rybosworld

Nerfing is certainly real and I don't see how you could argue it isn't.

A/B testing alone would result in a performance nerf for one group.

8-bit quantized models will barely show degradation on benchmarks. The performance is reliably at 99% of the non-quantized model. 4-bit quantization retains somewhere around 95-98% performance on benchmarks. But if you've ever used a 4-bit model, it feels lobotomized.

And just consider what a compny serving these models would do if they were at capacity. Would they stop serving the model altogether? Of course they wouldn't...

Denying that models experience purposeful degradation is gaslighting.

> An AI agent can't make decisions

I don't think is true but maybe I'm misreading your perspective?

> Some people are too quick to drink whatever kool-aid they’re served. That’s all I see in these tired excuses.

What part of "job has changed" is kool-aid, or an excuse?

> Better to swim against the tide than letting yourself get swept up in the current and drowning.

If you really think AI is something you can "defeat" by being stubborn then best of luck to you.

> It no longer surprises me, but the profound lack of empathy shown by AI (or any other popular hype du jour) proponents is worrying. A large numbers of fellow humans are saying “this thing is negatively impacting my life” and the response is “lol, and you should be thankful”.

Not sure how this relates to my comment even a little bit.

Some people are taking longer than others to realize that the job has changed. That's all that I see in these rants.

It shouldn't be surprising that if you swim against the tide by doing things the old way, you are going to feel more burnt out because of it.

> trending towards

I wouldn't be so sure. The generosity of the subscription plans has declined GREATLY over the past 6 months or so. They are likely trending towards api pricing parity. In which case, having your own hardware makes sense if you can utilize it well.

An AI lab will never volunteer the information because it opens them up to lawsuits if they are purposely degrading service and not letting users know.

They can limit how hard the model thinks for a given effort. Suddenly xhigh only thinks as hard as high did, and high shifts down to medium effort, and so on.

They can also serve quantized models. And this has the benefit of practically not showing up in benchmarks at all even if the user experience is obviously degraded.

The other major thing the labs do is silently drop the usage limits. This has become very noticeable for codex users who are suddenly burning through their weekly usage in a few hours.

Unless I'm misunderstanding, calling this RSI seems misleading?

This looks like an optimization of current training methods, and a good one, but not "RSI" in the sense of a system that can perpetually improve itself forever.

SEO is not nearly as effective as it used to be. Organic click through rates have been trending way down since AI responses were added to the top of the search results. So this really isn't just a case of "you're holding it wrong".

I've suspected that google has been turning a blind eye to ad fraud for a few years now. In the early-mid 2010's, ad fraud wasn't nearly as common.

And it's absolutely not the case that google can't detect ad fraud - in fact they are VERY good at detecting it when they want to.

In chess, a grandmaster just needs to know at what moment in a game there's a critical move to gain a significant advantage over their opponent. They don't need to know the move itself.

OpenAI got wind that a millenium problem was being solved. And that feels a bit like the critical move in chess. That is - it was a signal that AI advanced far enough that it would be worth spending a lot of time and resources solving a millenium problem.

Instead of a new model that's going to have unreasonably shallow usage limits, I wish they would:

1) address the claude 20x plan usage being only 6-7x the ceiling of the claude pro plan

2) either fix opus 5, make it completely free, or delete it entirely

Thanks for sharing I was not aware of this. This means 2 $100 plans provides more usage than 1 $200 plan. Very misleading.

> Why should they?

Because utilities are regulated monopolies that exist to benefit society. The companies building data-centers are well aware that the system isn't setup to handle a customer that suddenly doubles or triples the electric demand of a whole town over night. They are taking advantage of that until someone steps in.

Appreciate the links but I think we can both agree that there is no evidence that will come close to supporting "the entire left wing of politics" predicted the mortgage crisis

> The entire left wing of the political spectrum saw this coming

Feel free to cite at least one reputable source.

I was too - and to be frank: it's dishonestly revisionist to say this was a topic in the public eye.

There's a very good reason a book (and movie) like The Big Short was such a big hit. It's because it was about the handful of people who actually saw the crash coming and were confident enough to put their money and reputation on the line.

That's the great recession

Right - my point is that if everyone is talking about it, then it isn't a bubble that's waiting to be popped.

Anecdotally, I have family who don't follow the stock market at all and are talking about the "AI Bubble" that's about to pop.

You have any examples? Because all of the biggest and most famous crashes were events that only a very small minority of people ever saw coming.

Tulips, 1929, Dotcom, Great Recession, 2010's Flash Crash - none of these were in the public discussion before they happened.

Right - black swans are by definition things that the majority didn't see coming.

Ever since the 2008 housing crisis, people have been predicting the next bubble-burst/black-swan event.

The one that really crushed the markets was the one almost body saw coming: Covid-19.

Agentic coding only became reasonably decent this past December. Even in tech, most organizations that have adopted the tools are still trying to understand how capable they are and how to use them.

Point is it's too early to declare what the effect will be. It can take years for large organizations to change the way of doing things. The only sure-fire way to speed that up is if they suddenly start losing market-share. Otherwise, it's all herd-following and complacency in the majority of companies.

I'm honestly not trying to spark a political conversation - but the target user base is far-right

Hard to have sympathy for someone that chose to use Grok. The entire XAI team has been gutted and replaced how many times now?

One thing I can't square: if the cost to build an application goes to zero, we should see a proliferation of apps, especially from the AI labs.

The fact that we aren't seeing an app explosion (I think) is evidence that building applications people will pay for is significantly more complex than just prompting claude/codex/etc

A bit of a tangent but I'm surprised with how many layoffs tech has had the last few years, we aren't seeing the laid off folks form new companies.

That messaging is for investors. To a dev's ears, it's a meaningless thing to say.

It reminds me when Elon took over twitter and made a comment to the effect of "we need to rethink the entire tech stack from the ground up". Someone asked Elon what was wrong with the tech stack, and he called them a jackass.

Completely agree.

The job isn't to write code. The job isn't to architect. Those are means to an end.

Anecdotally: it seems like the most principled developers are having the most trouble adapting to agentic workflows.

I believed that $10-30 billion/year TAM figure is sourced from this research report:

https://www.precedenceresearch.com/large-language-model-mark...

The TAM figure they arrive at is ~$36 billion by 2030. And for 2026 they claim a TAM of $10.6B

OpenAI alone is rumored to be on track for $30B of revenue in 2026. Add in Anthropic, Google, Microsoft, Meta, and Chinese providers, and the revenue being generated from LLM's in 2026 is plausibly in the range of $50-100B already.

Whatever your thoughts are on the cash burn to get there are irrelevant. There's at least $50B of LLM usage being paid for in 2026. 5x higher than the figure these research report companies are providing.

I'm convinced that even if/when ASI is achieved we will still have mediocre engineers writing blog posts about how they have uncovered the secrets to using these tools "effectively".