial strategy and why the real opportunity is not just traditional military spending, but dual-use investment across AI, quantum, satellites, aerospace, and strategic infrastructure. The episode closes with a look at Andrej Karpathy's open-source Auto Research project and what it signals about the speed of AI progress, the democratization of research capabilities, and the growing pressure on knowledge workers and software engineers to keep up.If software moats are weakening, private credit is wobbling, and defence dollars are becoming innovation dollars, where will the next real edge come from?Constellation Software, AI Pressure, and the Future of Vertical SaaS (00:43)Matt and John break down Constellation Software's latest numbers, the market's growing skepticism toward legacy software businesses, and the bigger question of whether mission-critical vertical SaaS can stay resilient as AI chips away at traditional moats. They explore why trusted workflows and proprietary data still matter, but also why even durable software businesses may face long-term pressure.Salesforce's $25 Billion Debt Bet and What It Really Signals (06:28)Matt and John unpack Salesforce's plan to raise massive debt for share buybacks, debating whether this is efficient capital structure management or a defensive move from a software giant with fewer compelling growth opportunities. The bigger issue is what this says about confidence, capital allocation, and the mood inside mature SaaS companies right now.Private Credit Redemption Gates and the Fear Beneath the Surface (10:49)A wave of redemption limits across major private credit funds becomes the next flashpoint. Matt and John explain why retail money flooded into the asset class, how managers were pushed into riskier lending, and why the underlying concern is no longer just liquidity management, but whether private credit has been pricing equity risk like it was safe debt.Canada's Defense Strategy Is Really a Dual-Use Tech Strategy (16:29)Matt and John shift to Canada's defense industrial strategy and the National Research Council's planned investment, arguing that the real opportunity is in dual-use innovation. Rather than thinking only in terms of tanks and submarines, John reframes defense spending as investment in AI, quantum, satellites, aerospace, and strategic infrastructure that can serve both government and enterprise customers.The AI Catch-Up Panic Is Real (21:26)Matt and John zoom out from markets and policy to the personal reality of AI acceleration. John admits he feels both energized and behind, capturing the exact tension many operators and investors feel as new tools emerge faster than most people can realistically absorb them.Andrej Karpathy Auto Research and the One-GPU Research Lab Moment (22:58)The episode closes with Andrej Karpathy's open-source Auto Research project and why it matters. Matt explains how autonomous research loops, overnight experimentation, and low-cost GPU access could dramatically speed up model tuning, product testing, and AI development, making advanced experimentation far more accessible than before.Connect with John Ruffolo on LinkedIn: https://ca.linkedin.com/in/joruffoloConnect 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, we break down a whirlwind week across crypto, tech, and venture markets. We unpack Bitcoin's renewed rally into the 70s and explore how geopolitical tensions, hash‑rate swings, and macro narratives are driving price action. Then we dive into the CFTC's latest push to regulate prediction markets, and what it means for platforms like Polymarket and Kalshi as "gambling" and "forecasting" continue to blur. The conversation shifts to the so‑called "SaaS‑pocalypse" and whether the doom narrative holds up in a world where AI-native companies are generating 10× more revenue per employee. Finally, we spotlight the nearly 40 new unicorns minted this year, with AI dominating the list, and discuss what this potentially says about where capital, innovation, and excitement are flowing in 2026.  Remember to Stay Current!  To learn more, visit us on the web at https://www.morgancreekcap.com/morgan-creek-digital/. To speak to a team member or sign up for additional content, please email mcdigital@morgancreekcap.com  Legal Disclaimer  This podcast is for informational purposes only and should not be construed as investment advice or a solicitation for the sale of any security, advisory, or other service. Investments related to the themes and ideas discussed may be owned by funds managed by the host and podcast guests. Any conflicts mentioned by the host are subject to change. Listeners should consult their personal financial advisors before making any investment decisions. 

Juan Ignacio Garcia Braschi is a partner at L40, a boutique SaaS M&A advisory firm with offices in Madrid, Lisbon, and Miami. After two decades in banking, private equity, and operating roles, including serving as CFO of ride-hailing company Cabify, he now helps SaaS founders sell companies typically valued between $20M and $200M. L40 works primarily with B2B SaaS companies doing $5M–$50M ARR, most of them bootstrapped or lightly funded, including companies in Europe and Latin America. Juan explains how today's buyers evaluate SaaS companies, why Rule-of-40 performance still matters even with AI, and how growth rate, retention, and profitability determine valuation ranges of roughly 4–8x ARR. Key Takeaways Growth Drives Valuation: Growth rate correlates most strongly with SaaS multiples. Companies growing 50% command much higher valuations than those growing 20%. Rule Of 40 Still Matters: Buyers increasingly expect SaaS companies to combine strong growth with some profitability. Financial Buyers Dominant: Private-equity-backed platforms acquiring add-ons are the most active buyers for $50M–$100M SaaS companies today. Sell During Momentum: Smaller companies growing 20–40% annually can be an ideal window for acquisition before growth naturally slows. Quote from Juan Ignacio Garcia Braschi, Managing Director and Partner at L40 "If you think that you're going to sell your SaaS company, you should think of that two years ahead of when you want to sell. So don't wait until you're burned out. "Keep in mind that you will have to make a profit at some point to sell to serious financial buyers. So when your company is growing at decent 20, 30, 40% year over year rates, that's probably the sweet spot for selling.  "Significant funds have been raised in the past 24 months and that has to be deployed. Traditional private equity firms are more more interested in tech. These days you see more and more traditional private equity firms going into tech and that's increasing competition and driving multiples up." Links Juan Ignacio Garcia Braschi on LinkedIn L40 on LinkedIn L40 website Podcast Sponsor – Lighter Capital This podcast is sponsored by Lighter Capital. In the last 15 years, Lighter Capital has helped over 600 software and SaaS founders secure simple, non-dilutive financing to grow a little faster—without giving up any precious equity or board seats to investors.  Simple debt funding from Lighter Capital can range from $50K to $10 million, with straightforward terms, no personal guarantees or covenants, and up to a 4-year payback period. Go to LighterCapital.com to apply and get a quick pre-qualification. Then talk with their experienced team to create a practical funding plan to achieve your goals.  The Practical Founders Podcast Tune into the Practical Founders Podcast for weekly in-depth interviews with founders who have built valuable software companies without big funding. Subscribe to the Practical Founders Podcast using your favorite podcast app or view on our YouTube channel. Get the weekly Practical Founders newsletter and podcast updates at practicalfounders.com. Practical Founders CEO Peer Groups Be part of a committed and confidential group of practical founders creating valuable software companies without big VC funding.  A Practical Founders Peer Group is a committed and confidential group of founders/CEOs who want to help you succeed on your terms. Each Practical Founders Peer Group is personally curated and moderated by Greg Head.

Enregistré au One to One Retail E-Commerce 2026, cet épisode s'attaque à un sujet central pour tous les retailers : la personnalisation à grande échelle.Laurent reçoit Sonia Ouaksel, Chief of Brand and Experience Officer chez Reelevant - plateforme SaaS qui permet aux équipes CRM de délivrer un contenu individualisé à chaque client, sur tous les canaux.Au programme :Pourquoi la segmentation classique atteint ses limites à grande échelleLe cas Célio : 4 millions de clients, 7 personnes en CRM, et des résultats en hausseLa logique "no canal" : email, site, app - une seule expérience de marquePlace des Tendances et ses +43% de conversion sur la homepage personnaliséeSimone, l'IA de Reelevant, et ce que ça change pour les équipes marketingUn épisode pour toutes les marques qui ont de la donnée mais ne savent pas encore quoi en faire.Et quelques dernières infos à vous partager :Suivez Le Panier sur Instagram @lepanier.podcast !Inscrivez- vous à la newsletter sur lepanier.io pour cartonner en e-comm !Écoutez les épisodes sur Apple Podcasts, Spotify ou encore Podcast Addict.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

Justin sits down with Allison Skidmore, Chief Customer Officer at Optimizely, the world's first operating system for marketing teams.Allison brings a rich perspective shaped by stints at Adobe, Stackla, Gigya, and SAP across Asia Pacific before landing in the US to lead customer success at Optimizely. This episode explores how AI is fundamentally reshaping the marketer's daily workflow, what great onboarding looks like in an AI-native world, and what the CCO role must become as organizations race to stay ahead.Episode Notes & Key Topics1. Allison's Career JourneyStarted in SEM at a Sydney agency later acquired by Adobe, rode the wave of digital marketing's early SaaS transition.Spent six years at Adobe running customer success across Asia Pacific, building offshore teams and subscription services models.Moved through Stackla and Gigya (acquired by SAP nine months in), then scaled the CS role across all SAP lines of business in APAC.Joined Optimizely two years ago after reconnecting with CEO Alex Atzberger, bringing global enterprise CS experience to a fast-growing martech platform.2. What Stays the Same in Customer SuccessThe sales-to-CS handover friction is timeless: it never goes away regardless of company size or stage.Digital-first customer engagement (email, offshore teams, automation) has been a constant scaling challenge for decades.The shift from time-and-materials professional services to subscription models remains a dominant trend.Tech advancements create the inflection points:  AI is today's example.3. AI and the Marketer's Day-in-the-LifeAllison paints a vivid picture: by 10 AM, an AI-enabled marketer has completed a full week's worth of work.Optimizely's Opal AI product is provisioned across the entire team, enabling agent building, workflow automation, and access to tools like Claude and Gemini.The opportunity is not just efficiency, it's the ability to pull forward backlogged work and shrink implementation timelines (e.g., from 12 months to 3).The companies moving fastest are the ones blocking calendar time to train their teams on prompting and agent-building, not just giving access.4. Reimagining Onboarding and the Customer JourneyAllison's framework: great onboarding is the seamless alignment of three channels, human-to-human touchpoints, email marketing, and in-product experience.Customers now expect to self-serve answers (just like asking AI instead of calling a mechanic), human-heavy onboarding alone no longer cuts it.Consistency is the key: the message the customer gets in the product, in their inbox, and from their CSM should be identical, no basic repeats, no skipped steps.5. The Evolving Role of the CCOThe C-suite fundamentals don't change: stay curious, solve problems, skate to where the puck is going.Today, the puck is AI.  If you can't build an agent, you can't expect your team to.Allison is actively realigning roles, KPIs, and commissions around AI-native execution.The CCO who can't leverage AI to scale themselves and reimagine their business will become extinct, just like Blockbuster.Lego is the positive model: reinvention again and again.6. What's Top of Mind for 2026AI continues to dominate, but the customer journey evolution is a close second.Consumers are shifting from Google to ChatGPT and similar tools, which means brands must optimize for GEO (Generative Engine Optimization), not just SEO.Personalization is entering a new era:  every touchpoint, not just the website.

We unpack why the “SaaS-Pocalypse” is less about software dying and more about buyers finally right sizing cloud and marketplace deals with better data. We dig into AI unit economics, token driven cost volatility, and how procurement, FinOps, and venture capital are being rewritten in real time. • Flywl as a cloud meta marketplace across AWS, Azure, and Google Cloud • Buyer pain and buyer empathy as the product design center • Why AI inference costs make traditional FinOps reactive • Treating a marketplace purchase as a transaction lifecycle asset • Real time consumption tracking, alerts, and contract renegotiation timing • Outcome based pricing challenges with token variability and agentic workflows • Revenue recognition uncertainty in consumption and outcome models • Why humans still matter in go to market despite AI agents • The data cleanup problem in procurement and the need for universal product IDs • Why enterprises are not rushing to build all SaaS internally with AI • 2026 VC dynamics, mega rounds, capital concentration, and what counts as real recurring revenue “SaaS-Pocalypse” makes for a great headline, but the real shockwave is quieter and more disruptive: enterprise buyers finally understand their cloud environment well enough to demand better deals, better governance, and real proof of value. We sit down for a roundtable on cloud marketplaces, AI unit economics, and the new reality of software procurement where a purchase is no longer a static line item, it's a living asset you have to monitor, benchmark, and continuously right size. Ankur Srivastava, CEO and founder of Flywl, explains why he built a cloud meta marketplace to unify buying and selling across AWS Marketplace, Azure, and Google Cloud and why “buyer empathy” is the only way to fix a broken procurement playbook. Priya Ramachandran, founder and managing partner at Foster Ventures, connects the dots from operator experience to investing, and breaks down why traditional FinOps can't keep up with AI inference costs, token volatility, and outcome-based pricing models like per ticket resolved. Then we zoom out to the 2026 venture capital environment: mega rounds, capital concentration, and the debate over whether AI-native efficiency makes old funding assumptions obsolete. Along the way, we tackle an agentic economy question: when algorithms negotiate with algorithms, what happens to trust, brand, and human relationships in go to market?Ankur Srivastava: https://www.linkedin.com/in/ankursrivas/Ankur Srivastava is the CEO and Founder of Flywl, the world's first cloud meta-marketplace transforming how enterprises buy and sell software across AWS, Azure, and Google Cloud. Previously, he was an elite sales leader at Amazon Web Services (AWS), where he spent five years as Head of Field and Customer Business Development for the AWS Marketplace.Priya Ramachandran: https://www.linkedin.com/in/sivapriyaramachandran/Priya Ramachandran is the Founder and Managing Partner at Foster Ventures, an early-stage VC firm she built from the ground up to act as the "startup of the VC world". She is an operator-turned-investor with significant experience building and scaling products at companies like Coupa Software, BetterCloud, and Intel.Website: https://www.position2.com/podcast/Rajiv Parikh: https://www.linkedin.com/in/rajivparikh/Sandeep Parikh: https://www.instagram.com/sandeepparikh/Email us with any feedback for the show: sparkofages.podcast@position2.com

Atlassian announced that it is letting about 10% of its workforce go today. Management said it was because AI is making the company more efficient, but we're wondering if there is more to it than that. Plus, some napkin math on the Strategic Petroleum Reserve release and Dollar General's most recent earnings Tyler Crowe, Matt Frankel, and Jon Quast discuss: - Altassian's Layoffs - The challenges facing SaaS companies in an age of efficiency - Assessing the impact of the SPR release and how it changes our investing approach - Dollar General's earnings and its ongoing turnaround project Companies discussed: TEAM, XYZ, DG, FIVE, WMT, TGT Host: Tyler Crowe Guests: Matt Frankel, Jon Quast Engineer: Dan Boyd Disclosure: Advertisements are sponsored content and provided for informational purposes only. The Motley Fool and its affiliates (collectively, “TMF”) do not endorse, recommend, or verify the accuracy or completeness of the statements made within advertisements. TMF is not involved in the offer, sale, or solicitation of any securities advertised herein and makes no representations regarding the suitability, or risks associated with any investment opportunity presented. Investors should conduct their own due diligence and consult with legal, tax, and financial advisors before making any investment decisions. TMF assumes no responsibility for any losses or damages arising from this advertisement. We're committed to transparency: All personal opinions in advertisements from Fools are their own. The product advertised in this episode was loaned to TMF and was returned after a test period or the product advertised in this episode was purchased by TMF. Advertiser has paid for the sponsorship of this episode. Learn more about your ad choices. Visit ⁠⁠⁠⁠⁠⁠⁠⁠megaphone.fm/adchoices⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

It is a special midweek drop and the group chat is packed. This episode covers the forces quietly reshaping the economy right now — from AI slashing business costs overnight, to GLP-1 drugs gutting the snack industry, to private credit markets showing their first real cracks. The guys break down what is actually happening beneath the headlines, why sports viewership is at an all-time high, which consumer brands are quietly hitting $10 billion, and why the stock market may be setting up for a violent rally. No fluff, no filler — just the conversations happening in every serious group chat right now.   Topics Covered This Episode: 1. AI Is Replacing Your Entire Software Stack How using Claude cut one company's AWS bill from $9,500 a month down to a projected $500 — and what that means for every business owner still paying for legacy SaaS tools. Plus: Lovable jumps from a $300M to $400M run rate in a single month, and Anthropic adds $6 billion in run rate in two months. The AI economy is not coming — it is already here. 2. The GLP-1 Effect Is Hitting Corporate Earnings Campbell Soup's snack division dropped 6% in a single quarter with no obvious explanation other than 30 million Americans now on GLP-1 medications. The guys explore the downstream ripple effects — grocery aisles, fast food, supplement brands, and what retailers like Kroger do when people simply stop snacking. 3. Private Credit Is Cracking Blackstone, Blue Owl, and Cliffwater are all facing record redemption requests after two major auto suppliers backed by private credit funds went under. Is this an economy problem, a bad-lending problem, or a panic problem? The guys break it all down and explain why it matters even if you have never heard of private credit. 4. Sports Is on an Unprecedented Run Every sport — NFL, NBA, MLS, World Baseball Classic, UFC — is posting record ratings. The guys explain why gambling, fragmented media, and the death of cable news are all fueling the surge, and why the Tom Brady flag football league and the Gronk vs. Logan Paul beef are the perfect example of how modern sports entertainment actually works. 5. The $10 Billion Consumer Brands Nobody Is Talking About Quince hits a $10 billion valuation doing nearly $2 billion in revenue by going factory-direct to consumers. The guys break down why consumer investing is back, who is losing market share, and what the rise of brands like Keats and Whatnot means for traditional retail. 6. Millionaire Taxes, Fraud, and the Wealth Exodus Washington State's new 9.9% millionaire tax, the staggering scale of hospice care fraud in Los Angeles, and why billionaires — and now regular millionaires — are leaving high-tax states for Nevada, Texas, and Florida. The argument is simple: clean up the fraud first, and you would not need to raise taxes at all. 7. The Stock Market Rally Nobody Wants to Miss Goldman Sachs is calling for an extreme stock rally. The guys explain why $8.5 trillion sitting in money markets has nowhere else to go, why the US stock market is the only investable market left in the world, and why owning assets — not just earning a salary — is the only play that makes sense right now.   Group Chat News drops every week. Subscribe so you never miss the conversation.    

Raghu Bala is the CEO and Founder of Synergetics AI, a firm helping enterprises design and deploy agentic AI systems to drive growth, operational efficiency, and lasting competitive advantage. A serial entrepreneur with four startup exits, he has led Synergetics AI in developing cutting-edge solutions in autonomous and agentic AI, collaborating with organizations like MIT and HPE. Raghu previously held senior roles at Yahoo, InfoSpace, and PwC and holds degrees from The Wharton School and Stanford University. In this episode… Imagine a world where your digital twin shops for you, makes payments, and even negotiates on your behalf. Could AI agents transform both businesses and daily life by bringing seamless automation, security, and personalization? How are innovators building the infrastructure for this future? Raghu Bala, a seasoned entrepreneur and AI innovator, explains that agentic AI is redefining how enterprises and consumers interact with technology. He highlights that AI agents — autonomous digital entities — can automate workflows, manage transactions, and act independently across complex systems. With tools like LangTrain, AgentFlow, and AgentVM, these agents enable secure, efficient operations while paving the way for the agent economy. Raghu explains practical applications, from digital twins automating e-commerce purchases to AI supporting real-time addiction counseling in healthcare, illustrating how these systems can streamline tasks and unlock new opportunities. In this episode of the Inspired Insider Podcast, host Dr. Jeremy Weisz sits down with Raghu Bala, CEO and Founder of Synergetics AI, to discuss building the agent economy, the evolution of autonomous AI, and the integration of digital twins in business. They explore secure AI workflows, real-world applications across industries, and the future of agent-driven commerce. Raghu also shares his favorite productivity tools and insights on aligning technology with company culture.

No, they don't!

Turbopuffer came out of a reading app.In 2022, Simon was helping his friends at Readwise scale their infra for a highly requested feature: article recommendations and semantic search. Readwise was paying ~$5k/month for their relational database and vector search would cost ~$20k/month making the feature too expensive to ship. In 2023 after mulling over the problem from Readwise, Simon decided he wanted to “build a search engine” which became Turbopuffer.We discuss:• Simon's path: Denmark → Shopify infra for nearly a decade → “angel engineering” across startups like Readwise, Replicate, and Causal → turbopuffer almost accidentally becoming a company • The Readwise origin story: building an early recommendation engine right after the ChatGPT moment, seeing it work, then realizing it would cost ~$30k/month for a company spending ~$5k/month total on infra and getting obsessed with fixing that cost structure • Why turbopuffer is “a search engine for unstructured data”: Simon's belief that models can learn to reason, but can't compress the world's knowledge into a few terabytes of weights, so they need to connect to systems that hold truth in full fidelity • The three ingredients for building a great database company: a new workload, a new storage architecture, and the ability to eventually support every query plan customers will want on their data • The architecture bet behind turbopuffer: going all in on object storage and NVMe, avoiding a traditional consensus layer, and building around the cloud primitives that only became possible in the last few years • Why Simon hated operating Elasticsearch at Shopify: years of painful on-call experience shaped his obsession with simplicity, performance, and eliminating state spread across multiple systems • The Cursor story: launching turbopuffer as a scrappy side project, getting an email from Cursor the next day, flying out after a 4am call, and helping cut Cursor's costs by 95% while fixing their per-user economics • The Notion story: buying dark fiber, tuning TCP windows, and eating cross-cloud costs because Simon refused to compromise on architecture just to close a deal faster • Why AI changes the build-vs-buy equation: it's less about whether a company can build search infra internally, and more about whether they have time especially if an external team can feel like an extension of their own • Why RAG isn't dead: coding companies still rely heavily on search, and Simon sees hybrid retrieval semantic, text, regex, SQL-style patterns becoming more important, not less • How agentic workloads are changing search: the old pattern was one retrieval call up front; the new pattern is one agent firing many parallel queries at once, turning search into a highly concurrent tool call • Why turbopuffer is reducing query pricing: agentic systems are dramatically increasing query volume, and Simon expects retrieval infra to adapt to huge bursts of concurrent search rather than a small number of carefully chosen calls • The philosophy of “playing with open cards”: Simon's habit of being radically honest with investors, including telling Lachy Groom he'd return the money if turbopuffer didn't hit PMF by year-end • The “P99 engineer”: Simon's framework for building a talent-dense company, rejecting by default unless someone on the team feels strongly enough to fight for the candidate —Simon Hørup Eskildsen• LinkedIn: https://www.linkedin.com/in/sirupsen• X: https://x.com/Sirupsen• https://sirupsen.com/aboutturbopuffer• https://turbopuffer.com/Full Video PodTimestamps00:00:00 The PMF promise to Lachy Groom00:00:25 Intro and Simon's background00:02:19 What turbopuffer actually is00:06:26 Shopify, Elasticsearch, and the pain behind the company00:10:07 The Readwise experiment that sparked turbopuffer00:12:00 The insight Simon couldn't stop thinking about00:17:00 S3 consistency, NVMe, and the architecture bet00:20:12 The Notion story: latency, dark fiber, and conviction00:25:03 Build vs. buy in the age of AI00:26:00 The Cursor story: early launch to breakout customer00:29:00 Why code search still matters00:32:00 Search in the age of agents00:34:22 Pricing turbopuffer in the AI era00:38:17 Why Simon chose Lachy Groom00:41:28 Becoming a founder on purpose00:44:00 The “P99 engineer” philosophy00:49:30 Bending software to your will00:51:13 The future of turbopuffer00:57:05 Simon's tea obsession00:59:03 Tea kits, X Live, and P99 LiveTranscriptSimon Hørup Eskildsen: I don't think I've said this publicly before, but I just called Lockey and was like, local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you. But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working.So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people. We're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards. Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before.Alessio: Hey everyone, welcome to the Leading Space podcast. This is Celesio Pando, Colonel Laz, and I'm joined by Swix, editor of Leading Space.swyx: Hello. Hello, uh, we're still, uh, recording in the Ker studio for the first time. Very excited. And today we are joined by Simon Eski. Of Turbo Farer welcome.Simon Hørup Eskildsen: Thank you so much for having me.swyx: Turbo Farer has like really gone on a huge tear, and I, I do have to mention that like you're one of, you're not my newest member of the Danish AHU Mafia, where like there's a lot of legendary programmers that have come out of it, like, uh, beyond Trotro, Rasmus, lado Berg and the V eight team and, and Google Maps team.Uh, you're mostly a Canadian now, but isn't that interesting? There's so many, so much like strong Danish presence.Simon Hørup Eskildsen: Yeah, I was writing a post, um, not that long ago about sort of the influences. So I grew up in Denmark, right? I left, I left when, when I was 18 to go to Canada to, to work at Shopify. Um, and so I, like, I've, I would still say that I feel more Danish than, than Canadian.This is also the weird accent. I can't say th because it, this is like, I don't, you know, my wife is also Canadian, um, and I think. I think like one of the things in, in Denmark is just like, there's just such a ruthless pragmatism and there's also a big focus on just aesthetics. Like, they're like very, people really care about like where, what things look like.Um, and like Canada has a lot of attributes, US has, has a lot of attributes, but I think there's been lots of the great things to carry. I don't know what's in the water in Ahu though. Um, and I don't know that I could be considered part of the Mafi mafia quite yet, uh, compared to the phenomenal individuals we just mentioned.Barra OV is also, uh, Danish Canadian. Okay. Yeah. I don't know where he lives now, but, and he's the PHP.swyx: Yeah. And obviously Toby German, but moved to Canada as well. Yes. Like this is like import that, uh, that, that is an interesting, um, talent move.Alessio: I think. I would love to get from you. Definition of Turbo puffer, because I think you could be a Vector db, which is maybe a bad word now in some circles, you could be a search engine.It's like, let, let's just start there and then we'll maybe run through the history of how you got to this point.Simon Hørup Eskildsen: For sure. Yeah. So Turbo Puffer is at this point in time, a search engine, right? We do full text search and we do vector search, and that's really what we're specialized in. If you're trying to do much more than that, like then this might not be the right place yet, but Turbo Buffer is all about search.The other way that I think about it is that we can take all of the world's knowledge, all of the exabytes and exabytes of data that there is, and we can use those tokens to train a model, but we can't compress all of that into a few terabytes of weights, right? Compress into a few terabytes of weights, how to reason with the world, how to make sense of the knowledge.But we have to somehow connect it to something externally that actually holds that like in full fidelity and truth. Um, and that's the thing that we intend to become. Right? That's like a very holier than now kind of phrasing, right? But being the search engine for unstructured, unstructured data is the focus of turbo puffer at this point in time.Alessio: And let's break down. So people might say, well, didn't Elasticsearch already do this? And then some other people might say, is this search on my data, is this like closer to rag than to like a xr, like a public search thing? Like how, how do you segment like the different types of search?Simon Hørup Eskildsen: The way that I generally think about this is like, there's a lot of database companies and I think if you wanna build a really big database company, sort of, you need a couple of ingredients to be in the air.We don't, which only happens roughly every 15 years. You need a new workload. You basically need the ambition that every single company on earth is gonna have data in your database. Multiple times you look at a company like Oracle, right? You will, like, I don't think you can find a company on earth with a digital presence that it not, doesn't somehow have some data in an Oracle database.Right? And I think at this point, that's also true for Snowflake and Databricks, right? 15 years later it's, or even more than that, there's not a company on earth that doesn't, in. Or directly is consuming Snowflake or, or Databricks or any of the big analytics databases. Um, and I think we're in that kind of moment now, right?I don't think you're gonna find a company over the next few years that doesn't directly or indirectly, um, have all their data available for, for search and connect it to ai. So you need that new workload, like you need something to be happening where there's a new workload that causes that to happen, and that new workload is connecting very large amounts of data to ai.The second thing you need. The second condition to build a big database company is that you need some new underlying change in the storage architecture that is not possible from the databases that have come before you. If you look at Snowflake and Databricks, right, commoditized, like massive fleet of HDDs, like that was not possible in it.It just wasn't in the air in the nineties, right? So you just didn't, we just didn't build these systems. S3 and and and so on was not around. And I think the architecture that is now possible that wasn't possible 15 years ago is to go all in on NVME SSDs. It requires a particular type of architecture for the database that.It's difficult to retrofit onto the databases that are already there, including the ones you just mentioned. The second thing is to go all in on OIC storage, more so than we could have done 15 years ago. Like we don't have a consensus layer, we don't really have anything. In fact, you could turn off all the servers that Turbo Buffer has, and we would not lose any data because we have all completely all in on OIC storage.And this means that our architecture is just so simple. So that's the second condition, right? First being a new workload. That means that every company on earth, either indirectly or directly, is using your database. Second being, there's some new storage architecture. That means that the, the companies that have come before you can do what you're doing.I think the third thing you need to do to build a big database company is that over time you have to implement more or less every Cory plan on the data. What that means is that you. You can't just get stuck in, like, this is the one thing that a database does. It has to be ever evolving because when someone has data in the database, they over time expect to be able to ask it more or less every question.So you have to do that to get the storage architecture to the limit of what, what it's capable of. Those are the three conditions.swyx: I just wanted to get a little bit of like the motivation, right? Like, so you left Shopify, you're like principal, engineer, infra guy. Um, you also head of kernel labs, uh, inside of Shopify, right?And then you consulted for read wise and that it kind of gave you that, that idea. I just wanted you to tell that story. Um, maybe I, you've told it before, but, uh, just introduce the, the. People to like the, the new workload, the sort of aha moment for turbo PufferSimon Hørup Eskildsen: For sure. So yeah, I spent almost a decade at Shopify.I was on the infrastructure team, um, from the fairly, fairly early days around 2013. Um, at the time it felt like it was growing so quickly and everything, all the metrics were, you know, doubling year on year compared to the, what companies are contending with today. It's very cute in growth. I feel like lot some companies are seeing that month over month.Um, of course. Shopify compound has been compounding for a very long time now, but I spent a decade doing that and the majority of that was just make sure the site is up today and make sure it's up a year from now. And a lot of that was really just the, um, you know, uh, the Kardashians would drive very, very large amounts of, of data to, to uh, to Shopify as they were rotating through all the merch and building out their businesses.And we just needed to make sure we could handle that. Right. And sometimes these were events, a million requests per second. And so, you know, we, we had our own data centers back in the day and we were moving to the cloud and there was so much sharding work and all of that that we were doing. So I spent a decade just scaling databases ‘cause that's fundamentally what's the most difficult thing to scale about these sites.The database that was the most difficult for me to scale during that time, and that was the most aggravating to be on call for, was elastic search. It was very, very difficult to deal with. And I saw a lot of projects that were just being held back in their ambition by using it.swyx: And I mean, self-hosted.Self-hosted. ‘causeSimon Hørup Eskildsen: it's, yeah, and it commercial, this is like 2015, right? So it's like a very particular vintage. Right. It's probably better at a lot of these things now. Um, it was difficult to contend with and I'm just like, I just think about it. It's an inverted index. It should be good at these kinds of queries and do all of this.And it was, we, we often couldn't get it to do exactly what we needed to do or basically get lucine to do, like expose lucine raw to, to, to what we needed to do. Um, so that was like. Just something that we did on the side and just panic scaled when we needed to, but not a particular focus of mine. So I left, and when I left, I, um, wasn't sure exactly what I wanted to do.I mean, it spent like a decade inside of the same company. I'd like grown up there. I started working there when I was 18.swyx: You only do Rails?Simon Hørup Eskildsen: Yeah. I mean, yeah. Rails. And he's a Rails guy. Uh, love Rails. So good. Um,Alessio: we all wish we could still work in Rails.swyx: I know know. I know, but some, I tried learning Ruby.It's just too much, like too many options to do the same thing. It's, that's my, I I know there's a, there's a way to do it.Simon Hørup Eskildsen: I love it. I don't know that I would use it now, like given cloud code and, and, and cursor and everything, but, um, um, but still it, like if I'm just sitting down and writing a teal code, that's how I think.But anyway, I left and I wasn't, I talked to a couple companies and I was like, I don't. I need to see a little bit more of the world here to know what I'm gonna like focus on next. Um, and so what I decided is like I was gonna, I called it like angel engineering, where I just hopped around in my friend's companies in three months increments and just helped them out with something.Right. And, and just vested a bit of equity and solved some interesting infrastructure problem. So I worked with a bunch of companies at the time, um, read Wise was one of them. Replicate was one of them. Um, causal, I dunno if you've tried this, it's like a, it's a spreadsheet engine Yeah. Where you can do distribution.They sold recently. Yeah. Um, we've been, we used that in fp and a at, um, at Turbo Puffer. Um, so a bunch of companies like this and it was super fun. And so we're the Chachi bt moment happened, I was with. With read Wise for a stint, we were preparing for the reader launch, right? Which is where you, you cue articles and read them later.And I was just getting their Postgres up to snuff, like, which basically boils down to tuning, auto vacuum. So I was doing that and then this happened and we were like, oh, maybe we should build a little recommendation engine and some features to try to hook in the lms. They were not that good yet, but it was clear there was something there.And so I built a small recommendation engine just, okay, let's take the articles that you've recently read, right? Like embed all the articles and then do recommendations. It was good enough that when I ran it on one of the co-founders of Rey's, like I found out that I got articles about, about having a child.I'm like, oh my God, I didn't, I, I didn't know that, that they were having a child. I wasn't sure what to do with that information, but the recommendation engine was good enough that it was suggesting articles, um, about that. And so there was, there was recommendations and uh, it actually worked really well.But this was a company that was spending maybe five grand a month in total on all their infrastructure and. When I did the napkin math on running the embeddings of all the articles, putting them into a vector index, putting it in prod, it's gonna be like 30 grand a month. That just wasn't tenable. Right?Like Read Wise is a proudly bootstrapped company and it's paying 30 grand for infrastructure for one feature versus five. It just wasn't tenable. So sort of in the bucket of this is useful, it's pretty good, but let us, let's return to it when the costs come down.swyx: Did you say it grows by feature? So for five to 30 is by the number of, like, what's the, what's the Scaling factor scale?It scales by the number of articles that you embed.Simon Hørup Eskildsen: It does, but what I meant by that is like five grand for like all of the other, like the Heroku, dinos, Postgres, like all the other, and this then storage is 30. Yeah. And then like 30 grand for one feature. Right. Which is like, what other articles are related to this one.Um, so it was just too much right to, to power everything. Their budget would've been maybe a few thousand dollars, which still would've been a lot. And so we put it in a bucket of, okay, we're gonna do that later. We'll wait, we will wait for the cost to come down. And that haunted me. I couldn't stop thinking about it.I was like, okay, there's clearly some latent demand here. If the cost had been a 10th, we would've shipped it and. This was really the only data point that I had. Right. I didn't, I, I didn't, I didn't go out and talk to anyone else. It was just so I started reading Right. I couldn't, I couldn't help myself.Like I didn't know what like a vector index is. I, I generally barely do about how to generate the vectors. There was a lot of hype about, this is a early 2023. There was a lot of hype about vector databases. There were raising a lot of money and it's like, I really didn't know anything about it. It's like, you know, trying these little models, fine tuning them.Like I was just trying to get sort of a lay of the land. So I just sat down. I have this. A GitHub repository called Napkin Math. And on napkin math, there's just, um, rows of like, oh, this is how much bandwidth. Like this is how many, you know, you can do 25 gigabytes per second on average to dram. You can do, you know, five gigabytes per second of rights to an SSD, blah blah.All of these numbers, right? And S3, how many you could do per, how much bandwidth can you drive per connection? I was just sitting down, I was like, why hasn't anyone build a database where you just put everything on O storage and then you puff it into NVME when you use the data and you puff it into dram if you're, if you're querying it alive, it's just like, this seems fairly obvious and you, the only real downside to that is that if you go all in on o storage, every right will take a couple hundred milliseconds of latency, but from there it's really all upside, right?You do the first go, it takes half a second. And it sort of occurred to me as like, well. The architecture is really good for that. It's really good for AB storage, it's really good for nvm ESSD. It's, well, you just couldn't have done that 10 years ago. Back to what we were talking about before. You really have to build a database where you have as few round trips as possible, right?This is how CPUs work today. It's how NVM E SSDs work. It's how as, um, as three works that you want to have a very large amount of outstanding requests, right? Like basically go to S3, do like that thousand requests to ask for data in one round trip. Wait for that. Get that, like, make a new decision. Do it again, and try to do that maybe a maximum of three times.But no databases were designed that way within NVME as is ds. You can drive like within, you know, within a very low multiple of DRAM bandwidth if you use it that way. And same with S3, right? You can fully max out the network card, which generally is not maxed out. You get very, like, very, very good bandwidth.And, but no one had built a database like that. So I was like, okay, well can't you just, you know, take all the vectors right? And plot them in the proverbial coordinate system. Get the clusters, put a file on S3 called clusters, do json, and then put another file for every cluster, you know, cluster one, do js O cluster two, do js ON you know that like it's two round trips, right?So you get the clusters, you find the closest clusters, and then you download the cluster files like the, the closest end. And you could do this in two round trips.swyx: You were nearest neighbors locally.Simon Hørup Eskildsen: Yes. Yes. And then, and you would build this, this file, right? It's just like ultra simplistic, but it's not a far shot from what the first version of Turbo Buffer was.Why hasn't anyone done thatAlessio: in that moment? From a workload perspective, you're thinking this is gonna be like a read heavy thing because they're doing recommend. Like is the fact that like writes are so expensive now? Oh, with ai you're actually not writing that much.Simon Hørup Eskildsen: At that point I hadn't really thought too much about, well no actually it was always clear to me that there was gonna be a lot of rights because at Shopify, the search clusters were doing, you know, I don't know, tens or hundreds of crew QPS, right?‘cause you just have to have a human sit and type in. But we did, you know, I don't know how many updates there were per second. I'm sure it was in the millions, right into the cluster. So I always knew there was like a 10 to 100 ratio on the read write. In the read wise use case. It's, um, even, even in the read wise use case, there'd probably be a lot fewer reads than writes, right?There's just a lot of churn on the amount of stuff that was going through versus the amount of queries. Um, I wasn't thinking too much about that. I was mostly just thinking about what's the fundamentally cheapest way to build a database in the cloud today using the primitives that you have available.And this is it, right? You just, now you have one machine and you know, let's say you have a terabyte of data in S3, you paid the $200 a month for that, and then maybe five to 10% of that data and needs to be an NV ME SSDs and less than that in dram. Well. You're paying very, very little to inflate the data.swyx: By the way, when you say no one else has done that, uh, would you consider Neon, uh, to be on a similar path in terms of being sort of S3 first and, uh, separating the compute and storage?Simon Hørup Eskildsen: Yeah, I think what I meant with that is, uh, just build a completely new database. I don't know if we were the first, like it was very much, it was, I mean, I, I hadn't, I just looked at the napkin math and was like, this seems really obvious.So I'm sure like a hundred people came up with it at the same time. Like the light bulb and every invention ever. Right. It was just in the air. I think Neon Neon was, was first to it. And they're trying, they're retrofitted onto Postgres, right? And then they built this whole architecture where you have, you have it in memory and then you sort of.You know, m map back to S3. And I think that was very novel at the time to do it for, for all LTP, but I hadn't seen a database that was truly all in, right. Not retrofitting it. The database felt built purely for this no consensus layer. Even using compare and swap on optic storage to do consensus. I hadn't seen anyone go that all in.And I, I mean, there, there, I'm sure there was someone that did that before us. I don't know. I was just looking at the napkin mathswyx: and, and when you say consensus layer, uh, are you strongly relying on S3 Strong consistency? You are. Okay.SoSimon Hørup Eskildsen: that is your consensus layer. It, it is the consistency layer. And I think also, like, this is something that most people don't realize, but S3 only became consistent in December of 2020.swyx: I remember this coming out during COVID and like people were like, oh, like, it was like, uh, it was just like a free upgrade.Simon Hørup Eskildsen: Yeah.swyx: They were just, they just announced it. We saw consistency guys and like, okay, cool.Simon Hørup Eskildsen: And I'm sure that they just, they probably had it in prod for a while and they're just like, it's done right.And people were like, okay, cool. But. That's a big moment, right? Like nv, ME SSDs, were also not in the cloud until around 2017, right? So you just sort of had like 2017 nv, ME SSDs, and people were like, okay, cool. There's like one skew that does this, whatever, right? Takes a few years. And then the second thing is like S3 becomes consistent in 2020.So now it means you don't have to have this like big foundation DB or like zookeeper or whatever sitting there contending with the keys, which is how. You know, that's what Snowflake and others have do so muchswyx: for goneSimon Hørup Eskildsen: Exactly. Just gone. Right? And so just push to the, you know, whatever, how many hundreds of people they have working on S3 solved and then compare and swap was not in S3 at this point in time,swyx: by the way.Uh, I don't know what that is, so maybe you wanna explain. Yes. Yeah.Simon Hørup Eskildsen: Yes. So, um, what Compare and swap is, is basically, you can imagine that if you have a database, it might be really nice to have a file called metadata json. And metadata JSON could say things like, Hey, these keys are here and this file means that, and there's lots of metadata that you have to operate in the database, right?But that's the simplest way to do it. So now you have might, you might have a lot of servers that wanna change the metadata. They might have written a file and want the metadata to contain that file. But you have a hundred nodes that are trying to contend with this metadata that JSON well, what compare and Swap allows you to do is basically just you download the file, you make the modifications, and then you write it only if it hasn't changed.While you did the modification and if not you retry. Right? Should just have this retry loops. Now you can imagine if you have a hundred nodes doing that, it's gonna be really slow, but it will converge over time. That primitive was not available in S3. It wasn't available in S3 until late 2024, but it was available in GCP.The real story of this is certainly not that I sat down and like bake brained it. I was like, okay, we're gonna start on GCS S3 is gonna get it later. Like it was really not that we started, we got really lucky, like we started on GCP and we started on GCP because tur um, Shopify ran on GCP. And so that was the platform I was most available with.Right. Um, and I knew the Canadian team there ‘cause I'd worked with them at Shopify and so it was natural for us to start there. And so when we started building the database, we're like, oh yeah, we have to build a, we really thought we had to build a consensus layer, like have a zookeeper or something to do this.But then we discovered the compare and swap. It's like, oh, we can kick the can. Like we'll just do metadata r json and just, it's fine. It's probably fine. Um, and we just kept kicking the can until we had very, very strong conviction in the idea. Um, and then we kind of just hinged the company on the fact that S3 probably was gonna get this, it started getting really painful in like mid 2024.‘cause we were closing deals with, um, um, notion actually that was running in AWS and we're like, trust us. You, you really want us to run this in GCP? And they're like, no, I don't know about that. Like, we're running everything in AWS and the latency across the cloud were so big and we had so much conviction that we bought like, you know, dark fiber between the AWS regions in, in Oregon, like in the InterExchange and GCP is like, we've never seen a startup like do like, what's going on here?And we're just like, no, we don't wanna do this. We were tuning like TCP windows, like everything to get the latency down ‘cause we had so high conviction in not doing like a, a metadata layer on S3. So those were the three conditions, right? Compare and swap. To do metadata, which wasn't in S3 until late 2024 S3 being consistent, which didn't happen until December, 2020.Uh, 2020. And then NVMe ssd, which didn't end in the cloud until 2017.swyx: I mean, in some ways, like a very big like cloud success story that like you were able to like, uh, put this all together, but also doing things like doing, uh, bind our favor. That that actually is something I've never heard.Simon Hørup Eskildsen: I mean, it's very common when you're a big company, right?You're like connecting your own like data center or whatever. But it's like, it was uniquely just a pain with notion because the, um, the org, like most of the, like if you're buying in Ashburn, Virginia, right? Like US East, the Google, like the GCP and, and AWS data centers are like within a millisecond on, on each other, on the public exchanges.But in Oregon uniquely, the GCP data center sits like a couple hundred kilometers, like east of Portland and the AWS region sits in Portland, but the network exchange they go through is through Seattle. So it's like a full, like 14 milliseconds or something like that. And so anyway, yeah. It's, it's, so we were like, okay, we can't, we have to go through an exchange in Portland.Yeah. Andswyx: you'd rather do this than like run your zookeeper and likeSimon Hørup Eskildsen: Yes. Way rather. It doesn't have state, I don't want state and two systems. Um, and I think all that is just informed by Justine, my co-founder and I had just been on call for so long. And the worst outages are the ones where you have state in multiple places that's not syncing up.So it really came from, from a a, like just a, a very pure source of pain, of just imagining what we would be Okay. Being woken up at 3:00 AM about and having something in zookeeper was not one of them.swyx: You, you're talking to like a notion or something. Do they care or do they just, theySimon Hørup Eskildsen: just, they care about latency.swyx: They latency cost. That's it.Simon Hørup Eskildsen: They just cared about latency. Right. And we just absorbed the cost. We're just like, we have high conviction in this. At some point we can move them to AWS. Right. And so we just, we, we'll buy the fiber, it doesn't matter. Right. Um, and it's like $5,000. Usually when you buy fiber, you buy like multiple lines.And we're like, we can only afford one, but we will just test it that when it goes over the public internet, it's like super smooth. And so we did a lot of, anyway, it's, yeah, it was, that's cool.Alessio: You can imagine talking to the GCP rep and it's like, no, we're gonna buy, because we know we're gonna turn, we're gonna turn from you guys and go to AWS in like six months.But in the meantime we'll do this. It'sSimon Hørup Eskildsen: a, I mean, like they, you know, this workload still runs on GCP for what it's worth. Right? ‘cause it's so, it was just, it was so reliable. So it was never about moving off GCP, it was just about honesty. It was just about giving notion the latency that they deserved.Right. Um, and we didn't want ‘em to have to care about any of this. We also, they were like, oh, egress is gonna be bad. It was like, okay, screw it. Like we're just gonna like vvc, VPC peer with you and AWS we'll eat the cost. Yeah. Whatever needs to be done.Alessio: And what were the actual workloads? Because I think when you think about ai, it's like 14 milliseconds.It's like really doesn't really matter in the scheme of like a model generation.Simon Hørup Eskildsen: Yeah. We were told the latency, right. That we had to beat. Oh, right. So, so we're just looking at the traces. Right. And then sort of like hand draw, like, you know, kind of like looking at the trace and then thinking what are the other extensions of the trace?Right. And there's a lot more to it because it's also when you have, if you have 14 versus seven milliseconds, right. You can fit in another round trip. So we had to tune TCP to try to send as much data in every round trip, prewarm all the connections. And there was, there's a lot of things that compound from having these kinds of round trips, but in the grand scheme it was just like, well, we have to beat the latency of whatever we're up against.swyx: Which is like they, I mean, notion is a database company. They could have done this themselves. They, they do lots of database engineering themselves. How do you even get in the door? Like Yeah, just like talk through that kind of.Simon Hørup Eskildsen: Last time I was in San Francisco, I was talking to one of the engineers actually, who, who was one of our champions, um, at, AT Notion.And they were, they were just trying to make sure that the, you know, per user cost matched the economics that they needed. You know, Uhhuh like, it's like the way I think about, it's like I have to earn a return on whatever the clouds charge me and then my customers have to earn a return on that. And it's like very simple, right?And so there has to be gross margin all the way up and that's how you build the product. And so then our customers have to make the right set of trade off the turbo Puffer makes, and if they're happy with that, that's great.swyx: Do you feel like you're competing with build internally versus buy or buy versus buy?Simon Hørup Eskildsen: Yeah, so, sorry, this was all to build up to your question. So one of the notion engineers told me that they'd sat and probably on a napkin, like drawn out like, why hasn't anyone built this? And then they saw terrible. It was like, well, it literally that. So, and I think AI has also changed the buy versus build equation in terms of, it's not really about can we build it, it's about do we have time to build it?I think they like, I think they felt like, okay, if this is a team that can do that and they, they feel enough like an extension of our team, well then we can go a lot faster, which would be very, very good for them. And I mean, they put us through the, through the test, right? Like we had some very, very long nights to to, to do that POC.And they were really our biggest, our second big customer off the cursor, which also was a lot of late nights. Right.swyx: Yeah. That, I mean, should we go into that story? The, the, the sort of Chris's story, like a lot, um, they credit you a lot for. Working very closely with them. So I just wanna hear, I've heard this, uh, story from Sole's point of view, but like, I'm curious what, what it looks like from your side.Simon Hørup Eskildsen: I actually haven't heard it from Sole's point of view, so maybe you can now cross reference it. The way that I remember it was that, um, the day after we launched, which was just, you know, I'd worked the whole summer on, on the first version. Justine wasn't part of it yet. ‘cause I just, I didn't tell anyone that summer that I was working on this.I was just locked in on building it because it's very easy otherwise to confuse talking about something to actually doing it. And so I was just like, I'm not gonna do that. I'm just gonna do the thing. I launched it and at this point turbo puffer is like a rust binary running on a single eight core machine in a T Marks instance.And me deploying it was like looking at the request log and then like command seeing it or like control seeing it to just like, okay, there's no request. Let's upgrade the binary. Like it was like literally the, the, the, the scrappiest thing. You could imagine it was on purpose because just like at Shopify, we did that all the time.Like, we like move, like we ran things in tux all the time to begin with. Before something had like, at least the inkling of PMF, it was like, okay, is anyone gonna hear about this? Um, and one of the cursor co-founders Arvid reached out and he just, you know, the, the cursor team are like all I-O-I-I-M-O like, um, contenders, right?So they just speak in bullet points and, and facts. It was like this amazing email exchange just of, this is how many QPS we have, this is what we're paying, this is where we're going, blah, blah, blah. And so we're just conversing in bullet points. And I tried to get a call with them a few times, but they were, so, they were like really writing the PMF bowl here, just like late 2023.And one time Swally emails me at like five. What was it like 4:00 AM Pacific time saying like, Hey, are you open for a call now? And I'm on the East coast and I, it was like 7:00 AM I was like, yeah, great, sure, whatever. Um, and we just started talking and something. Then I didn't know anything about sales.It was something that just comp compelled me. I have to go see this team. Like, there's something here. So I, I went to San Francisco and I went to their office and the way that I remember it is that Postgres was down when I showed up at the office. Did SW tell you this? No. Okay. So Postgres was down and so it's like they were distracting with that.And I was trying my best to see if I could, if I could help in any way. Like I knew a little bit about databases back to tuning, auto vacuum. It was like, I think you have to tune out a vacuum. Um, and so we, we talked about that and then, um, that evening just talked about like what would it look like, what would it look like to work with us?And I just said. Look like we're all in, like we will just do what we'll do whatever, whatever you tell us, right? They migrated everything over the next like week or two, and we reduced their cost by 95%, which I think like kind of fixed their per user economics. Um, and it solved a lot of other things. And we were just, Justine, this is also when I asked Justine to come on as my co-founder, she was the best engineer, um, that I ever worked with at Shopify.She lived two blocks away and we were just, okay, we're just gonna get this done. Um, and we did, and so we helped them migrate and we just worked like hell over the next like month or two to make sure that we were never an issue. And that was, that was the cursor story. Yeah.swyx: And, and is code a different workload than normal text?I, I don't know. Is is it just text? Is it the same thing?Simon Hørup Eskildsen: Yeah, so cursor's workload is basically, they, um, they will embed the entire code base, right? So they, they will like chunk it up in whatever they would, they do. They have their own embedding model, um, which they've been public about. Um, and they find that on, on, on their evals.It. There's one of their evals where it's like a 25% improvement on a very particular workload. They have a bunch of blog posts about it. Um, I think it works best on larger code basis, but they've trained their own embedding model to do this. Um, and so you'll see it if you use the cursor agent, it will do searches.And they've also been public around, um, how they've, I think they post trained their model to be very good at semantic search as well. Um, and that's, that's how they use it. And so it's very good at, like, can you find me on the code that's similar to this, or code that does this? And just in, in this queries, they also use GR to supplement it.swyx: Yeah.Simon Hørup Eskildsen: Um, of courseswyx: it's been a big topic of discussion like, is rag dead because gr you know,Simon Hørup Eskildsen: and I mean like, I just, we, we see lots of demand from the coding company to ethicsswyx: search in every part. Yes.Simon Hørup Eskildsen: Uh, we, we, we see demand. And so, I mean, I'm. I like case studies. I don't like, like just doing like thought pieces on this is where it's going.And like trying to be all macroeconomic about ai, that's has turned out to be a giant waste of time because no one can really predict any of this. So I just collect case studies and I mean, cursor has done a great job talking about what they're doing and I hope some of the other coding labs that use Turbo Puffer will do the same.Um, but it does seem to make a difference for particular queries. Um, I mean we can also do text, we can also do RegX, but I should also say that cursors like security posture into Tur Puffer is exceptional, right? They have their own embedding model, which makes it very difficult to reverse engineer. They obfuscate the file paths.They like you. It's very difficult to learn anything about a code base by looking at it. And the other thing they do too is that for their customers, they encrypt it with their encryption keys in turbo puffer's bucket. Um, so it's, it's, it's really, really well designed.swyx: And so this is like extra stuff they did to work with you because you are not part of Cursor.Exactly like, and this is just best practice when working in any database, not just you guys. Okay. Yeah, that makes sense. Yeah. I think for me, like the, the, the learning is kind of like you, like all workloads are hybrid. Like, you know, uh, like you, you want the semantic, you want the text, you want the RegX, you want sql.I dunno. Um, but like, it's silly to like be all in on like one particularly query pattern.Simon Hørup Eskildsen: I think, like I really like the way that, um, um, that swally at cursor talks about it, which is, um, I'm gonna butcher it here. Um, and you know, I'm a, I'm a database scalability person. I'm not a, I, I dunno anything about training models other than, um, what the internet tells me and what.The way he describes is that this is just like cash compute, right? It's like you have a point in time where you're looking at some particular context and focused on some chunk and you say, this is the layer of the neural net at this point in time. That seems fundamentally really useful to do cash compute like that.And, um, how the value of that will change over time. I'm, I'm not sure, but there seems to be a lot of value in that.Alessio: Maybe talk a bit about the evolution of the workload, because even like search, like maybe two years ago it was like one search at the start of like an LLM query to build the context. Now you have a gentech search, however you wanna call it, where like the model is both writing and changing the code and it's searching it again later.Yeah. What are maybe some of the new types of workloads or like changes you've had to make to your architecture for it?Simon Hørup Eskildsen: I think you're right. When I think of rag, I think of, Hey, there's an 8,000 token, uh, context window and you better make it count. Um, and search was a way to do that now. Everything is moving towards the, just let the agent do its thing.Right? And so back to the thing before, right? The LLM is very good at reasoning with the data, and so we're just the tool call, right? And that's increasingly what we see our customers doing. Um, what we're seeing more demand from, from our customers now is to do a lot of concurrency, right? Like Notion does a ridiculous amount of queries in every round trip just because they can't.And I'm also now, when I use the cursor agent, I also see them doing more concurrency than I've ever seen before. So a bit similar to how we designed a database to drive as much concurrency in every round trip as possible. That's also what the agents are doing. So that's new. It means just an enormous amount of queries all at once to the dataset while it's warm in as few turns as possible.swyx: Can I clarify one thing on that?Simon Hørup Eskildsen: Yes.swyx: Is it, are they batching multiple users or one user is driving multiple,Simon Hørup Eskildsen: one user driving multiple, one agent driving.swyx: It's parallel searching a bunch of things.Simon Hørup Eskildsen: Exactly.swyx: Yeah. Yeah, exactly. So yeah, the clinician also did, did this for the fast context thing, like eight parallel at once.Simon Hørup Eskildsen: Yes.swyx: And, and like an interesting problem is, well, how do you make sure you have enough diversity so you're not making the the same request eight times?Simon Hørup Eskildsen: And I think like that's probably also where the hybrid comes in, where. That's another way to diversify. It's a completely different way to, to do the search.That's a big change, right? So before it was really just like one call and then, you know, the LLM took however many seconds to return, but now we just see an enormous amount of queries. So the, um, we just see more queries. So we've like tried to reduce query, we've reduced query pricing. Um, this is probably the first time actually I'm saying that, but the query pricing is being reduced, like five x.Um, and we'll probably try to reduce it even more to accommodate some of these workloads of just doing very large amounts of queries. Um, that's one thing that's changed. I think the right, the right ratio is still very high, right? Like there's still a, an enormous amount of rights per read, but we're starting probably to see that change if people really lean into this pattern.Alessio: Can we talk a little bit about the pricing? I'm curious, uh, because traditionally a database would charge on storage, but now you have the token generation that is so expensive, where like the actual. Value of like a good search query is like much higher because they're like saving inference time down the line.How do you structure that as like, what are people receptive to on the other side too?Simon Hørup Eskildsen: Yeah. I, the, the turbo puffer pricing in the beginning was just very simple. The pricing on these on for search engines before Turbo Puffer was very server full, right? It was like, here's the vm, here's the per hour cost, right?Great. And I just sat down with like a piece of paper and said like, if Turbo Puffer was like really good, this is probably what it would cost with a little bit of margin. And that was the first pricing of Turbo Puffer. And I just like sat down and I was like, okay, like this is like probably the storage amp, but whenever on a piece of paper I, it was vibe pricing.It was very vibe price, and I got it wrong. Oh. Um, well I didn't get it wrong, but like Turbo Puffer wasn't at the first principle pricing, right? So when Cursor came on Turbo Puffer, it was like. Like, I didn't know any VCs. I didn't know, like I was just like, I don't know, I didn't know anything about raising money or anything like that.I just saw that my GCP bill was, was high, was a lot higher than the cursor bill. So Justine and I was just like, well, we have to optimize it. Um, and I mean, to the chagrin now of, of it, of, of the VCs, it now means that we're profitable because we've had so much pricing pressure in the beginning. Because it was running on my credit card and Justine and I had spent like, like tens of thousands of dollars on like compute bills and like spinning off the company and like very like, like bad Canadian lawyers and like things like to like get all of this done because we just like, we didn't know.Right. If you're like steeped in San Francisco, you're just like, you just know. Okay. Like you go out, raise a pre-seed round. I, I never heard a word pre-seed at this point in time.swyx: When you had Cursor, you had Notion you, you had no funding.Simon Hørup Eskildsen: Um, with Cursor we had no funding. Yeah. Um, by the time we had Notion Locke was, Locke was here.Yeah. So it was really just, we vibe priced it 100% from first Principles, but it wasn't, it, it was not performing at first principles, so we just did everything we could to optimize it in the beginning for that, so that at least we could have like a 5% margin or something. So I wasn't freaking out because Cursor's bill was also going like this as they were growing.And so my liability and my credit limit was like actively like calling my bank. It was like, I need a bigger credit. Like it was, yeah. Anyway, that was the beginning. Yeah. But the pricing was, yeah, like storage rights and query. Right. And the, the pricing we have today is basically just that pricing with duct tape and spit to try to approach like, you know, like a, as a margin on the physical underlying hardware.And we're doing this year, you're gonna see more and more pricing changes from us. Yeah.swyx: And like is how much does stuff like VVC peering matter because you're working in AWS land where egress is charged and all that, you know.Simon Hørup Eskildsen: We probably don't like, we have like an enterprise plan that just has like a base fee because we haven't had time to figure out SKU pricing for all of this.Um, but I mean, yeah, you can run turbo puffer either in SaaS, right? That's what Cursor does. You can run it in a single tenant cluster. So it's just you. That's what Notion does. And then you can run it in, in, in BYOC where everything is inside the customer's VPC, that's what an for example, philanthropic does.swyx: What I'm hearing is that this is probably the best CRO job for somebody who can come in and,Simon Hørup Eskildsen: I mean,swyx: help you with this.Simon Hørup Eskildsen: Um, like Turbo Puffer hired, like, I don't know what, what number this was, but we had a full-time CFO as like the 12th hire or something at Turbo Puffer, um, I think I hear are a lot of comp.I don't know how they do it. Like they have a hundred employees and not a CFO. It's like having a CFO is like a runningswyx: business man. Like, you know,Simon Hørup Eskildsen: it's so good. Yeah, like money Mike, like he just, you know, just handles the money and a lot of the business stuff and so he came in and just hopped with a lot of the operational side of the business.So like C-O-O-C-F-O, like somewhere in between.swyx: Just as quick mention of Lucky, just ‘cause I'm curious, I've met Lock and like, he's obviously a very good investor and now on physical intelligence, um, I call it generalist super angel, right? He invests in everything. Um, and I always wonder like, you know, is there something appealing about focusing on developer tooling, focusing on databases, going like, I've invested for 10 years in databases versus being like a lock where he can maybe like connect you to all the customers that you need.Simon Hørup Eskildsen: This is an excellent question. No, no one's asked me this. Um, why lockey? Because. There was a couple of people that we were talking to at the time and when we were raising, we were almost a little, we were like a bit distressed because one of our, one of our peers had just launched something that was very similar to Turbo Puffer.And someone just gave me the advice at the time of just choose the person where you just feel like you can just pick up the phone and not prepare anything. And just be completely honest, and I don't think I've said this publicly before, but I just called Lockey and was like local Lockie. Like if this doesn't have PMF by the end of the year, like we'll just like return all the money to you.But it's just like, I don't really, we, Justine and I don't wanna work on this unless it's really working. So we want to give it the best shot this year and like we're really gonna go for it. We're gonna hire a bunch of people and we're just gonna be honest with everyone. Like when I don't know how to play a game, I just play with open cards and.Lockey was the only person that didn't, that didn't freak out. He was like, I've never heard anyone say that before. As I said, I didn't even know what a seed or pre-seed round was like before, probably even at this time. So I was just like very honest with him. And I asked him like, Lockie, have you ever have, have you ever invested in database company?He was just like, no. And at the time I was like, am I dumb? Like, but I think there was something that just like really drew me to Lockie. He is so authentic, so honest, like, and there was something just like, I just felt like I could just play like, just say everything openly. And that was, that was, I think that that was like a perfect match at the time, and, and, and honestly still is.He was just like, okay, that's great. This is like the most honest, ridiculous thing I've ever heard anyone say to me. But like that, like that, whyswyx: is this ridiculous? Say competitor launch, this may not work out. It wasSimon Hørup Eskildsen: more just like. If this doesn't work out, I'm gonna close up shop by the end of the mo the year, right?Like it was, I don't know, maybe it's common. I, I don't know. He told me it was uncommon. I don't know. Um, that's why we chose him and he'd been phenomenal. The other people were talking at the, at the time were database experts. Like they, you know, knew a lot about databases and Locke didn't, this turned out to be a phenomenal asset.Right. I like Justine and I know a lot about databases. The people that we hire know a lot about databases. What we needed was just someone who didn't know a lot about databases, didn't pretend to know a lot about databases, and just wanted to help us with candidates and customers. And he did. Yeah. And I have a list, right, of the investors that I have a relationship with, and Lockey has just performed excellent in the number of sub bullets of what we can attribute back to him.Just absolutely incredible. And when people talk about like no ego and just the best thing for the founder, I like, I don't think that anyone, like even my lawyer is like, yeah, Lockey is like the most friendly person you will find.swyx: Okay. This is my most glow recommendation I've ever heard.Alessio: He deserves it.He's very special.swyx: Yeah. Yeah. Yeah. Okay. Amazing.Alessio: Since you mentioned candidates, maybe we can talk about team building, you know, like, especially in sf, it feels like it's just easier to start a company than to join a company. Uh, I'm curious your experience, especially not being n SF full-time and doing something that is maybe, you know, a very low level of detail and technical detail.Simon Hørup Eskildsen: Yeah. So joining versus starting, I never thought that I would be a founder. I would start with it, like Turbo Puffer started as a blog post, and then it became a project and then sort of almost accidentally became a company. And now it feels like it's, it's like becoming a bigger company. That was never the intention.The intentions were very pure. It's just like, why hasn't anyone done this? And it's like, I wanna be the, like, I wanna be the first person to do it. I think some founders have this, like, I could never work for anyone else. I, I really don't feel that way. Like, it's just like, I wanna see this happen. And I wanna see it happen with some people that I really enjoy working with and I wanna have fun doing it and this, this, this has all felt very natural on that, on that sense.So it was never a like join versus versus versus found. It was just dis found me at the right moment.Alessio: Well I think there's an argument for, you should have joined Cursor, right? So I'm curious like how you evaluate it. Okay, I should actually go raise money and make this a company versus like, this is like a company that is like growing like crazy.It's like an interesting technical problem. I should just build it within Cursor and then they don't have to encrypt all this stuff. They don't have to obfuscate things. Like was that on your mind at all orSimon Hørup Eskildsen: before taking the, the small check from Lockie, I did have like a hard like look at myself in the mirror of like, okay, do I really want to do this?And because if I take the money, I really have to do it right. And so the way I almost think about it's like you kind of need to ha like you kind of need to be like fucked up enough to want to go all the way. And that was the conversation where I was like, okay, this is gonna be part of my life's journey to build this company and do it in the best way that I possibly can't.Because if I ask people to join me, ask people to get on the cap table, then I have an ultimate responsibility to give it everything. And I don't, I think some people, it doesn't occur to me that everyone takes it that seriously. And maybe I take it too seriously, I don't know. But that was like a very intentional moment.And so then it was very clear like, okay, I'm gonna do this and I'm gonna give it everything.Alessio: A lot of people don't take it this seriously. But,swyx: uh, let's talk about, you have this concept of the P 99 engineer. Uh, people are 10 x saying, everyone's saying, you know, uh, maybe engineers are out of a job. I don't know.But you definitely see a P 99 engineer, and I just want you to talk about it.Simon Hørup Eskildsen: Yeah, so the P 99 engineer was just a term that we started using internally to talk about candidates and talk about how we wanted to build the company. And you know, like everyone else is, like we want a talent dense company.And I think that's almost become trite at this point. What I credit the cursor founders a lot with is that they just arrived there from first principles of like, we just need a talent dense, um, talent dense team. And I think I've seen some teams that weren't talent dense and like seemed a counterfactual run, which if you've run in been in a large company, you will just see that like it's just logically will happen at a large company.Um, and so that was super important to me and Justine and it's very difficult to maintain. And so we just needed, we needed wording for it. And so I have a document called Traits of the P 99 Engineer, and it's a bullet point list. And I look at that list after every single interview that I do, and in every single recap that we do and every recap we end with.End with, um, some version of I'm gonna reject this candidate completely regardless of what the discourse was, because I wanna see people fight for this person because the default should not be, we're gonna hire this person. The default should be, we're definitely not hiring this person. And you know, if everyone was like, ah, maybe throw a punch, then this is not the right.swyx: Do, do you operate, like if there's one cha there must have at least one champion who's like, yes, I will put my career on, on, on the line for this. You know,Simon Hørup Eskildsen: I think career on the line,swyx: maybe a chair, butSimon Hørup Eskildsen: yeah. You know, like, um, I would say so someone needs to like, have both fists up and be like, I'd fight.Right? Yeah. Yeah. And if one person said, then, okay, let's do it. Right?swyx: Yeah.Simon Hørup Eskildsen: Um. It doesn't have to be absolutely everyone. Right? And like the interviews are always the sign that you're checking for different attributes. And if someone is like knocking it outta the park in every single attribute, that's, that's fairly rare.Um, but that's really important. And so the traits of the P 99 engineer, there's lots of them. There's also the traits of the p like triple nine engineer and the quadruple nine engineer. This is like, it's a long list.swyx: Okay.Simon Hørup Eskildsen: Um, I'll give you some samples, right. Of what we, what we look for. I think that the P 99 engineer has some history of having bent, like their trajectory or something to their will.Right? Some moment where it was just, they just, you know, made the computer do what it needed to do. There's something like that, and it will, it will occur to have them at some point in their career. And, uh. Hopefully multiple times. Right.swyx: Gimme an example of one of your engineers that like,Simon Hørup Eskildsen: I'll give an eng.Uh, so we, we, we launched this thing called A and NV three. Um, we could, we're also, we're working on V four and V five right now, but a and NV three can search a hundred billion vectors with a P 50 of around 40 milliseconds and a p 99 of 200 milliseconds. Um, maybe other people have done this, I'm sure Google and others have done this, but, uh, we haven't seen anyone, um, at least not in like a public consumable SaaS that can do this.And that was an engineer, the chief architect of Turbo Puffer, Nathan, um, who more or less just bent this, the software was not capable of this and he just made it capable for a very particular workload in like a, you know, six to eight week period with the help of a lot of the team. Right. It's been, been, there's numerous of examples of that, like at, at turbo puff, but that's like really bending the software and X 86 to your will.It was incredible to watch. Um. You wanna see some moments like that?swyx: Isn't that triple nine?Simon Hørup Eskildsen: Um, I think Nathan, what's calledAlessio: group nine, that was only nine. I feel like this is too high forSimon Hørup Eskildsen: Nathan. Nathan is, uh, Nathan is like, yeah, there's a lot of nines. Okay. After that p So I think that's one trait. I think another trait is that, uh, the P 99 spends a lot of time looking at maps.Generally it's their preferred ux. They just love looking at maps. You ever seen someone who just like, sits on their phone and just like, scrolls around on a map? Or did you not look at maps A lot? You guys don't look atswyx: maps? I guess I'm not feeling there. I don't know, butSimon Hørup Eskildsen: you just dis What about trains?Do you like trains?swyx: Uh, I mean they, not enough. Okay. This is just like weapon nice. Autism is what I call it. Like, like,Simon Hørup Eskildsen: um, I love looking at maps, like, it's like my preferred UX and just like I, you know, I likeswyx: lotsAlessio: of, of like random places, soswyx: like,youswyx: know.Alessio: Yes. Okay. There you go. So instead of like random places, like how do you explore the maps?Simon Hørup Eskildsen: No, it's, it's just a joke.swyx: It's autism laugh. It's like you are just obsessed by something and you like studying a thing.Simon Hørup Eskildsen: The origin of this was that at some point I read an interview with some IOI gold medalistswyx: Uhhuh,Simon Hørup Eskildsen: and it's like, what do you do in your spare time? I was just like, I like looking at maps.I was like, I feel so seen. Like, I just like love, like swirling out. I was like, oh, Canada is so big. Where's Baffin Island? I don't know. I love it. Yeah. Um, anyway, so the traits of P 99, P 99 is obsessive, right? Like, there's just like, you'll, you'll find traits of that we do an interview at, at, at, at turbo puffer or like multiple interviews that just try to screen for some of these things.Um, so. There's lots of others, but these are the kinds of traits that we look for.swyx: I'll tell you, uh, some people listen for like some of my dere stuff. Uh, I do think about derel as maps. Um, you draw a map for people, uh, maps show you the, uh, what is commonly agreed to be the geographical features of what a boundary is.And it shows also shows you what is not doing. And I, I think a lot of like developer tools, companies try to tell you they can do everything, but like, let's, let's be real. Like you, your, your three landmarks are here, everyone comes here, then here, then here, and you draw a map and, and then you draw a journey through the map.And like that. To me, that's what developer relations looks like. So I do think about things that way.Simon Hørup Eskildsen: I think the P 99 thinks in offs, right? The P 99 is very clear about, you know, hey, turbo puffer, you can't run a high transaction workload on turbo puffer, right? It's like the right latency is a hundred milliseconds.That's a clear trade off. I think the P 99 is very good at articulating the trade offs in every decision. Um. Which is exactly what the map is in your case, right?swyx: Uh, yeah, yeah. My, my, my world. My world.Alessio: How, how do you reconcile some of these things when you're saying you bend the will the computer versus like the trade

In this episode of Tank Talks, host Matt Cohen sits down with Didier Lavallée, Founder and CEO of Tetra Digital Group, to explore one of the most important frontiers in Canadian fintech: regulated digital assets and the rise of a sovereign Canadian stablecoin.Didier shares his journey from more than a decade in capital markets and custody roles at RBC to founding Tetra following the collapse of QuadrigaCX, an event that exposed the need for secure and regulated digital asset custody in Canada. His experience in trading desks, foreign exchange, and global custody infrastructure helped shape his vision for building institutional-grade digital asset infrastructure.Didier also discusses Tetra's growing platform, including Tetra Trust, Canada's regulated digital asset custodian, and Tetra Unity, a custody orchestration SaaS platform designed to help institutions manage digital asset infrastructure. He explains how these tools bridge the gap between traditional financial systems and blockchain technology.From the launch of CADD, Tetra's upcoming Canadian dollar-backed stablecoin, to the partnerships powering its ecosystem with companies like Wealthsimple, Shopify, and National Bank, Didier dives into the future of digital payments, cross-border settlement, and programmable financial infrastructure.Whether you're interested in fintech innovation, digital assets, or the evolution of global payments, Didier's perspective offers valuable insights into how Canada can build the next generation of financial infrastructure.The QuadrigaCX Collapse and the Birth of Tetra (10:12)* How the QuadrigaCX scandal exposed the need for regulated custody* The founding of Tetra to provide institutional digital asset security* Building a regulatory framework for digital asset custody in Canada* Why secure custody is foundational to the digital asset ecosystemBuilding Institutional-Grade Infrastructure for Digital Assets (14:35)* Why Tetra positioned itself as a regulated financial institution first* The development of Tetra Unity, its custody orchestration platform* How APIs and automation help reconcile transactions across crypto networks* Turning internal infrastructure into a scalable SaaS platformThe Vision for Canada's Stablecoin: CADD (16:40)* Why Canada has lagged behind other jurisdictions in stablecoin development* How CADD aims to become Canada's regulated fiat-backed stablecoin* Partnerships with Wealthsimple, Shopify, National Bank, and others* The importance of regulatory clarity for stablecoin innovationStablecoins and the Future of Payments Infrastructure (21:50)* How stablecoins enable 24/7 programmable settlement* Why traditional payment rails struggle with cross-border transfers* The role of stablecoins in treasury management and automation* How global companies could use stablecoins to streamline paymentsThe Role of Banks in the Digital Asset Transition (26:54)* Why traditional financial institutions must adapt or risk disruption* How fintech platforms are redefining customer expectations* The generational wealth transfer shaping financial innovation* Why blockchain infrastructure may operate invisibly behind consumer appsTetra's Business Model and Growth Strategy (30:49)* The three pillars of Tetra's business: custody, software, and stablecoins* How the Unity platform generates SaaS revenue* Custody services and institutional digital asset management* How stablecoin reserves generate yield and network incentivesCanada's Opportunity in Digital Asset Infrastructure (36:56)* Why Canada once led the digital asset industry but has fallen behind* The need for clear regulatory frameworks to unlock institutional adoption* Tetra's goal to become the institutional backbone of digital assets in Canada* Why 2026 could be a breakthrough year for the Canadian ecosystemAbout Didier LavalléeDidier Lavallée is the CEO of Tetra Digital, a Canadian digital asset infrastructure company focused on custody, stablecoins, and institutional blockchain services. With a background in financial markets and banking, Didier is building infrastructure designed to help financial institutions and businesses adopt digital assets securely and efficiently.Connect with Didier Lavallée on LinkedIn: https://www.linkedin.com/in/didier-lavalleeVisit Tetra Digital Group Website: https://tetradg.com/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

What sparked the recent “SaaSpocalypse” conversation across social media, news outlets, and investor circles? In episode #358 of SaaS Metrics School, Ben Murray explains how the debate around AI potentially disrupting SaaS began. Ben breaks down what actually started the conversation, the major concerns investors and operators are discussing, and why SaaS founders and CFOs should pay attention to the shift. Resources Mentioned Ben's blog post: The SaaSpocalypse — Bull Case, Bear Case, and How to Assess SaaS Defensibility: https://www.thesaascfo.com/the-saaspocalypse-ai-agents-vibe-coding-and-the-changing-economics-of-saas/ What You'll Learn What triggered the “SaaSpocalypse” narrative in early 2026 Why AI coding tools are accelerating the build vs. buy decision for software How agentic workflows could pressure traditional SaaS products Why seat-based pricing models may face scrutiny in an AI-driven world How investors may rethink the durability of SaaS revenue and growth Why It Matters AI agents capable of executing workflows could reshape how software is delivered SaaS pricing models tied to seats may become less durable if AI reduces headcount needs The build vs. buy equation is shifting as AI coding tools make software easier to create Investors may begin reassessing SaaS valuations based on AI disruption risk SaaS operators must stay informed and proactive as AI reshapes the software landscape

There are loads of apps that post to social media. So how did Nevo David get Postiz to take off? He started selling it to AI agents Nevo David is the founder of Postiz, an open-source social media scheduling platform designed for automation and AI-driven workflows. By leaning into open source, building tools for agents like OpenClaw, and simplifying integrations through a CLI interface, Nevo turned a crowded product category into a fast-growing bootstrapped SaaS. Today Postiz generates over $45K in monthly recurring revenue while continuing to evolve for the emerging agent ecosystem. Sponsored byZapier More interviews -> https://mixergy.com/moreint Rate this interview -> https://mixergy.com/rateint

The tech industry is split between two fantasies  – that AI writes production software while you get coffee, and that everything AI touches is slop. The reality is messier and more interesting: AI tools are force multipliers for people who already know what good looks like, and an expertise amplifier disguised as an easy button. ... Read more »

Carl Quintanilla and Jim Cramer delved into two big stories of importance to investors and consumers: With the Iran conflict sparking energy market volatility, the price of crude rose despite reports of International Energy Agency plans for a record release of oil from its strategic reserves. February CPI showed consumer inflation up 2.4% from a year ago — but the data had been collected prior to the Iran war. Oracle shares surged on a Q3 beat and raised revenue guidance. The anchors explored what it all means for the AI trade and the software "SaaS-pocalypse." Also in focus: Nvidia to invest $2 billion in Nebius, JPMorgan Chase limits private credit lending, Microsoft backs Anthropic's lawsuit against the Pentagon, Campbell's in the soup.   Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

I break down Andrej Karpathy's new open-source project, Autoresearch: what it is, how it works, and why some of the smartest people in tech are losing their minds over it. I walk through 10 concrete business ideas you can build on top of Autoresearch loops, from niche agent-in-a-box products to always-on A/B testing agencies. I also cover Karpathy's companion launch, Agent Hub, share community reactions, and show you step by step how to get started using Claude Code and a Colab GPU. I'm hosting a free workshop so you can build your business in the age of AI. Sign up here: https://startup-ideas-pod.link/build-with-ai-2026 Links Mentioned: Autoresearch Github: https://startup-ideas-pod.link/autoresearch Timestamps 00:00 – Intro 00:45 – How Autoresearch Actually Works 02:40 – Visual Walkthrough of the Autoresearch Loop 03:37 – Mental Model: Your Research Bot That Runs While You Sleep 05:26 – Idea 1: Niche Agent-in-a-Box Products 06:48 – Idea 2: A/B Testing for Marketing (Landing Pages & Ads) 08:45 – Idea 3: Research as a Service 09:43 – Idea 4: Power Tool Inside Your Own SaaS 10:49 – Idea 5: Agency That Runs 100× More Tests 12:05 – Idea 6: Auto Quant for Trading Ideas 13:44 – Idea 7: Always-On Lead Qualification & Follow-Up 14:21 – Idea 8: Finance Ops Autopilot for Businesses 15:09 – Idea 9: Internal Productivity Lab for Your Org 15:53 – Idea 10: Done-for-You Research & Due Diligence Shop 16:41 – Non business use cases 18:27 – Karpathy's Agent Hub Announcement 19:50 – How to Get Started with Autoresearch 22:21 – Final Thoughts Key Points Autoresearch is an open-source AI agent that sets a goal, runs experiments in a loop on a GPU, keeps the winners, and discards the rest — all while you sleep. You need an NVIDIA GPU to run it (tested on H100), but you can rent one cheaply through Lambda Labs, Vast AI, RunPod, Google Cloud, or Google Colab. The fastest way to get started is to use Claude Code to walk you through installation, then run it on Google Colab with a T4 GPU runtime. Ten business ideas built on Autoresearch span niches like SaaS optimization, A/B testing agencies, trading backtests, CRM lead scoring, and done-for-you due diligence. Karpathy also launched Agent Hub — essentially a GitHub designed for agent swarms to collaborate on the same codebase. The project already has 25,000+ GitHub stars and is growing fast; early movers who tinker now build an unfair advantage. The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/ FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/

Max Jungestål, CEO of Legora, joins Jacob Effron and Logan Bartlett to discuss the company's $550M Series D and share a candid account of what building an AI-native company at speed actually looks like from the inside. Max argues that the AI application layer requires a fundamentally different operating model than traditional SaaS, one built on low ego, constant reinvention, and a willingness to watch nine months of work get washed away by a model update. He walks through how step-function improvements in the underlying models, particularly Opus 4.5 and 4.6, have repeatedly forced Legora to rebuild core product features from scratch, and why he sees that as a feature, not a bug. On the legal industry, Max offers a ground-level view of how AI is actually diffusing through law firms, less through top-down mandates and more through competitive pressure between firms and, increasingly, from enterprise clients demanding efficiency from their outside counsel. He pushes back on the viability of AI-native law firms, dismisses outcome-based pricing as harder than it looks, and makes the case for why foundation model competition creates tailwinds rather than threats for a company with Legora's depth. The episode closes with a detailed look at the US expansion strategy, including the deliberate cultural decisions, like flying all New York hires to Stockholm for onboarding, that Max believes are the real source of Legora's compounding advantage.   [0:00] Intro [1:16] Legora's Series D Story [3:24] Why You Need Low Ego to Build in AI [5:58] From 60% to 100% Accuracy in One Summer [7:04] Law Firm Economics Shift [14:09] Pricing Seats Vs Outcomes [18:31] Why Foundation Models Entering Legal Helps Legora [30:10] Convincing a 75-Year-Old Partner to Go All In [33:02] Hiring Legal Engineers [34:32] Running an AI-Native Company [35:57] The Opus 4.5 Christmas Breakthrough [40:02] Building With Customers [44:01] All In On US Expansion [51:22] Stockholm Startup DNA   With your co-hosts:  @jacobeffron  - Partner at Redpoint, Former PM Flatiron Health  @patrickachase  - Partner at Redpoint, Former ML Engineer LinkedIn  @ericabrescia  - Former COO Github, Founder Bitnami (acq'd by VMWare)  @jordan_segall  - Partner at Redpoint

My guest today is Shyam Sankar, the CTO of Palantir Technologies. In this conversation, we explore the ideas that shape how Shyam thinks about technology, talent, and national power. We discuss the origins of Palantir's forward-deployed engineering model and the lessons he learned from Alex Karp about identifying people's "superpowers". We also talk about Shyam's fascination with the "heretics" of American history, the unconventional builders who challenged bureaucracy and created many of the systems that powered America's military and industrial success. Shyam argues that the United States must reindustrialize after decades of moving production overseas, and explains what we can learn from America's industrial past. In a new Colossus profile, our Editor in Chief Jeremy Stern tells the story of how Shyam became one of the most important but largely unseen figures behind Palantir, tracing his journey from immigrant roots to employee #13 and the architect of the company's success and distinctive culture. 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. Subscribe at ⁠colossus.com/subscribe⁠. ----- ⁠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. ----- Trusted by thousands of businesses, ⁠Vanta⁠ continuously monitors your security posture and streamlines audits so you can win enterprise deals and build customer trust without the traditional overhead. Visit ⁠vanta.com/invest⁠.  ----- ⁠WorkOS⁠ is a developer platform that enables SaaS companies to quickly add enterprise features to their applications. Visit⁠⁠ ⁠WorkOS.com⁠⁠⁠ to transform your application into an enterprise-ready solution in minutes, not months. ----- ⁠Rogo⁠ is an AI-powered platform that automates accounts payable workflows, enabling finance teams to process invoices faster and with greater accuracy. Learn more at ⁠Rogo.ai/invest⁠. ----- ⁠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. Visit⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ridgeline.ai⁠. ----- Editing and post-production work for this episode was provided by The Podcast Consultant (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://thepodcastconsultant.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠). Timestamps: (00:00:00) Welcome to Invest Like the Best (00:02:43) Intro: Shyam Sankar (00:03:24) Defining Heretics in US Military History (00:05:01) The Story of Hyman Rickover (00:09:55) Formative Experiences & Worldview (00:14:50) Components of American Greatness (00:17:56) How to Unlock Talent (00:25:56) Palantir's Distinct Culture (00:28:15) Origin of Forward Deployed Engineering (00:34:24) What Does Palantir Actually Do? (00:36:19) Example: Airbus (00:40:20) State of the US Military Today (00:47:33) The U.S. Needs to Reindustrialize (00:52:19) Perspective of China (00:55:56) Our Key Asymmetric Advantages (01:00:57) Executive Orders for a Day (01:02:37) Negative Aspects of US Culture (01:04:47) Managing Rapid Pivots (01:09:17) Where Will AI Value Accrue? (01:12:37) Undeclared State of Emergency (01:15:45) Surprising Aspects of Palantir (01:17:50) To Do or To Be (01:18:50) Reflecting on Fatherhood (01:19:46) The Kindest Thing

Ryan Levesque built a SaaS company, had a life-changing exit offer fall apart at the 11th hour, and then realized something that shook him:  he'd started hating his business, and as a result, he'd started hating himself. What followed was a radical reorientation: making a public confession to his audience that none of his emails over the past seven years had actually been written by him, moving to a Vermont farm, and writing every word of his newsletter without AI.  This is a conversation about what it costs to take your voice back and why the unscalable things are the only real moat left in a post AI world. RESOURCES: Ryan's newsletter » CONNECT WITH ME Newsletter Instagram TikTok X (Twitter) LinkedIn Facebook  

Dale Renner is the Founder and CEO of Redpoint Global, a software company that helps businesses collect, organize, and use customer data to improve marketing, customer experiences, and business decisions. Since 2006, he has led the company's vision of enabling marketers to orchestrate meaningful customer interactions across channels using advanced data capabilities. Dale brings more than 25 years of experience in CRM consulting, data processing, and analytics software. Earlier in his career, he was a global managing partner at Accenture, where he founded the firm's Global CRM practice. In this episode… Clean data rarely gets the spotlight in AI discussions, yet it often determines whether AI succeeds or fails. Companies invest heavily in analytics and automation, but fragmented data can undermine even the most advanced systems. What happens when organizations finally unify and master their customer data? Dale Renner, a veteran enterprise software entrepreneur and data strategy expert, explains that AI only works when the underlying data is reliable and unified. He emphasizes that companies often rush toward analytics and machine learning before fixing foundational data issues, which leads to faster but flawed results. Clean, governed data enables organizations to personalize experiences, make accurate decisions, and scale engagement across millions of customers. He also notes that industries with massive datasets — like healthcare, finance, and insurance — especially benefit from strong data architecture. Without that foundation, even the most advanced AI tools struggle to deliver meaningful outcomes. In this episode of the Inspired Insider Podcast, Dr. Jeremy Weisz sits down with Dale Renner, Founder and CEO of Redpoint Global, to talk about how clean unified data powers AI and better customer experiences. They discuss the origins of the customer data platform, transforming fragmented data into actionable insights, why regulated industries rely on robust data governance, and how AI is shaping enterprise sales and marketing.