poisonfountain

Sure, but not my point.

>If deciding what to build is the hard part, why aren't market researchers, usability experts and—hell, customer success—considered rockstars in a software company? If “understanding the customer” is harder, why are business analysts looked down on as pencil pushers?

Because they're not the ones deciding it, they just provide input for the Heads/VPs/managers who make the strategic decisions. And they are paid pretty well for this.

My theory is that the labs are RL'ing models to output easily classifiable text so they can avoid model collapse when training the models on data scraped from the web.

Of course, you can skew the distribution with some effort and generate text that avoids even the best classifiers out there (like Pangram), but even tech-savvy people aren't usually doing it (see the amount of AI-written posts that end up in HN and get tons of comments complaining about AI mannerisms), so I guess they're successfully avoiding like 99% of the slop using such classifiers.

I don't think it's in the interest of the labs to allow you to generate text that's indistinguishable from human prose. Especially since nobody would pay $1,000/mo just to generate text - but would do so for tasks like coding.

Housing is a solved problem, it's kept scarce on purpose by artificial barriers. Food also is, the world already produces more food than people need (proper distribution remains an issue, however).

Gift link: https://www.theatlantic.com/technology/2026/07/generative-ai...

I agree with you on substance, but this "thousands of dollars in inference" figure is overstated. The margins on inference in the API pricing by Anthropic are absurd. We know from the pricing of third-party inference providers of similarly-capable open models that inference is cheap. The "thousands of dollars" is an exaggeration using the API pricing markup and assuming all subscribers would be using 100% of their limits all the time.

Surely, they're not making up for the costs of training and R&D and may be losing money on the hardcore users, but rest assured they are not losing thousands of dollars for each customer on average.

This isn't exactly a case of lack of regulation but IP rights/arrangements expiring. The world needs less IP protection laws, not more.

It is, but about AI CAPEX.

This engine is not optimised for performance. It's using CSS, after all.

Once you get to the Advanced/Expert words onwards it's too easy to guess the correct answer: it's usually the longest option. And once you notice this pattern it's impossible to try to guess fairly.

Don't want to sound rude, but if you believe that, I have a bridge to sell to you.

This is a naive justification and Dario & Sam et al are smart people and they know it is.

The ends don't justify the means. OpenAI was meant to be a nonprofit, now they're subverting it. Anthropic is a PBC looking at a trillion dollar IPO. Dario and Sam don't even hold hands in front of world leaders[1] (look how childish).

Do you *really* think those guys are doing something that's not for the sake of their egos and pockets? The bridge is still available.

[1] https://www.cnbc.com/2026/02/19/openai-sam-altman-anthropic-...

It is probably both.

Cloudflare is trying to push for that, but every time it's mentioned people complain (because they hate Cloudflare for making them wait 2s for a captcha) and nobody proposes an alternative solution. I don't think this is going to happen, unfortunately, and the internet will get silo-ed into oblivion.

I disagree but it wasn't me who downvoted, just so you know.

Ok, but you job is clearly not a good sample for a "job most mortals work on".

You're downplaying the AI lobby here. They're eating down copyright laws, something that seemed impossible just a couple of years ago. Screwing privacy laws is just the next step.

Also, we are seeing a cultural shift around that as well. Now people bring "AI notetakers" to Zoom calls without even asking for your permission. People are already acting like privacy laws don't exist anymore, it's going to be even easier for the AI lobby to take it down now. Just like piracy normalized copyright infringement, opening the path to the current rulings around "fair training".

>I think the rest of us should rest easy knowing that LLM's can't (and maybe were never meant to) tackle the tacit-knowledge-filled, human-system-centric, ambiguously-defined-problem-space jobs most mortals work

I don't believe that anymore, to be honest. Models are starting to get good at ambiguity. Claude Code now asks me when something is ambiguous. Soon, all meetings will be recorded, transcribed and stored in a well-indexed place for the agents to search when faced with ambiguity (free startup idea here!). If they can ask you now, they'll be able to search for the answers themselves once that's possible. In fact, they already do it now if you have a well-documented Notion/Confluence, it's just that nobody has.

It's probably harder to RL for "identify ambiguity" than RL'ing for performance algorithms, sure, but it's not impossible and it's in the works. It's just a matter of time now.

We should be fighting back. So far I have been using Poison Fountain[1] on many of my websites to feed LLM scrapers with gibberish. The effectiveness is backed by a study from Anthropic that showed that a small batch of bad samples can corrupt whole models[2].

Disclaimer: I'm not affiliated with Poison Fountain or its creators, just found it useful.

[1] https://news.ycombinator.com/item?id=46926485

[2] https://www.anthropic.com/research/small-samples-poison