If that is the case, it's a very different claim than that AI is plagiarizing Tailwind (which was somewhat of a reach, given the permissiveness of the project's MIT license). Achieving such mass adoption would typically be considered the best case scenario for an open source project, not harm inflicted upon the project by its users or the tools that promoted it.
The problem Tailwind is running into isn't that anything has been stolen from them, as far as I can tell. It's that the market value of certain categories of expertise is dropping due to dramatically scaled up supply — which is basically good in principle, but can have all sorts of positive and negative consequences at the individual level. It's as if we suddenly had a huge glut of low-cost housing: clearly a social good on balance, but as with any market disruption there would be winners and losers.
If Tailwind's primary business is no longer as competitive as it once was, they may need to adapt or pivot. That doesn't necessarily mean that they're a victim of wrongdoing, or that they themselves did anything wrong. GenAI was simply a black swan event. As a certain captain once said, "It is possible to commit no mistakes and still lose. That is not a weakness; that is life.".
I see what you're getting at, but CSS is as much an open standard as the law. Public legal docs written against legal standards aren't fundamentally dissimilar to open source libraries written against technical standards.
While I am all for working out some sort of compensation scheme for the providers of model training data (even if indirect via techniques like distillation), that's a separate issue from whether or not AI's disruption of demand for certain products and services is per se harmful.
I'm not going to dogpile criticism on Tailwind or Adam, whose behavior seems quite admirable, but I fundamentally agree with the thrust of the parent comment. It's unfortunate for Tailwind and anyone who was invested in the project's pre-2022 trajectory, but no one is entitled to commercial engagement by unaffiliated third parties.
Here's a similar example from my own experience:
* Last week, I used AI to help me prepare a set of legal agreements that would have easily cost $5k+ in legal fees pre-2022.
* I've also started a personal blog and created a privacy policy and ToS that I might otherwise have paid lawyers money to draft. Or more realistically, I'd have cut those particular corners and accepted the costs of slightly higher legal risk and reduced transparency.
* In total, I've saved into the five figures on legal over the past few years by preparing docs myself and getting only a final sign-off from counsel as needed.
One perspective would be that AI is stealing money from lawyers. My perspective is that it's saving me time, money, and risk, and therefore allowing me to allocate my scarce resources far more efficiently.
Automation inherently takes work away from humans. That's the purpose of automation. It doesn't mean automation is bad; it means we have a new opportunity to apply our collective talents toward increasingly valuable endeavors. If the market ultimately decides that it doesn't have sufficient need for continued Tailwind maintenance to fund it, all that means is that humanity believes Adam and co. will provide more value by letting it go and spending their time differently.
Thanks for sharing, that is really bad. I did say "mostly", though. A bizarre anomaly that will most likely get thrown out in court is still pretty different from comparable practices in e.g. the UK that are standard procedure.
The transparency I referred to was primarily to the American political system's airing of its dirty laundry out in the open, which is inherently going to look more chaotic than disputes between internal factions of a single party because so much of it is performative.
Anyone who spends any amount of time perusing discussions on social media will quickly observe what a rare gift strong reading comprehension turns out to be.
I think there's an important material difference between the two. China's single party is authoritarian and uncontested. America's two major parties are mildly authoritarian on different axes, but average out to a mostly liberal status quo in practice. The relative chaos and transparency of America's system are what they are, but it isn't an autocracy at this point.
There's also a significant growing political push to transition away from FPTP voting in the US, which would dismantle the current duopoly.
In summary, the Luddites had a point. It doesn't mean they were ultimately correct, just that their concerns were valid.
Regardless of anyone's thoughts on genAI in particular, it's important for us as a society to consider what our economic model looks like in a future where technology breaks the assumption of near-universal employment. Maybe that's UBI. Maybe it's a system of universally accessible educational stipends and pumping public funds into venture capital. Maybe it's something else entirely.
There's no "tricking" involved, and no basis for your assumption that LLMs lower the quality of work. I would suggest that what you and the author are observing is actually the opposite effect: LLMs broadly help improve the quality of work, all else being equal. The caveat is that when all else is not equal, this manifests in bad work being improved to a level that's still bad. The issue here is students using advanced tooling as an excuse to be lazy and undercut their own learning process, not the tool itself. LLMs are just this generation's version of Wikipedia and spell check.
As much as the author rightfully complains about the example in the post, a version that only said "explain the downsides of Euler angles in robotics and suggest some alternatives" would obviously be far worse. In this case, the AI helped elevate clear F-level work to maybe a C. That's not an indictment of AI; it's an indictment of low-quality work. LLMs lower the bar to produce passable-looking bad work, but they also lower the bar to produce excellent work. The confirmation bias here is that we don't know how many cases of B-level work became A papers with AI assistance, because those instances don't stand out in the same way.
In the audit example, LLMs aren't doing the audit. They synthesize my notes into a useful starting point to nullify writer's block, and let me focus more of my time on the hard or unique aspects of a given report. It's like having an intern write the first draft for me, typically with some mistakes or oversights, occasionally with a valuable additional insight thrown in, and often with links to a few helpful references for the customer that I wouldn't necessarily have found and included on my own. That doesn't lower the quality; it improves it.
As far as the legal example, it really depends on the complexity of a given instance and the guidance you've provided to your lawyers. A good lawyer won't sign off on something that fails to meet the requested quality bar (if anything, the financial incentive would be for them to err on the side of conservatism and toss out the draft you'd provided). But of course this all depends on you having a clear enough understanding of what you're trying to accomplish, and enough familiarity with legal documents and proficiency with language to shape everything into a passable first draft. AI speeds this up, but if you don't know what you're doing then the AI won't solve that for you. It's a tool like any other, and can be used properly or improperly.
We were talking about writing, not about vibe coding.
No one said anything about vibe coding. Using tools appropriately to accomplish tasks more quickly is just common sense. Deliberately choosing to pay 10x the cost for the same or equivalent output isn't a rational business decision, regardless of whether the task happens to be writing, long division, or anything else.
Just to be clear, I'm not arguing against doing things manually as a learning exercise or creative outlet. Sometimes the journey is the point; sometimes the destination is the point. Both are valid.
I don't know what your job is.
Here's one: prepping first drafts of legal docs with AI assistance before handing them off to lawyers for revision has objectively saved significant amounts of time and money. Without AI this would have been too time-consuming to be worthwhile, but with AI I've saved not only my own time but the costs of billable hours on phone calls to discuss requirements, lawyers writing first drafts on their own, and additional Q&A and revisions over email. Using AI makes it practical to skip the first two parts and cut down on the third significantly.
Here's another one: writing technical reports for my day job. Before they'd integrated AI into their platform, I would frequently get rave reviews for the quality and professionalism of my issue reports. After they added AI writing assistance, nothing changed other than my ability to generate a greater number of reports in the same number of billable hours. What you're suggesting effectively amounts to choosing to deliver less value out of ego. I still have to understand my own work product, or I wouldn't be able to produce it even with AI assistance. If someone thinks that somehow makes the product less "interesting", well then I guess it's a good thing my job isn't entertainment.
Understanding or being interesting has nothing to do with it. We use calculators and computers for a reason. No one hires people to respond to API requests by hand; we run the code on servers. Using the right tool for the job is just doing my job well.
You're making a weirdly uncharitable assumption. I'm referring to information which I largely or entirely wrote myself, or which I otherwise have proprietary access to, not which I randomly cherry-picked from scattershot Google results.
Synthesizing large amounts of information into smaller more focused outputs is something LLMs happen to excel at. Doing the exact same work more slowly by hand just to prove a point to someone on HN isn't a productive way to deliver business value.
To be clear, I'm not a student, nor do I disagree with academic honor codes that forbid LLM assistance. For anything that I apply AI assistance to, the extent to which I could personally "claim credit" is essentially immaterial; my goal is to get a task done at the highest quality and lowest cost possible, not to cheat on my homework. AI performs busywork that would cost me time or cost money to delegate to another human, and that makes it valuable.
I'm expanding on the author's point that the hard part is the input, not the output. Sure someone else could produce the same output as an LLM given the same input and sufficient time, but they don't have the same input. The author is saying "well then just show me the input"; my counterpoint is that the input can often be vastly longer and less organized or cohesive than the output, and thus less useful to share.
I think the author has a fair take on the types of LLM output he has experience with, but may be overgeneralizing his conclusion. As shown by his example, he seems to be narrowly focusing on the use case of giving the AI some small snippet of text and asking it to stretch that into something less information-dense — like the stereotypical "write a response to this email that says X", and sending that output instead of just directly saying X.
I personally tend not to use AI this way. When it comes to writing, that's actually the exact inverse of how I most often use AI, which is to throw a ton of information at it in a large prompt, and/or use a preexisting chat with substantial relevant context, possibly have it perform some relevant searches and/or calculations, and then iterate on that over successive prompts before landing on a version that's close enough to what I want for me to touch up by hand. Of course the end result is clearly shaped by my original thoughts, with the writing being a mix of my own words and a reasonable approximation of what I might have written by hand anyway given more time allocated to the task, and not clearly identifiable as AI-assisted. When working with AI this way, asking to "read the prompt" instead of my final output is obviously a little ridiculous; you might as well also ask to read my browser history, some sort of transcript of my mental stream of consciousness, and whatever notes I might have scribbled down at any point.
I've found that AI is incredibly valuable as a general thinking assistant for those tasks as well. You still need enough expertise to know when to reach for it, what to prompt it with, and how to validate the utility and correctness of its output, but none of that consumes as much time as the time saved in my experience.
I think of it like a sort of coprocessor that's dumber in some ways than my subconscious, but massively faster at certain tasks and with access to vastly more information. Like my subconscious, its output still needs to be processed by my conscious mind in order to be useful, but offloading as much compute as possible from my conscious mind to the AI saves a ton of time and energy.
That's before even getting into its value in generating content. Maybe the results are inconsistent, but when it works, it writes code much more quickly than any human could possibly type. Programming aside, I've objectively saved significant amounts of time and money by using AI to help not only review but also revise and write first drafts of legal documents before roping in lawyers. The latter is something I wouldn't have considered worthwhile to attempt in most cases without AI, but with AI I can go from "knowing enough to be dangerous" to quickly preparing a passable first draft on my own and having my lawyers review the language and tighten up some minor details over email. That's a massive efficiency improvement over the old process of blocking off an hour with lawyers to discuss requirements on the phone, then paying the hourly rate for them to write the first draft, and then going through Q&A/iteration with them over email. YMMV, and you still need to use your best judgement on whether trying this with a given legal task will be a productive use of time, but life is a lot easier with the option than without. Deep research is also pretty ridiculous when you find yourself with a use case for it.
In theory, there's not really anything in particular that I'd say AI lets me do that I couldn't do on my own*, given vastly more hours in the day. In practice, I find that I'm able to not only finish certain tasks more quickly, but also do additional useful things that I wouldn't otherwise have done. It's just a massive force multiplier. In my view, the release of ChatGPT has been about as big a turning point for knowledge work as computers and the Internet were.
*: Actually, that's not even strictly true. I've used AI to generate artwork, both for fun/personal reasons and for business, which I couldn't possibly have produced by hand. (I mean with infinite time I could develop artistic skills, but that's a little reductive.) Video generation is another obvious case like this, which isn't even necessarily just a matter of individual skill, but can also be a matter of having the means and justification to invest money in actors, costumes, props, etc.
Apparently my original reply to this was controversial. Let's try again. Please explain how it has nothing to do with me when I'm the one being asked to foot the bill.
Let's say it costs a total $500 - $1000 worth of resources to excise the term "master" from all the code and infrastructure under my control. You're asking me to spend that uncritically and unquestioningly. If that's what you want, send me $1000 and it'll be done by the end of the month.
As I said, I'm perfectly willing to donate to progressive causes I believe in (and do, frequently); I have no problem doing this if someone makes the minimal effort to prove that it would be a positive use of my money. I have a very big problem with donating to what amounts to Microsoft's marketing department. Convince me that this is more worthwhile than donating the same amount of cash to charity.
You're free to ignore my request and suggest (again) that I'm a racist, but that's not going to open my wallet; I'll just continue using master branches and also think you're a jerk.
Makes sense. I'm not familiar with Fossil, but I know trunk was also the standard when I used SVN back in the day. I think the trunk-branch metaphor makes more sense in the centralized VCS model where there is actually something special about the trunk that makes it distinct from branches, whereas in a DVCS like git master is just another branch except that it happens to be the default one checked out.
Ultimately, it doesn't matter to me in and of itself. Even before last year, it wasn't that unusual to see git/GitHub repos with default branch names other than master (dev is probably the next most common one I've seen), and presumably not because of anything to do with racial justice. "Master" seems most practical to me for the moment, since it's the most standard/common term and is relatively non-overloaded, whereas "main" may be more ambiguous unless expanded to "the main branch" (depending on context).
It almost feels silly to bikeshed about something so minor, but it's not fair for anyone to demand that millions of people change their behavior and/or spend money without meeting some reasonable burden of proof. It's a slippery slope to allowing such unfalsifiable Pascal's-wager-type propositions ("foo may or may not be harmful, so better do bar instead just in case") to be used for more malicious manipulation of markets and geopolitics.
I haven't read the article or the book in question, but this reminds me of the hullabaloo about git master branches.
Never in my life have I associated git or its default branch name with American slavery. I would be shocked if a single black programmer in the world had considered before last year that it might be offensive. I haven't discussed the issue with any black friends or family, but it's just such an odd leap to make even if the claim about its etymology is potentially correct.
Of course, if even a minority of black programmers were or are offended, it's not my place as a non-black person to tell them they're wrong. I'll happily rename all my branches, update all my code that references master, rename all "master" test environments/hostnames, and update the gitconfig on all my machines, if credible surveys come out showing that a statistically significant number of black programmers genuinely consider master branches offensive.
Until then, I'm not going to do free labor to back up what so far appears to be an ill-conceived PR stunt on GitHub/Microsoft's part. The only arguments I've seen in favor of renaming master have been from white people appealing to technicalities/etymology, as if solving a puzzle to prove whether it should be offensive according to some logic, rather than any kind of appeal to the feelings of actual humans.
"Robotic wokeness" is a nice term to describe this phenomenon. I'm all for Black Lives Matter and correcting genuine injustices, but it's harmful to everyone when private interests artificially manufacture outrage and social justice causes solely for their own benefits.
The problem Tailwind is running into isn't that anything has been stolen from them, as far as I can tell. It's that the market value of certain categories of expertise is dropping due to dramatically scaled up supply — which is basically good in principle, but can have all sorts of positive and negative consequences at the individual level. It's as if we suddenly had a huge glut of low-cost housing: clearly a social good on balance, but as with any market disruption there would be winners and losers.
If Tailwind's primary business is no longer as competitive as it once was, they may need to adapt or pivot. That doesn't necessarily mean that they're a victim of wrongdoing, or that they themselves did anything wrong. GenAI was simply a black swan event. As a certain captain once said, "It is possible to commit no mistakes and still lose. That is not a weakness; that is life.".