I have no idea how you come to this conclusion, when the evidence on the ground for those training models suggests it is precisely the opposite.
We are much further along the path of writing code than writing new maths, since the latter often requires some degree of representational fluency of the world we live in to be relevant. For example, proving something about braid groups can require representation by grid diagrams, and we know from ARC-AGI that LLMs don't do great with this.
Programming does not have this issue to the same extent; arguably, it involves the subset of maths that is exclusively problem solving using standard representations. The issues with programming are primarily on the difficulty with handling large volumes of text reliably.
Grid Diagrams can be specified (hopefully) through algebraic equations.
The way that most math is currently done is that someone provides an extremely specified problem and then one has to answer that extremely specified problem.
The way that programming is currently done is through constructing abstractions and trying to create a specification of the problem.
Of course I'm not saying we're close to creating a silicon Grothendieck (I think that Bourbaki actually reads like a codebase) but I'm saying that we're much closer to constructing algorithms that can solve specified programs as opposed to specifying underspecified problems
Think about the difference in specificity of
Prove Fermat's last theorem vs Build a web browser
Nah, LLM's are solving unique problems in maths, whereas they're basically just overfitting to the vast amounts of training data with writing code. Every single piece of code AI writes is essentially just a distillation of the vast amounts of code it's seen in it's training - it's not producing anything unique, and it's utility quickly decays as soon as you even move towards the edge of the distribution of it's training data. Even doing stuff as simple as building native desktop UI's causes it massive issues.
Yeah, it's hard to compare management and programming but they're both multimodal in very different ways. But there's gonna be entire domains in which AI dominates much like stockfish, but stockfish isn't managing franchises and there is no reason to expect that anytime soon.
I feel like something people miss when they talk about intelligence is that humans have incredible breadth. This is really what differentiates us from artificial forms of intelligence as well as other animals. Plus we have agency, the ability to learn, the ability to critically think, from first principles, etc.
Oooh yeah that's really good framing. Humans have been building machines that outperform humans for hundreds of years at this point, but all in problems which are extremely well specified. It's not surprising LLM's are also great in these well specified domains.
One difference between intelligence and artificial intelligence is that humans can thrive with extremely limited training data, whereas AI requires a massive amount of it. I think if anybody is worried about being replaced by AI, they should look at maximising their economic utility in areas which are not well specified.
But LLMs have proven themselves better at programming than most professional programmers.
Don't argue. If you think Hackernews is a representative sample of the field then you haven't been in the field long enough.
What LLMs have actually done is put the dream of software engineering within reach. Creativity is inimical to software engineering; the goal has long been to provide a universal set of reusable components which can then be adapted and integrated into any system. The hard part was always providing libraries of such components, and then integrating them. LLMs have largely solved these problems. Their training data contains vast amounts of solved programming problems, and they are able to adapt these in vector space to whatever the situation calls for.
We are already there. Software engineering as it was long envisioned is now possible. And if you're not doing it with LLMs, you're going to be left behind. Multimodal human-level thinking need only be undertaken at the highest levels: deciding what to build and maybe choosing the components to build it. LLMs will take care of the rest.
A bit optimistic I'd say. It's put some software engineering within reach of some people who couldn't do it prior. Where 'some' might be a lot, but still far from all.
I was thinking the other day of how things would go if some of my less tech savvy clients tried to vibe code the things I implement for them, and frankly I could only imagine hilarity ensuing. They wouldn't be able to steer it correctly at all and would inevitably get stuck.
Someone needs to experiment with that actually: putting the full set of agentic coding tools in the hands of grandma and recording the outcome.
It's still going to take a knowledgeable person to steer an LLM. The point is that code written entirely by humans is finished as a concept in professional work—if you're writing it yourself you're not working efficiently or employing industry best practice.
I think it's dramatic to say it's the end of hand written code. That's like saying it's the end of bespoke suits. There are scenarios where carefully hand written and reviewed code are still going to have merit - for example the software for safety critical systems such as space shuttles and stations, or core logic within self-driving vehicles.
Basically when every single line needs to be reviewed extremely closely the time taken to write the code is not a bottleneck at all, and if using AI you would actually gain a bottleneck in the time spent removing the excess and superfluous code it produces.
And my intuition is that the line between those two kinds of programming - let's call them careful and careless programming to coin an amusing terminology - I think that line may not shrink as far back as some think, and I think it definitely won't shrink to zero.
The code lets you shoot yourself in the foot in a lot more ways than a spec does, though. Few people would make specs that include buffer overflows or SQL injection.
That is akin to saying if you aren't using an IDE you are not working efficiently or employing industry best practice, which is insane when you consider people using Vi often run rings around people using IDEs.
AI usage is a useless metric, look at results. Thus far, results and AI usage are uncorrelated.
I keep hearing anecdata that suggest significant to huge productivity increases—"a task that would have taken me weeks now takes hours" is common. There is currently not a whole lot of research that supports that, however:
1) there hasn't been a whole lot of research into AI productivity period;
2) many of the studies that have been done (the 2025 METR study for example) are both methodologically flawed and old, not taking into account the latest frontier models
3) corporate transitions to AI-first/AI-native organizations are nowhere near complete, making companywide productivity gains difficult to assess.
However, it isn't hard to find stories on Hackernews from devs about how much time generative AI has saved them in their work. If the time savings is real, and you refuse to take advantage of it, you are stealing from your employer and need to get with the program.
As for IDEs, if you're working in C# and not using Visual Studio, or Java and not using JetBrains, then no—you are not working as efficiently as you could be.
Actually I will argue. Complex systems are akin to a graph, attributes of the system being the nodes and the relationships between those attributes being the edges. The type of mechanistic thinking you're espousing is akin to a directed acyclic graph or a tree, and converting an undirected cyclic graph into a tree requires you to disregard edges and probably nodes as well. This is called reductionism, and scientific reductionism is a cancer for understanding complex phenomena like sociology or economics, and I posit, software as well.
People and corporations have been trying for at least the last five decades to reduce software development to a mechanistic process, in which a system is understandable solely via it's components and subcomponents, which can then be understood and assembled by unskilled labourers. This has failed every time, because by reducing a graph to a DAG or tree, you literally lose information. It's what makes software reuse so difficult, because no one component exists in isolation within a system.
The promise of AI is not that it can build atomic components which can be assembled like my toaster, but rather that it can build complex systems not by ignoring the edges, but managing them. It has not shown this ability yet at scale, and it's not conclusive that current architectures ever will. Saying that LLM's are better than most professional programmers is also trivially false, you do yourself no favours making such outlandish claims.
To tie back into your point about creativity, it's that creativity which allows humans to manage the complexity of systems, their various feedback loops, interactions, and emergent behaviour. It's also what makes this profession broadly worthwhile to its practitioners. Your goal being to reduce it to a mechanistic process is no different from any corporation wishing to replace software engineers with unskilled assembly line workers, and also completely misses the point of why software is difficult to build and why we haven't done that already. Because it's not possible, fundamentally. Of course it's possible AI replaces software developers, but it won't be because of a mechanistic process, but rather because it becomes better at understanding how to navigate these complex phenomena.
This might be besides the point, but I also wish AI boosters such as yourself would disclose any conflict of interests when it comes to discussing AI. Not in a statement, but legally bound, otherwise it's worthless. Because you are one of the biggest AI boosters on this platform and it's hard to imagine the motivation of spending so much time hardlining a specific narrative just for the love of the game, so to speak.
> Saying that LLM's are better than most professional programmers is also trivially false, you do yourself no favours making such outlandish claims.
You grossly underestimate how awful one can be and still call themselves a "professional" in the field. Software engineering has effectively no standard certification of competence, which is part of why it's not actually an engineering field at all. So I stand by my statement that LLMs are better at writing code than most people working professionally as programmers. Again, Hackernews is not a representative sample, let alone the kind of programmer we admire and view as authoritative here on Hackernews. Most programmers require considerable oversight as well as detailed standards to follow in order to produce work without gumming up a code base. If you want to know why so much enterprise stuff is so bloated with heavy frameworks and a twisty maze of best practices like OO, SOLID, GoF patterns, etc. it's for this reason. The LLMs have access to a vast (if compressed/summarized) repository of knowledge about programming problems and commonly employed solutions in a variety of languages, and the ability to draw upon it instantly. Most humans, including myself, do not.
Anyway, as Tim Bryce observed in 2005, based on his father Milt's work in the 70s, most of the creativity and human in software development happens in the business/systems analysis phase, not programming, at least if you're employing a structured, rigorous, proven methodology. Milt Bryce turned systems design from an art into a proven, repeatable science, and with that a view of programming that's largely mechanistic. "There are very few true artists in programming; most programmers are just house painters."
> This might be besides the point, but I also wish AI boosters such as yourself would disclose any conflict of interests when it comes to discussing AI.
I'm not boosting squat. I'm telling it like it is, and talking about decisions in our field that have already been made. It is no longer up for debate that AI use is an integral part of software engineering now, and writing code "the old way", in an editor with maybe autocomplete, refactoring tools, etc., will soon go the way of punchcards. The business class that actually runs things has already decided this. If you're getting suspicious and demanding conflict-of-interest disclosures from someone who spells this out, your understanding is out of date.
Of course, because it takes multi-modal intelligence to manage a McDonalds. I.e. it requires human intelligence.
> I predict that in the future people will ditch LLMs in favor of AlphaGo style RL
Same for coding as well. LLM's might be the interface we use with other forms of AI though.