Podbit · Planet Money
Monopsony — one employer dominating a labor market — gives companies the power to underpay workers and impose bad conditions because workers can't easily leave. Dube argues this isn't just a sci-fi scenario; it's a hidden feature of today's economy.
Where this was said
At 14:00 · chapter starts 13:20
The hosts have been dancing around the term for a while, deliberately avoiding it so Dube can land it himself. When he finally says 'monopsony,' the hosts react with mock horror — fitting, since they're talking about a horror movie. Dube's quip that monopsony is scarier to him as a labor economist than the xenomorph gets a big laugh, but it also lands a serious point: the power that one-employer dominance gives over workers' lives is genuinely frightening at scale. The hosts then give a clean definition: monopoly is one seller in a market; monopsony is one buyer — and in labor markets, that buyer is the employer. They note that Weyland-Yutani operates as a clear monopsony in the Alien universe, and the question is how much that maps onto our world. [1] — Arin Dube "When asked whether monopsony or the xenomorph is scarier, labor economist Arin Dube doesn't hesitate: monopsony wins. It's a punchline, but…" 13:20
When asked whether monopsony or the xenomorph is scarier, labor economist Arin Dube doesn't hesitate: monopsony wins. It's a punchline, but it's also a thesis — monopsony's grip on real labor markets is a bigger everyday threat than any sci-fi monster.
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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