The Learning Corner by Precursor

The Learning Corner by Precursor

By Mia Farnham, Charles HudsonBusinessTechnologyInvesting
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The Learning Corner by Precursor episodes

  • Episode #90: Is Series A Broken?, Are Winners Ever Found In Hype Markets?, The Power Of The Builder Archetype
    This week on The Learning Corner, Mia and Charles dig into Gil Dibner's argument that Series A investors have fundamentally abandoned their core function, opting instead to invest way too late, way too early, or simply accumulating options without conviction. They then unpack Sequoia's twenty-year hype analysis that shows the most valuable companies are almost never founded in the peak hype year of their category, and what today's signals might tell us about the next big wave. The episode closes with Maple VC's Talent Arbitrage framework and why the Builder, the nonlinear, hardest-to-categorize founder, is the most important and most overlooked signal in early stage investing.

    Is Series A Broken?

    Should You Chase Hype or Ignore It?

    The Talent Arbitrage

    (0:00) The broken state of pre seed and series a labels, introduction, and welcome
    (1:01) Gil Dibner's take and current series a market dynamics
    (2:11) Charles Hudson's perspective on market changes
    (3:43) Alternative funding routes for founders
    (4:40) Series a market viability for various companies
    (6:08) Opportunity for new series a investors
    (7:47) Analysis of hype data vs. valuable companies
    (9:15) Challenges and humility in focusing on non-hyped themes
    (12:59) Communicating value of lesser-known companies
    (15:31) Duration and impact of current market hype
    (16:52) The talent arbitrage and evaluating founder archetypes
    (22:26) Impact of losing inventor founders on startups
    (22:53) The role of inventors across the company lifecycle and closing remarks
    24 min
  • Episode #89: Industrializing the Blame Game, VC-Backed Startups and Fraud, Will You Quit on Me?
    This week on The Learning Corner, we dig into three pieces that all orbit the same uncomfortable question: who is actually accountable when things go wrong? Angela McNeal's "The Architecture of Blame" argues that AI does not invent accountability gaps, it industrializes them at machine speed across every workflow at once. New academic research reveals that VC-backed startups are statistically more likely to commit fraud, and that investors are not passive victims but active co-creators of the conditions that make it happen. We close with a Wall Street Journal investigation into how the AI talent war is turning founders into acquihire targets, with VCs now explicitly asking in diligence: will you quit on me?

    The Architecture of Blame

    VC-Backed Startups Commit More Fraud, and Researchers Think They Know Why

    VCs Are Asking Founders: "Will You Quit on Me?"

    (0:00) Founder commitment and podcast introduction
    (1:09) AI, accountability, and the architecture of blame
    (6:12) Role of AI agents and employee responsibility
    (9:57) Fraud patterns in VC-backed startups
    (12:18) Handling and prevalence of fraud in venture capital
    (15:08) SEC audits and the cycle of fraud
    (18:29) Founder poaching and acqui-hires in the AI sector
    (22:58) Founder pressure, investor expectations, and scaled outcomes
    (24:32) Founders' desire to be high performers
    (25:21) Impact of external signals on perceived performance
    (26:00) Realism of founder commitments and episode closing
    27 min
  • Episode #88: Supporting Young Founders Well, YC's Fall Requests For Startups, Why Labs Say Frontier Not Foundation
    This week Mia and Charles are joined by Precursor intern Aleena to dig into three good reads. First up, Ashley Smith's piece on the pressures young founders face today, including predatory incubators that trade small checks for outsized equity stakes. Then, YC's Fall 2026 Requests for Startups, featuring the first ever entry from a sitting US Secretary of the Army. They close with Frontier, a piece unpacking why AI labs stopped calling their models foundation models and started calling them frontier models, and what that shift reveals about how durable their moats really are.

    The Young Founder Experience

    YC Requests for Startups
    Frontier

    (0:00) Introduction, welcome, and Ashley Smith's article
    (2:36) Charles Hudson's reaction and takeaways
    (6:04) Balancing honesty and fearlessness in young founders
    (7:13) The myth of building great products
    (9:04) Y Combinator's fall 2026 request for startups
    (10:18) Significance of government officials' participation
    (12:18) Consumer products and the return of YC
    (17:15) Request for startups from Precursor's perspective
    (18:59) Frontier models vs. foundation models in AI
    (25:00) Closing remarks
    (25:12) Sponsor: Precursor Ventures
    26 min
  • Episode #87: Patience Pays In Venture, Why Humans Beat AI, Managing Your Agent Workforce
    This week on The Learning Corner, Mia and Charles dig into Nick Chirls' case for patience as an underrated venture strategy, using the owners versus tenants framing to explain why short term thinking is eating the industry. They then explore David Deming's argument that humans still hold a real edge over AI in social learning, especially in reading people and building trust from just a handful of interactions. The episode closes with George Sivulka's provocative piece comparing AI agents to a workforce, drawing parallels between tokenmaxxing and headcount bloat, and looping and meetings about meetings. Tune in for a conversation on long term thinking, the future of human judgment, and how to actually manage an AI agent workforce well.

    Patience (and on owners v. tenants) - Nick Chirls

    Efficient Social Learning Is the Human Advantage Over AI - David Deming

    You Just Hired a Million Bad Employees - George Sivulka

    (0:00) Introduction, AI readiness, and episode overview
    (0:47) Patience in venture: Nick Charles' post and fund manager-LP alignment
    (2:14) The game on the field and investor awareness
    (3:07) Revenue durability and legibility to LPs
    (5:30) The conundrum and risks of patience in venture capital
    (7:23) Impact of patience on founders, employees, and social values
    (10:10) David Deming's Substack: social learning vs. AI
    (12:22) Comparative advantage, human leadership, and people management in the AI era
    (17:24) George Sowellka's post on AI agents and management
    (19:18) Favorite comparisons from George’s piece
    (20:13) Forward deployed engineers and business transformation
    (21:14) Challenges of AI-powered customer support
    (22:02) Consulting’s role in business transformation
    (22:52) Managing dysfunction: AI vs. humans and software checkpoints
    (24:34) Emotional differences, managing AI agents, and closing remarks
    27 min
  • Episode #86: Paying For AI Twice, Sierra's Bet On One AI Agent, Data's Next Trillion Dollar Category, NY Halts Data Centers
    This week Mia and Charles dig into whether using AI tools means quietly giving away your most valuable knowledge, what Sierra learned building a single company wide AI agent instead of one per department, and why data, not tokens, might be the next trillion dollar category in AI. They also touch on New York's new one year moratorium on large data center construction. Tune in for a debate filled conversation about what it really means to adopt AI at work today.

    The Reverse Information Paradox

    AI-pilling Our Company: Lessons Learned
    The Task Economy
    New York Becomes First State to Impose Data Center Moratorium

    (0:00) Introduction, tech opposition, and hosts
    (0:45) Satya Nadella's reverse information paradox
    (2:04) Data boundaries, proprietary info, and AI
    (6:03) Sierra's AI acceleration team and productivity gains
    (9:42) Everett Randall's task economy and expertise
    (12:16) Transfer of human knowledge to AI models
    (14:21) White collar professionals and AI model contributions
    (15:26) New York's moratorium on new data centers
    (17:06) Tech's communication battle and public sentiment
    (18:50) Closing remarks and thank yous
    20 min
  • Episode #85: Venture's Cognitive Dissonance Moment, Cold Pitches Actually Work, Building Your Moat Later
    Is AI venture living through its biggest bubble yet, or its biggest winner-take-all cycle ever? Charles and Mia break down Samir Kaji's read on the duality driving valuations to record highs. They also dig into a Stanford field experiment proving cold pitches to investors work far more often than founders think, and challenge Tomasz Tunguz's take on why most startups don't actually have a real moat at founding. A fast, sharp look at three ideas shaping venture right now.

    Crazy, and Rational: Venture's Cognitive Dissonance Moment

    Cold Pitch: How to Ensure the Investor Gets Back to You
    The Moat You Haven't Earned Yet

    (0:00) Cold emails and episode introduction
    (0:52) AI venture market trends and sentiment shift
    (3:29) Narrowed AI opportunities and public market impact
    (5:20) Future growth in technology cycles
    (7:18) Stanford's cold email experiment
    (8:51) VC attitudes towards cold outreach
    (12:10) Effective VC outreach to founders
    (13:05) Addressing and evolving the "moat" question in pitches
    (17:47) Episode conclusion and thank you
    19 min
  • Episode #84: Convicted vs. Disciplined Healthcare VCs, The Capitalist Multiverse, The Untrainable
    This week we dig into a piece from Halle Tecco exploring how healthcare VCs are splitting into two camps on ownership discipline as AI drives valuations to decade highs. We also break down Sam Lessin's argument that the SpaceX IPO signals the end of the DCF as the only globally scalable story of value, and what it means that minority belief systems can now pool conviction at trillion-dollar scale. We close with Sarah Guo's "The Untrainable," a sharp framework for understanding where real moats exist in the AI era and why the defensible work is exactly the stuff that can't be benchmarked. Tune in for a packed episode touching on venture math, narrative-driven markets, and what it actually takes to build something durable when the models keep getting smarter.

    Convicted or Disciplined: How Healthcare VCs Are Split on Investing - Halle Tecco

    The Real SpaceX IPO Lesson: We Live in a Capitalist Multiverse of Value - Sam Lessin

    The Untrainable - Sarah Guo

    (0:00) Episode introduction and overview
    (1:04) Health care investment strategies
    (2:29) Broader implications of AI on industries
    (3:20) Balancing conviction and discipline in venture capital
    (5:09) The impact of momentum in venture investing
    (8:17) Generalist vs. specialist investment strategies
    (9:23) SpaceX IPO analysis and alternative value beliefs
    (10:27) Entertainment and identity in modern investing
    (15:25) Sarah Guo's essay: AI and venture defensibility
    (19:11) AI models, consumer apps, and innovation vs. compliance
    (20:01) Evaluating AI agents and workforce transformation
    (21:23) Process redesign and early AI implementation
    (21:53) Episode wrap-up and upcoming break
    23 min
  • Episode #83: Tony Fadell on Building Products That Last, SAFE Adjusted TVPI, Kirkland's $500M AI Bet
    This week we featured Tony Fadell's interview with Lenny Rachitsky, covering how great products are built with pain at the center and why fast software is the new fast fashion. We also dig into Hunter Walk's practical framework for how early-stage VCs should communicate SAFE note markups to their LPs. And we close out with Reuters' reporting on Kirkland and Ellis committing $500 million to build a fully proprietary AI platform, and what that signals about where the legal industry is heading.

    Father of the iPod and iPhone on Building Taste, Judgment, and Creativity in the AI Era | Lenny's Podcast

    SAFE Adjusted TVPI: How Early Stage VCs Should Communicate SAFE Note Markups to Their LPs | Hunter Walk
    Kirkland and Ellis to Spend $500 Million Developing Its Own AI Platform | Reuters

    (0:00) Introduction and the need for a 10x better product
    (0:54) Tony Fadell on AI, fast software, and product success
    (2:25) Importance of marketing, sales, and judgment in product outcomes
    (4:19) Persistence versus foolishness in product development
    (7:45) External triggers and market timing
    (12:40) Communicating portfolio value with SAFEs (Hunter Walk's blog)
    (16:05) Showcasing portfolio progress to LPs
    (17:20) Sponsor: Kirkland & Ellis
    (20:30) Benefits of AI investment and justifying premium fees for service firms
    (21:11) Closing remarks and announcements
    22 min
  • Episode #82: What Will Be Scarce in an AI World, The Real Cost of Vibe Coding, The End of the Software Engineer
    This week on The Learning Corner, economist Alex Imas makes a counterintuitive case that AI will not eliminate human labor but instead relocate scarcity toward a "relational sector" of nurses, teachers, craftspeople, and care workers where human presence is the product itself. Lisa Kostova shares one of the most honest accounts of vibe coding gone wrong, arriving at a major conference with 40 ready buyers and a product too broken to sell. We close with Boris Cherny, creator of Claude Code at Anthropic, who argues the title of software engineer is dissolving but predicts 100 times more people will be writing code in the near future. Three reads, one big question: what does human work actually look like in an AI economy?

    What Will Be Scarce? by Alex Imas

    40 Buyers and Nothing to Sell by Lisa Kostova

    Claude Code's Creator on the End of the Software Engineer by Casey Newton

    (0:00) Episode introduction, presentation prep, and Cloud Code issues
    (0:48) AI, the relational sector, and the value of human interaction
    (2:29) Human labor as a competitive moat in service industries
    (4:43) Comparing customer experiences at different coffee shops
    (5:30) The future of service jobs and compensation
    (7:16) High-income influence and AI's deflationary impact
    (8:34) Introduction to vibe coding and Lisa Kustova's article
    (11:04) Hype vs. reality and technical challenges in AI development
    (14:08) The importance of technical understanding in AI coding
    (16:12) Maintaining AI agents and practical experiences
    (16:51) Casey Newton's interview with Boris Churney
    (17:45) Hackathon winners and AI tool accessibility
    (18:25) AI tools democratizing coding and barriers to entry
    (21:21) Rapid dissemination of coding knowledge and technological anxiety
    (22:27) Using AI tools in real-life scenarios and the generational impact
    (23:24) Conclusion and closing remarks
    24 min
  • Episode #81: Does Your Boss Have AI Brain?, Career Bets That Compound, Too Much Is Happening Too Fast
    This week on The Learning Corner, we dig into Rachel Karten's viral piece on AI-obsessed leadership and what happens when organizations adopt AI without any real strategy. We then discuss Karan Dhir's framework for career bets that actually compound versus the ones that just look like progress, including a debate on whether the AI era has quietly rehabilitated the generalist. We close with Charlie Warzel's Atlantic piece on AI malaise and whether the anxiety around AI is actually a geography problem more than a technology one.

    Does Your Boss Have AI Brain? - Rachel Karten

    The Career Bets That Compound (And the Ones That Don't) - Karan Dhir

    Too Much Is Happening Too Fast - Charlie Warzel, The Atlantic

    (0:00) High status jobs vs. startup adventure and podcast introduction
    (0:41) Sponsor: AngelList
    (1:17) AI integration in workplaces and lack of clear strategies
    (2:09) Creativity, judgment, and over-reliance on AI in marketing
    (4:22) Efficiency vs. human value and challenges under AI-obsessed leadership
    (6:56) Loss of employee trust and satisfaction from AI-driven processes
    (7:47) Career bets, opportunity costs, and the adventure vs. certainty dilemma
    (12:49) Patience, long-term commitment, and working at iconic companies
    (15:46) Making informed career choices and understanding personal goals
    (16:21) Generalists, AI’s impact on specialized roles, and industry messaging
    (18:45) AI industry’s marketing problem and public perception
    (21:24) New job opportunities and the downside of careerism in higher education
    (23:49) AI’s impact on job markets and supporting displaced workers
    (25:19) The future of software development and the importance of personal branding
    (26:30) The value of 9-to-5 jobs in a changing work landscape
    (27:21) Closing remarks and acknowledgments
    (27:34) Thanking listeners and encouraging subscriptions
    28 min

About The Learning Corner by Precursor

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Welcome to the Learning Corner, a weekly Precursor Ventures podcast, where members of the Precursor team walk through their favorite articles and news snippets across the venture ecosystem.