My recommendation would be to encourage students to ask the LLM to quiz and tutor them, but ultimately I think most students will learn a lot less than say 5 years ago while the top 5% or so will learn a lot more
My recommendation would be to encourage students to ask the LLM to quiz and tutor them, but ultimately I think most students will learn a lot less than say 5 years ago while the top 5% or so will learn a lot more
We’ll see a new class division scaffolded on the existing one around screens. (Schools in rich communities have no screens. Students turn in their phones and watches at the beginning of the day. Schools in poor ones have them everywhere, including everywhere at home.)
I specifically remember Telluride Mountain School’s banners in town advertising a low-tech approach.
If we assume that AI will automate many/most programming jobs (which is highly debatable and I don't believe is true, but just for the sake of argument), isn't this a good outcome? If most parts of programming are automatable and only the really tricky parts need human programmers, wouldn't it be convenient if there are fewer human programmers but the ones that do exist are really skilled?
AI does not think
Ergo, AI will not take "programming jobs"
It may highlight some "fraud people" (do not know how to say it in english .. you know, people who fake the job so hard but are just clowns, do not produce anything, are basically worthless and just here to grab some money as long as the fraud is working)
Why?
Because LLMs are capable of sometimes working snippets of usually completely unmaintainable code?
Sure. So we can keep paying money to your employer, Anthropic, right?
on some ChatGPT interfaces.
>AI will improve more rapidly than the education system can adapt.
Is entirely obvious, and:
> Within a few years it won't make sense for people to learn how to write actual code, and it won't be clear until then which skills are actually useful to learn.
is not obvious, but quite clear from how things are going. I expect actual writing of code "by hand" to be the same sort of activity as doing integrals by hand - something you may do either to advance the state of the art, or recreationally, but not something you would try to do "in anger" when faced with a looming project deadline.
This doesn’t seem like a good example. People who engineer systems that rely on integrals still know what an integral is. They might not be doing it manually, but it’s still part of the tower of knowledge that supports whatever work they are doing now. Say you are modeling some physical system in Matlab - you know what an integral is, how it connects with the higher level work that you’re doing, etc.
An example from programming: you know what process isolation is, and how memory is allocated, etc. You’re not explicitly working with that when you create a new python list that ends up on the heap, but it’s part of your tower of knowledge. If there’s a need, you can shake off the cobwebs and climb back down the tower a bit to figure something out.
So here’s my contention: LLMs make it optional to have the tower of knowledge that is required today. Some people seem to be very productive with agentic coding tools today - because they already have the tower. We are in a liminal state that allows for this, since we all came up in the before time, struggling to get things to compile, scratching our heads at core dumps, etc.
What happens when you no longer need to have a mental model of what you’re doing? The hard problems in comp sci and software engineering are no less hard after the advent of LLMs.
Architects are not civil engineers and often don't know details of construction, project management, structural engineering etc. For a few years there will still be a role for a human "architect" but most of the specific low level stuff will be automated. Eventually there won't be an architect either but that may be 10 years away
Coding via prompt is simply a new form of coding.
Remember that high level programming languages are "merely" a sop for us humans to avoid low level languages. The idea is that you will be more productive with say Python than you would with ASM or twiddling electrical switches that correspond to register inputs.
A purist might note that using Python is not sufficiently close to the bare metal to be really productive.
My recommendation would be to encourage the tutor to ask the student how they use the LLM and to school them in effective use strategies - that will involve problem definition and formulation and then an iterative effort to solve the problem. It will obviously involve how to spot and deal with hallucinations. They'll need to start discovering model quality for differing tasks and all sorts of things that look like sci-fi to me 10 years ago.
I think we are at, for LLMs, the "calculator on digital wrist watch" stage that we had in the mid '80s before the really decent scientific calculators rocked up. Those calculators are largely still what you get nowadays too and I suspect that LLMs will settle into a similar role.
They will be great tools when used appropriately but they will not run the world or if they do, not for very long - bye!
It's obviously not quite the same as programming, but my English professor assigned an essay a few weeks ago where we had to ask ChatGPT a question and then analyze its response, check its sources, and try to spot hallucinations. It was worth about 5% of our overall grade. I thought that it was a fascinating exercise in teaching responsible LLM use.
This reminds me of folks teaching their kids Java ten years ago.
You’re teaching a tool. Versus general tool use.
> Those calculators are largely still what you get nowadays too and I suspect that LLMs will settle into a similar role
If correct, the child will be competent in the new world. If not, they will have wasted time developing general intelligence.
This doesn’t strike me as a good strategy for anything other than time-consuming babysitting.
High-level languages are deterministic and reliable, making it possible for developers to be confident that their high-level code is correct. LLMs are anything but deterministic and reliable.
Checking output can be done by testing but test code in itself can be unreliable and testing in itself is no correctness guarantee.
The only way reliable code could be produced without human touching it would be using formal specifications, having the LLM write the formal proof at the same time as the code and using some software to validate the proof. The formal specification would have to be written using some kind of programming language, and then we're somewhat back to square one (but with maybe a new higher level language where you only define the specs formally rather than how you implement them).
It isn’t deterministic like a real programmer isn’t deterministic, and that’s why iteration is necessary.
Therefore, I still see a need for highlevel and even higher level languages, but ones which are easy for humans to understand. AI can help of course but challenge is how can we unambiguously communicate with machines, and express our ideas concisely and understandably for both us and for the machines.
No, it isn't. "Write me a parser for language X" is like pressing a button on a photocopier. The LLM steals content from open source creators.
Now the desperate capital starved VC companies can downvote this one too, but be aware that no one outside of this site believes the illusion any longer.
I am sure that the 360° performance reviews have never looked better.
Your experience is contradicted by the usually business friendly Economist:
https://www.economist.com/finance-and-economics/2025/11/26/i...
jokes aside I do trust economist’s heart is in the right place but misguided IMO. “the investors” (much like many here on HN) expected “AI” to be magic thing and are dealing with some disappointment that most of us are still employed. the next stage of “investor sentiment” just may be “shoot, not magic but productivity is through the roof”
Where are the hard numbers? Number of games on Steam, new GitHub projects, new products released, GDP growth—anything.
since you referenced a trusted Economist here’s much-more-we-know-what-we-are-talking-about MIT saying 12% of workforce is replaceable by AI (I think this is too low) - https://iceberg.mit.edu/
So far no verifiable metrics show any hint of a 3x productivity boost.
Not according to court cases.
Courts ruled that machine learning is a transformative use, and just fine.
Pirating material to perform the training is still piracy, but open source licenses don't get that protection.
A summary of one such court case: https://www.jurist.org/news/2025/06/us-federal-judge-makes-l...
> "Write me a parser for language X" is like pressing a button on a photocopier.
What is the prompt "review this code" in your view? Because LLM-automated code review is a thing now.
To retroactively grant propriety AI training rights on all copyrighted material on the basis that it's no different from humans learning is, I think, misguided.
That's a fair position: laws are for the nation (and in a democracy, that's supposed to mean the people), and the laws we make are not divine or perfect.
But until the laws change, it is what it is.
> To retroactively grant propriety AI training rights on all copyrighted material on the basis that it's no different from humans learning is, I think, misguided.
I would say it's not retroactive, it's the default consequence of what already is. Changing the law so this kind of thing is no longer allowed in the future is one thing, but it would be retro-active to say it had always been illegal.