show me your claude chat
In a world where humans and AI are collaborating, you're not hiring a person anymore. You're hiring a human + AI agent system. Most interviews aren't designed for this.
If you only evaluate the human half, you're reading the conclusion without checking the working. You can't assess the quality of what you're working with.
This matters most in roles where the signal-to-noise ratio is low — product, marketing, HR, strategy. Take a product decision. A team bets on a feature after deep customer research and first principles reasoning. It flops — a competitor shipped the same thing three weeks earlier. The outcome tells you nothing about the quality of thinking that produced it.
A good poker player understands this. The decision and the outcome are two different things. In low-SNR roles, the decision-making process is the best proxy for a good outcome.
So the question is — how do you evaluate the thinking, not just the person? Ask to see their Claude chat. Or whichever AI they use most, which itself tells you something about how they work.
Most people accept the first answer that AI gives them. They don't push back, change their minds, or evolve their thesis. Asking to see a Claude chat they're proud of gives you a glimpse into whether they performed expertise or chased truth. That's the one disposition you can't train into someone after they join.
The gut feeling experienced founders and hiring managers have built over years of hiring will start to fail them. Not because it was wrong — but because the world it was trained on no longer exists. Hiring for human+AI systems requires a new evaluation framework. The Claude chat is where that starts.
What to Watch For
When reviewing someone's AI conversation, here's what separates real thinking from performed expertise:
Signs of deep thinking
| Marker | What it looks like | What it signals |
|---|---|---|
| Reframing the question | The person restates the problem differently after the AI's first answer | They're not outsourcing thinking — they're using the AI to sharpen their own framing |
| Disagreeing with evidence | "I don't think that's right because [specific reason]" | They have a thesis before asking, and test it against the AI's response |
| Changing their mind explicitly | "Actually, your point about X changes how I see this. Let me revise..." | Intellectual honesty — they update beliefs when presented with better reasoning |
| Escalating specificity | Questions get narrower and more precise over the conversation | They're drilling toward truth, not browsing for confirmation |
| Introducing outside context | They bring in data, anecdotes, or domain knowledge the AI doesn't have | They're treating the AI as a collaborator, not an oracle |
| Killing their own darlings | They abandon a direction they were pursuing when the reasoning doesn't hold | They value being right over being consistent |
Red flags
- Every prompt is a one-shot question with no follow-up
- They never disagree or push back
- Questions are generic, never anchored in specific context
- The conversation is a straight line — no backtracking, no revision
- The AI is doing all the heavy lifting while the human just says "great, now do X"
Appendix: When Everyone Starts Optimizing Their Claude Chat
When a measure becomes a target, it ceases to be a good measure. Three ways to stay ahead:
- Watch it live. Give the candidate a novel problem and let them use AI in front of you. You can't fake the instinct of what to ask next.
- Depth-probe the artifact. Ask why they asked a specific question, what they considered but didn't ask, and what they'd have done if the AI disagreed. A person who lived through the thinking can reconstruct the tree. A performer can't.
- Ask for volume. Request 3–5 chats, not 1. Patterns are harder to fake than highlights.