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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.