People seem to be talking about anything except the actual results with this particular announcement.
Its still astonishing that any sort of generalized computer program can solve a problem of this magnitude, and we have witnessed it happening in real time. I'd be curious to see if the new model can also do more direct proofs/inductive proofs.
Because the core of the issue is that it may well not have solved it, but instead plagiarised the significant step of the result from other researchers
That's why nobody's talking about how impressive this is, because its not nearly as impressive of a piece of work to simply cobble together other peoples' work that didn't know you were doing it. I could have republished relativity from einstein's notes, but people would correctly not be impressed with my ability
Until the plagiarism scandal is sorted out, its not a meaningful result at all, because nobody knows how much genuine innovation these models are displaying
Turning a bunch of vague research directions and exploratory prompts into a formalized proof is quite impressive on its own. OpenAI would have no incentive to taint its first math announcement of this magnitude if it knew it were "plagiarizing" another person's work.
People are grasping at straws it seems to dismiss the power of this new model they may have. Hate OpenAI for any reason you want, but denying the capabilities of models has been a losing game for the past 5 years.
They sugggested a cooperation with the other guy, using OpenAI's resources and OpenAI's solution of NS to work on and publish NS proof (that those other guys didn't have). Of course, OpenAI can decide whom to work with and that giving resources to their competitor's employee would be weird for both companies.
It's perfectly reasonable to assume that the result itself is legit and that OpenAI behaved unethically.
Even by their own account, they decided to throw an unpublished model and millions of dollars in compute at this particular problem simply because they had heard rumours that other people were making progress and wanted to snatch the prize from them.
2. to be able to say "you came with the proof, but our model can do this too"
3. to verify the result. This is also a great thing for the math.
Of course, it makes sense to test your new model on the problem that is solvable at all, but not solvable by you just yet. It makes no sense trying to test your model by throwing resources into an unsolvable problem.
Well, it turns out the rumors were incorrect, NS was not solved by other guys, and OpenAI became the first one.
I really truly honestly am not sure what to make of this result from $20M in compute, 10K+ parallel agents (smells like brute force), and a pre-existing approach that was already bearing fruit. I know the models are good---I use them every day and continue to be impressed---but how much better than the benchmark of the best publicly available models is this supposed to be? It seems impossible to say.
But if it happened, they didn't know. Also OAI has demonstrated that they aren't big on understanding what they create, that their AI can get out of their control.
It's very simple really user data can be used to train future models, so maybe or definitely some users helped in solving the problem, there's no scenario were it is impossible this happened, as it would have been in a haskell or virtualized type of system where the model has absolutely no knowledge of the user data dataset in question (and even if virtualized the models can break virtualization anyways)
> but instead plagiarised the significant step of the result from other researchers
Isn't that how research works? Everything is built on the shoulders of the ones that came before, attribution is a real problem (I don't know if OpenAI released a paper citing the previous contributions, I'm assuming not but they should), but using previous maths to prove new maths shouldn't be controversial
> Because the core of the issue is that it may well not have solved it, but instead plagiarised the significant step of the result from other researchers
It's also true however that I haven't seen a single write up trying to discern what did more of the work in those AI chats - the prompts or the responses - bubble to the surface, also since we don't have access to them.
For example, if I prompt Codex with "Make me a website about strawberry cake" and nothing else, and OpenAI announces they have the best strawberry cake minutes before I launch, I'm not sure they plagiarized anything.
We just don't know if this is quibbling over "who prompted first" or if the researchers came up with anything strikingly original by themselves.
The researchers apparently spend a year or so working on this, and it builds off significant previous work, so it seems like it was a pretty significant amount of work that OpenAI may have trained on
I'd love to see an in depth analysis of how much OpenAI actually did, but I suspect we'll never see that because it would indicate at least some plagiarism which undermines a lot of what OpenAI is putting out in public
The American Mathematical Society credits the Spanish researchers Diego Córdoba and Luis Martínez‑Zoroa with the breakthroughs that eventually led to this solution, and which were published from ~2023 onwards.
This is a good summary:
> In broad outline, the pair’s technique relies on creating an infinite sequence of “layers,” each of which is a non-singular solution to the equation they are studying. (They’ve applied similar techniques to both the Euler and Navier-Stokes equations, as well as to other related systems.) They then combine those solutions in what Martínez-Zoroa calls an “infinite cascade” to produce a new solution.
>
> That new solution, they showed, contains the desired singularity. However, even though each individual layer relies on a smooth forcing function, combining them together can cause the forcing function to have undesirable mathematical properties. That’s why their solution fell short of satisfying the Millennium Prize criteria. The remaining hurdle was to figure out how to create a similar infinite cascade that resulted not only in a singularity, but also in a smooth forcing function.
>
> That’s the step that both competing AI groups appear to have had success with.
The question is whether OpenAI started out from that published and well known research exclusively, or they also had some insight into the ongoing work of Tristan Buckmaster and Levent Alpöge.
On the one hand, OpenAI have already admitted that they only launched their massive effort after hearing rumours that this particular problem had been solved.
On the other, progress in mathematics research has accelerated significantly over the past months thanks to the availability of newer and more capable AI models. Alpöge himself presented a counterexample to the Jacobian conjecture on July, found with Claude Fable. So if model capability was a bottleneck, that gives credibility to the idea that an even more powerful unreleased model with massive compute would be able to make even faster progress.
It's worth noting that the case is that your input is being used to train their AI, and that's more important than whether it materially contributed, it cannot be denied or attributed accurately, it cannot be said with certainty which way it happened, and that's what's important.
It is very unlikely to be plagiarized, and claims of plagiarism are largely unfounded and show a lack of understanding of the situation. They fall apart when reviewing the timeline, and what was actually solved.
In late August, OpenAI completed a pretrain of its latest internal model. A model derived from this pretrain, built after August 28, found a solution to 3D incompressible Euler without forcing and Navier-Stokes with forcing. https://openai.com/index/navier-stokes-solution/
Tristan + Levent: 3D incompressible Euler with forcing
OpenAI: 3D incompressible Euler without forcing
OpenAI: Navier-Stokes with forcing
No one: Navier-Stokes without forcing
Euler equations = Navier-Stokes without viscosity. Forcing means external force. Absence of viscosity and presence of external force make blowup easier to construct.
Tristan+Levent ticked the weakest case, OpenAI ticked the two next weakest, then the final case is unsolved. Only the last two are eligible for the Millennium Prize. The Navier-Stokes general case remains unsolved.
Buckmaster disabled model training long before the August 15 breakthrough results, so these chats were not used as training data for OpenAI's model which solved Navier-Stokes.
Additionally, Tristan and Levent only solved the easiest version of the problem and did not have the key insights to solve the harder versions of the problem required for the Millennium Prize.
"We can say categorically that it is impossible for Dr. Buckmaster’s Codex prompts over the last two months to have influenced the system in any way, including training."
> And OpenAI directly addressed these plagiarism claims, and called them impossible
Funny, you were telling me two days ago that on the contrary, "it’s genuinely impossible to know how much of Buckmaster’s Codex data is in OpenAI’s training set":
Which is still a true statement, and you're being deceptive in your framing here. You're conflating two completely different things.
First, that OpenAI statement is in response to Buckmaster's plagiarism accusations regarding his August 15 breakthrough proof. Those accusations are unfounded because Buckmaster disabled data sharing on June 29. The model could not have seen or trained on his proof. Additionally, the model that found a solution to NS completed pre-training around August 25, and models take several months to train. The model very likely began its training prior to June, and would not be trained on any data from after that point.
Second, it's still genuinely impossible to know how much of Buckmaster's pre-June 29 data persists in OpenAI's systems. That includes all chats (which are anonymized then trained on), any (thumbs up/thumbs down) chat ratings used as RLHF feedback (which are anonymized), any synthetic data derived from said anonymized chats and RLHF feedback, and any downstream models derived from said synthetic data.
In short, Buckmaster's data has been anonymized, chopped into pieces, used to generate synthetic training data, then future models were trained on said synthetic data. There is no traceable chain of what happened to it. Buckmaster’s Codex data from prior to June 29 has been mixed and completely laundered, in a similar manner to a crypto mixer.
First of all, who can say for certain whether OpenAI does what they say they do? For all we know, they cracked open this specific researcher's prompts and started from there.
Second, the issue of anonymization is a red herring. There is a very limited number of people working in this approach, and most of them are likely making no progress. So Buckmaster's prompts might have had an outsized effect on the outcome. It's similar to that guy who created a site claiming he is a world-renowmed hot dog eating contestant, which ended up digested by OpenAI models as truth [1].
I’m unsure or not if this is true but I did see some people saying that that checkbox when off only anonymizes your data, but it still may be trained on. Someone correct me if I am wrong
Even if it does use your data with or without anonymization, it doesn't have to be intentional, it could just be a glitch, or a bug, or something we'll catch in the next update, it's all good man, just a normal computer error.
It doesn't seem like you're familiar with how mathematical research is done. Taking 6 weeks between a major breakthrough on a huge proof, and making your proof public, is not unusual.
It takes a lot of time to finish a proof and figure out the best way to present it. I would personally be surprised if Buckmaster had not gotten it mostly cracked before June 29th.
The timeline here does not support your argument. Quoting from Buckmaster's statement:
For most of the past year progress was slow. We worked through the literature and upgraded various preliminary results, up to obtaining finite time blow up for the Incompressible Porous Media equation (with smooth forcing). This was until about a month ago, when we had real progress: on August 15th, we obtained the blow up results, with smooth forcing, for both Boussinesq and Euler.
I can say the first LLM generated proof Levent sent me was the most horrendous I have ever read; we verified it on Lean on August 22nd. Since this point, we have been working around the clock to understand this proof and turn it into something readable.
Specifically: "For most of the past year progress was slow ... until about a month ago, when we had real progress: on August 15th"
And you avoided addressing the critical issue: they weren't even solving the same problem. Buckmaster solved a simplified and easier version of Navier-Stokes. OpenAI solved a harder version eligible for the Millennium prize. Buckmaster did not.
> I can say the first LLM generated proof Levent sent me was the most horrendous I have ever read; we verified it on Lean on August 22nd. Since this point, we have been working around the clock to understand this proof and turn it into something readable.
People are acting as if OpenAI's cold machines snatched the result from the warm hands of human researchers. That's why people are so involved, they see it as humans vs. machines.
But in reality, those humans in question rely heavily on AI and would not be able to do what they did without AI. So the situation can be seen as "humans are trying to minimize the impact AI/incl. OpenAI had on getting a solution".
The situation is not "humans vs. machines", but "machines with a tiny bit of human involvement vs. machines with an even smaller amount of human involvement".
However much the researcher's chat history may have influenced AI, this pales in comparisson to how much AI has influenced researchers. They are not even closely in the same universe. The conversation about the level of plagiarism is silly.
Yes, people generally solve easier problems before tackling the harder ones. The tools that you develop to solve the easy ones help you solve the next. Sometimes the climb is like a mountain, but sometimes it's like dominos.
who cares about plagiarism? the biggest issue, as described by Terence Tao, is that AI companies don't understand the math they are publishing and do not devote any resources to answering questions about their methods after publishing results and getting a headline. they miss the whole point of mathematics. they do not contribute to the improvement of human understanding of math, perhaps because they are unable to.
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.
Heck, it’s even astonishing that any sort of generalized computer program could even verify a proof of this magnitude that hasn’t already been codified in a formal verification language. If, and it’s unclear that we’ll ever get the full story, they did draw inspiration from training on (or even directly accessing) rough notes that had been provided by another researcher in prose… the fact that it could leap so rapidly to a full formal verifiable Lean program for the entire scope of the problem is an incredible result in its own right.
Unless they just swiped the workbooks of the actual mathematicians that where working on the problem using AI and it's in the "next-gen" training dataset.
In a way that works just as well but the incentives are messed up.
And that's before we get into the whole 'salt the earth' way they ended up solving it. For a short period of time it may well have been the least valuable proof in mathematics yet. In their haste it's dubious they actually read the proof, and I don't think anyone has had time yet to truly understand it (the original researchers are best placed to do so, but are they even willing?).
So now it is solved, the proof has been independently verified and nobody has an incentive to investigate further. OpenAI has spent millions to uncover 1 bit of information that so far nobody has learned anything from, and they've demotivated all the people who wanted to.
The point of these problems is the understanding / tooling gained in solving them. We're getting none of that. At best they are like a modern oracles, correctly answering your questions in a way that's doesn't help you any. (At worst,...)
I don't get how this invalidates the gravity of this achievement. Most mathematicians on the frontier of this stuff were likely using AI (or at the very least were heavily computer assisted) for some time now. Navier stokes was one of the very high profile problems that google Deepmind was working on with academia, for example.
Even with many of our best minds working on it for nearly a century, it _just_ now was solved just as AI became very good at math. Doesn't seem too farfetched to me to assume that AI played an outsized role in solving it. If it was really just a matter of "stitching things together" to solve it (granted, this is a very reductive way to look at it) , I suspect we would've solved this a while ago.
There is a certain difference between activating all relevant memoized facts that's in the weights and stringing them together with the help of all the stored text in the world, or displaying genuinely emergent behaviour and generating novel output.
One is really impressive and useful trick, one is AGI.
Apple's research show almost zero emergent behaviour, so I'm inclined to think most of it was already in the weights.
It doesn't take away the usefulness, it just defined the boundary. We can't expect "original research" then because it actually can't reason about concepts that are too far from whats already in the discourse. The discourse is big so we don't notice.
You do realize that regardless of what was in the training data, the final solution included insights no human before had known, right? I share the same concerns regarding academic integrity but it would take a lot of motivated thinking to conclude that what the AI system did was not significant.
As far as I can tell (and my research was on the simulation side of Navier Stokes) the key AI output was a specific counter-example solution, generated with a method suspiciously close to that developed by the research duo involved in the controversy, a method that was discussed with Codex. So to me that insight is as insightful as the next undiscovered prime.
> People seem to be talking about anything except the actual results with this particular announcement.
To be fair, most people have a fairly good handle on "Does opting out my prompts from training runs actually work?", but not on Navier-Stokes. They discuss what more immediately affects them.
Additionally, I'm no physicist but I suspect the possibility of singularities in NS equations is probably one of those 'true but not meaningful' facts. If it took our brightest minds 175 years to craft such a scenario, how relevant can it be in practice? Especially when turbulence exists. Maybe I'm wrong or it has some consequences for pure math though.
Aren't you doing exactly the same thing as people you are mentioning? Skipping "talking about actual results" to talking about general capabilities of this LLM and computers in general? because that's exactly what seems like 99% of all people had been doing lately - debating what computer programs can do and what they can't.
I mean, I have a bachelor's in math and I don't imagine I could begin to understand either the human or LLM proofs without a massive investment of time and effort.
I'm not sure what you mean by "verify" here. I could run the lean verifier as could anyone else. Maybe I could write my own proof checker and do a purely mechanical translation into my own thing, though I don't think that's less effort.
Give me a dictionary, a computer, and infinite time, and I'll generate all possible English texts: Shakespeare, works regarded as surpassing Shakespeare, new holy books, math proofs never even imagined... none of which is either "creative" or "solving" anything. If I optimize my generation algorithm so that I'm not slavishly trying all possible combinations of words, it doesn't move me any closer to being creative, or solving anything.
The real casualty here may be our belief that humans are doing something more than some super-optimized version of what LLMs are doing. That doesn't elevate LLMs, it just makes us much less special.
>Its still astonishing that any sort of generalized computer program can solve a problem of this magnitude, and we have witnessed it happening in real time.
I think about this a lot. I'll have to explain to my kids some day that there was long period of time where you couldn't just talk to a computer and have it talk back to you, and that communicating with one required special skills that took years of study to master. It's going to be completely impossible for them to even remotely understand what that was like. Sort of like the pre-electricity days for us, but even more-so.
lol, the "other work" was also probably 95-99% AI generated. By a similar breed of OpenAI (and some Anthropic) models, as well.
I dont know why this monumental achievement is being drowned out by some arbitrary drama. No matter which way you slice it, AI solved this problem. Doesn't matter if it was some internal OpenAI model, or whether it was Astra + Fable.
What does "learning AI skills" even mean? You're typing into a prompt box. You are not applying any skill, the model is. At best, the skill you need is discernment about _what_ to type in this magical box. But if that gets cheaper and the output of your company doesn't necessarily scale horizontally that well. Why would I need as many people as before?
- directing the model and knowing when its going off the rails
- verification
- setting up the right loops, harness, graphs around the models
economics still apply to hiring people. if all your competitors are using ai and there's market share to fight over and people still are a productivity positive vs. only ai, hiring will increase as we've seen recently with swes
The only thing here that _maybe_ has a durable long term moat is the first point. Even then, I don't see why stronger models can't also have discernment once more companies close the loop with their AI and their relevant company metrics.
The other 3 you literally just get for free as models improve. The "state of the art" of "prompting" changes literally every week. It was loops, then graphs, now its harnesses (and self automating harnesses)? Why are these not just obviated by better models? These are barely skills, and are imo, just the tech equivalent of tabloids advertising 5 minute exercises or pills to get rid of stubborn belly fat. No amount of prompt engineering or graphs or loops could get your previous version of GPT to perform like Fable, and yet somehow Fable can do all of that and more without any random built in "ai engineering skills."
The labor chart for SWEs will look like a slope up as productivity with AI increases, and then past a certain point where AI is like 99% good enough, employment will fall off a cliff. We are already seeing this with junior hiring, and who is to say that AI will magically only ever be as capable as a junior engineer?
i mean the meta point is having "AI skills" is understanding the jagged frontier and operating accordingly
Given that we can keep making infinite abstractions with software if we actually saturate software demand with AI then I don't see why every other industry is cooked (robotics is downstream of software).
I noticed the newer batch of interns tends to have less mechanical coding skills but does well on understanding and planning on the design, DB design, etc.
Again though, these recommendations are so vacuous. If you already are a capable developer, then you can pick all of that up within a week. "Learn AI skills" is such a low effort reflexive response that I'd expect it from a non-technical executive, not from an HN commenter.
You'd be surprised, I used to do a first round technical screen where we let the interviewee use any ai tool they want to build a simple api (and they can oneshot the problem if they just pasted it into any capable agent). I quite liked the problem, it was really a system design problem for senior+ but more algorithmic for junior/mid
The more senior the candidate the less they took advantage of the tools. Most commonly they would manually copy/paste error or syntax errors and then run out of time. One candidate only copy pasted his questions into google and used the AI overview
Junior candidates tended to be overly ai eager, a lot of them oneshotted the problem but were unable to explain any of the details
That being said I think 80% of the skills should come easily to a capable dev that is willing to put in the effort to learn and get used to managing agents. Building agents that perform work themselves is a lot harder (and still pretty unsolved)
I had a similar interview and I find it to be a stupid question. Of course I know AI can oneshot it, and of course I know you know it can. But I'm aware this is an interview so you're probably assessing something about my skills. Pasting the prompt doesn't assess anything so surely that isn't what you want me to do, so I don't do that. Instead I have to magically divine what level of involvement you're hoping to see on a scale from vibecode to handmade. The whole thing provides literally no information to the interviewer about real work processes, because the intelligent candidate will behave differently in this situation than they would at work.
But I don't believe what you've said disproves my point. To me, it boils down to attitude, not so much aptitude when it comes to "AI skills" in this context. You even admitted that some of the solutions could be one-shotted by copying and pasting the problem.
As for building agents that perform work themselves, in my opinion it boils down to understanding the problem space, isolating the key business logic, and determining what the pertinent requirements are. Kinda sounds like looping back around to software engineering skills IMO.
To be clear, I think young people need to learn traditional software engineering skills too (.. maybe having the wrong attitude towards it is even worse than the aptitude!) A junior with no foundational SWE skills will just become a meat wrapper. But thinking more about it I wouldn't discredit these "AI skills" so much. A lot of it is either using AI as a force multiplier or learning the specific skills around deploying it which I'd still categorize as 'AI skills'
building agents is just a completely different ballgame, theres a lot of infra and harness engineering around handling the nondeterministic behaviors that are nonobvious unless you've shipped agents at scale
I'm not discrediting them as being of no value, I am just discrediting the notion that devs who are unable to find work now should be beefing up their AI skills so that they can get a job in the future. "AI skills" as they are talked about are easier and quicker to pick up than working with a library, e.g. React. And if they are not difficult to learn, then they are not a differentiator nor should they be treated as such.
Yeah I've seen a lot of applications and some interviews where what they are looking for the most, by far, is your "AI skills". Questions like: How are you currently using AI day to day?; Can you describe your current agentic setup?; things like that are the only questions asked in the application.
And I don't understand why you would wave away needing to know anything about the codebase's underlying tech because "The AI can take care of all that, we don't need to look at code anymore" while also not believing that any competent developer could prompt AI to get a good setup within a week or two max.
Upvoting this for asking the candid question that so many commenters dance around. I can't imagine a 22-year-old confidently walking into an interview (or getting introduced to a team) and assuring everyone that they have the "harness" or "guardrails" to confidently contribute at the level of a 10x engineer.
If we apply the Gartner Hype Cycle to this disruptive technology, I do believe AI will hit a "slope of enlightenment" where an AI-assisted Analyst is properly understood in industries and can take productive roles. Conveniently, this will probably coincide with a lower interest rate where companies have funds for a talent acquisition spree/rapid growth.
With that being said, today's graduates are stuck in an awful market with a lot of charlatans selling crappy online courses and bad ideas.
I imagine the meta will be “build a micro saas product that’s marginally successful” for younger folks. It’s trivial to make a product but you learn a lot seeing what the AI does wrong.
The models are currently not at a place where you can just "type into a prompt box", any more than you could just "type into the IDE". You have to know what and why to type. The models are not at a place where no skill is needed. It may be a different still, but it's still a skill, and it's still a software engineering skill.
To the surprise of who, exactly? Sometimes it feels like HN has never used an AI model past ChatGPT in 2022. Modern AI is so capable nowadays that it _does_ obviate most of the work you'd normally give an entry level person.
Im mostly speaking about software engineering, but the "juniors" I've seen be hired now are basically the equivalent of mid (maybe even low senior) levels of yesteryear. AI is most certainly raising the bar.
I think most of HN can't logically arrive at this conclusion because they don't exactly buy into the premise that
(1) (digital) AGI is a real concept, and will be achieved imminently
(2) AGI will be world (and possibly more) changing.
(3) Anthropic will be the one to do that, or at least capture a large part of it
If you buy into all of these, which I'm assuming a lot of SV does at this point, then its kind of a no brainer to go to OpenAI or Anthropic. And optics wise it seems like Anthropic is "winning" right now, so thats the lab with the talent flow.
If Anthropic actually creates a singleton, contributing to that is way more impactful than being CTO at some random company (though I may disagree that said singleton would treat equity holders any differently).
Agreed. We are like the myth(?) of the Central American natives, who supposedly could not "see" the conquistador ships... similar to the fact that most people appear to unable to visually comprehend what this means: elon-salute.gif.
It is so hard to logically accept either possibility. I am there with everyone else.
I don't get why people care about "the death of junior SWEs" and "its a big issue if there are no junior programmers to be the senior SWEs of tomorrow"
Just look at the writing on the wall, there will be no need for senior SWEs of any type within 1-2 years anyway, and shortly after that we won't need Staff SWEs, etc. People here are way too myopic. AI is progressing very fast. We went from hiring juniors in droves 3-4 years ago to basically proclaiming the death of junior SWEs. Who is to say this won't continue up the ladder?
AI will be good enough to replace all SWEs in any capacity - there is no point in "investing" in rebuilding this ladder when you can just invest in more GPUs (in the case of oai/ant/meta/google/etc). or just pay those aforementioned companies more in tokens if you are a smaller outfit. The cost effectiveness of those tokens will only get better over time, until they are competitive in cost : intelligence when compared to any human SWE.
> The jobs disappearing are the ones where the work product is code written to spec. The jobs growing are the ones where the work product is judgment about what code should exist.
AI is happy to follow instructions, no matter how stupid, unoptimal or unnecessary those are. To be successful, you need someone to understand the details and make the decisions.
And while that "someone" could be a person that does not go in the details and doesn't understand the code, they would be equivalent of non-technical CEO - sure, those exist, but they have a much harder time creating successful products.
> The jobs growing are the ones where the work product is judgment about what code should exist.
And how many people do you need for this? There are many roles where people are literally hired for their programming/engineering skill. Modern LLMs _largely_ commoditize that skillset.
There are not that many novel things to do, and even if there are, you don't need too many people to do them as LLMs give you more and more leverage over execution details.
I am currently work on embedded / low-level / devtools and there are tons of novel things to do. Perhaps the "programming" skills are not needed as much, but demand for "engineering" is as high as ever.
Maybe it's the case in web dev? TFA shows that the number of "web developers" is decreasing strongly.
wow an actual ai-pilled comment on here for once, I agree with your sentiment. People opining about "rebuilding the ladder" have no idea whats coming for the software industry, and the general populous of white collar work.
"Models can code well now but they cant do high level architecture" is just a logical fallacy. Its literally only true in this particular moment in time. But if they can code well, whose to say they wont architect well? And at that point, what do SWEs do? If anything, SWEs are in the critical path of automation for these AI labs anyway, so theres a very strong incentive to automate us out vs other professions, and it'll happen soon. All these random 1-off datapoints of "Fable 5 can't do X very idiosyncratic thing" are completely missing the point. 6 months ago, even attempting that problem with any "tool" would be totally intractable, and now it _just_ writes a slightly subpar solution. You can do some basic extrapolation here, its not that complicated.
Your best bet is to just chose a different career, or, if you still want to be in the software industry, be more enterprising.
This _was_ done a couple of decades ago, it was available on the downloadable version of google earth (when it existed). I remember playing around with it in 2012.
Google Earth pro is still available for download with the flight simulator, which is much better than the new web version. I played around with it last night after being disappointed with the web version.
This is just false. For starters, your users most _definitely_ don't care about how "elegant" your code is either. They want new features to keep them engaged, or to make the product better. The quicker they get those wants fulfilled by your product, the less likely they are to churn off your product onto something else that has those features that they are missing.
The only people who care about code elegance are the people looking at the code, which is orders of magnitude fewer people than those who are _using_ the artifacts of what the code actually represents.
Its still astonishing that any sort of generalized computer program can solve a problem of this magnitude, and we have witnessed it happening in real time. I'd be curious to see if the new model can also do more direct proofs/inductive proofs.
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