I'd recommend you listen to at least the last half; In particular Emmanuel's views on AI adoption inside industries and companies are pretty eye-opening, including how much leverage he is seeing himself already (we're not talking 10-20%).

A gaming ad CEO in a list about AI? Yes because AI is just that, an enabling piece of technology. And the greatest CEOs put it to use everywhere. Adam does just that, inside of a real business. He's been switching his algorithm to ML fast but much more important, he's streamlining his whole company by empowering people with AI. The transformative force of AI is very visible inside of AppLovin.

Yes this is a crypto episode, but I still believe its very useful for anyone in AI to understand the story of Rain, because of the pace it operates at AND for the world it outlines in where agents are already paying for services autonomously (already happening, can't really ignore it).

Another nugget is the idea of product-team fit, which Farooq discusses quite early (there are great ideas that simply aren't made for you as a team).

Love this episode. Tara is an amazing builder and I've listened to this episode multiple times now and recommended it to multiple product related people at my company.

I think my two biggest take aways are the "three mantras" Tara shares about "how to build products at OpenAI" and her thinking on "the only possible planning horizon".

The three mantras:

1. Are you “mainlining it yet”? Are you bringing all your taste on here? Are you using this all day?
2. Is this maximally accelerated? Can you go any faster here? Can you get this into the hands of (early!) users faster?
3. Are you being as ambitious as possible? Scope? Blow it up if you can!

A post-mortem of the July 2026 sell-off from someone who calls it 2022 packed into a single month. Useful counterweight if you only ever read the optimistic version.

The case that the next era is not faster chatbots but background agents running on their own for hours or days — and what has to get cheaper before that works at all.

Multi-agent systems, and what the current run of mathematics results suggests about automating AI research. The harder question sits underneath: how would you even know whether a model is aligned.

Two hours arguing about recursive self-improvement: whether roughly a year after human-level intelligence you slingshot far past it. It is a debate rather than a briefing, and that is exactly why it is worth the time — you hear where the disagreement actually sits instead of a confident summary.

330,000 cars in 79 years at an average of half a million dollars each. Scarcity manufactured on purpose, and the genuinely hard part is what it costs to keep holding that line when you could obviously sell more.

An ownership structure — not a product — ended up the largest equity owner of most of the S&P 500, on behalf of fifty million people. If you take one thing from this, take the structure.