"Guilty until proven innocent" is an authoritarian, quasi-Stalinist view. There is nothing intrinsically wrong with seeking profit, and most people who do so are completely within ethical bounds. For the ones who do unethical things, we have laws and courts to decide that and to punish them appropriately.
There is also a "profit motive makes it wrong" idea permeating at least parts of the culture, which is equally suspect. We should apply standards consistently and judge the ethics by the amount of harm that was caused.
If we've learned anything from modern physics, it's that our regular intuitions are often wrong. The framework makes perfect sense mathematically, and it makes the right predictions experimentally. It's still controversial how exactly the framework relates to reality, which is where you get into interpretations of quantum mechanics.
Nielsen and Chuang's "Quantum Computation and Information" Chapter 2. (Available online, but legality depends on where you live)
If you're interested primarily in QM rather than linear algebra, I'd recommend not working through all of Axler, but instead using Nielsen and Chuang to learn about qubits and observables, and using Axler to fill in the gaps.
So many entry-level explanations of quantum mechanics start with classical physics, waves, particles, the double-slit experiment, etc. and then jump straight to infinite-dimensional Hilbert spaces. In my opinion, there's way too much conceptual baggage that comes with these concepts [0].
As a non-physicist, QM only clicked for me when I learned the linear algebra of one- and two-qubit systems and observables on those systems [1]. The math is accessible at a pre-calculus level, and the most "counter-intuitive" aspects of QM (interference, the uncertainty principle, entanglement) make perfect sense in this framework. I think it's the best starting point for understanding what QM really says.
[0] That's not to say they aren't important historically or for a complete understanding of QM. I'm talking about conceptual understanding for someone, especially an amateur, encountering QM for the first time.
[1] Chapter 2 of Nielsen and Chuang is what did it for me. It's heavygoing if you've never seen linear algebra before, but I don't know of a clearer explanation of the postulates of quantum mechanics.
On the one hand yes, Perelman was known within his subfield. On the other hand, I find it hard to believe that if some random person had published the same papers, they would have gone unnoticed, since as far as I understand it these were important results that would have been worth evaluating based on the titles and the abstracts.
Volume isn't really a problem going forward, at least in mathematics, since every proof can be accompanied by a Lean verification. Whether other disciplines can implement automated verification or quality assessment will depend on the details of the field.
Reputation is already a lot more about networking than a specific publishing system. Researchers get recognized by producing quality work and telling people about it in person and on the internet. Reputation spreads by word of mouth. I don't see that changing much in an open publishing world.
I agree, the people who care will still push themselves to learn. There's a concern about education for the ones who don't care, but that's a separate issue and was present long before AI came along.
You're thinking in terms of the traditional peer review model. I'm imagining a more organic process where papers are scrutinized to the extent that they're considered important or promising. For example, how Grigori Perelman's papers on arXiv about the Poincaré conjecture were reviewed by peers. There's no assignment of individual reviewers in this model, it's a natural byproduct of researchers discussing and collaborating in public.
Doing things by hand is part of the learning process. Sure, you can prompt an AI to one-shot a 3D open world game, but you'll learn nothing about programming, computer graphics, or game design. That domain knowledge makes you more effective with the AI tools when you choose to use them. What you're saying might apply to experienced engineers, but not a high school kid learning the craft for the first time.
And yes, there will be people who have no interest in learning and just want to manage the AI to do everything for them. I remain skeptical that (manager + AI) will beat (domain expert + AI). It's possible that the relevant kinds of expertise will evolve, but I'm betting on some form of expertise remaining relevant for a long time.
An alternative to journals would be to switch to the arXiv model everywhere: let anyone publish with a bare minimum of moderation, make papers explicitly versioned, and let peer review happen out in the open. Already for decades, the importance of a paper has been measured by citations, not publication in a prestigious journal.
It's fine as long as you set up sane style guidelines (only allocate memory at init, always use explicit buffer lengths, etc.), and you have a mandatory test pass running with UBSan/ASan/Valgrind.
I'm pretty sure most big software projects (Windows, Linux, Chrome, gcc, LLVM, AAA game engines, ...) are still writing a lot of new C and C++. Pretty much everyone in the embedded world is as well.
As a solo programmer, I still prefer C and C++. In fact, as I've incorporated LLMs in to my workflow, I've been increasingly drawn toward C and its simplicity. A lot of the QoL features of C++ simply aren't needed when you're writing less code manually.
>Making money is going to be everyone’s primary goal, especially if you’re now competing against machines as well
What you've just stated is a Marxist/materialist perspective. Up to a certain point of meeting basic survival needs, it's probably true. But there are other ways to think about what ultimately determines human goals once survival needs are met. This illustrates my point about the humanities, often we put our thinking into boxes without realizing that the box exists at all.
A lot of people still feel this way, possibly the majority. I think we're already starting to see a backlash against the cynical politics of the past decade and a renewed interest in shared values.
I hope we see a revival of the humanities in the coming decades. The original meaning of "liberal" in liberal education referred to cultivating free human beings (Latin libertas). Arguably, the most important skills going forward are the ability to think independently and critically, to adapt to new circumstances, to formulate one's thoughts in a coherent and convincing manner, and to understand things in a broader historical perspective.
Yes, people would seek out those services elsewhere because clearly there's a demand for them. I find this attitude very paternalistic. Why do you care how other people choose to spend their time? Is it the technology you don't like, or the people who use it?
Facebook is one of the most successful products of all time. Billions of people use it because they get value out of it. You can raise all of the legitimate criticisms of Facebook that you want, and there are many, but saying they have contributed nothing to society is delusional.
I'm still not clear on what you're really saying. If a human programmer's input doesn't matter at all, then anyone should be able to prompt a model until it spits out the software they want. That might work today for building fun tech demos, but I don't see it happening on projects that have a minimum bar for security/reliability and long-term maintenance. Would you say, for example, a team of 10 managers with no software experience can be as successful as a team of 10 expert programmers at building a large project like an operating system, with both teams having access to the same AI models and budget?
You didn't address the argument about tech debt. How do you get the greatest quantity of maintainable, high quality code within a given token budget? The answer, at least so far, seems to be human experts in the loop. We've also been hearing for years that LLMs are going to automate away software development entirely, but software job postings are steadily rising.
When I said "in my experience" I meant literally that, my experience so far. I'm not making any predictions. I'm definitely anticipating things improving at a rapid pace, which is why I think it's good to run frequent experiments. If in a month I find that an agent can one-shot a 20k line project without creating an unreadable, unmaintainable mess, that will be great.
I wonder if we will see new platforms emerge around human-only collaboration. There's nothing stopping people from continuing to work on projects in the traditional way. It's just harder on big platforms like GitHub which are designed around public bug reports and pull requests.
You can work the way you want to work. In my experience, letting the agent make every decision leads to a big ball of mud that requires touching dozens of files to make a change. More context is needed, it burns through tokens much faster, and the agent takes shortcuts and builds technical debt. So the way I prefer to work is to have changes targeted to a single file or small set of files where I've defined the API. Maybe that will change in the future, I'm not set in stone here.
I was fortunate and saved up enough to live frugally and just work on passion/learning projects for a while. I've been considering going back into commercial software development as a solo dev and leveraging AI, but keeping the focus on quality and craftsmanship. In my opinion there's never been a better time to start a new, ambitious project if you're passionate about quality software.
Yeah, I imagine it's something that needs to be enforced at the organizational level. I sympathize with anyone who takes pride in their work and needs to deal with mindless coworkers pushing slop without any accountability. I left corporate software development (before LLMs were a thing) over frustration with the general culture and I can only imagine it's gotten worse since then.
Don't get me wrong, LLMs are way better than me at most things, but I don't think you can deny that the current generation of models makes questionable decisions sometimes. Especially on project structure, API boundaries, and code duplication. I also think they just have bad taste in general, though of course that's more subjective.
I agree with this approach, but I'm a bit surprised this has to be said at all. These are basic software design principles and I imagine it's the default way any experienced programmer would approach the problem. I wonder if the people not using this approach are primarily junior developers who have never seen how real software is built, or if it's leaking into the senior levels as well.
I don't think the metaphor works because a job is an agreement to exchange labor for money, not a service or a product that you purchase. But for the sake of argument, let's grant it. If a restaurant gives you a peanut butter sandwich instead of a steak, you can go to another restaurant that provides better service. Similarly, if an employee doesn't like the terms set by their employer, they can move to a different company, or start their own company on their own terms.
I'm curious if there are specific examples of the kind of thing that motivated this post. What comments or articles are suggesting that it's not ok to enjoy programming?