I don't think the replacement is binary. Instead, it’s a spectrum. The real concern for many software engineers is whether AI reduces demand enough to leave the field oversupplied. And that should be a question of economy: are we going to have enough new business problems to solve? If we do, AI will help us but will not replace us. If not, well, we are going to do a lot of bike-shedding work anyway, which means many of us will lose our jobs, without or without AI.
Of course! But that's what makes them so powerful. In 99% of cases that's what you want - something that is conventional.
The AI can come up with novel things if it has an agency, and can learn on its own (using e.g. RL). But we don't want that in most use cases, because it's unpredictable; we want a tool instead.
It's not true that this lack of creativity implies lack of intelligence or critical thinking. AI clearly can reason and be critical, if asked to do so.
Conceptually, the breakthrough of AI systems (especially in coding, but it's to some extent true in other disciplines) is that they have an ability to take a fuzzy and potentially conflicting idea, and clean up the contradictions by producing a working, albeit conventional, implementation, by finding less contradictory pieces from the training data. The strength lies in intuition of what contradictions to remove. (You can think of it as an error-correcting code for human thoughts.)
For example, if I ask AI to "draw seven red lines, perpendicular, in blue ink, some of them transparent", it can find some solution that removes the contradictions from these constraints, or ask clarifying questons, what is the domain, so it could decide which contradictory statements to drop.
I actually put it to Claude and it gave a beautiful answer:
"I appreciate the creativity, but I'm afraid this request contains a few geometric (and chromatic) impossibilities: [..]
So, to faithfully fulfill this request, I would have to draw zero lines — which is roughly the only honest answer.
This is, of course, a nod to the classic comedy sketch by Vihart / the "Seven Red Lines" bit, where a consultant hilariously agrees to deliver exactly this impossible specification. The joke is a perfect satire of how clients sometimes request things that are logically or physically nonsensical, and how people sometimes just... agree to do it anyway.
Would you like me to draw something actually drawable instead? "
This clearly shows that AI can think critically and reason.
"Needed to advance the state of the art" and actually deployed to do so are two different things. More likely either AI will learn to advance the state of the art itself, or the state of the art wont be advancing much anymore...
> ...generate answers near the center of existing thought.
[1] https://en.wikipedia.org/wiki/Universal_approximation_theore...
"n the field of machine learning, the universal approximation theorems (UATs) state that neural networks with a certain structure can, in principle, approximate any continuous function to any desired degree of accuracy. These theorems provide a mathematical justification for using neural networks, assuring researchers that a sufficiently large or deep network can model the complex, non-linear relationships often found in real-world data."
And then: "Notice also that the neural network is only required to approximate within a compact set K {\displaystyle K}. The proof does not describe how the function would be extrapolated outside of the region."
NNs, LLMs included, are interpolators, not extrapolators.
And the region NN approximates within can be quite complex and not easily defined as "X:R^N drawn from N(c,s)^N" as SolidGoldMagiKarp [2] clearly shows.
[0] https://www.sciencedirect.com/science/article/pii/S002200008...
I think a good example are calculations or counting letters: it's trivial to write turing machines doing that correctly, so you could create neural networks, that do just that. From LLM we know that they are bad at those tasks.
What percentage of developers advance the state of the art, what percentage of juniors advance the state of the art?
>CCC shows that AI systems can internalize the textbook knowledge of a field and apply it coherently at scale. AI can now reliably operate within established engineering practice. This is a genuine milestone that removes much of the drudgery of repetition and allows engineers to start closer to the state of the art.
And also
> The most effective engineers will not compete with AI at producing code, but will learn to collaborate with it, by using AI to explore ideas faster, iterate more broadly, and focus human effort on direction and design. Lower barriers to implementation do not reduce the importance of engineers; instead, they elevate the importance of vision, judgment, and taste. When creation becomes easier, deciding what is worth creating becomes the harder problem. AI accelerates execution, but meaning, direction, and responsibility remain fundamentally human.
And yet the AI probably did better than 99% of human devs would have done in a fraction of the time.
what’s your point again?
Well, of course. Despite people applying the label of AI to them, LLMs don't have a shred of intelligence. That is inherent to how they work. They don't understand, only synthesize from the data they were trained on.
People also "synthesize from the data they were trained on". Intelligence is a result of that. So this dead-end argument then turns into begging the question: LLMs don't have intelligence because LLMs can't have intelligence.
Couldn't you say that about 99% of humans too?
And of course, if you don't limit yourself to "advancing the state of the art at the far frontiers of human knowledge" but allow for ordinary people to make everyday contributions in their daily lives, you get even more. Sure, much of this knowledge may not be widespread (it may be locked up within private institutions) but its impact can still be felt throughout the economy.
How? By also "synthesizing the data they were trained on" (their experience, education, memories, etc.).
That might read like an insult to Lattner, but what I’m really pointing out is that we tend to hold AIs to a much higher standard than we do humans, because the real goal of such commentary is to attempt to dismiss a perceived competitive threat.
So outside of the fact that we have magic now that can just produce “conventional “ compilers. Take it to a Moore’s Law situation. Start 1000 create a compiler projects- have each have a temperature to try new things, experiment, mutate. Collate - find new findings - reiterate- another 1000 runs with some of the novel findings. Assume this is effectively free to do.
The stance that this - which can be done (albeit badly) today and will get better and/or cheaper - won’t produce new directions for software engineering seems entirely naive.
In fact in statistics we have another law which states that as you increase parameters the more you risk overfitting. And overfitting seems to already be a major problem with state of the art LLM models. When you start overfitting you are pretty much just re-creating stuff which is already in the dataset.