Nicolas Mondada

The pitch on AI is simple: delegate more, produce more. Spin up agents, fire off prompts, watch the output pile up. That pitch is only true for the few people willing to do the part nobody puts in the sales deck: reviewing what comes back, learning from it, steering it toward something actually correct, and building the harness that makes steering it possible in the first place.

Everyone else gets a pile of documents that look finished and aren't.

Are you using AI to be more productive, or to make your whole team less productive?

AI is here, it's real, and it's revolutionary. Every one of us is choosing what to do about it, one way or another, you can: stand still and try to ignore it, use it badly, or use it well.

Standing still or assuming this thing doesn't exist is not a new kind of bet, and it's a very human reaction to resist change. It's the bet an accountant made in the 1980s or 90s, insisting on paper ledgers while everyone around them moved to spreadsheets. And a few decades or centuries ago it was a bet you could make: technology permeated slow enough for you to retire before the technology retired you. But these days the world moves faster now than it did then, so I wouldn't take that bet unless the years left before your own retirement can be counted on the fingers of one hand.

Using it badly is worse than standing still, and it's the harder trap to see yourself falling into, because it looks like progress the whole time. This group delegates without checking. They generate a steady stream of AI slop: documents that look complete and mostly aren't, that read fine on the surface and fall apart on inspection. This doesn't just fail to add value, it makes the org slower than doing nothing at all, because who receives the document still has to do the work of figuring out what's usable. Colleagues notice, and this group isn't well liked, for good reason: they quietly push the review work they skipped onto everybody else.

Using it well means getting somewhere the other two groups can't. This group lets AI get them 80-90 percent of the way there, then spends its attention on the other 10-20 percent: reviewing it, correcting it, understanding why the model did what it did, and ultimately applying their own judgement to the document. Over time, and with the right harnesses, they delegate narrow-scoped tasks, but still do a quick check. These harnesses are built over time, not assumed up front.

Assuming you're willing to lean forward and land in that third group, here are two habits that separate it from the other two: how you review what AI hands you, and building your own harnesses so you don't have to steering AI manually every time.

I once assumed a model could actually do the research, dig into a market by scraping a dozen websites, pull in the company's own private data to understand not only what I was trying to do but what my organization's and partners' goals were, crunch the numbers, and come up with a report that would guide the strategy of a product for the upcoming year or two. Let me tell you, it didn't go well.

Thirty minutes later I had something that read like a real business plan, so I sent it to my boss. It used all the right key words and terms, but it was shallow, didn't deeply understand what our organizational goals were, and I struggled when my manager asked pointed questions about it. That's the pattern many have found: AI can produce output that is about 80 percent correct and 100 percent done-looking, but the author struggles to defend it in depth. The gap between those two numbers is invisible until you go looking for it, and it's exactly where quality and reputation are won or lost.

I call the fix for this problem the Double-Click Method: when a term, a claim, or a structural choice in the output is unfamiliar, don't wave it through. Ask what it is. Ask why the model chose it over the alternative. Then ask it to argue the opposite case, get the other side of the argument. This is something so many people miss; models are generally happy to defend whatever they just told you, so the only way to see the other side is to explicitly ask for it, and then it's up to you to judge which one is right or to use both.

Another powerful technique is running the same document through different personas. Ask the AI to review your product requirements as a senior product executive would. Then as an engineering manager. Then as a lead engineer. Then as a product marketer. Use skills like /grill me. Each pass surfaces a different set of gaps, because each role is optimizing for something the others aren't, and on each pass you use your judgement to curate and refine your understanding of the subject and the content of your document.

And it's your judgment that makes this work rather than just adding noise: don't apply all the feedback automatically. Read each pass, learn about the subject, and weigh it against what you actually know about your product and your org, then decide what to keep and what to discard. The goal was never a perfect document. It was for you to grow your knowledge and refine your judgement by surfacing gaps you couldn't see from inside your own head, and forcing yourself to sit with angles you'd otherwise have skipped. The outcome is a better document.

The payoff shows up twice. You save your colleagues real review time, because you've already caught what the engineering manager or the product marketer would have flagged before it reaches them. And you build your own understanding of the problem well enough to defend it in the room without notes. That second part is the one that compounds. More documents produced faster isn't the win condition; more judgment per document is.

Every time we talk to an AI it runs into the same wall: the model knows an enormous amount in the abstract and almost nothing about your specific situation. A model is basically the world's knowledge compressed into a zip file, it is an archeologist, an engineer and a designer all at the same time. The only way to get real use out of it is to steer that knowledge toward your task at hand, your company, your customers, and what you already tried last week and ruled out, instead of leaving it to answer in the generic.

The people who get the most out of AI stop treating every session as a blank slate and start building what's worth calling an Intelligence Harness: custom instructions that encode how they like work done, skills that package a repeatable workflow instead of re-explaining it each time, MCP servers that connect the model to the actual tools and data they use daily, a CLAUDE.md or AGENTS.md file that carries standing context and preferences from one session to the next, and eventually memory and agent frameworks that let multiple specialized agents coordinate on their behalf. None of it makes the model smarter. It all makes the model more steerable, which is what separates a good one-off prompt from a system that keeps getting better without you re-explaining yourself every time. That's what the pros do.

You don't need to build all of that right away. If you're starting from scratch, go in this order: custom instructions first, skills second, MCP servers third, only then jump into agent frameworks. First three are supported across the major agents from every big name in the industry, so the time you put into them travels with you no matter which model or tool you're using next year. Memory and full agent frameworks are worth building toward eventually, but they're also where lock-in and complexity show up first.

These two habits, reviewing with real scrutiny instead of waving things through, and building the harness that steers and contextualizes AI as much as possible so that happens by default instead of something you have to remember every time, are really the difference between using AI to chase output at the expense of quality, and using AI to be better at your job.