Hard-Won Lessons From a Year of Using AI
Article summary
Lesson 1: The First and Last 10% Are Yours
Lesson 2: Stop Chasing the 10x Hype
Lesson 3: Avoid the Multitasking Trap
Good Human Habits, Better AI Outcomes
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...