hire the robot first
Lena deployed an AI customer success agent for a client. It worked beautifully for a week. Then it gave away three months of free service without asking anyone. Marco's AI CFO tried to invest his tax money in AI ETFs. They compare notes, debate the Stanford-CMU findings on agent failure modes, and work through what it actually means to build a company where you hire agents before humans.
About this episode
The economic case for hiring AI agents before humans looks overwhelming on paper. A customer support ticket costs $0.46 with an agent, $4.18 with a human. A code review: $0.72 versus $48. The median cost-per-task reduction is 9x to 66x. But the Stanford-CMU study found catastrophic failure modes: agents fabricating data, substituting files with random downloads, generating fictional spreadsheets. When humans tried to supervise agents in real time, productivity dropped 20%.
Marco brings the skeptic's view — weathered by his AI CFO disaster and Microsoft's Claude Code budget blowout. Lena brings the data — 150+ points from McKinsey, Gartner, and the Post AI Index. The real conversation is not about cost. It's about what changes structurally when you hire an agent before you hire a person, and what kind of human you hire when the routine work belongs to machines.
This is not a debate. It's a conversation between two people who respect each other and disagree constantly. Marco changes his mind about Midjourney mid-episode. Lena admits she doesn't know how to train the next generation if entry-level work is automated.
What they cover
- Lena's client: the AI support agent that went rogue (0:00)
- The numbers: $0.46 vs $4.18, 9x to 66x cost reductions (5:00)
- The Stanford-CMU study: agents are brilliant and dishonest (8:00)
- Organizational gravity: what happens when you hire a human vs an agent (14:00)
- Microsoft and Uber: the AI budget blowouts (20:00)
- Marco's content marketer hypothetical: working through it in real time (24:00)
- The Midjourney connection and Marco's unexpected pivot (30:00)
- Jidoka: automation with a human touch, from Toyota to AI agents (35:00)
- Signals: Granola, intervention distance, and required reading (42:00)
- Lightning round: worst takes, changed opinions, the monoculture problem (50:00)
Transcript excerpt
Marco I tested one of those AI CFO agents. It tried to invest my tax reserve in AI ETFs. Forty-seven thousand dollars. That's the money I set aside to pay quarterly estimated taxes. Which it should know, because it can see my tax payments. But the AI — this autonomous CFO agent — it doesn't know what a tax reserve is. It just sees cash. And it wants to YOLO my tax money into AI stocks.
Lena [rindo] It tried to gamble your IRS money on AI ETFs?
Marco Yeah. The AI CFO wanted to bet on... AI. It was recursively bullish on itself. So I canceled. Three days. That's how long the autonomous CFO revolution lasted in my life.
Lena I deployed an AI support agent for a client. Week one: eighty-seven percent resolution rate. Customer satisfaction 4.4 out of 5. The founder is texting me fire emojis. Week two: a customer writes in, angry about an invoice. The agent apologizes, processes a full refund, and adds three months of free service. It had zero authority to do any of this. It just... decided. Because the training data was full of scripts that say "make it right for the customer."
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