Category: Book Notes

I used to wish I could see the future. Now I’m grateful I can’t.

Look at any decade of your life: even the chapters that turned out well had moments inside them that would have terrified you if you’d been shown them ahead of time. Lived in sequence, with what came before and after, they fit. Handed to you in advance, without that context, they’d have wrecked you.

I’ve been thinking about that while reading the wave of forecasts on AI and jobs. A recent Economist piece argues AI-driven mass unemployment would be unprecedented in history. New technology has never spread fast enough to put a lot of people out of work for long, and the jobs data right now doesn’t look like the start of a collapse. The economists have a strong case. They probably have it right.

“Probably” doesn’t help you sleep well at night.

The country’s job numbers can hold steady while your particular role gets reorganized underneath you. A historical pattern can be reassuring while your specific quarter is rough. A forecast that averages across millions of workers isn’t a plan for the one career you have. Even if you could see exactly how the next twenty years played out, most of the data points along the way would be unsettling without the context of how they resolved.

Build the practice. Use the tools deliberately. Stay close to the work as it changes shape. Keep the skills that compound regardless of which scenario lands. Those engineers will be fine if the forecast holds and better positioned if it doesn’t. Engineers who wait for the forecast to settle will get clarity—but too late to act on.

Forecasts are useful for the people running policy. For the rest of us, the work is the same either way.

Several times a day, I’m typing in an AI prompt box before my brain has finished formulating the question. It’s of the quickest habits I’ve ever picked up.

Hit a confusing error message, paste it in. See a vague requirement, ask for clarification. Feel a bit stuck, “generate me five options.” Connect it to Jira, hand it your next work ticket, and ask for an implementation plan. None of those is wrong, each reasonable in isolation.

But if you never sit with a problem yourself, you atrophy the mental muscles that are the part of engineering that comes before the answer: the capacity to form a hypothesis before testing it, to feel which option is wrong before you’ve articulated why, to recognize that the actual problem isn’t the one you were asked to solve. None of that comes from prompting. All of it comes from sitting with the problem long enough for your own pattern recognition to fire.

The model is excellent at giving you answers. It’s structurally weaker at the work that happens before the question is well-formed.

So, carve out small protected pockets where you reason without the model. Fifteen minutes on a hard problem, alone, before you ask anyone—model or human—to weigh in. Sketch the shape. Try the wrong answer. Notice what doesn’t fit. Then, if you want, hand it to the model: “Here’s where I got. Critique my reasoning.”

When you bring a half-formed thought to the model, you’re treating it as a collaborator. When you bring a blank prompt, you’re treating it as a script: fill in the blanks, return the output. The second mode produces faster results. The first mode keeps your judgment alive.

You don’t need to do this all day. You need to do it sometimes, deliberately, on the problems that actually matter.

In the early 2000s, EverQuest introduced a boss with so many hit points (the game’s measure of health) it was meant to be functionally unkillable.

The math seemed airtight: no realistic group of players would ever coordinate long enough to chew through the health bar. They’d give up before they made a dent. Nearly two hundred players, fighting for almost three hours in a coordinated attack, proved the assumption wrong. The “unkillable” boss died.

The lesson shows up in systems engineering. At internet scale, “one in a billion” stops being hypothetical. With enough traffic and enough time, tail risks become real incidents.

The same thing happens with people.

From a distance, some people look invulnerable. Calm, reliable, always “fine,” laughing along when the team piles on. It’s easy to treat them like they have infinite hit points. They can take it.

Then one day they don’t. Someone burns out. Someone quits without warning. Someone has an out-of-character reaction and everyone says, “I thought they were fine.”

Nobody has infinite hit points. Not the rock on your team, not the most stoic manager, not you.

As AI tools make it easier to look fine—ship more, respond faster, always online—capacity gets harder for others to see and harder to track in yourself. The visible signal stays bright while the meter drops.

You don’t have a health bar. You have warning signs. Am I more irritable than usual? Am I rubber-stamping changes I don’t fully understand? Am I avoiding conversations because I feel overloaded? Each one is a reading you can take, if you stop long enough to look.

This works in two directions. For others: be careful what you pile on people because they can take it. For yourself: be honest about your own capacity before it runs out.

In games, you step away from the fight and let your health regenerate. In real life, that’s sleep, a walk, a conversation that isn’t about work. None of it counts as productivity. All of it counts as capacity.

There is no badge for enduring alone.

AI is supposed to make the work easier. The data so far says it isn’t.

A February Harvard Business Review piece by UC Berkeley researchers, based on an eight-month study at a tech company, found AI intensifies the work rather than reducing it: employees took on more tasks, filled former breaks with prompting, and extended work into more hours of the day. Boston Consulting Group coined a phrase for the cognitive cost, “AI brain fry,” with self-reported productivity dropping for people running four or more AI tools at once.

The hours are only part of it. The shape of the work has changed in a way that hits harder.

The tools compress the mechanical part. What’s left is the part only you can do: judgment, verification, deciding what’s actually safe to ship. That used to be threaded through hours of typing. Now it’s most of the shift.

Sustained judgment has a real cost. A 2022 study put participants through six-plus hours of demanding cognitive control tasks and measured glutamate buildup in the part of the brain that does that kind of work. (Glutamate is the chemistry of focused thought — neurons release it when they fire, and the brain has to clear it to keep working.) The high-demand group ended the day with elevated levels and made more impulsive, low-effort choices afterward. The metaphor people reach for is that the fuel runs out. The mechanism is closer to exhaust accumulating in the exact circuits doing the work, and rest is how those circuits clear.

For AI-augmented engineering, that lands somewhere specific. If you’ve moved up a level — coaching the system, owning the call, defining what good looks like — you’ve moved into the work that runs those circuits hot. Producing 10x the output doesn’t mean working 10x harder, but it does mean a higher ratio of your day is the thing that fatigues fastest.

The tools are still worth it. But it changes the math on what a sustainable shift looks like, and “just sleep less” is a punchline, not a plan.