When Faster Doesn’t Set You Free

The first time I used AI to code, it was honestly a shock. Suddenly I was moving ten times faster than before. Changes that used to eat up an afternoon were finished before lunch. There’s a kind of high that comes from watching pull requests drop like dominoes, backlog tickets falling as soon as you stand them up. I remember thinking, well, so this is it. I’ve cracked the problem. Got more code out, built more features, finally outpaced the to-do list.

I thought that would mean afternoons actually clearing—time for a walk, time to finally let my mind settle for once. Maybe even end a week with less on my plate than I started with. You picture yourself finally living instead of chasing fires, and you start thinking about how to prioritize before automation so the extra space actually sticks.

But the promise never arrived. Every bit of room I made just filled up. No one told me to do this. It’s like the world has an auto-expand feature the second you free up minutes. Each productivity boost quickly becomes the new normal. Deadlines shrink, projects multiply, and complexity piles up, but resources stick to the same limits. It hit me: AI didn’t free me. It just gave me more rope. That’s not the future I signed up for.

So if you’re thinking that more speed alone will buy you space, here’s the thing. Prioritize before automation by prioritizing non-negotiable outcomes, or all you get is more work, not more life.

A Simple Framework: Defined vs. Messy Work

There was a recent piece making the rounds—nothing flashy, but the concept stuck with me. Intelligence, at least by most definitions, solves well-defined problems. Clear tasks, stable rules, obvious success criteria. You hand AI the box, tell it the boundaries, and let it run.

But the challenges that actually shape work and teams? That’s where things get murky. Most value-heavy decisions are poorly defined problems. Messy, open-ended, the sort of thing that never comes with a checklist. Even official frameworks push teams to weigh unique generative AI risks—and recommend actions that actually fit their real goals and priorities. You can automate what’s mapped out, but the work that matters tends to resist that clarity. If it feels complicated because it involves people, tradeoffs, or values, it probably isn’t meant for AI to decide.

This distinction shows up every day in engineering. Fixing a bug is well-defined. You know exactly what’s broken and what working looks like. Deciding which bugs matter, or which refactors are worth investing in? That’s full of ambiguity. Refactors are clear. Roadmaps, not so much.

Here’s the personal truth. I always gravitate to optimizing the stuff that’s already mapped out, postponing the hard, ambiguous choices. I code faster, clear space—and then end up asking, “How do I actually use the space I’ve created?” That’s the part AI can’t resolve.

Protecting Human Judgment: How to Prioritize Before Automation

Start here. Set your priorities and boundaries before you throw acceleration at your workflow. Speed isn’t the lead variable. Clarity is.

Now, what do I mean by boundaries? Make three non-negotiables explicit in your life. They’re not theoretical. They need calendar real estate and show up in your actual commitments. For me, it’s health, deep work, and keeping relationships alive. Health means blocking out time to actually move or step away—no Slack exceptions. Deep work isn’t just “get things done.” It’s time for thinking that doesn’t get interrupted by dings, meetings, or context switches. And a system to defend time for deep work.

Relationships? That’s leaving enough room for check-ins that aren’t rushed. You can pick your own, but whatever holds the most value shouldn’t get traded for one more ticket. Framing cuts down back-and-forth. When you calendar your priorities, treat them as the frame for everything else. I keep my most critical blocks in system reminders, color-coded, non-cancellable unless there’s fire.

Ambiguous work—the stuff that AI can’t automate—gets special handling. Block out short sessions on your schedule for human judgment before automation, just for making decisions. Not for cranking tasks out, but for sitting down, eyeballing the options, and settling what actually matters that week. These are 20-minute standalones, not endless marathons. Put them on recurring, and only use them for the hard calls.

Six months ago I was convinced these boundaries would make me less responsive. Kept checking my inbox like I was waiting for a bomb. Here’s the reframe. Boundaries don’t mean being unavailable or unreliable. They put a fence around the stuff that keeps you sane and sharp. If you draw lines and communicate them clearly, you don’t lose trust. You build it. Most teams will respect a system if they see you stick to it and still deliver on what really matters.

If you need help sticking the landing with your team, use written agreements for service-level response times, escalation windows, or even on-call rotations. Try scripts like: “I protect these hours for judgment work—urgent asks get a flagged window every Friday, everything else waits.” Loop back on this regularly, and tweak as needed. It’s the only way boundaries last longer than a sprint.

Matching the Tool to the Task: Living With the Framework

Here’s the rule I come back to, week after week. If you prioritize before using AI, anything that’s clear, bounded, and has a right answer—send it to the AI within a judgment-centered workflow design that keeps constraints and audits in place. If the edges are blurry or if you feel that sinking “I’m not sure what ‘done’ looks like” in your gut, keep it human. When a task isn’t clear, the first investment needs to be your judgment, not automated speed. That’s not about hoarding the messy stuff, it’s about making sure you’re actually solving the right problem.

For example: generating a prompt library, automating basic test scaffolds, or even rewriting chunks of code—I hand those to AI without hesitation. Documenting code or writing new functions—classic examples of AI for well-defined tasks—often takes about half the time with generative AI on standby, according to McKinsey. But the second I’m setting priorities for the sprint, structuring a refactor that cuts across teams, or facing a strategy call, I stop. That’s the ambiguous territory. For me: “How do I get 10x more done?” is well-defined. “What actually needs doing?” never is.

If you need a mental reset, think bigger for a second. I remember watching coverage of polio eradication and hearing proclamations about computers in every pocket—big headlines that made focus seem simple. Now, everyone’s racing toward AGI like it’s the finish line and assuming that whoever gets there first will clean up everything downstream. But hype always loops back around to reality. It turns out the hard part isn’t superintelligence, it’s actually aiming the thing. Most of what eats our time isn’t about shortage of tools, but shortage of defined targets.

And it’s not just code or product strategy. Even in the most basic corners of my life, the pattern repeats. Used AI to convert a vague goal like eating healthier into a concrete plan—full grocery list, weekly meal plan, even nutrition summaries. Still ended up eating noodles over the sink twice that week. The bottleneck wasn’t lack of information or options. It was the discipline to execute. So, clarity matters, but don’t kid yourself that automation means results.

Every Monday, I do three things that center on how to prioritize before automation. Set my non-negotiable priorities, batch the clearly defined tasks for AI, and block off calendar time for human judgment—strategy, trade-offs, or anything squishy. That “no net-new reclaimed-time rule” isn’t just aspirational. It’s a standing appointment. Guard your gains, or you’ll lose them before you enjoy them. Still, I haven’t found a way to stop that reclaimed time from evaporating, instantly filled by the next urgent ask or shiny new to-do. Maybe that’s just how brains are wired. I’d like to crack it, but for now, the tension’s real.

Elevating Efficiency to a Team Habit

If you want these boundaries to stick, you need more than personal willpower. Set up team agreements that set boundaries for AI. Service-level expectations, firm decision windows, recurring focus days, and clear guardrails for AI use—plus an explicit path for real escalations. These aren’t just policies. They’re rituals that keep everyone clear on what must be preserved and when it’s actually safe to disrupt.

So, draw your lines first—and only then let the AI stretch your horizons. That’s how you keep the room you fought to create.