A totally realistic image of my office that is totally not AI generated slop.

Anyone who knows me understands I’ve been all aboard the AI choo choo train since the early days. At work, we have something like 3,000 engineers and I’m number 4 in the entire company in terms of AI spend. It’s insane.

That said, a recent article in the Harvard Business Review highlighted something I’ve been feeling pretty deeply as of late: “AI Brain Fry” (archive link to article).

I find that I’m busier than I’ve ever been in my engineering career, juggling multiple tasks at once: build this feature in one worktree while fixing 2 separate bugs in additional worktrees, oh and we need to look at performance improvements in this other one.

My teammates are all onboard too, as we feel like we all have to maintain ridiculously high levels of output. Our jobs have changed. Coding was something I’ve always loved. I get to solve these interesting puzzles and challenges every day. Now it’s shifting to one of my personal least favorite, but most important, parts of the job: code review.

Because there is a lot of AI generated code to review.

We often joke about how many browser tabs someone has open. The joke is shifting to how many terminal tabs running multiple instances of Claude Code and Codex.

Meanwhile, we have senior leadership saying things like, “AI should improve your productivity. I’m not talking 10x. I’m talking 100x, 1,000x!” It feels like it’s all just becoming this impersonal numbers game that measures raw output above all else.

I think part of it is that our immediate team is working in scrappy startup mode, competing with a product offering from our parent company. That underdog feeling is fun and exciting. And we’re pushing boundaries and pioneering workflows that the rest of the company hasn’t embraced yet. The average engineer company-wide closes something like 2-3 Jira tickets per sprint. We’re average about 25!

But oh, man. There is so much to do and maybe 4 of us working on it. It’s crazy.

Needless to say, from the article:

Unsurprisingly, workers are finding themselves up against the limits of their cognitive abilities when working this way. In recent weeks, online AI users have described increased cognitive load, “saturated” attention, and mental fatigue in social media posts. Engineer Francesco Bonacci, founder of Cua AI, wrote a popular X post titled “Vibe Coding Paralysis: When Infinite Productivity Breaks Your Brain” in which he lamented: “I end each day exhausted—not from the work itself, but from the managing of the work. Six worktrees open, four half-written features, two ‘quick fixes’ that spawned rabbit holes, and a growing sense that I’m losing the plot entirely.”

I’ve definitely feel like I’ve been hitting a wall. What was once a blistering pace has been dialed back a bit as I try to better balance work, life, and the incessant demand for ever increasing output.

The HBR article goes on to recommend how leaders can better set expectations related to AI use:

When organizations celebrate “productivity gains” without clarifying workload implications, employees interpret this as work intensification. That ambiguity alone may increase stress. Leaders reduce strain when they clearly define AI’s purpose in the organization, articulating how it reshapes role scope, setting guidance around oversight, and clarifying how workload will evolve.

[…]

Incentivizing quantity of use will lead to waste, low quality work, and unnecessary mental strain. Start from a clear, strategic north star business objective, with measurable outcomes. Exercise caution in responding to efficiency innovation. Don’t rush to backfill work recently automated by an ingenious worker; doing so immediately will feel punitive and disincentivize further innovation.

[…]

Some of the most valuable human skills today, including discernment, decision making, and strategic planning, require focused attention. While burnout has become a point of concern in many workplaces, mental fatigue is more likely go undetected in existing workplace surveys. Organizations should evolve people analytics measures to monitor cognitive load overall, and safeguard against mental fatigue with AI use as a novel job-related risk. Cultures, teams, and leaders that prioritize cognitive thriving can expect to see better judgments, fewer errors, and higher retention rates for top talent.

Anyway, forgive me, for Claude beckons and the AI isn’t going to solve this bug completely by itself (not yet, at least…).