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This first-person sketch imagines a near-future engineering workflow in which agents generate, review, and fix code, and humans mostly triage and approve. It is an anecdote, not evidence, but it raises a concrete question: as AI produces more output, human review becomes the bottleneck, and reviewers may skim more than they realize. For AI readers, the useful signals are where agents add value (catching technical issues) and where human judgment remains essential (business logic, critical systems).
Sources coveringEffective Altruism Forum, Quantum Computing Report, LessWrong, The Verge
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

A day in the life of a 2026 software engineer

Effective Altruism Forum3 min read

Published on October 18, 2026 5:12 AM GMT I bike to work and get in at 9am. I check my code review dashboard. There are 5 PRs* to review. 2 are automated cleanups by bots, I approve them without reading them. The other 3 PRs are written by more expensive models (mostly Gemini 4), at the request of my colleagues. I open conversations to have my own agents review them, while I skim the code myself. I catch one business logic mistake. The agents catch 5 technical issues (mostly poor error handling, or inefficiency), and 3 issues that are nits I don't care about (I delete these comments). I approve all the PRs, except the one with the business logic mistake. My colleagues tell their agents to respond to my replies, and then push the results. I check my email. There is one design document someone wants me to review. It was written by AI. It is good, but very long. I skim it, and approve it on a broad strokes basis (it is not a critical part of the system, I don't need it to be exactly correct). There are 4 bugs that have been forwarded to me. 3 are automated alerts. I tell my agents to try to investigate them and create a fix. They create plausible fixes in all cases, and I send my own code out for review. I send the important fixes to my peer who reviews code within an hour, the rest to folks who take a day or two. It is now 11:30, time for lunch. I have done more work this morning than I did in a week last year. At lunch with my team, we discuss AI. The topic rotates between 'man Gemini is so silly I dont know how it could make this mistake' and 'Gemini saved more than my salary in compute, when I spent 5 minutes asking it to make one our jobs more efficient'. People repeatedly say that AI is a bubble, and the models are going to hit a wall. They say models won't get better than they are now. We got work access to Gemini 4.0 only 3 weeks ago. After lunch, I conduct an interview for an intern candidate. I tell the candidate I may need them to close their eyes and describe the algorithm (AI assistance is not allowed since Opus 5.5 would demolish the interview). They struggle with basic syntax, and seem not to have coded much recently. I haven't either. At the end of the interview, they ask me how I see AI changing the industry. I decide to be honest and say that I'm scared shitless. I don't see many parts of my job that I'm better at than the frontier models. The candidate laughs, but then says he agrees. After writing up interview feedback, I spend an hour flipping between reviewing PRs, asking agents to fix old bugs in our system, and reading Astral Code Ten. Oh, no, it was 3 hours and its 6pm. My sense of time is all wonk from flipping between contexts constantly. I go home. *I work at Waymo as a Senior Software Engineer (L5). We would call PRs 'CLs' and would generally refer to Gemini as 'Jetski', which is Google's internal version of Antigravity. Other details are largely correct. Discuss

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