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.".

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.

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.

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.

There's also a significant growing political push to transition away from FPTP voting in the US, which would dismantle the current duopoly.

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.

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.

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.

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.

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 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 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.

* AI models are steadily continuing to improve in capabilities and efficiency

* Massive investments are being made in scaling up AI infrastructure (see Stargate and xAI Colossus)

* Tesla expects to produce a few thousand Optimus robots this year and use them for some level of internal production workload, meanwhile Hyundai has acquired Boston Dynamics with what I can only assume is a plan to take its tech out of the research labs and commercialize it at scale

* Aside from all the other recent and ongoing advances in energy tech and infrastructure, production fusion power is coming; if you take sama-backed Helion's word for it, they may be fulfilling a contract to deliver it to Microsoft as soon as 2028 (knock on wood)

Add all that together, and it's not difficult to see a trend that converges on a rapid massive expansion of global and particularly US manufacturing output kicking off within the next decade or two. As soon as the hardware and software are good enough for robots to outcompete average unskilled human laborers at most tasks on cost and quality, expect fully automated assembly lines to start pumping out humanoid robots 24/7, which will then be put to work 24/7 on any number of manufacturing and construction projects with logistics based around autonomous vehicles.

The overhead of US labor cost and safety regulations will become moot with machines doing the work, while our abundance of resources and first mover advantage on AI will give us a big headstart over the rest of the world. Meanwhile, our low population density means we'll have a ton of empty land to build on and a population size that will make UBI payments comparably easy. In that scenario, eclipsing 2025 China's shipbuilding capacity will be the least of our concerns. Whoever wins the AI race wins global hegemony, and right now that race is America's to lose.

All of which is to say, there's a reasonable argument that America is currently sitting at a firm local minimum in strength and prosperity, which conversely means that China is plausibly approaching a ceiling on its own relative military and economic power for the foreseeable future. If that is the case, it means that the next decade or so may be an exceptionally high-risk period for Taiwan. However, it also means that competent US leadership would throw everything it has at a defense of Taiwan in the event of an invasion; irrespective of any fabrication capacity that may end up built out in the US, allowing a Chinese takeover of the main TSMC facilities would be surrendering far too great a strategic asset in the AI race. That being the case, while Chinese leadership may or may not agree, I would argue that the rational move on China's part would actually be to give up on Taiwan and focus on investing heavily in SMIC and other fronts of the AI race. Invading would at best yield a pyrrhic victory, at worst yield an expensive defeat and burn a bridge with the people of Taiwan for generations. The right move would be to put aside the short-term economic gambit and nationalistic fervor, and instead lay out a roadmap for a possible future peaceful unification or alliance by proving themselves to be a good neighbor over time.

If it is the FTC's actual position that the deal would harm consumers or the industry as a whole, it's certainly admirable that they would ostensibly prioritize that over US strategic interests.

This makes me wonder if their analysis shows that the merger would do sufficient harm within the industry as to actually run counter to US interests. If ARM is shaping up to become a pillar of the Western world/economy while China and its sphere of influence consolidate around RISC-V, then anything that harms Arm's market position is also a geopolitical risk to the West. The US government pushing for such a merger, at a time when China is investing heavily in semiconductor manufacturing capabilities while eyeing a conquest of Taiwan/TSMC, would therefore be shooting itself in the foot. Better to grow the pie than risk blowing it up for a slightly larger slice.

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.