It was. That's when I stopped coding most things by hand. The difference in what I could reliably get AI to do for me in February 2025 vs February 2026 is just massive. I immediately became a huge advocate amongst my peers for going all-in on automated engineering because this curve is about to get very steep and if you're not staying ahead, you might get left behind.
There are two types of AI users: those who chase the rabbit and those who do not. Some people bounce from tool to tool QuantumLeap-style hoping that maybe the next tool will be the big thing to solve all their issues. Other people use AI to do actual work. Once they find a tool that works, they stick with that tool until they feal a need to upgrade.
It is like people fishing. Some people go out and catch fish with the tools they know will work. For other people, every day at the lake requires a new boat/rod/lure. They spend more time figuring out how to use their new toy than they do catching fish.
AI is becoming a more and more powerful lever that lets you move greater loads per application; it is in a real sense giving you more leverage. But the lever is difficult to use effectively, and the methods for using the level change every week or so.
> methods for using the level change every week or so
hmm doesn't mean noob will become better? if what I'm "learning" about AI is going to get deprecated every week... then it doesn't make sense to "keep up"
Here's how I think about it. There's two different things "knowing how to use AI effectively" and "knowing how to leverage effective AI to improve your project".
1. Both are multipliers on skills you already have. A "noob" will be at a disadvantage.
2. The second one is a complex blend of technical skill, domain knowledge and human factors (knowing your user-base, knowing your product/project, understanding UX/DX/whatever) that you probably will always have an edge on.
Because improved models seem to increase, not decrease, the gap between what you get from really expert supervision (prompting, steering, hand-corrections along the way, etc.) vs what you get from fairy-dust and wishes.
It requires skill to prompt the AI to do best possible work. I have senior colleagues that produce horrible slop and others that always produce great results. Managing the context, knowing when to stop the AI. Knowing what to question, what to "trust" in is a big deal.
Also knowing what models are good for what. There was time half a year ago when Google genuinely had a better model than everyone else. I used it for everything and 3 weeks later they lobotomise it (sorry "optimised") and I went back to opus...
I think Anthropic is like a drug dealer, giving us the sweet sweet drug for free (I don't think our $200 a month subscriptions even cover the electricity for our use) and the time to pay will come very soon...
I expect this subscription will cost $2k a month. Will it be a normal increase? Or will the "enshittify" existing models to the point you'll pay $2k to get fable 6 to do what opus 4.8 did fine in July 2026?
I was referring to the amount of people in the future who might be employed at today's engineering wages, and the differential between those people and the average employed engineer today.
What will that differential be? A large component will be social reinforcement: generational wealth, connectedness, and such. Things like merit might take more of a backseat. So people who do not have the necessary social capital may be fighting each other for very limited amounts of positions.
That is the steepening of the curve for people like me: I was homeless at 16 and finished high school on my own, was given a full ride to LSU, grants, room and board and a job in the Comp Sci department, but lost all of it after an immature and vindictive high school teacher illegally modified my grade in a core class in order to fuck me over. I didn't have parents to back me up at the school board and make things right.
Instead I suffered through years of homelessness and had to find my own path into the industry by starting companies with friends and doing all of the engineering. Since then I've led multiple teams, made some connections, shipped a lot of cool stuff and bring to the table a wealth of experience and a generalist skillset that is both wide and deep. Yet, I too wonder where my place in this changing industry will be once things settle a bit. Probably less engineering and more focus on business development.
The flipside though is as you've said: The fruits of engineering are more accessible than ever to the layman, and individuals can currently possess an unprecedented amount of agency and leverage. I think that is amazing and am fully behind it. I do know that it means the process of renormalization is going to be very rough, given the similarly unprecedented rate of industry change these technologies are bringing.
I think this is both true and untrue. For a long time, I was an AI skeptic, perhaps even a hater. A chauvinist for writing code by 'hand.' But it's become very difficult, as I've gradually migrated to AI-authoring of code, to go back to writing code by hand and retain the same velocity.
Part of this is certainly that my hand-authoring code skills have atrophied, sure, but my workflow has also radically changed. Previously, I would spent a lot of time and focus on a single work item, and only context switch to other tasks whenever I would wait on CI or a long build. It meant that I spent a lot of time understanding one thing at a time, and interruptions (forced context switches) incurred a massive switching cost.
Now, having moved to largely AI-authored code, I find myself necessarily working on multiple threads at the same time. This means I can meaningfully progress each of those threads in parallel, with a much-reduced overhead on context switching, since I don't have my head down focusing on all the details of the work. And if the task really demands it, I can still stop and focus on one thread to sketch out the code manually, think about the concepts more deeply, etc.
It's a very different workflow, and there are certainly downsides, but the upside is that the rate of which I've been able to put up good-quality PRs has measurably increased. It's not quite 2x, and it's certainly not 10x, but it's definitely noticeable. I do understand a bit less, but there was always more work than time to understand things in full detail. I guess only time will tell if that missing understanding was actually vital to the long-term success of my work.
yes that's fine, i have a similar experience. but there is a difference between saying what you did, and the type of AI religious fundamentalism where you got "converted" when truely good AI coding models revealed themselves to you, and then you go around trying to save those who would otherwise be "left behind".
Well it's a good thing that isn't what I said or implied, and that you instead smugly misinterpreted my comment.
The person you're replying to had the decency to ask me for clarification; for you, I'd suggest brushing up on your reading comprehension and learning to respect the rules of this site, which include interpreting comments in their best light and focusing on positive, substantial contributions instead of negative, inflammatory posts.
Again, I'm going to refer to the advice I just laid out for you. I'm not responsible for you and I don't need to cater my words to your lack of reading comprehension.
