Videos

Lenny's Podcast. Hamel Husain and Shreya Shankar walk through the entire eval workflow on a real property-management AI assistant — looking at traces, open and axial coding of errors, deciding when to stop, building an LLM-as-judge, and validating it against human judgment. This is the rare long-form conversation that is genuinely aimed at PMs and team leads rather than ML engineers, and it covers the same "30 minutes a week after setup" rhythm the article recommends.

Liam Ottley. A live build of an AI voice agent that calls inbound leads, qualifies them, and tries to book a discovery call — Make.com plus a voice provider, with the qualification script and handoff logic shown. Good complement to the email side: same enrichment-then-personalization pattern, different channel, different failure modes.

Greg Isenberg. A wider tour of the current AI marketing stack — workflow automation, model routing, AI video and voice tools, ad creation from competitor analysis. Good way to see which tools are doing what across the category before you decide where to put the first three Zaps or n8n flows for your own team.

Big Think. A tight 8-minute version of Mollick's "four scenarios" model — static, linear, exponential, AGI — and why teams should plan against scenario two or three rather than betting everything on either extreme. Useful when you're trying to get a leadership team to agree on what they're actually preparing for before you write the playbook.

Sana. An hour with Mollick on what AI inside organizations actually looks like — why "cut costs" is the wrong framing, why traditional org charts are bending, and what "AI-native" teams do differently. Sits below the usual 100k bar but it is the cleanest practitioner-level conversation about adoption strategy from the researcher most consistently cited on this topic, and the playbook concerns in the article map almost 1:1 onto his framing.