Posted by yegg 6 hours ago
If you consider things like the machine learning filters in your smartphone camera and Google's AI Overviews for searches it's entirely plausible that the US is currently at 75%+ of AI usage.
A small group of developers at my company have set up volumes of skill.md and other instructions for the AI to write Jira tickets, then take action on those Jira tickets by writing the code. The AI submits a pull request. Then there's another AI to review the code. They've written the game plan for the AI to do all of this. All the human does now is click "approve" without even reading the PR, and then someone clicks "merge". There's no coding, no critical thinking by a human anymore except for telling the AI what to do... which really anyone at the company could do. I doubt I'll have a job at this company much longer after 8 years employed there.
If you're working in some vanishingly rare domain then maybe it's not yet, but most coding challenges are very much in the wheelhouse of the current frontier models.
I am constantly looking for a new job, but all of them are also require AI coding experience.
i am not saying it's really powerful or great. but the lure is undeniable. because of how low friction it has become.
Everybody is using LLMs/AI. All the time. It's in every facet of your life. Just because you didn't input the prompt, doesn't mean you're not consuming the end product of LLMs all day, everyday, on websites like this one, reddit, tiktok, instagram, facebook, etc.
Addressing the article, if you're hyperfocused on whether people are using AI and only consider AI use a chatbot... well, you're not honestly covering all the AI use out there. And reading the other stats, it seems like this article is trying to paint a narrative. Why is the Datos stat only considering "Desktop use" for instance.
Not to mention their stats are actually astounding and DON'T show what the headline is trying to assert. 1/3 of people using AI regularly is a FUCK TON of people in a VERY short span of time to uptake a new technology.
They are great on exploring, understanding and finding bugs in existing codebase.
They are great for simple or one time scripts/programs.
They are terrible, really terrible coders. The overengineering is so deep in their training that no matter what is your prompt, your skills or agents.md/claude.md, if you don't babysit them continuously, at some point they will just fuck up your codebase.
Software engineers are definitely in a bit of a bubble here. Are we just early adopters who see the value sooner, or does it uniquely benefit software engineering, or do we just like cool automation and we're deluding ourselves that this adds value beyond the cost?
The moment you have to interact with the physical world or humans (psychological, imaginative, aesthetic, etc), there are often undiscovered or changing rules—or no rules at all. Or systems are subject to perturbations beyond a defined scope.
The other thing I believe is software developers are experts at doing the things that allow them to make doing those very things easier and more automated. And they do this in public, perfectly documented online.
Both because of the things I described above and because software developers have created the largest machine-accessible training set for plying their trade of any trade, ML—that is ultimately interpolating massive datasets to do things—is unsurprisingly uniquely successful for software tasks.
The less popular a language, the more models struggle.
Writing, UI, and presentations have similar knowledge bases.
Outside of those, quality becomes much more hit and miss. If you ask for a recipe you may get something good, or you may get something completely inedible and random.
"Domain specific knowledge" really means "strong foundations and relevant abstractions" and LLMs just don't do that reliably.
> Computers should adapt to people. Asking people to make themselves more legible to software — to turn themselves into a database — is a doomed idea.
I've been in software a long time, and I do sort of see this trend, but I think it's because these are tools that build other tools. The interface has always been a 'best I can do for now' thing, with the focus on doing things that are useful. Computers were just calculators in the beginning, which led to more complex calculators, instruction sets, programming languages, operating systems, GUIs, interconnectivity, etc.
What people are doing today is experimenting, like they always have. They're putting their experiments out there so that others can use them and build on them. Some will use those tools to build other tools, and some won't. But over time, the experiments that work will get distilled and turn into real products that people who 'do not yearn for automation' will still want to use, so it seems like the value is there.
I guess the real question is whether they will create value that offsets the near-term costs, because I don't think the billions in investments are sustainable, and I'm not convinced the centralized data center paradigm is the right way.