I know software engineers who love writing code. They enjoy solving problems, of course. But they also enjoy the act of writing. Choosing how to represent something. Finding a simpler way to express an idea. Spending an afternoon making a messy piece of code clearer. There is pleasure in the work itself, including parts that someone watching from the outside might consider tedious.
When we tell these engineers that AI will free them to focus on the interesting work, we may be talking past them. They were already doing something they found interesting. They had also managed to find people willing to pay them to do it. That combination is easy to take for granted until it begins to come apart.
The same is true across film, music and other creative fields. A screenwriter may discover the story while rewriting a scene. An actor may find the character in rehearsal with another performer. A visual effects artist may love the painstaking work of making light and movement convincing. In his essay on AI and art, Ted Chiang argues that the small decisions made during execution matter as much as the large decisions about what to create. That resonates with me. Sometimes we only discover what we want to say through the act of making it.
I love building with AI. It has allowed me to attempt things I would previously have struggled to find the time or resources to build. I understand the excitement of watching an idea take shape in hours. But I am increasingly aware that what I am happy to delegate may be the very thing someone else loves doing. We can enter the same profession and want quite different things from our working day.
AI may separate the work someone loves from the livelihood that lets them spend their life doing it.
That possibility has stayed with me while reading the recent debate about AI and mathematics. On 11 September, Terence Tao published a declaration initially signed by 25 Fields Medallists. It argues that producing mathematical results can become disconnected from the understanding and human development that give those results their purpose. A problem is also something through which students learn, mathematicians develop ideas, and a community comes to understand more than it did before.
Timothy Gowers, in his explanation of why he did not sign, adds a distinction I find particularly useful. Mathematicians differ in what motivates them. Some put conceptual understanding first. Others are drawn to solving problems. There is room for both. We should be careful about telling someone what they must really have valued in the work they chose to do.
Imagine a mathematician who has spent decades thinking about a problem. Those years contain failed approaches, conversations, small discoveries and occasional moments when something finally makes sense. We readily recognize this as a creative life when we are talking about a novelist or a musician. Mathematics belongs in that conversation too. Now imagine an AI solves the problem overnight. Assume the solution is correct, elegant and understandable. The mathematician might celebrate the discovery and still feel a profound loss. Nobody is entitled to keep a problem unsolved (another human could have got there first). But if this happens across a field, it raises a larger question: will people still be supported in spending years doing this kind of work?
For the fortunate person who loves their profession, earning a living has made sustained attention possible. The programmer gets to spend the working week writing software. The musician gets to practice, compose, rehearse and perform. There are deadlines and frustrations and plenty of days that are no fun at all. Still, the activity they care about occupies a substantial part of their life. They do not have to find room for all of it after doing something else to pay the bills.
These livelihoods are often sustained by ordinary assignments: composing music for an advertisement, revising a screenplay, performing a supporting role or working on a film’s effects. Such jobs pay for years spent practicing a craft, alongside people who can help you improve. If AI reduces those commissions or turns them into reviewing generated material, both the income and the opportunities to practice can change.
This is why I find the reassurance that people can always continue as a hobby incomplete. Of course they can. A hobby can be deeply serious and satisfying. But telling a professional musician to make music after work quietly inserts another job into their day. That job needs time and energy. The freedom to keep playing remains, while the hours available to play may shrink dramatically. A livelihood may also have supplied collaborators, equipment and difficult assignments that helped the musician improve. Continuing alone in the evenings is a different arrangement.
Those years of paid practice also help develop the judgment we now expect people to bring to AI output. An experienced engineer can spot a fragile design or recognize that a plausible solution answers the wrong question because they have spent years building, debugging and living with the consequences of their decisions. A film editor learns to recognize why a scene drags by cutting scenes, watching them and trying again. Repetition, mistakes and feedback teach them what to look for. A task that feels routine to them may still be essential practice for someone starting out. If fewer people get to do that work, where will the next generation acquire the experience to assess what the machines produce and make it useful? We risk depending on expertise while making it harder for people to develop it.
Even keeping the job may not preserve what someone loved about it. An engineer could remain well paid, produce more software and spend most of the day directing agents. Some will enjoy that immensely. Others will miss writing code. In my essay on decision fatigue, I wrote about the strain of this new working day: long periods of intense effort interrupted by a stream of decisions. Here I am concerned with something that could remain even if the agents become completely dependable. Supervising excellent work can still feel different from doing the work yourself.
This is where I see the misalignment. An employer wants useful software delivered quickly. A customer wants a reliable product at a lower price. The engineer may want both of those things and also want to spend the day writing code. AI can meet the first two goals while making less room for the third. The efficiency the employer gains can come from taking away the part of the job the engineer most wanted to do. The people involved simply value different parts of the same activity, and the person buying the work usually has more say in how it gets done.
I don’t have a neat resolution to this. Making useful things cheaper and more accessible matters. So does opening creation to people who could not previously participate. But I would like the people doing the work to have a voice in deciding what they hand over. I would like employers to consider what makes a working day worth returning to, alongside how much it produces. And I worry about a future in which spending your days on a practice you love increasingly requires money earned somewhere else.
I keep returning to the engineer who enjoyed writing code. They had found something they wanted to get better at, and a way to support themselves while doing it. That is a lovely thing to have found. As we celebrate everything AI makes possible, I want us to take that person’s loss seriously too. The work we have made unnecessary may have been the work they wanted to spend their life doing.