The news
Nate's Substack published a piece arguing that comprehension — not output — is the differentiating skill in an AI-saturated market. The argument: AI has made production cheap and fast, so the pile of demos, prototypes, and portfolios keeps growing at machine speed. What's now scarce, and therefore valuable, is the ability to read a situation, evaluate what's actually good, and make judgment calls that AI can't make for you.
Our take
This lands hard for GTM teams, and not in a comfortable way.
The instinct right now — and dAIs sees this across nearly every client conversation — is to reach for output as proof of AI adoption. More sequences, more content briefs, more reports, faster. The CRO wants to see AI being used. So the team shows volume. That's not wrong, exactly. But it's also not the thing that compounds.
What actually compounds is the layer underneath: knowing which leads are worth the sequence in the first place, knowing which message variant reflects something true about the segment, knowing when the AI-generated scoring model is reflecting real buying behavior versus training data noise. That's comprehension. That's judgment. And it's genuinely hard to build because it requires deep familiarity with your market, your process, and your data — not just the tools.
Here's the uncomfortable part: most GTM teams are skipping that layer entirely. They're automating on top of undocumented, untested assumptions and calling it AI adoption. When the output underperforms, they blame the tool. The tool isn't the problem.
The teams dAIs has seen actually pull ahead aren't the ones generating the most — they're the ones who've gotten specific enough about their ICP, their process, and their quality bar that they can tell immediately when AI output is off. They've built the internal model to evaluate. That's not a portfolio skill. It's an operator skill.
The so-what
Comprehension doesn't show up in a demo. It shows up when you catch a bad ICP match before it burns a sequence, when you know a nurture track is working because engagement is meaningfully different — not just higher. GTM teams who want AI to actually move the number need to invest in that internal calibration, not just the tools on top of it.
- Audit one AI-assisted workflow this week. Not for output volume — for whether the inputs and assumptions driving it are documented and defensible.
- Identify one person on your team who's good at catching when something's "off." That's your comprehension anchor. Build around them.
- Before adding another AI tool, ask: do we have a clear quality bar for what good looks like here?
The teams that win the next two years won't be the ones who shipped the most. They'll be the ones who built the judgment to know what was worth shipping.