Episode Summary
Executive Summary: The episode centers on Reddit’s IPO, arguing that its long-term community moat, user influence, and valuable data make it far more strategic than its current ad revenue suggests. The panel then broadens into AI investing, warning that many AI startups are overhyped while infrastructure, proprietary data, and small, efficient teams may create the biggest winners. They also debate corporate VC, especially OpenAI’s fund, and end with portfolio picks.
Main Topics: Reddit’s IPO and monetization story (Priority: 5/5): Brian Rosenblatt and Jason Calacanis discuss Reddit’s growth from a tiny ad business into a public company, emphasizing community longevity, user authenticity, and the challenge of monetizing a discerning audience. AI investing: moats, stages, and speed (Priority: 5/5): The panel argues that AI is still early, with the strongest opportunities in infrastructure and data-rich applications, while many consumer and app-layer bets remain fragile or easily copied. Corporate venture capital and OpenAI’s fund (Priority: 4/5): They examine OpenAI’s corporate fund, weighing its unique informational advantages and talent access against conflict-of-interest risks and the downside of strategic investors. Capital efficiency and the end of ZIRP excess (Priority: 4/5): The speakers criticize oversized rounds, venture debt misuse, and operational bloat, arguing that founders often do better with less capital and tighter focus. Portfolio construction in a concentrated market (Priority: 4/5): The conversation covers how fund size changes return expectations, why Series B/C may increasingly be secondary-driven, and how smaller managers can use follow-on capital to back outliers. Latest investments and market signals (Priority: 3/5): Each speaker shares recent investments, revealing patterns: healthcare workflows, creator monetization, AI-enabled staffing, defense tech, and tooling for developers and product creation.
Key Arguments: Reddit’s value is driven less by ad scale and more by durable communities, influential users, and proprietary data that could support licensing or AI products. Reddit succeeded by keeping its interface stable while slowly improving usability, proving that longevity and community can outperform flashy redesigns. In AI, proprietary data and technical founder backgrounds matter more than broad market enthusiasm; many startups lack a real moat. The strongest AI opportunities may be in infrastructure and workflow layers, not simple apps that could be absorbed by OpenAI or other major model providers. Corporate VC can be strategically useful when it brings access and information, but it often creates control issues, special rights, and alignment problems. OpenAI is unusually powerful as a corporate investor because it knows its roadmap, product gaps, and which startups it may or may not compete with. The market is moving toward smaller, more efficient companies; some AI startups can scale revenue with very small teams, changing venture economics. Founders frequently overraise and then distort their behavior with excess cash or venture debt, which can obscure product-market fit and weaken discipline. For venture funds, second and third checks into true winners can dramatically improve fund returns, especially when early small-fund managers identify breakout companies. Series B and C may increasingly involve secondary/tender offers rather than large primary financings, especially for capital-efficient AI winners.
Data Points: Reddit revenue: $804 million - Brian references Reddit’s disclosed annual revenue in the IPO filing discussion. Reddit revenue at Brian’s arrival: less than $10 million - Brian says Reddit was tiny when he joined in 2015. Google licensing deal: $60 million - Jason cites a reported yearly data licensing deal between Reddit and Google. Reddit users daily: almost 100 million - Jason describes Reddit’s daily usage scale. Reddit users monthly: 500 million - Jason cites monthly active users while assessing its influence. Public media company performance: down about 50% over the last two years - Michael notes the difficult backdrop for publicly traded media companies. OpenAI Fund commitments: $175 million - Axios-reported amount already invested by OpenAI’s corporate fund. OpenAI launch timing: November 2022 - Michael notes ChatGPT’s relatively recent launch as a benchmark for AI’s early stage. OpenAI-related revenue: $1.7 billion - Michael says most of this revenue is consumer-driven. Fast-growing AI company example: $1M to $20M ARR in about six months - Michael describes a small team company showcasing AI-era speed. Small team example: 16 people - Used in the same example of rapid AI-driven growth. Midjourney revenue example: over $200 million a year - Jason cites Midjourney as a highly efficient AI business with a tiny team. Midjourney team size: 11 full-time employees - Used to illustrate AI-era capital efficiency. Kraft fund math: 3.5x return on emerging manager funds; 4–6x on direct investments - Michael explains the firm’s underwriting expectations. Emerging manager portfolio size: 24–25 funds - Michael says the firm backs this many small managers. Typical small fund size: $20M–$40M, average around $30M - Michael describes the managers in his strategy. Jason’s investment cadence: 100 new investments per year - Jason explains his power-law approach. Jason’s portfolio concentration: half the capital reserved for top 5% - He says he concentrates follow-on capital into breakout names.
Pivotal Quotes: "92% of those companies had a small fund like yours, Jason, early... but then... less than 5% of the time did they ever even participate beyond the seed stage" — David Weisbert: Introduces the key empirical point about small funds missing later rounds in unicorns. "It literally feels like you're driving in the storm and you can't really see." — Brian Rosenblatt: Describing the uncertainty and rapid change in AI investing. "I'd rather have founders who are pirates, not people pleasers" — Michael Downing: Arguing that startups should avoid becoming dependent on OpenAI as a strategic investor or vendor.
Implications: Listeners should expect AI to reward proprietary data, technical founders, and extreme efficiency, while public-market and venture norms may shift toward smaller rounds and more secondaries. Reddit’s IPO reinforces that long-lived communities can become strategic data assets, not just ad platforms.
About This Week in Startups
Jason Calacanis covers startups, tech, markets, media, and all the hottest topics in business and technology. He also interviews the world’s greatest founders, operators, investors, and innovators.