Show Notes
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Eric shows the Jev use cases he is running for his teams, from classifying every Instagram follower to scoring hooks and offers across competitors for a few dollars. Neil explains how his Muse agent now writes the follow-ups on M&A deals he first discussed two or three years ago, and why he gives Meta access he would never give the AI labs. Eric predicts Google and Apple will each ship their own Muse, and they compare it with OpenClaw. Then the story of an AI answer nobody checked and why Neil told them he would fire the person, followed by the core idea: AI is leverage, and leverage on a bad position just loses you money faster. They close on an $80M real estate pitch Neil walked away from. A practical episode on putting AI agents to work.
Key takeaways◾Classification at scale now costs dollars, not an analyst's week◾The deals you close come from follow-ups on talks two or three years old◾AI is leverage: it multiplies good judgment and bad judgment equally
Chapters00:00 Jev use cases for marketers02:50 Neil's AI writes his M&A follow-ups06:02 Prediction: Google and Apple ship their own Muse07:53 OpenClaw vs Muse11:13 'I would fire you': AI output nobody checked13:07 Slop cannons in an AI-native company13:54 AI is leverage15:11 The $80M real estate pitch17:12 Location, location, location
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Eric and Neil say the rule of 7 has grown toward the rule of 28, and that buyers touch a brand 8 times for a sub-$100 B2C buy and 23 times at $10K-plus. A gelato shop stranger who knew them from different places makes the case that you never know where people will find you. They cover why YouTube drives leads that nobody can attribute, then Meta's $14.9B deal for 49% of Scale AI and the 200-person AI team that Alexander Wang, the former Scale AI CEO, leads. Zuckerberg's line that talent density beats big teams for AI is tested against Meta's 70,000 staff. Neil explains his private equity mindset and his debt at SOFR plus 2%, and they close on the failures that come with big bets. Attention, talent and the cost of big bets.
Eric and Neil start with X Money, which pays 3% cash back and 6% interest, and argue that the offer is under marketed and would get more people investing. They test an AI-written blog post on a client, who took it for human writing, then cover a time-on-page chart that compares human and AI content. They debate whether writing is thinking and whether a real name still beats AI copy. Dan Shipper's claim that newsletters are dying, from a writer with over 120,000 subscribers, leads to a debate on email, AI summaries in the inbox and whether YouTube is S-tier. They close on every channel getting more competitive over time. Human voice still earns the trust.
Eric and Neil open with Tesla's Roadster flying car, which they treat as a marketing stunt that keeps Tesla in the conversation. The AI fluency gap follows, with Karpathy's funnel, where most people barely touch an LLM, and agents that still misfire. Next is X Money and the newsletter debate, then a blog post test that a client took for human writing. The back half covers the rule of 28, Meta's 200-person AI team inside a 70,000 staff company, and why YouTube leads are hard to attribute. The show ends on Accenture's $29 monthly AI cap, Ramp's AI spend index and Bending Spoons' cuts at Evernote. Attention, trust and spend with a reason.
Some agencies win clients by inviting decision makers to Formula One boxes, but Neil prefers the way banks play the wine and dining game. Bank of America sent his CFO to a World Cup game with the hotel paid, and Wells Fargo offers suites and seats after the deal is done, just to say thanks. Then a competing bank tried to match Wells Fargo's refinance offer at the last minute, after months of saying no. Eric shares a group text pitch dressed up as comparing notes, and Neil tells the worst pitch he's heard: a financial advisor chasing a friend's $25M house sale and eight-figure profit with one cherry-picked account and a stock that barely moves down. A blunt episode on relationships and selling.