> a third of them were instantly converted to being very pro-LLM. That suggests that practical experience
I wasn't aware one could get 'practical experience' "instantly." I would assume that their instant change of heart owes more to other factors. Perhaps concern over the source of their next paycheck? You have admitted you just "forced" them to do this. Isn't the question then, why didn't they do it before? Shouldn't you answer that before you prognosticate?
> that junior developers will still be needed, if nothing else because they are open-minded about LLMs
You're broadcasting, to me, that you understand all of the above perfectly, yet instead of acknowledging it, you're planning on taking advantage of it.
> I think the equivalent of cruft is ignorance
Exceedingly ironic.
> Will two-pizza teams shrink to one-pizza teams
The language you use to describe work, workers, and overcoming challenges are too depressing to continue. You have become everything we hated about this profession.
If you haven't experenced a post-November-2025 coding agent before and someone coaches you through how to one-shot prompt it into solving a difficult problem in your own codebase that you are deeply familiar with I can see how you might be an almost instant convert.
(Based on your comment history I'm guessing you haven't experienced this yourself yet.)
You're right, and I enjoy using coding agents too. I've built some things with them I wouldn't have otherwise.
However, it's been a full quarter now since November 2025.
Based on facts on the ground, i.e. the rate and quality of new software and features we observe, change has been nowhere as dramatic as your comment would suggest.
It seems to me that a possible explanation is that people get very excited about massive speedups in specific tasks, but the bottleneck of the system shifts somewhere else immediately (e.g, human capacity for learning, team coordination costs, communication delays).
That "full quarter" included the Christmas holidays for many people, during which not a lot of work gets done.
I think it's a bit early to expect to see huge visible output from these new tools. A lot of people are still spinning up on them - learning to use a coding agent effectively takes months.
And for people who are spun up, there's a lot more to shipping new features and products that writing the code. I expect we'll start to see companies ship features to customers that benefited from Opus 4.5/4.6 and Codex 5.2/5.3 over the next few months, but I'm not surprised there hasn't been a huge swell in stuff-that-shipped in just the ~10 weeks since those models become available.
There is one notable example that's captured the zeitgeist: https://github.com/openclaw/openclaw had its first commit on November 25th 2025, 3 months later it's had more than 10,000 commits from 600 contributors, attracted 196,000 stars and (kind-of) been featured in a Superbowl commercial (apparently that's what the AI.com thing was, if anyone could get the page to load - https://x.com/kris/status/2020663711015514399 )
This rings true for me. Up until the end of 2025 I had my doubts. I haven't fully adopted AI, but I am using it for several side projects where I normally would not have made much progress. The output w/Claude Code is solid.
The challenges I have were selected because I enjoy solving them and because very few, if any, people have taken the time to work on them already. As such I have no desire to "one-shot" a solution and I additionally have serious doubts that any model trained on any existing code could possibly output anything useful or anything that truly fits into the design of the system. These projects are written for style and are to explore ideas and gain experience. Inviting an LLM in out of laziness is completely the opposite of my intentions.
The only other code that I write is for a handful of industry specific products that are not challenging in any way to code but are fun to design for the specific needs of my users and are informed by their incredible feedback from the field. The time and effort to play games with an LLM prompt would have effectively zero value here and again is the opposite of what makes these products great enough to be sold by word of mouth alone.
Aside from all of this I have no desire to pay a subscription to a service that requires me to submit all of my code to their engine for output. Given their models apparent fondness for taking copyrighted code and passing it off as it's own I would not put it past them to play games behind my back with my work.
Finally I see no new "AI billionaires" suddenly rising out of the field and I see no "AI heavy" companies suddenly increasing their profits, productivity or quality in any way. I hear what you are saying, and you're certainly not alone in saying it, but I see zero evidence that it's actually meaningful in the real world software market.
I would be very happy to solve problems that "very few, if any, people have taken the time to work on them already."
My experience (as someone how works with a team of PhD's) is that code is about 30% of what we do but in these, 75% are "trivial things" (building charts, quickly designing apps to process information, etc). Out of these 75%, AI certainly helps us at least 50% of the time (and amazes me 10% of the time :-))
> I see no new "AI billionaires" suddenly rising out of the field and I see no "AI heavy" companies suddenly increasing their profits, productivity or quality in any way.
Exactly what I was telling myself yesterday. That's rather not in line with the media coverage.
We need a new AI "Code Panther".
I have literally heard this exact vague phrase about every single stupid model that has come out, plus more than a few companies.
So far it's all been endless unfounded FOMO hype by people who have something to sell or podcasts to be on. I am so tired of it.
Ask around and see if you can find anyone you know who's experienced the November 2025 effect. Claude Code / Codex with GPT-5.1+ or Opus 4.5+ really did make a material difference - they flipped the script from "can write code that often works" to "can write code that almost always works".
I know you'll dismiss that as the same old crap you've heard before, but it's pretty widely observed now.
I’ve been living this experience and using latest models in work throughout this time. The failure modes of LLMs have not fundamentally changed. The makers are not awfully transparent about what exactly they change in each model release the same way you know what changed in i.e., a new Django version. But there’s not been a paradigm shift. I believe/guess (from outside) the big change you think you’re experiencing could be result of many things like better post training processes (RLHF) for models to run a predefined set of commands like always running tests, or other marginal improvements to the models and focusing on programming tasks. To be clear these improvements are welcome and useful, just not the groundbreaking change some claim.
the perimeter of the tasks the LLMs can handle continuously expands at a pretty steady pace
a year ago they could easily one shot full stack features in my hobby next.js apps but imploded in my work codebase
as of opus 4.6 they can now one shot full features in a complex js/go data streaming & analysis tool but implode in low latency voice synthesis systems (...for now...)
just depends on how you're using it (skill issues are a thing) and what you're working on