What is Changing?
I stand continually amused and concerned at the gap between real-industry adoption of AI and the discourse online. If you survey the Internet, and ask It how the software industry is adopting AI, you will receive the entire range of answers: one corner will say "trepidatiously"; the next will tell you "carelessly"; some people believe everyone is on the adoption curve, and yet some will claim that no serious engineers touch the stuff.
Well, let me give you an accurate testimony in mid-2026:
- I do not personally know a single engineer who does not use some kind of AI tooling in the workplace.
- Almost all Enterprise shops are using Copilot, with CC/Codex usage limited or prohibited.
- Serious work with AI is done almost strictly in the process-level harnesses of Claude Code or Codex. If you are writing by hand or copy and pasting code, I have some bad news to deliver from 2025.
- Serious engineering with AI involves a lot of architecture, documentation, and a high-level understanding of traditional software practices and the SDLC. To date, there is still no accounting for taste. The gap between juniors and seniors is widening for this reason.
- Almost all serious folks embracing this movement are consuming an entire $200-price-point subscription (or multiple) every month, which is a massive subsidy compared to the rates we pay over API / enterprise billing. Generally, folks are capping their weekly limit every week. The $200 subscription point goes from looking expensive to seeming cheap pretty quickly. To most of us, it feels somewhat magical very suddenly.
If these takes, to you, are not self-evident or uncontroversial, you might not be a software professional, or you are in one of its niche extremities. There are few problems today that are tractable to a human senior, but intractable to a senior using CC/Codex under supervision or well-structured guidance.
Almost Nothing is Changing, in Theory
Yeah, not that much at least. Tone and conversation are changing faster than anything else, which is not abnormal for a hype-driven industry. The projects that I see being worked on with AI are the same projects that preceded it. The dev-teams that are adopting it are, generally, the same ones who were there prior. There are changes being made around hiring and firing, but these are pretty speculative and also hype-based (I do think some traditional junior engineers struggle right now, but you would be wrong if you thought juniors walking in with AI-nativity aren't sought after).
Dev practices are exceedingly the same, though you could argue that a change in pace so substantive constitutes a change in substance all-together. If you want to know more about my position, read on to my second post in which I defend another hot take: that the successful AI-native dev cycles are just the proven human dev cycles, and the effective process with AI is essentially just the same process every senior engineer is acquainted with already. Walking into 2026 as a senior engineer, you need very little new education to become extremely proficient with a harness and some tokens.
So Where Are We Then?
Despite stating that almost nothing is changing, it's also a time when we seem to be on the brink of massive change, so I'm not sure how to reconcile those things. This is not a piece of prophetic text, I am not telling anyone where the industry is going, and I am not capable of divining what capacities the model-providers will attain (anyone who can, including the providers, stands to make absurd amounts of money, so I think I'm in good company here).
Do You Have Advice?
Yeah, actually. Recently a prominent member of the Anthropic team said that their job has become "writing loops," and this is accurate. You should be thinking really carefully about exactly what work you do, how you do it, and how you keep it on track as a a human. Then, try to figure out an iterative approach to repeating those tasks in a cycle.
If this isn't already what you're up to, this post is a simple explanation of how you run Claude Code "in a loop," which is a fancy way to say, "with a bunch of procedure being executed and re-evaluated."
The Bottleneck is Time Management (still)
Basically, we are at a place where the time-management of your own thoughts, and the time-management of verification, have eclipsed all other inputs to software (not token cost). I would argue that this is not new, and this was actually always the case-- if you really think about the causes of SDLC failure in the past, they essentially boil down to the same things. Anyone who has ever tried to scale architecture to meet evolving client needs, or tried to scale the quality of a QA organization, or has tried to write tests for a system that was created without them-- these were problems of organization, process, and time-allocation.
If you feel differently, hit me up, I guess, and if you live in the Minneapolis or San Francisco area I'll buy you a beer and talk it out. But in terms of the online discourse, I'm not really interested in the debate, because the above is essentially a sort of "lived experience" one finds hard to disbelieve.
Proof
So if you don't believe me that this is the way that software is being made, take a look at my github repo. It's what I used to generate this entire website (no, not this post, I'm still writing my English by hand) in a single pass of Claude Code, an alltogether unremarkable feat in 2026. You can look at the core loop and the entrypoint/initial prompt.
I executed the prompt in README.md and went for a walk. Claude Code built and deployed what you're looking at. It took 2h 26m minutes.