Here’s the thing from the skeptic perspective: This statement keeps getting made on a rolling basis. 6 months ago if I wasn’t using the life-changing, newest LLM at the time, I was also doing it wrong and being a luddite.
It creates a never ending treadmill of boy-who-cried-LLM. Why should I believe anything outlined in the article is transformative now when all the same vague claims about productivity increases were being made about the LLMs from 6 months ago which we now all agree are bad?
I don’t really know what would actually unseat this epistemic prior at this point for me.
In six months, I predict the author will again think the LLM products of 6 month ago (now) were actually not very useful and didn’t live up to the hype.
Consider that what you're reacting to is a symptom of genuine, rapid progress.
> An exponential curve looks locally the same at all points in time
If your curve is continuous, it is locally linear.
There's no use in talking about the curve being locally similar without the context of your window. Without the window you can't differentiate an exponential from a sigmoid from a linear function.
Let's be careful with naive approximations. We don't know which direction things are going and we definitely shouldn't assume "best case scenario"
The crying wolf reference only makes sense as a soft claim that LLM’s better or not, are not getting better in important ways.
Not a view I hold.
LLMs get better over time. In doing so they occasionally hit points where things that didn't work start working. "Agentic" coding tools that run commands in a loop hit that point within the past six months.
If your mental model is "people say they got better every six months, therefore I'll never take them seriously because they'll say it again in six months time" you're hurting your own ability to evaluate this (and every other) technology.
Yes, but other smart people were making this argument six months ago. Why should we trust the smart person we don't know now if we (looking back) shouldn't have trusted the smart person before?
Part of evaluating a claim is evaluating the source of the claim. For basically everybody, the source of these claim is always "the AI crowd", because those outside the AI space have no way of telling who is trustworthy and who isn't.
In general, part of being an effective member of human society is getting good at evaluating who you should listen to and who is just hot air. I collect people who I consider to be credible and who have provided me with useful information in the past. If they start spouting junk I quietly drop them from my "pay attention to these people" list.
Today it works, it didn't in the past, but it does now. Rinse and repeat.
You write like this is some grand debate you are engaging in and trying to win. But to people on what you see as the other side, there is no debate. The debate is over.
You drag your feet at your own peril.
Really think about it and ask yourself if it's possible that AI can make any, ANY work a little more efficient?
I've been using Cline and it can do a few of the things suggested as "agentic", but I'd have no idea how to leave it writing and then running tests in a VM and creating a PR for me to review. Or let it roam around in the file tree and create new files as needed. How does that work? Are there better tools for this? Or do I need to configure Cline in some way?
I actually do most of my "agentic coding" (not a fan of the term, but whatever) in ChatGPT Code Interpreter, which hasn't changed much in two years other than massive upgrades to the model it uses - I run that mainly via o4-mini-high or o3 these days.
OpenAI's Codex is a leading new thing, but only if you pay $200/month for it. Google's equivalent https://jules.google/ is currently free.
GitHub Copilot gained an "agent mode" recently: https://github.blog/ai-and-ml/github-copilot/agent-mode-101-...
There's also Copilot Coding Agent, which is confusingly an entirely different product: https://github.blog/changelog/2025-05-19-github-copilot-codi...
If you ask different people the above question, and if you vary it based on type of task, or which human, you would get different answers. But as time goes on, more and more people would become impressed with what the human can do.
I don't know when LLMs will stop progressing, but all I know is they continue to progress at what is to me a similar astounding rate as to a growing child. For me personally, I never used LLMs for anything, and since o3 and Gemini 2.5 Pro, I use them all the time for all sorts of stuff.
You may be smarter than me and still not impressed, but I'd try the latest models and play around, and if you aren't impressed yet, I'd bet money you will be within 3 years max (likely much earlier).
In this context, never. Especially because the parent knows you will always ask 2+2 and can just teach the child to say “four” as their first and only word. You’ll be on to them, too.
On the assumption that you'll always only ask it "what's 2+2?" Keywords being "always" & "you".
In aggregate, the set of questions will continuously expand as a non-zero percentage of people will ask new questions. The set of questions asked will continue to expand, and the LLMs will continue to be trained to fill in the last 20%.
Even under the best interpretations, this is the detractors continuously moving goalposts, because the last 20% will never be filled: New tasks will continuously be found, and critics will point to them as "oh, see, they can't do that". By the time that the LLMs can do those tasks, the goalpost will be moved to a new point and they'll continue to be hypocrites.
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> > At what point would you be impressed by a human being if you asked it to help you with a task every 6 months from birth until it was 30 years old?
Taking GP's question seriously:
When a task consisting of more than 20 non-decomposable (atomic) sub-tasks is completed above 1 standard deviation of the human average in that given task. (much more likely)
OR
When an advancement is made in a field by that person. (statistically much rarer)
Using Claude Sonnet 4, I attempted to add some better configuration to my golang project. An hour later, I was unable to get it to produce a usable configuration, apparently due to a recent v1-to-v2 config format migration. It took less time to hand-edit one based on reading the docs.
I keep getting told that this time agents are ready. Every time I decide to use them they fall flat on their face. Guess I'll try again in six months.
I made the mistake of procrastinating on one part of a project thinking "Oh, that is easily LLMable". By God, was I proven wrong. Was quite the rush before the deadline.
On the flip side, I'm happy I don't have to write the code for a matplotlib scatterplot for the 10000th time, it mostly gets the variables in the current scope that I intended to plot. But I've really not had that much success on larger tasks.
The "information retrieval" part of the tech is beautiful though. Hallucinations are avoided only if you provide an information bank in the context in my experience. If it needs to use the search tool itself, it's not as good.
Personally, I haven't seen any improvement from the "RLd on math problems" models onward (I don't care for benchmarks). However, I agree that deepseek-r1-zero was a cool result. Pure RL (plain R1 used a few examples) automatically leading to longer responses.
A lot of the improvements suggested in this thread are related to the infra around LLMs such as tool use. These are much more well organised these days with MCP and what not, enabling you to provide it the aforementioned information bank easily. But all of it is built on top of the same fragile next-token generator we know and love.
You can give it the docs as an "artifact" in a project - this feature has been available for almost one year now.
Or better yet, use the desktop version + a filesystem MCP server pointing to a folder containing your docs. Tell it to look at the docs and refactor as necessary. It is extremely effective at this. It might also work if you just give it a link to the docs.
I threw in the towel and had a working config in ten minutes.
The article doesn't explicitly spell it out until several paragraphs later, but I think what your quoted sentence is alluding to is that Cursor, Cline et al can be pretty revolutionary in terms of removing toil from the development process.
Need to perform a gnarly refactor that's easy to describe but difficult to implement because it's spread far and wide across the codebase? Let the LLM handle it and then check its work. Stuck in dependency hell because you updated one package due to a CVE? The LLM can (often) sort that out for you. Heck, did the IDE's refactor tool fail at renaming a function again? LLM.
I'm remain skeptical of LLM-based development insofar as I think the enshitification will inevitably come when the Magic Money Machine breaks down. And I don't think I would hire a programmer that needs LLM assistance in order to program. But it's hard to deny that it has made me a lot more productive. At the current price it's a no-brainer to use it.
(I should know since I've created half-a-dozen tools for this with gptel. Cline hasn't been any better on my codebase.)
otherwise, yes, you'll continue to be irritated by AI hype, maybe up until the point where our civilization starts going off the rails
- they can't be aware of the latest changes in the frameworks I use, and so force me to use older features, sometimes less efficient
- they fail at doing clean DRY practices even though they are supposed to skim through the codebase much faster than me
- they bait me into inexisting apis, or hallucinate solutions or issues
- they cannot properly pick the context and the files to read in a mid-size app
- they suggest to download some random packages, sometimes low quality ones, or unmaintained ones
That's mostly solved by the most recent ones that can run searches. I've had great results from o4-mini for this, since it can search for the latest updates - example here: https://simonwillison.net/2025/Apr/21/ai-assisted-search/#la...
Or for a lot of libraries you can dump the ENTIRE latest version into the prompt - I do this a lot with the Google Gemini 2.5 models since those can handle up to 1m tokens of input.
"they fail at doing clean DRY practices" - tell them to DRY in your prompt.
"they bait me into inexisting apis, or hallucinate solutions or issues" - really not an issue if you're actually testing your code! I wrote about that one here: https://simonwillison.net/2025/Mar/2/hallucinations-in-code/ - and if you're using one of the systems that runs your code for you (as promoted in tptacek's post) it will spot and fix these without you even needing to intervene.
"they cannot properly pick the context and the files to read in a mid-size app" - try Claude Code. It has a whole mechanism dedicated to doing just that, I reverse-engineered it this morning: https://simonwillison.net/2025/Jun/2/claude-trace/
"they suggest to download some random packages, sometimes low quality ones, or unmaintained ones" - yes, they absolutely do that. You need to maintain editorial control over what dependencies you add.
This is where collaboration comes in play. If you solely rely on the LLM to “vibe code” everything, then you’re right, you get whatever it thinks is best at the time of generation. That could be wrong or outdated.
My workflow is to first provide clear requirements, generally one objective at a time. Sometimes I use an llm to format the requirements for the llm to generate code from. It then writes some code, and I review it. If I notice something is outdated I give it a link to the docs and tell it to update it using X. A few seconds later it’s made the change. I did this just yesterday when building out an integration with an api. Claude wrote the code using a batch endpoint because the steaming endpoint was just released and I don’t think it was aware of it. My role in this collaboration, is to be aware of what’s possible and how I want it to work (e.g.. being aware of the latest features and updates of the frameworks and libraries). Then it’s just about prompting and directing the llm until it works the way I want. When it’s really not working, then I jump in.
of course they can, teach them / feed them latest changes or whatever you need (much like another developer unaware of the same thing)
they fail at doing clean DRY practices even though they are supposed to skim through the codebase much faster than me
tell them it is not DRY until they make it DRY. for some (several projects I’ve been involved with) DRY is generally anti-pattern when taken to extremes (abstraction gone awry etc…). instruct it what you expect and it and watch it deliver (much like you would another developer…)
they bait me into inexisting apis, or hallucinate solutions or issues
tell it when it hallucinates, it’ll correct itself
they cannot properly pick the context and the files to read in a mid-size app
provide it with context (you should always do this anyways)
they suggest to download some random packages, sometimes low quality ones, or unmaintained ones
tell it about it, it will correct itself
yes. this happens to me almost every time i use it. I feel like a crazy person reading all the AI hype.
All of the state-of-the-art models are online models - you have no choice, you have to pay for a black box subscription service controlled by one of a handful of third-party gatekeepers. What used to be a cost center that was inside your company is now a cost center outside your company, and thus it is a risk to become dependent on it. Perhaps the risk is worthwhile, perhaps not, but the hype is saying that real soon now it will be impossible to not become dependent on these closed systems and still exist as a viable company.
For coding it seems to back itself into a corner and never recover from it until i "reset" it .
AI can't write software without an expert guiding it. I cannot open a non trivial PR to postgres tonight using AI.
100% true, but is that really what it would take for this to be useful today?
Granted I was trying to do this 6 months ago, but maybe a miracle has happened. But I'm the past I had very bad experience with using LLMs for niche things (i.e. things that were never mentioned on stackoverflow)
I have no way of evaluating these myself so they might just be garbage slop.
But for each nine of reliability you want out of llms everyone's assuming it's just a linear growth. I don't think it is. I think it's polynomial at least.
As for your tasks and maybe it's just cuz I'm using chat GPT, but I asked it to Port sed, something with full open source code availability, tons of examples/test cases, a fully documented user interface and I wanted it moved to Java as a library.
And it failed pretty spectacularly. Yeah it got the very very very basic functionality of sed.
I've tried everything. I have four AI agents. They still have an accuracy rate of about 50%.
Tell me about this specific person who isn't famous
Create a facebook clone
Recreate Windows including drivers
Create a way to transport matter like in Star Trek.
I'll see you in 6 months.
People making arguments based on sweeping generalizations to a wide audience are often going to be perceived as delusional, as their statements do not apply universally to everyone.
To me, thinking LLMs can code generally because you have success with them and then telling others they are wrong in how they use them is making a gigantic assumptive leap.
Dude, just try the things out. It's just undeniable in my day-to-day life that I've been able to rely on Sonnet (first 3.7 and now 4.0) and Gemini 2.5 to absolutely crush code. I've done 3 side projects in the past 6 months that I would have been way too lazy to build without these tools. They work. Never going back.
I tried Copilot a few months ago just to give it a shot and so I could discuss it with at least a shred of experience with the tool, and yea, it's a neat feature. I wouldn't call it a gimmick--it deserves a little more than that, but I didn't exactly cream my pants over it like a lot of people seem to be doing. It's kind of convenient, like a smart autocomplete. Will it fundamentally change how I write software? No way. But it's cool.
The top of SWE-bench Verified leaderboard was at around 20% in mid-2024, i.e. AI was failing at most tasks.
Now it's at 70%.
Clearly it's objectively better at tackling typical development tasks.
And it's not like it went from 2% to 7%.
The pressure for AI companies to release a new SOTA model is real, as the technology rapidly become commoditised. I think people have good reason to be skeptical of these benchmark results.
But there's a plenty of people who actually tried LLMs for actual work and swear they work now. Do you think they are all lying?..
Many people with good reputation, not just noobs.