Podbit · The Prof G Pod with Scott Galloway
GMO research shows every 1% rise in market cap correlates with a 7.5% drop in the following year's returns. Applied to the AI IPO wave, that points to a potential 40% market decline. The catch: when analysts say it's 1999, it's often actually 1997 — and the NASDAQ could double from here.
Where this was said
At 6:05 · chapter starts 5:40
PitchBook analyst Harrison Rulfs had argued that large institutional investors — pension funds, sovereign wealth funds, and large-cap growth mandates — would fund their AI IPO allocations by liquidating existing big tech positions in NVIDIA, Microsoft, Google, and Meta. Galloway respectfully disagrees. His theory centers on investor psychology and investor overlap: the people most excited about SpaceX, OpenAI, and Anthropic are the same cohort that piled into Bitcoin and crypto on Elon Musk's enthusiasm. They're the innovation-chasing crowd, not the pension fund managers. Crypto is already about 50% off its highs, Galloway notes [1] — Scott Galloway "Forget NVIDIA and Meta taking the hit from large AI IPOs. Galloway says the real pressure will come on crypto — already 50% off its highs —…" 05:30 , and the AI IPO wave will accelerate that rotation out of digital assets and into AI equity. It's a more behaviorally-grounded prediction than the institutional portfolio rebalancing thesis, and it has significant implications for anyone holding Bitcoin while also wanting exposure to AI.
Galloway believes crypto is approximately 50% off its high and will come under further stress as AI IPO enthusiasm draws capital away.
GMO research found every 1% increase in market cap in a given month is historically associated with a 7.5% decrease in the subsequent 12-month stock market return.
SiteGPT attracted over 1 million visitors and $500K in total revenue without spending a cent on paid marketing. The secret: engineering as marketing — building free tools that rank on Google.
Bhanu quit his first job after just 8 months, moved back to his parents' house to cut costs, and started building. One product sold for $250K; the next hit $10K MRR in its first month.
90% of SiteGPT's Google search traffic comes not from the main product but from ~50 free tools Bhanu built. Each tool targets a low-competition keyword and funnels users back to the paid product.
50,000 monthly visitors become 200 leads, 60 trials, and roughly 15–24 new customers per month at ~$100 average revenue each. Add a $1,700–$1,800 LTV and you have a very healthy SaaS.
Start with a blank Ahrefs search, layer in keyword filters (include term, KD < 10, volume > 1,000), list candidates in Notion, design a CTA linking to your main product, then score by volume, difficulty, build effort, and product relevance. That's the whole playbook.
Marketing feels painful for most builders. Engineering as marketing flips the script: instead of writing cold emails or blog posts, you build things — and those things rank on Google forever.
Don't spend months perfecting before launch. Ship the core feature, get real users, and let their feedback dictate the product roadmap. Premature polish is a trap.
SiteGPT launched and hit $10,000 MRR within its first month. That momentum was so overwhelming that Bhanu sold his existing SaaS, Feather, for $250,000 to free up all his time.
PropGPT launched with 20 downloads a day and strong influencer marketing but hit a ceiling at $1,000–$2,000 MRR. High download numbers masked a critical flaw: almost nobody stuck around after the free trial ended.
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