tjr

I don't think it's quite at trillions to zero, but I do think the numbers are way, way out of balance so far. I'm still at the point where, if AI disappeared tomorrow, I would be, at worst, mildly inconvenienced.

Its also worth noting that if you can create a business with an LLM, so can everyone else.

One possibility may be that we normalize making bigger, more complex things.

In pre-LLM days, if I whipped up an application in something like 8 hours, it would be a pretty safe assumption that someone else could easily copy it. If it took me more like 40 hours, I still have no serious moat, but fewer people would bother spending 40 hours to copy an existing application. If it took me 100 hours, or 200 hours, fewer and fewer people would bother trying to copy it.

Now, with LLMs... what still takes 40+ hours to build?

Fair; that was an example instance. People interested in “Free software” rather than “open source” seem to often favor the GPL, though other licensing options also count as “free software”.

But in any case, the question really refers to, can the LLM-generated software be copyrighted? If not, it can’t be put under any particular license.

I’ve seen different opinions. Can LLM-generated software be licensed under the GPL?

I see AI coding as something like project management. You could delegate all of the tasks to an LLM, or you could assign some to yourself.

If you keep some for yourself, there’s a possibility that you might not churn out as much code as quickly as someone delegating all programming to AI. But maybe shipping 45,000 lines a day instead of 50,000 isn’t that bad.

Go enough shoulders down, and someone had to have been the first giant.

There has long been debate that software development is not even an engineering discipline at all, because it lacks certain characteristics from "real world engineering". I have worked with software mostly in aerospace, and I believe that what is typically done in that industry counts as "engineering". Reams of requirements, boatloads of tests (including simulated testing, testing in hardware labs, and testing on the plane), and sign-offs from multiple people who attest to software quality.

I would further think that the same practices could be applied to any software, whether if it was safety-critical or not. If software development isn't engineering, it's not because it can't be, but because not every project is critical enough to warrant the extra time and expense.

I think a similar train of thought applies here. As the article points out, skipping reading the code is probably not a good idea for safety-critical software, but for less critical things, it may be fine.

If someone told me that they applied avionics-level rigor to an iOS puzzle game, I would think that (a) it's probably very solid software, but also (b) they were probably wasting their time. But on the flip side, if someone wanted to spend their time making their puzzle game rock-solid, I don't think that's necessarily a bad thing to do. It's not harmful to have especially robust puzzle games.

Is it worth it to review LLM-generated code? For some projects, maybe not. Even for many projects, maybe not. But I'm not sure that it should be frowned upon either. It might turn up something interesting. Put in whatever level of rigor matches your project needs, personal interest, and schedule!

One of the main points of the GPL was to prevent software from being siphoned up and made part of proprietary systems.

I personally disagree with the rulings thus far that AI training on copyrighted information is "fair use", not because it's not true for human training, but because I think that the laws were neither written nor wielded with anyone but humans in mind.

As a comment upstream a bit said, some people are now rethinking even releasing some material into the public, out of not wanting it to be trained by AI. Prior to a couple of years or so ago, nearly nobody was even remotely thinking about that; we could have decades of copyrighted material out there that, had the authors understood present-day AI, they wouldn't have even released it.

Is your proposition that programmers are now incapable of writing code?

I don't see why it's not possible.

In your example scenario, it sounds like Bob is vibe-coding a project in assembly language, and then using an existing assembler to create the final binary?

I have not tried it, but I would imagine that current LLMs are relatively weak on generating assembly language, just due to less thorough training compared to higher-level languages, but even if so, that's surmountable.

As for, what I think you are suggesting, having the LLM also do the assembly step? Again, in theory, sure, but I would think just using the existing known-good assembler would be preferable to training an LLM to convert assembly language to binary. I'm not sure what you would gain in terms of either speed or overall productivity to have the LLM itself do the assembler step?

As described above, I think with AI coding, our role shifts from "programmer" to "project manager", but even as a project manager, you can still choose to delegate some tasks to yourself. Whether if you want to do the hard stuff yourself, or the easy stuff, or the stuff that happens on Thursdays. It's not about what AI is capable of doing, but rather, what you choose to have it do.

Most of my career has been as an individual engineer, but the past few years I have been a project manager. I find this to be very much like using AI for coding.

Which also makes me refute the idea that AI coding is just another rung up on the programming abstraction ladder. Depending on how much you delegate to AI, I don't think it's really programming at all. It's project management. That's not a bad thing! But it's not really still programming.

Even just in the context of my human team, I feel less mentally engaged with the code. I don't know what everything does. (In principle, I could know, but I don't.) I see some code written in a way that differs from how I would have done it. But I'm not the one working day-in, day-out with the code. I'll ask questions, make suggestions, but I'm not going to force something unless I think it's really super important.

That said, I don't 100% like this. I enjoy programming. I enjoy computer science. I especially enjoy things more down the paths of algorithm design, Lisp, and the intersection of programming with mathematics. On my team, I do still do some programming. I could delegate it entirely, but I indulge myself and do a little bit.

I personally think that's a good path with AI too. I think we're at the point where, for many software application tasks, the programming could be entirely hands-off. Let AI do it all. But if I wish to, why not indulge in doing some myself also? Yeah, I know, I know, I'll get "left behind in the dust" and all of that. I'm not sure that I'm in that much of a hurry to churn out 50,000 lines of code a day; I'm cool with 45,100.

I wonder how much more it would take Anthropic to make CCC on par with, or even better than, GCC.

I’m glad cheap stuff exists. Sometimes I really do need something quickly, and borderline-disposable quality is good enough. But I also want the option to buy better than that.

I installed some drywall a few years ago. I plan to install a room of drywall exactly never again. Not worth it for me to buy the best drywall tools.

But I have installed multiple wood floors, replacing old carpet, and would do so again if needed. I’d rather get higher quality tools there so I can keep them and reuse them for years.

Will the agents also be the ones using the software?

What compiler accepts cash as input?

Maybe. This book from 1990

https://mitpress.mit.edu/9780262526401/artificial-intelligen...

envisions a future of AI assistance that looks not too far off from today.

Right. I work in aerospace software, and I do not know if this option would ever be on the table. It certainly isn't now.

So I think this question needs to be asked in the context of particular projects, not as an industry-wide yes or no answer. Does your particular project still need humans involved at the code level? Even just for review? If so, then you probably ought to retain human-oriented software design and coding techniques. If not, then, whatever. Doesn't matter. Aim for whatever efficiency metric you like.

I think that's the question. Is a programmer expected to ever touch the source code? Or will AI -- and AI alone -- update the code that it generated?

Not entirely unlike other code generation mechanisms, such as tools for generating HTML based on a graphical design. A human could edit that, but it may not have been the intent. The intent was that, if you want a change, go back to the GUI editor and regenerate the HTML.

Maybe AI was driving his car.

Delegation, yes.

I do not see LLM coding as another step up on the ladder of programming abstraction.

If your project is in, say, Python, then by using LLMs, you are not writing software in English; you are having an LLM write software for you in Python.

This is much more like delegation of work to someone else, than it is another layer in the machine-code/assembly/C/Python sort of hierarchy.

In my regular day job, I am a project manager. I find LLM coding to be effectively project management. As a project manager, I am free to dive down to whatever level of technical detail I want, but by and large, it is others on the team who actually write the software. If I assign a task, I don't say "I wrote that code", because I didn't; someone else did, even if I directed it.

And then, project management, delegating to the team, is most certainly nondeterministic behavior. Any programmer on the team might come up with a different solution, each of which works. The same programmer might come up with more than one solutions, all of which work.

I don't expect the programmers to be deterministic. I do expect the compiler to be deterministic.

Sometimes determinism is exactly what one wants. For avionics software, being able to claim complete equivalence between two builds (minus an expected, manually-inspected timestamp) is used to show that the same software was used / present in both cases, which helps avoid redundant testing, and ensure known-repeatable system setups.

People often compare working with AI agents to being something like a project manager.

I've been a project manager for years. I still work on some code myself, but most of it is done by the rest of the team.

On one hand, I have more bandwidth to think about how the overall application is serving the users, how the various pieces of the application fit together, overall consistency, etc. I think this is a useful role.

On the other hand, I definitely have felt mental atrophy from not working in the code. I still think; I still do things and write things and make decisions. But I feel mentally out of shape; I lack a certain sharpness that I perceived when I was more directly in tune with the code.

And I'm talking, all orthogonal to AI. This is just me as a project manager with other humans on the project.

I think there is truth to, well, operate at a higher level! Be more systems-minded, architecture-minded, etc. I think that's true. And there are surely interesting new problems to solve if we can work not on the level of writing programs, but wielding tools that write programs for us.

But I think there's also truth to the risk of losing something by giving up coding. Whether if that which might be lost is important to you or not, is your own decision, but I think the risk is real.

In Soviet Russia, this thread brings Slashdot back to YOU!

It's not illegal for me to drive from New York to San Francisco on Interstate 80. But if I were able to endlessly duplicate myself and my car, about a million of me could hog the entire country-wide highway. Not sure if even that would be "illegal", but it would sure be annoying, and I suspect there would need to be laws updated / rewritten to account for my ability to duplicate myself without limit. I doubt society would just agree, "yep, one of you on I-80 is okay, and so a million of you on I-80 is also okay."

Imagine if RMS could have just vibe-coded a new printer driver.

So far, very little.

Per corporate policy -- in the name of safety and legal reasons -- the most extensive public uses of AI, like vibe coding and agentic systems, aren't really options. The most common usage I have seen is more like consulting AI as a fancy StackOverflow.

Will this change? I personally don't expect to ever see pure vibe coding, with code unseen and unreviewed, but I imagine AI coding uses will expand.

What would be some examples of some current software products for which you expect the game is up?

Are there any software products that you think will survive?

what are the kinds of tests do people write in other areas of the industry?

Aerospace here. Roughly this would be typical:

- comprehensive requirements on the software behavior, with tests to verify those requirements. Tests are automated as much as possible (e.g., scripts rather than manual testing)

- tests are generally run first in a test suite in a completely virtual software environment

- structural coverage analysis (depending on level of criticality) to show that all code in the subsystem was executed by the testing (or adequately explain why the testing can't hit that code)

- then once that passes, run the same tests in a hardware lab environment, testing the software as it runs on the the actual physical component that will be installed on the plane

- then test that actually on a plane, through a series of flight tests. (The flight testing would likely not be as entirely comprehensive as the previous steps)

A full round of testing is very time-consuming and expensive, and as much as possible should be caught and fixed in the virtual software tests before it even gets to the hardware lab, much less to the plane.

Interesting that it "generated" such a large chunk of code that it just copied from somewhere else.