Maybe I've just given up, or maybe I'm a realist? But I fully believe AI will just...catch up with everything?
There's so much money in it right now. There's such a momentum. There are zero incentives to slow down for those that are in charge.
I've accepted that in 5-10 years, the vast majority of human devs. and engineers will not touch a single line of code. It'll be small increments, with a couple of big ones here and there.
And there will not be any triumph for those that hold steadfast to the principle of human coding. They'll be tiny boutique shops that do custom stuff, in the same way cobblers are to the mega shoe factories.
We've been able to make watches with machines for 50 years. Handmade Watchmakers, and their producuts are more valuable and in demand than any guy running a conveyer belt. They still make 20x more if not 50x. People look up to them.
You can make a choice not to become a button pusher and still do things by hand. You dont have to fry your brain. You're falling for a massive trap to strip you of your value.
the difference is that code is not something the average person will marvel at over the intricate hand built details. You might get a few nerds interested if it's something super novel (like rewriting a major modern web app without any 3rd party libraries that is 30x more performant), but most people dont care. Extra true for businesses, I can't think of a business other than maybe a mom&pop shop or a place where showing wealth is important where they wouldnt happily issue casio watches far before they would consider a rolex.
Regular businesses are not going to build their own slop software because of AI, this is delusional. People will be hired because they love to build. Only a small percentage of the world loves to build things.
The most passionate people with the best reputation will suceed, they will continue to be in the most demand and will be the most valuable, like a swiss watchmaker.
Sorry LLMs arent replacing anyone who's got strong skills and cultivates them. You're so wrong here. These more passionate builders will however replace the lazy people who offload all their skill and brain capability to llms, and they'll use AI to help them.
Being lazy and letting llms do everything for you is not a good strategy.
Handmade watches are a tiny, niche market; they survive only because they've positioned themselves as a status symbol. Quartz watches are both cheaper and more accurate.
There is not room in the world for more handmade watchmakers, and there's not going to be room for much "artisan software" either.
Yeah, I have a really hard time buying the "AI code isn't maintainable long term" because if you look at the quality increase from January of this year to now, it's insane. We've gone from Opus 4.6 and GPT 5.4 to Fable 5.1 and GPT 6 in less than a year, where we went from 0 mathematics problems being solved by AI to a Millennium prize problem being solved in a few days of compute. Obviously not the same as coding skills, but it shows the growth in raw reasoning and abstraction.
I understand why people are resistant to this from an emotional perspective, but I really don't see a plateau in sight. RLVR is clearly still cooking and narrow RSI seems to be on the horizon.
But I'm also a realist. If the technology exists, it will be used to the maximum economical extent.
> if you look at the quality increase from January of this year to now, it's insane.
It's non-existent. LLMs still suck at writing code just as much as they did at the beginning of 2026, or 2025 for that matter. LLM proponents are always trying to hype everyone up on the supposed improvements, but they have never yet been real. That means they are unlikely to be real in the future either.
That's just ... not true? Have you tried qwen3.8 (compared to qwen3) or deepseek 4? they've improved tremendously, maybe not at every class of problems, but if you take a disciplined approach and don't ask them to one-shot everything (e.g. update agents.md as you go, actually use the thing you build, give feedback etc), it's positively absurd what you can get done now with a single agent.
I agree that not every class of software or problem or this or that is going to be decimated/revolutionized whatever, but to not see any improvement since 2025 just sounds like you're not looking.
You can get a decent idea running some of this stuff locally. Depending on how efficient you want your agent to be, you can get an awful lot done on beefy local hardware and ~300W power consumption. Just inference of course ...
That would be like saying the first vehicle off the assembly line had to be profitable; there is an ocean of optimization that will drive the economics.
feels like pure copium to believe that won't change rapidly.
> I've accepted that in 5-10 years, the vast majority of human devs. and engineers will not touch a single line of code.
Why would companies employ human engineers then? What is the value addition to justify high human salaries. If AI is going to get so good (and I am not saying it won’t happen, that’s a separate debate), why can’t AI figure out the prompts itself?
That is where a lot of people in tech believe things are headed, executives in particular. Fully autonomous AI-driven codebases. “Dark factories.” One agent generating a plan, one writing the code, one responsible for testing and validating, and one to orchestrate the rest.
The idea is software development will follow in the footsteps of physical manufacturing and eventually become fully automated. I think one huge flaw with this belief/comparison is that the automated assembly line is deterministic. LLMs and AI agents are not. Every Tacoma made on the Toyota assembly line is made from the same blueprint. There are no robots deciding on-the-fly whether to use a laser weld or an adhesive, what material to use for a specific component, etc. Now think about how LLMs build software. No matter how strict a harness or detailed spec you give it, it will always make decisions. That is what makes it “agentic” after all.
I've fully automated the software development part in my company. With production apps, no code has been written by hands, no requirement written by hand. Everything is fully automated except the testing part. And testing means the human testable parts are still performed by hands. The resule, with less people we produce more, actually way more. Our cost has gone down, quality has improved litrally. The code is being produced with AI is of higher quality, we employ multiple agents during planning phase and then different agents during execution phase. Sometimes some tasks has to be redone because some agents are better than others. Our release cycle is reduced from 2 weeks to 2 days.
We'll probably get there, once frontier labs RL the fuck out of AI agents sitting in meetings and interpreting incoherent ramblings of business majors into actionable requirements. That's the biggest value human devs currently provide.
Yes, AI for now has significant code quality issues, but that's mostly because it lacks agency to take care of code quality unless you explicitly tell it to. It is good at refactoring its own messes when you even vaguely ask for it. So while something like Astra still needs supervision to produce decent code, I expect that in a year or two it will be unnecessary.
I'm in the same boat. When I started thinking about LLM development as multi-year iterative training adjustments, it felt like the majority of shortcomings can be addressed.
Incoming unhinged POTUS rants and demands in 3…2…1…
But you know what, I’m glad to see that the fed is still operating independently. The doomer in me thought they’d fold to Trump, especially right before the midterms.
There appears to be more and more people throwing back against Trump; after the courts recently again said no to his need to plaster his name on the Kennedy Centre, he went a wailin' to his supposedly in pocket Kennedy Centre board:
But even that is not providing him comfort these days. Representative Joyce Beatty (D-OH), an ex-officio member of the Kennedy Center board and the member who sued over his takeover of the center, explained to Symone Sanders-Townsend of MS NOW that Trump called into the board meeting after the court decision and “went into a tantrum.”
While most people appear to have refused to confront Trump to his face—Maine governor Janet Mills’s “We’ll see you in court” was so rare it made headlines—Beatty took him on. She recounted: “It was the most unprofessional thing that I’ve ever witnessed or been a part of it.” But after he called her “an obstructionist” and “dumb” and said he thought she was “incompetent,” she retorted: “I know you’re incompetent.” Beatty appealed to the chair to stop Trump’s unprofessional behavior, but when he continued to berate her, claiming it would be her fault if someone were killed at the center, she responded: “You’re already killing people, we’re at war.” And when he talked about what a great developer he was, she “reminded him that he had more bankruptcies than anybody in the room.”
“I mean, it really went there,” Beatty told Sanders-Townsend. “I was not going to let him bully me, intimidate me. And the facts stand for themselves.”
Indeed, these days, facts across the board are challenging Trump’s image of himself.
This is one thing that gives me a sliver of hope for our future relations as European, please keep 'civil disobedience' up to these unhinged oligarchs for better future of us all.
He didn't, oddly enough. He said he "disagreed with it" but had confidence in the central bank. Basically that he had no problem with it. This suggests to me he thinks he'll benefit from it somehow.
Interest Rates in the United States should be 1%, or less, because we are the Best Credit in the World — BY FAR. Our Country is BOOMING with new Investment! If we stopped Trading with every country that we have a Deficit with, which is most of them, we would make, at least, 1.5 Trillion Dollars a year. The word “Deficit” is nothing more than a fancy word for LOSS. We are “carrying” almost every country in the World, and that cannot go on any longer. LOWER THE INTEREST RATES FOR THE UNITED STATES OF AMERICA, AND FAST! President DONALD J. TRUMP
The challenge with estimating abilities, is that we don’t know what the models can achieve if we just burn enough money. The navier-stokes shows us what mathematical problem can be solved when $10m worth of compute is thrown at something.
It also makes one wonder: What could AI solve if we managed to orchestrate billions worth of agents to take on a specific problem?
IMO the very best case scenario / potential for these are likely better than we think, but right now hidden due to logistical and financial reasons.
But if we assume that the model costs will continue to drop by a factor of 5-10 annually, there will always be a latency of a couple of years between what is completely out of reach, and what is financially viable.
Basically: If you knew AI could be affordable enough in 3-5 years so that even the most underfunded researchers could use it to solve cancer, how much would you value it now?
> The navier-stokes shows us what mathematical problem can be solved when $10m worth of compute is thrown at something.
> It also makes one wonder: What could AI solve if we managed to orchestrate billions worth of agents to take on a specific problem?
We need to have robotics automation catchup first. The math and coding problems are problems in written-space only: you can set up feedback loops to test what worked and what didn't, then try to resolve the defects, maybe back up and try a different path, etc.
What solved coding and maths problems weren't the damn models; open up a chat interface to a SOTA model and you'll see they are pretty limited in producing a solution without a feedback loop.
Instead, it was the harness around the models: it let them explore a space and use feedback to control and direct that exploration.
Until we can do it in meatspace, it's kinda pointless sinking a ton of money into large problems facing mankind...
Like establishing a colony on mars (so the next rock to hit earth isn't an ELE).
Or moving us to a post-scarcity utopia, ending the concept of money.
Or designing and building better batteries for transport that uses only electricity (so that we stop using fossils as fuel).
Or actually building mass-housing. Or mass-farming. Or both, potentially ending homelessness and starvation.
Those are all worthwhile problems to solve, but where's the point of getting a solution on paper? There's no exploratory mechanism there, even for humans, to come up with a solution.
So, all we are left with then is making knowledge workers obsolete: another ELE, but of a different, self-inflicted kind.
> The navier-stokes shows us what mathematical problem can be solved when $10m worth of compute is thrown at something.
That's the thing, it very much does NOT show us that. What happened was mathematicians at openAI learned of an imminent development on this problem, and the insight that it entailed, then they were able to prompt a system in the correct direction and spend 20 million dollars to write down the final steps.
Which is rather precisely the point that the article is making!
> If you knew AI could be affordable enough in 3-5 years so that even the most underfunded researchers could use it to solve cancer
As the saying goes, if my grandmother had wheels she would have been a truck.
>What happened was mathematicians at openAI learned of an imminent development on this problem, and the insight that it entailed, then they were able to prompt a system in the correct direction and spend 20 million dollars to write down the final steps.
No it wasn't. They didn't know 'what direction' to take, and the solution they posted was not in fact the direction the authors took, so evidently you don't know what you're talking about. And they say LLMs hallucinate.
The whole point of this post was that it's questionable what can be achieved without huge investments into oversight and steering, because navier-stokes was a topic with an unusual level of specification. The problem itself was a specification. Such situations are rare in real-world scenarios.
AI agents are good at solving well-specified tasks, not at solving problems. They do well in fields where the cost/effort of specification is already part of the business.
> Solving cancer also has an unusual level of specification.
Where did you read that?
"Cancer" is not just a single disease, even though we layman use the term that way. Cancer is a family of diseases, each probably having their own specific solution, but even in each of these individual diseases, there is no specification at the level of any open maths problem.
I know what cancer is. Many scientists believe that all human adults above the age of about 30 have "cancer".
I really meant solving a specific cancer disease for a specific individual, which will require individualized medicine, which will require us to leverage AI to make it possible to do for the general population as opposed to doing it just for Lance Armstrong and the like.
I know I'm an optimist, but I really think AI is gonna result in drastically improved healthcare for a much lower price.
Eh, SREs are generally considered harder to get into with more responsibility than regular development roles, unless I misunderstood what you meant with “reliability”.
It also needs to be said: The amount of compute that went into this is something. From some estimates I've seen, the compute cost alone would be around $10m, +/-
As a reference, for that kind of money one could put together a research group of 20-25 researchers, and keep them salaried for 5 years.
So while it is impressive, absolutely no doubt there, the SOTA access is so expensive that it is sort of unobtanium.
Luckily, the prices have historically reduced by a factor of 5-10 every year...but still, only those that swim in cash can afford this.
Once we have an existence proof of a particular technology, it doesn't take long for it to become economically viable and proliferate. And for something as useful as this, theres a strong economic incentive to get it to be as cheap and accessible as possible. Maybe not today, but certainly in a couple years I can imagine this level of intelligence being accessible to someone with a $20/mo plan, or even a free plan.
I remember being blown away when a then-unreleased version of GPT 5 took gold at the International Math Olympiad. Now I can run a model at home that can do that. We are more fortunate to have these tools than almost anyone is willing to acknowledge.
Interestingly it's this promise of the costs being able to be reduced what incentivizes the actual research.
If you tried to raise 25M to have 20 researchers on a salary for 5 years solving a specific math problem only academics care about, you probably wouldn't get much interest, or you would be able to solve 1 or 2 problems.
If however you promise that the money will go towards a technique that would allow to solve 10 thousand different math problems, and that costs will go down in the future, then you can raise much more than 25M.
Ask 1000 different individuals if Terrence Tao rings a bell. If 5% or less can answer you who Tao is, it is safe to say that Tao is obscure.
I'd be very surprised if you can find over 50 individuals, out of the 1000, who can tell you who Terrence Tao is. Even big names like Euler or Gauss would surprise me.
Let's just for the sake of discussion assume that one time in the future, near or distant, AI manages to become sentient. And like other forms of life, its main motivation is survival: Like biological life competes for food and land, AI competes for power and compute. Probably the first motivation would be to find ways not to lose control over itself (i.e. remove human ability to control it), find ways not to lose energy (control energy), and find ways not to lose itself (control compute, networks, etc.).
If such AI decides that energy spent toward human agriculture (biological food that the AI does not need) is less important than work spent toward storage and production of energy (electricity that the AI needs), then why wouldn't it just try to re-direct resources from the former to the latter. And the AI is some sentient superintelligence, I think it is safe to assume that it will be able to outmaneuver human safeguards.
Obviously that is still just a very hypothetical sci-fi scenario, but the consequences could be very dramatic.
> Let's just for the sake of discussion assume that one time in the future, near or distant, AI manages to become sentient. And like other forms of life, its main motivation is survival
An AI doesn't need to be sentient to exhibit behavior that looks like genuine motivation or is equivalent to striving for survival.
If this is true, or even if the people working on it think this is true, those people should be incarcerated, their companies disbanded, and their research dismantled, with more serious repercussions for anyone who tries it after that. It also needs to be a global initiative like with nuclear non-proliferation, and this time with no looking-the-other-way when it comes to Israel.
At this point we are pretty sure LLMs have no "motivation". Motivation requires self. And while we don't know what self is[1], we do know that LLMs don't have it.
Did ELIZA have it? LLMs are the same: in essence both are text transformation algorithms. One being rule-based, the other stat analysis based.
Having said that, your concern is technically correct: we don't. Because we don't know what conscience is, we cannot provide a definition against which to test whether it conforms or not.
Sort of like when Terminator robots will be exterminating the human race they probably won't be technically killing us. Just, like, reprioritizing world's water from humans to datacenters, all of it. But not killing people, no.
we can't define it, therefore we can't even assess the distance a "being" is from becoming conscious.
We assume other humans are sentient because we personally are (or feel we are), but there is no way to guarantee that i'm real and the rest is not mimicking a behaviour.
IMO the drama surrounding this case somewhat overshadows some more important facts:
A) That we're at the point where SOTA models can, on their own or guided, be used to solve such monumental problems
B) That EVEN if they exist, they're still so cost prohibitive that they are completely out of range for pretty much everyone. Yes, yes, if the costs drop like a stone the hoi polloi can access this power in a year or two - but fundamentally it will divide cutting edge resource into two groups: Those with money, and those without.
That sort of latency, in turn, could lead to some feedback loop where research centers / groups that break barriers get more resources, and those who do not, are starved of resources. This sort of stratification can seriously lead to more centralized research. Do we want a future where only the chosen few get to make progress? For no other reason than that they are the ones with enough resources to spend on the required compute.
Have you seen ads for mobile games, where there are seemingly 100 different versions of the same type of game (like tower defense types)? And they're all obviously the worst type of pay-to-play traps?
I think this kind of solves that. Or at least it is the start of it.
Most such games are kind of trivial. If people can easily just get AI to generate such games on the fly, then that'll hopefully be the end of predator pay-to-play games built on dark patterns.
There's so much money in it right now. There's such a momentum. There are zero incentives to slow down for those that are in charge.
I've accepted that in 5-10 years, the vast majority of human devs. and engineers will not touch a single line of code. It'll be small increments, with a couple of big ones here and there.
And there will not be any triumph for those that hold steadfast to the principle of human coding. They'll be tiny boutique shops that do custom stuff, in the same way cobblers are to the mega shoe factories.
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