You know that feeling when you finish a task with AI in 10 minutes that used to take an hour… and then immediately start three new things because now you “have time”?
Yeah, that’s not a productivity win. That’s a trap.
New research from Harvard Business Review studied how generative AI changed work habits at a ~200-person tech company over eight months. The finding? AI tools didn’t reduce workloads. They consistently intensified them.
Here’s what happened
- People took on work that wasn’t theirs. Product managers started writing code. Researchers attempted engineering tasks. AI made everything feel doable, so people just… did more.
- Breaks disappeared. Workers started prompting AI during lunch, between meetings, even right before leaving their desks (“let me send one last prompt so it can work while I’m gone”). Work became, as the researchers put it, “ambient”; always there, always possible, always tempting.
- Multitasking exploded. Employees ran multiple AI agents in parallel, revived old tasks they’d been putting off, and juggled more threads than ever. They felt productive. They also felt busier than before AI.
One engineer summed it up perfectly:
“You thought that because you could be more productive with AI, you’d save time and work less. But you don’t work less. You work the same amount or even more.”
Honestly? We felt this one personally. AI doesn’t feel like work. It feels like play. You’re not “writing a report,” you’re “chatting with Claude.” You’re not “doing research,” you’re “exploring an idea.” That friction that used to force you to take a break (waiting for a colleague, staring at a blank page, not knowing where to start) is gone.
And without that friction… you just never stop.
So what’s the fix?
Not “set better boundaries.” That’s the email advice we ignored 15 years ago.
The better answer is compound engineering, a concept from Kieran Klaassen at Every. The core idea: instead of using AI to do more work, use it to build systems where every piece of work makes the next one easier. Your codebase (or workflow) gets simpler over time, not more complex.
Here are the key principles worth stealing, even if you’re not an engineer:
- Spend 80% planning, 20% building. Most people do the opposite. But with AI, “plans are the new code”; if the plan is good, the output will be good. Brainstorm and plan thoroughly, then let AI execute.
- The 50/50 rule. Spend half your time on the actual task and half on improving the system itself (templates, prompts, reusable patterns). This is the compounding part; you’re investing in making tomorrow’s work faster.
- Build safety nets, not checkpoints. Instead of reviewing every line of AI output, build monitoring and tests that automatically catch problems. Trust the process, then verify at the PR level (for non-coders, that would be the final output before you “publish” the work)
- Let go of “code is self-expression.” This applies beyond coding. If your identity is tied to doing the work rather than directing it, you’ll resist the shift. The new skill is taste, not typing.
As for the HBR researchers, they recommend “intentional pauses” and “sequencing.” That’s fine. But compound engineering goes further: it turns the AI treadmill into a flywheel. Instead of doing more things at the same speed, you do the same things faster each time, which is what actually frees up real time.
Watch the full breakdown here (13 min). Even if you’re not a developer, the philosophy applies to any AI-assisted workflow.
Editor’s note: This content originally ran in the newsletter of our sister publication, The Neuron. To read more from The Neuron, sign up for its newsletter here.