> This is such a weird point to make

I think it is a great point to make, because if everyone really believed that AIs will do everything without human intervention in a handful of years, as the marketing repeats again and again (AGI, singularity, etc.) and have been saying for years... why then get bothered?

Because we DO know LLMs have their hallucinations, limitations, perform tasks not previously seen way worse than humans, etc. And it seems that, for now, there is not a good or magic solution to it, it is inherent limitations of the paradigm.

Yes, you can feed more and more and more (curated data) and eventually make AIs excellent at task X or Y, but then you spend your time specializing those engines. So the work does not really disappear, it just shifts and you make it more replicable for a bound set of problems.

Needless to say that at some point I prefer to learn (and combine with AIs, it is ok) than acritically getting inputs from something until I become totally useless.

Unless we have a paradigm for which a fully autonomous AI can do everything, this will just become improving our productivity in some ways, with all the in-between bottlenecks that it has.

I think you meant to post this in response to https://news.ycombinator.com/item?id=49174900?

Same reason some think preserving the environment is pointless because the believers will ascend to heaven, either way. It’s a religion. It’s dogmatic nihilism.

> I, as the human, still have to do the thinking as Claude still 'can't jump'

I still have to do quite a bit of thinking but the amount of of thinking I do per task is trending down. I agree LLMs are not good at abduction but very few humans are either and very few jobs/tasks require it. I can't talk for researchers jobs though. But perhaps fewer researchers would be desired by these labs (not none).

although, if i'm out of tokens and have to wait a full day, i won't bother doing some things manually because the day i'll spend doing something won't take more than 1 hour the next day when tokens are available again.

A fully automated utopia isn't just going to happen. Even with frontier models, the integrations, the evals, the UX, need a lot of work and someone needs to do it. After I've automated this thing I'll move on to the next task, this is what it means to be a software engineer.

Robot powered factories still have humans there, the gist is that they are a tiny fraction of what a classical factory would require 50 years ago.

AI has made 'jumps' in demanding fields like leading mathematical research and has made advancements in AI research itself. Is now a good time to start a maths career? Is there a field of research (yours?) which is inherently (more) AI proof?

Btw, I think the discussion of Einstein's career in the paper you link is historically wrong in many respects, particularly the argument about 'weak signal'. Einstein was in fact working on some of the most mainstream and widely discussed problems in physics of the day, he is admired for the creativity of his solutions to those problems, and much of his work built incrementally on ideas and breakthroughs that came (long) before (as all research does).

Article suggests that a central motivation of Einstein's work was resolving action-at-a-distance in Newtonian mechanics - yet Maxwell introduced the same Lagrangian field theories for electromagnetism we use today 50 years earlier to solve the same problem for Farraday's laws of electromagnetism. Similar wave equations existed even earlier. Heaviside in 1893 extended this technique to gravity (matching 'weak field' GR) 20 years earlier. So this is perhaps the one aspect of gravity that had actually already been solved before Einstein. Authors might be conflating his work on action-at-a-distance in QM.

Einstein's GR extended the linear 'weak field' understanding of gravity to include the non-linear self-referential case where masses themselves create gravity. This was mathematically incredibly difficult but was necessary precisely because SR's mass energy equivalence created so many strong signals that were unresolved. For example: if finite energy is mass, then mass changes as objects accelerate past a large mass like a start, and hence their propagation in space could not be explained by linear EM style field equations. Many such considerations were causing very 'strong signals' in SR, and there were analogous problems in QM atomic models being developed at the same time.

SR was also a solution to a problem that was actively being worked by many of the leading physicists of the day. SR actually does match Newtonian mechanics for a single observer - it resolves contradictions in the case of separate observers, by allowing them to assign different values to the speeds, masses, etc of objects such that each object appears to follow Newtonian mechanics for each observer. Again, this was necessary because of a lot of contradictions related to the behavior of light that had been well-known for ~20 years at the time.

Personally, I don't consider this kind of reasoning to be beyond the capabilities of future LLMs (even current LLMs if the task was broken into technical rather than philosophical problems). Personally, I doubt that such problems could stand open for 20+ years waiting for a creative genius to solve them in the modern world.

And don't get me started on the philosophy.

Sorry where in the article does it make this point? I cannot find the text "On my visits to the Bay Area" anywhere in the original article.

Also people tend to forget that LLMs still just work on compressed data... Where are the MAJOR breakthroughs? Where is all the "crazy" AI output going? Software seemed to degrade in quality a lot in the recent years. All "improvements" LLMs go through are simply improvements on how to burn more tokens out of my pockets given that Claude now want an actual browser extension to "visually" confirm small changes every time I use it for UI. They are still just data parrots.

It also seems kinda tone deaf. If someone basically told me I was wasting my time and asked what I would do in the future, I would not bother giving them a particularly thoughtful answer because trying to spend effort justifying my life choices to them would be the actual waste of time.

What kind of answers were they expecting to get?

By trade I'm a UX Researcher/Designer who designs in code (HTML/CSS) and have done so since 2009. Recently I vibe coded an entire python app with a database and each time I didnt know what to do I would just feed screenshots to Gemini or Codex for guidance (i think i could share my screen with Codex and it can guide me via a voice conversation). I know I could follow up and build a companion iPhone and Android app using these tools.

Overall, I'd like to understand those who have a positive outlook on design and software engineering as a career. Where do you see the opportunity where I just see a bleak one where anyone can do this stuff by typing or talking to AI? Myself, after 17 years in the field I am begrudingly back in school for a new medical career. As well, anytime an IT recruiter reaches out I am getting responses back only after under-cutting the hourly rate I use to demand and what others probably are still trying to get. And with it feels even bleaker as it becomes a race to the bottom!

It reminds me of Richard Hamming’s notorious question. I like the summary at https://bestjelly.substack.com/p/hamming-questions (which starts out with a quote from another site):

> > Mathematician Richard Hamming used to ask scientists in other fields "What are the most important problems in your field?" partly so he could troll them by asking "Why aren't you working on them?" and partly because getting asked this question is really useful for focusing people's attention on what matters.

> I imagine someone being asked this question, and how they should respond. I think like so - ‘Fuck off Richard’.

> This is partly because I imagine this question being asked in a kind of snarky, gotcha kind of way, with some sort of nerdy superiority. Like ‘ha your behaviour is inconsistent with your implied preferences, you idiot, do you even von Neumann–Morgenstern?’

Also, if you believe your well-paying job is eventually going to be automated you would be prudent to bank the money while you prepare for the future.

You read my mind. I suspect it is a transient spike while I blow through the backlog and I'll run out of things that AI can vastly accelerate in, say, 6 months. But right now? I'm spending 80-90% of my time blasting through implementing features, finding bugs, fixing old bugs, writing and improving my tools... Code was never the majority of my job. But right now the bar to doing things is so low and the sheer joy of blasting through some previously tedious, low to medium hanging fruit is thrilling.

>> We already know developers don’t actually spend most of their time writing code, with studies at Microsoft and elsewhere showing it’s closer to 14 percent.

> Anyone else finding they're spending more time writing code (or at least driving agents to write code) now?

Not really, as once it is time to write code, the problem has been defined/understood (to the degree possible with knowledge acquired at the time), and encoding it is largely an exercise in typing along with verifying assumptions via test suites.

Does GenAI quicken some portions of the above workflow? Sure, in the same way IDEs with contextual code snippet suggestions can make encoding faster.

I used it to write SQL and make dashboards. Back in the day, I would spend a lot of time doing that, then I changed roles. I dipped my toe in it recently and used AI exclusively. I would send a prompt, see the output, decide if that is what I wanted or not. I kept my brain in "what-if mode" and I let the LLM handle the technical specs.

Does the code get reviewed? How do you deal with increased amount of code that may need to be looked at?

Yeah. I spend most of my day driving agents to write code, verifying the results, orchestrating work streams, and so on. The rest of the time, a Fable agent is organizing work in Linear/Jira and making sure coworkers are getting their stuff done in a way that won’t conflict.

With GenAI, we can now produce something without caring about it - or while caring about it very little.

And the parts we don't care about aren't necessarily worse, they are just... arbitrary. Could be good, could be bad, no one knows, because no one really cares.

I find that if I care about something a lot, it's a pretty similar time investment than pre LLMs. And it makes me feel invested and proud in the result, motivated to show it and improve it.

If I care about something very little, in the past I just wouldn't have done it at all. Now I might, but I feel that same disconnect you mentioned.

I think being strategic in what we do and do not care about is likely the key skill we'll have to build to actually make the best of the tech.

Yep. ADHD very strong in this one, so LLM code generation takes pretty much all joy out of coding. It’s like watching a computers play chess. Yeah, no thanks.

Same thought - 80% through reading it occurred to me to check the citations. A few items from 2025 and most well before that.

So much has changed since late 2025 one can’t really draw any conclusions from this.

In fact, I’m guessing things will continue to move so fast that by the time one were to execute a survey of developers, many of the responses and findings are no longer relevant.

I felt the same and why didn’t the authors look over METR’s recent material?

https://metr.org/blog/2026-05-11-ai-usage-survey/

Okay.

Show me the evidence that AI has an impact on productivity when doing design work. Or reducing meeting load.

My own experience is that AI doesn't tighten the design cycle, and in fact might extend it by encouraging gold plating.

Yeah, this seriously drives me nuts.

That meeting that you spent an hour in to understand the requirements? You don't need that meeting if you're not writing the code. That sync up with the QA engineer you did to hand it off to them? Don't need that meeting if you're not writing the code. That half hour you spent installing vim extensions? Don't need 'em if you don't open vim anymore.

There are engineers whose jobs go well beyond coding, of course. Staff engineers and principal engineers have had their jobs radically change because of AI, but not because it's writing all their code.

But there are also a lot of engineers -- your standard mid-level engineer, or even senior engineers at a lot of orgs with title inflation -- whose job is almost entirely about delivering code, and who spend all day either writing code or engaging in scaffolding around code-writing activities. Let's not pretend that automating away that code writing is a 15% boost.

Also AI is now drafting design docs, generating PLC work products, entering it all in Jira, characterizing and root causing bugs... It's speeding up the 86% of my job that isn't coding. The article is a bit myopic and frankly contradicts itself.

If Claude told you to work on a task that you don't want to work on, or make a design choice that you think is wrong, would you do it? If not, then it can't really replace things like design or meetings. (Note that this is subtly but importantly different than the "vibecoding" model, where you just don't bother to supervise Claude's decisions.)

I don't know about others, but at work, the reason I only spend like 14% of my day coding is because I'm lazy, not because I'm actually doing other stuff.

> That was because coding was expensive and we needed to be sure we didn't code the wrong thing.

Coding has never been expensive as it is nothing more than a reification of a solution to a problem as it is understood at that time.

It is the underlying understanding of the problem which has always been expensive and remains so.

So you're suggesting that coding will take more of the PRD phase?

Getting a usable PRD is often the bottleneck.

You forgot to add "coordination" to that pipeline. That is easily far and away the biggest source of delays.

That includes talking to vendors, meetings with every layer of stakeholder when just one person digs their heels, etc.

That is truly the final frontier for "AI", and one that it will likely never cross. That would be when even the execs and upper management feel threatened by "AI". But, since they also delegate so much, you often see someone at the bottom of the totem pole in those meetings. This is why nobody is getting replaced by "AI". We really need to move this discussion away from the scifi stupidity already. There is no singularity or godlike AGI about to take over the world.

I hate to use awful terms like "synergy" and "teamwork", but they do have a lot more substance and truth to them than any perceived threat from "AI".

how so?

I’m very suspicious of this objection, because when Claude first landed the same people now saying “code is not the bottleneck” were saying “the generated code doesn’t work.” Smacks of moving goalposts.

The only solid objection to “AI is going replace developers” is “AI is an accelerant.” It helps developers move faster. I haven’t seen anywhere it has fully replaced developers.

Whether this leads to a large number of job losses depends on whether you think we can increase software output by the same factor as the acceleration and still be profitable. I think we can, latent software demand is extremely high. I also think we’re nearing the limit of capability with current models.

Situation could change if more advanced models emerge, but some of the more foreseeable advances probably have compute requirements beyond today’s hardware.

I’ve had people tell me, with a completely straight face, that they expected 10-100x productivity improvements. This is at the executive and VC level. The mania is extreme.

As an individual contributor I do not have insights into office politics. While we have >10k developers, it is not a software company.

Top leadership and/or investors believe in the benefits of AI.

Thus, skeptics stay silent. Who wants to loudly contradict their bosses' boss?

In practice, I do not see a big shift yet in workflows that would require a lot of politics. We just have the agent implement the code, and then it still goes through the usual code review and QA processes.

Only now with more effective models and harnesses do many developers realize how good these tools are at investigating bugs, etc.

Before the price-decreased GPT 5.6 Luna we barely had access to enough cheap AI to last for a month of work.

In my company GitHub CoPilot was rolled out on a global scale last year.

LLMs from Anthropic and co we get through a central tool which buys api tokens from Azure and AWS.

Its surprisingly fast, probably thefastest i have seen technology getting rolled out.

Im more worried about the small/middle sized companies which are software companies but don't get that they are. You know the companies 100% depending on IT but not having the right or good or enough people who often in interviews struggle with basics like code review.

And time in coding is like time on highways for taxi drivers. A fairly useless metric.

Not so much due to length of time, but because of the Opus shockwave than fell within that period.

In my open source work I believe this is the same. I don't have numbers, but I'm sure the vast majority of my time isn't spent writing code. Of course, it depends on how you define "writing code".

It is not urgency. Large production systems mean you are doing mostly unsexy operational planning. If I had a dollar each time I hear the word "data migration" I reckon I could do well.

Then yeah you might not be a good developer.

LLMs are also quite good in writing unit tests and understanding bugs a lot faster than I do, now.

Just a few month back i looked at some yaml stuff for like 20 minutes, played around with it, looked at formatting etc. then i asked the LLM, it immediadly told me what was wrong. I was just blind to that particular wrong char.

Logic bugs? Yeah it can find them too.