We are producing more with AI. What we’re producing less of, apparently, is honest reflection on what that actually means. The internet is full of frameworks, prompt guides, and tutorials promising you’ll “10x your productivity overnight.” The question nobody seems to want to answer is whether any of it is actually good. Most of it is hype without a clear value proposition. This is my attempt at something more honest.
After a year of genuinely integrating AI into my daily work, not just experimenting with it, here’s what I’ve actually learned. These aren’t my original ideas. Some I picked up from people smarter than me, some I arrived at through trial and error. I can’t say I follow all of them perfectly, and it’s easy to slip when things get busy. But they’ve shaped how I try to work, and I keep coming back to them.
Lesson 1: The First and Last 10% Are Yours
I first came across this framing in a LinkedIn post by Matt Przegietka, and it clicked immediately. The idea: think of AI-assisted work in three unequal parts.
The first 10% is yours: the framing, the strategic thinking, the definition of what actually needs to happen. This is where you decide what problem you’re solving, why it matters, and what “done” looks like. AI can’t do this for you, and it’s tempting to think it can. But this is critical thinking, judgment, and human intelligence. It’s the stuff that sets everything else up for success or failure.
The middle 80% is where AI can do the heavy lifting. Research, drafting, synthesizing, iterating, generating options. All of it is on the table. This is where it genuinely earns its place in a workflow.
The last 10% is yours again: the review, the refinement, the final judgment call. Does this make sense? Is it true? Is it good? AI will produce confident, polished output that is sometimes subtly wrong, occasionally embarrassingly wrong, and sometimes genuinely excellent. You can’t tell the difference without showing up at the end.
This only works if you’re using tools that let you take outputs somewhere and do something with them. When you stay trapped in a chat window, handing everything back to the AI with each iteration, you end up in an endless loop, essentially like asking an intern to push pixels around while you hover over their shoulder. They fix one thing and break something else. You lose control of the work, and the last 10% never really happens. Getting that final stretch right means pulling the output out of the generative tool and finishing it yourself, in a space where you’re actually in charge.
The temptation is to skip the edges entirely: hand AI a vague goal, accept whatever it spits out, and call it done. I’ve done it. The results are rarely something I’m proud of, and often something I have to quietly fix later anyway.
Lesson 2: Stop Chasing the 10x Hype
I’m skeptical of the big productivity numbers I hear thrown around. Not because AI doesn’t make you faster. It does. But not in the way the hype would have you believe. A study out of Stanford and MIT found that AI tools boosted productivity for knowledge workers by about 14% on average. Real, and worth celebrating. Not a revolution, but real.
In my experience, 2x is achievable and repeatable. With the right setup and a task that plays to AI’s strengths, I can genuinely get roughly twice as much done in the same amount of time. It compounds over weeks and months.
Three times is possible sometimes, usually on narrow, well-defined tasks where I’ve done the upstream work carefully and I’m reviewing just as carefully.
Beyond that, I get skeptical. Five times starts to feel like cutting corners and rushing past the parts of work that actually matter. And 10x? If someone tells you they’re 10x more productive with AI, they’ve either redefined what “done” means or they’re not reviewing their work closely enough. My hunch is it’s probably both.
The real risk isn’t underusing AI. It’s chasing a multiplier that sets you up to produce more output in less time without actually improving the quality of what you’re making. That’s a trap. More shit, faster, should not be the goal.
Set realistic expectations. Celebrate 2x. Build the habits that make it sustainable.
Lesson 3: Avoid the Multitasking Trap
This one surprised me, not because I didn’t know multitasking was bad, but because AI made the temptation so much worse.
When you have an agent running in the background, it’s easy to think: I’ll just kick off three things at once and get ahead. So you do. You’re half-reviewing one output, half-crafting another prompt, half-reading a Slack thread. And technically, technically, you can do all three. Nothing stops you. But here’s the thing: your brain is paying for all of it, and the work shows it.
Multitasking isn’t just inefficient, it’s neurologically impossible. The brain doesn’t run two cognitive tasks in parallel; it switches between them rapidly, and every switch carries a cost. The American Psychological Association cites research showing that task-switching can eat up to 40% of productive time. Psychologist Sophie Leroy’s work on “attention residue” shows that even after you’ve moved on to the next thing, part of your brain is still stuck on the previous one. You’re never fully present on what’s in front of you.
AI doesn’t fix this. It makes it worse because the faster you can generate output, the more pressure you feel to keep all the plates spinning. The result is three tasks done poorly instead of one done well, and then extra time spent fixing all three.
My colleague Rachael Hodder wrote a helpful post on this recently. Her framing is useful—the “agent wait” is a real category of time now, and it deserves intentional use. But her most important point is buried in the last section: “The best thing you can do with the wait is stay one step ahead of the agent. Review the last output. Queue the next prompt. Plan the verification.”
That’s the key. When an agent is running, you don’t have to sit still. But if you decide to keep going, you should connect to the work at hand. Re-read what you’ve been prompting. Review the context you’ve gathered. Draft your next prompt. Prepare how you’ll verify what comes back. This keeps you in the work rather than escaping it, and it means you’ll be sharp and ready when the output lands instead of scrambling to reorient. It can also truly help to just step away and take a much-needed human break. AI is supposed to free up time in our lives, not keep us glued to a never-ending, artificial productivity cycle.
The alternative—pinging three Slack channels, opening a totally different project, catching up on email—isn’t rest, and it isn’t progress. It’s the worst of both.
Good Human Habits, Better AI Outcomes
I don’t follow these perfectly. Some days I catch myself running too many things at once, or accepting a draft I know deserves another pass, or jumping straight into prompting before I’ve actually thought through what I want. The craze around AI makes it easy to slip. There’s always pressure to move faster, do more, and squeeze another percentage point of efficiency out of everything. These lessons are my pushback against that pressure, even when I don’t manage to live up to them.
The tools are only as useful as the habits around them. Without mindful, human judgment guiding the process, all we’re really doing is automating mediocrity and making more work for ourselves.