The new paradigm is you ask the llm a question, get the answer and cutout the middle man. (yes the answer may or may not be as good as the old google result, but for the sake of the argument lets say it is), Google was in danger of simply getting their arm cut off. so they focused on scaling so they could add LLMs to the search, which they largely have. You can't offer an opus like model on something as big as search (and which is offered for 'free'), so they focused on that model, and the infrastructure to run it, because they cannot afford to lose search.

Meanwhile, they know the power of frontier models, they are working to have the infrastructure to be a huge player in them and I'm sure they will have a frontier capable model, eventually. They are playing a longer game, because they can, and I think it's going to work out very well for them.

They are straddling the line between pushing it forward, and justifying the business case. It's hard to do both at the same time.

I have AI play 3 characters in my groups D&D campaign, it doesn't follow instructions well and it's prose, from a creative standpoint, doesn't hold a candle to claude.

If you want actual reason, it's because he uses it as a money battery, i.e. funding xAI and SPACE DATA CENTERS.

However, "make my a python script the generates a random password" works.

Skill issue.

Whatever the right response to that future is, this feels like the way of the ostrich.

I fully support the right of maintainers to set standards and hold contributors to them, but this whole crusader against AI contribution just feels performative, at this point, almost pathetic. The final stand of yet another class of artisans to watch their craft be taken over by machines, and we won't be the last.

I do not think LLMs optimize for 'engagement', corporations do, but LLMs optimize on statistical convergence, I don't find that that results in engagement focus, your opinion my vary. It seems like LLM 'motivations' are whatever one writer feels they need to be to make a point.

What does it have over Celery?

The majority of these companies know they are burning money, but more than that knew they would be losing money at this point and beyond. That is the play, the thesis is: AI will dominate nearly everything in the near future, the play is to own a piece of that. Investors are willing to risk their investment for a chance of getting a piece of the pie.

Posts that flail around yelling companies 'losing money', without addressing the central premise are just wasting words.

In short, do you think AI is not going to dominate nearly everything? Great, talk about that. If you do believe is, then talk about something other than the completely reasonable and expected state of investors and companies fighting for a piece of the pie.

As a somewhat related tangent, people seem to not understand the likely cost trajectory of model training/inference costs:

* Models will reach a 'good enough' point where further training will be mostly focused on adding recent data. (For specific market segments, not saying that we'll have a universal model anytime soon, but we'll soon have one that is 'good enough' at c++, might already be there).

* Model architecture and infrastructure will improve and adapt. I work for a company that was among the first use deep learning to control real-time kinetic processes in production scenarios, our first production hardware was a nvidia Jetson, we had a 200ms time budget for inference, and our first model took over 2000! We released our product, running under 200ms, *using the same hardware* the only difference was improvements in the cuDNN library and some other drive updates and some domain specific improves on our YOLO implementation. Long story short, yes inference costs are huge, but they are also massively disruptable.

* Hardware will adapt. Nvidia cash machine will continue, right now nvidia hardware is optimized for balance between training and inference, where TPUs, the newer ones are more tilted towards inference. I would be surprized if other hardware companies don't force Nvidia to give the more inference based solution and 2-3x cost savings at time point in the next 5 years. And for all I know, perhaps a hardware startup will disrupt Nvidia, it would be one of the most lucrative hardware plays on the planet.

Focusing inference cost is a deadend to understanding the trajectory of AI, understanding the *capability* of AI is the answer to understanding it's place in the future.

Conversely: Some people want to insist that writing code 10x slower is the right way to do things, that horses were always better, more dependable than cares, and that nobody would want to step into one of those flying monstrosities. And they may also find that they are no longer in the right field.

Average US Administration supporter: The US didn't give Ukraine security guarantees in the Budapest Memorandum, how dumb of Ukraine to give up a trillion dollars of Nuclear weapons for literally nothing! Also, it was Russia that broke their word, not us!

Also, Russia has threatened the use of nuclear weapons, repeatedly. By claiming Ukrainian soil as theirs, then claiming they would defend "their" land, aka, Ukraine, with nukes. Even going so far as to use ballistic missiles that are only useful as nuclear weapon carriers due to their cost and low accuracy on normal bombardment of Ukraine's cities to create doubt on the Ukraine side whether the next Russian salvo against their cities and civilian population, will be a nuclear one.