What Does It Look Like When Your Job Is Done For You?
We’re building the tools but not the discipline to use them
The conversation about AI and work keeps circling the same questions. Will it take jobs? Which jobs? How fast? But there’s a more interesting question that almost nobody’s asking: if AI does your core tasks, what are you actually supposed to do?
Not in the abstract future. Right now. Because for a lot of knowledge workers, this isn’t hypothetical anymore. The tools already exist to automate much of what fills their days. The question isn’t whether the tech can do it. It’s what happens when it does.
The Obvious Answer Doesn’t Work
The natural response is: you do higher-level work. Strategy instead of execution. Creativity instead of production. Thinking instead of doing.
But this breaks down fast. Most jobs aren’t structured that way. They’re not a neat pyramid with thinking at the top and doing at the bottom. They’re a mix. And critically, a lot of the thinking happens through the doing. You don’t really understand a problem until you’re elbow-deep in trying to solve it.
When the doing goes away, something strange happens. You don’t automatically get better at thinking. You often get worse at it, because you’ve lost the feedback loop that made your thinking good in the first place.
What Executives Already Know
Here’s what’s interesting: this problem is already solved in one place. Executives don’t execute. They set direction, ask questions, and watch metrics. When something’s off, they dive into the details just enough to understand what’s broken, then pull back out.
This is exactly the position most knowledge workers are moving into with AI. You’re not in the weeds by default anymore. You’re overseeing execution, checking if it’s working, and only going deep when you need to course-correct.
The skills that matter are the same ones executives need. Knowing which metrics actually matter. Asking the right questions to surface problems early. Understanding causation well enough to know when correlation is misleading. Having enough domain knowledge to spot when something looks wrong, even if you can’t immediately articulate why.
The hard part is that most people weren’t trained for this. Executives get there gradually, building this muscle over years. But AI is compressing that timeline. You go from doing everything yourself to overseeing it much faster than the typical career path allows.
Why AI Management Needs To Be A Real Thing
Right now, most people using AI tools are doing one of two things. Either they’re micromanaging every output, essentially doing the work twice. Or they’re trusting blindly and catching mistakes too late.
Neither works at scale. The first defeats the purpose. The second is dangerous.
The gap is management discipline. We need frameworks for oversight that aren’t just “check everything” or “hope for the best.” We need metrics that actually tell you if AI is performing well on what matters, not just whether it completed tasks.
Everyone’s focused on making AI better at doing tasks. Faster models, better reasoning, longer context windows. But almost nobody’s working on the actual hard problem: how do you manage AI effectively?
What Makes This Different
Managing AI is harder than managing people in some ways, easier in others.
Harder because AI fails in unpredictable ways. People make mistakes, but you can usually spot patterns in how they fail. AI can be perfect for 1000 iterations and then completely miss something obvious on 1001. You need different error detection systems.
Harder because AI doesn’t push back. A good employee will tell you when a request doesn’t make sense or when they need more context. AI just does what you ask, even if it’s the wrong thing. You lose that feedback loop.
Easier because you can instrument everything. With people, you can’t watch their every move without destroying trust. With AI, you can log every input and output, measure every decision, A/B test every approach. The data is there. We just don’t know what to do with it yet.
Easier because iteration is fast. If you realize your approach isn’t working, you can restructure it immediately. No reorganization, no hurt feelings, no adjustment period.
The Patterns That Work
Some patterns are starting to emerge from people doing this well:
Staged verification. Don’t check everything, but don’t trust blindly. Check strategically at decision points. Like code review but for reasoning.
Output metrics that matter. Not “did AI complete the task” but “did this move us toward the goal.” Most AI tools measure completion. Almost none measure impact.
Failure cataloging. When AI gets something wrong, documenting the pattern matters more than fixing the instance. People who do this build mental models of how their AI fails, which makes them much better at catching it.
Appropriate delegation. Knowing which tasks can be fully automated versus which need human checkpoints versus which AI shouldn’t touch. This varies by domain and by person’s risk tolerance, but getting it right is the difference between force multiplication and costly mistakes.
Feedback loops. The best AI users have tight loops between output and refinement. They don’t just accept results. They use bad outputs to improve their prompts, their processes, their understanding of what AI can and can’t do.
Why This Needs To Become A Discipline
Right now everyone’s learning this independently. Trial and error. Sharing tips on Twitter. But this is too important to leave to informal knowledge transfer.
We need actual research on what works. We need frameworks that generalize across domains. We need training that teaches people how to think about AI management, not just how to write better prompts.
Think about how project management became a discipline. Or how DevOps emerged as a real field with principles and practices. AI management needs the same thing. Not just best practices, but a coherent theory of how to oversee AI work effectively.
What Changes
I think we’ll see new roles emerge. Not “AI managers” in the HR sense, but people who specialize in designing AI workflows, building verification systems, and catching failure patterns.
We’ll see new tools built specifically for AI oversight. Not just better AI, but better ways to monitor, measure, and manage what AI produces. We’ll see organizations restructure around this. Teams that used to be organized around execution will reorganize around verification and direction-setting. The skill mix will shift.
And we’ll see education catch up. Right now business schools teach management but not AI management. Engineering schools teach AI but not how to deploy it responsibly at scale. Someone will bridge that gap.
The Real Work
The people who struggle aren’t the ones who can’t think strategically. They’re the ones who can’t resist diving back into execution mode, or who don’t trust the output enough to let go. They end up doing the work twice: once by AI, once by themselves to check it.
The people who thrive are the ones who can genuinely shift into that executive mode of thinking. Set clear direction, define success metrics, spot when things are off, intervene surgically when needed.
If your job is being done for you, the answer isn’t to find more tasks. It’s to figure out what you should be thinking about instead. And to build the systems that let you know whether what’s being done is actually working.
That’s the real work now. And we’re just starting to figure out how to do it well.