The Difference Between a Job and Work
And why automation just forced the issue.
Previously on Giuseppe’s Glimpse: In the last episode, we explored 5 and a half ways to constructively dissent. Missed it? Catch up here! ✨
Buongiorno everyone 👋
I was on a call last week with a client who kept using the word “optimize“ to describe how they wanted to use AI. Optimize the workflow. Optimize the hiring process. Optimize the decision-making. And I kept thinking: optimize for what? Speed? Cost? Efficiency?
The question underneath that is: what are you actually optimizing?
Because if you’re trying to optimize something that’s just a transactional task, you optimize for speed and cost. But if you’re trying to optimize something that someone experiences as work, something bound up in how they see themselves, then optimizing purely for efficiency might be completely wrong. You’d be solving for the wrong thing entirely.
As Seth Godin has recently suggested, this all comes down to something we rarely talk about: the difference between a job and work.
A job is transactional. You do X, you get paid Y. It has a description, deliverables, measurable output. You can hand it off and make it more efficient.
Work is different. It’s what you do when the doing itself matters to you, when it’s connected to how you see yourself or what you care about.
A surgeon can perform a job: run through a procedure, execute it flawlessly. But the moment they genuinely care about the person on the table, when they’re making real judgments about trade-offs that no protocol covers, when they’re carrying actual responsibility, that’s work.
It’s the same with a teacher who notices something’s wrong with a student before anyone else does, or a craftsperson who cares about the quality of something no one will ever inspect.
Where it gets tricky
The complicated part is that these things aren’t always separate.
Sometimes a job becomes work through repetition and care. Sometimes something that starts as pure drudgery, like running reports, processing data, gradually accumulates meaning as you get better at it and understand what it actually means.
And of course this also works the other direction. Work can gradually become just a job. The thing that mattered starts feeling like routine, the passion fades and you’re left executing the task without the engagement.
And this brings us to the problem we face today. Right now, as organizations are figuring out what to automate, they’re not really distinguishing between these.
They’re asking: what can AI do faster and cheaper? And the answer to that question doesn’t really take into account whether what you’re automating is a job or work.
Why this is important
When you automate something, you’re removing more or less from people’s lives depending on what that something is.
Some things are just jobs. They’re repetitive, exhausting, something you endure. For those people, automation is a relief. It would be patronizing to pretend that everything that feels like drudgery is secretly noble.
Then there’s a middle ground. Those activities that start as routine and gradually accumulate meaning through mastery and engagement. You run reports and eventually understand what they mean. You answer customer questions and develop genuine care.
Again, the reverse happens too. Something you used to care about becomes mechanical. The engagement fades and it’s just the task. These in-between things are easy to overlook when you’re automating, but they matter because the meaning isn’t fixed, it depends on how you’re doing the work.
Finally, there are the things where doing it is part of who you are. A radiologist who spent years developing diagnostic intuition doesn’t just lose a job if an algorithm surpasses their technical skill. They lose something about how they understand themselves.
The problem is that the tendency today is to automate without asking which category something falls into. Lots of organizations just optimize for efficiency across all three without asking what they’re actually removing.
The blind spots in this argument
I need to be honest about the limitations of this thinking, though.
First, there’s a privilege embedded in it. The ability to distinguish between job and work, to care about meaning in your labor, is largely a luxury of people whose work already contains it.
For someone working multiple jobs to survive, the philosophical concern about losing meaning can sound like a complaint from people who’ve never had to choose between meaning and rent.
Second, meaning is fluid and deeply personal. Humans adapt quickly. We build profound identities around things that didn’t exist twenty years ago: software engineering, social media management, digital marketing.
What matters to one person might not matter to another. The work of tomorrow might look completely unrecognizable to us today. Which means you can’t just set universal rules about what’s meaningful and what’s not.
The answer changes depending on who’s doing the work and when. We might lose less and gain more than we think, or the opposite. The point is we can’t know in advance.
So what’s actually at stake?
The real risk isn’t that AI takes over human activity. We’ve been automating things for centuries.
The real risk is that we optimize for efficiency without ever asking what we’re optimizing for. Organizations that treat AI as purely a cost-reduction machine will end up with environments where people feel like machine supervisors rather than people doing something that matters.
Organizations that actually ask how AI can amplify human judgment and care rather than just replace it build something different.
At an individual level, it means asking yourself honestly: as more of what I do gets delegated or automated, what’s left that I would actually call my work? And am I investing in it, or just hoping it takes care of itself?
The thing about work that actually matters to people is it’s never just about the task. It’s about doing something you’re genuinely invested in, something you care about. That’s what’s at stake when we automate things away without thinking about which things those are.
Stay curious 🙌
-gs
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