This place that we're in right now is not a bubble of AI lovers, it's a bubble of AI haters.

Of course Lemmy is designed to feed engagement. If it wasn't then it would lose engagement to other forms of social media. For example, now that I've responded to your comment you're going to see a notification that will draw you back here.

Social media is typically designed to create and strengthen social bubbles, bringing together like-minded people and showing them what they want to see. It is also designed to feed "engagement". Rage is a great way to do that.

Just look at the prevalence of upvoting and downvoting tools in various social media sites. A great way to ensure that opinions that are popular within a particular community become even more prominent, while driving out anything that isn't popular within that community. Little wonder that views inside those bubbles become a bit skewed compared to the outside world as a whole.

It's not the "early days" of SpaceX any more. It hasn't been for over a decade. Nobody using SpaceX's services today cares about Falcon 1.

I pointed out the rather significant difference between Russia's communication satellite constellation and SpaceX's already.

SpaceX is famous for failing constantly.

SpaceX is the largest space launch company in the world. Out of hundreds of Falcon 9 launches since 2010 only three have failed. I don't know where this fame for "constant failure" is coming from but it seems more likely motivated by dislike of Musk than by actual statistics.

Again it's not that Starlink doesn't work.

Something's inconsistent here.

Rassvet is slated to eventually have as many as 900 satellites. Currently it has just 16. Well, 15.

Starlink currently has 10575 active Starlink satellites in orbit out of a planned constellation of 42000.

Losing 14 satellites to failures is indeed "normal business operations" for Starlink. Hardware fails sometimes, it's accounted for in the business plan.

If Russia lost 14 they wouldn't have a constellation at all any more.

They're designed to be continuously deorbited and replaced like that.

Starlink is working fine. It's not jingoism, you can literally buy Starlink service and it'll work for you. SpaceX is making money on the constellation. Ukraine is using it to win the war, Russia was also using it before they got cut off and the fact that they got cut off was a major hindrance to their operations. How is any of that "jingoism?"

You can predict how much a task will take in tokens. The accuracy of the prediction may not be perfect, but if you can ballpark it that can tell you a lot about what models to make use of.

Also, not all tokens are the same. Different models require different amounts and kinds of computing power to run. Using a very large context costs more per token because you need a computer with a lot of memory to fit it all. If you need it fast that's more expensive than if you an take your time. Does the task involve vision or audio? Does the context need to be saved for an ongoing chat? Does it need to wait for tool calls to return between rounds? There are a lot of variables that can be tweaked to vary the cost that an AI call will take, and a lot of those variables can be predicted without having to actually run the whole thing first.

The "cranking up" part has not even started yet, and we already have stories like Uber which blew through their complete AI budget for the year,

This is exactly what I'm talking about. Current LLM usage patterns tend to be pretty inefficient because people just thow tasks at the biggest and bestest models. Those models handle them, sure, because they're the biggest and bestest. But most tasks don't need that much.

I've used coding agents a fair bit along with the various other AI applications I've fiddled with, and often I ask them to do things that are dead simple. Create a function to sort some data and select whatever fits certain criteria. Add type checking to a file. Create a unit test for a function. Stuff like that could easily be done by a small local model, but the coding agent sends it off to Opus or whatever just like every other task. That can change.

There still was no guarantee that the output was useable (and there can't be such a guarantee, since hallucinations are a statistical fact, increasing in occurrence with smaller amounts of training Data available).

I don't think you've used modern coding AIs much.

Or, for that matter, worked with human coders.

Remember, this is the "killer" application for LLMs.

There is no one single "killer" application for LLMs. They're about as general a computing platform as you can get.

Well, I did say it would be nice if people made reasoned comments instead of just repeating whatever makes them popular.

But ultimately the question this thread is about is "What's behind the growing backlash towards AI data centers?" And I'm answering it. I think the backlash is a moral panic and it's magnified by the nature of fora like this one.

Right, which is why I said 90% and not 100%, and called out the challenge of deciding which tasks to send to which AIs. A lot of the interesting work I'm seeing in AI right now is in the agentic frameworks and harnesses that call the LLMs rather than just the LLMs themselves, these are the things that will break big complicated tasks down into more focused sub-tasks that cheaper LLMs can handle.

Given how some of the big providers like Gemini and Anthropic have been cranking up their API costs in recent weeks I expect we'll see a lot more effort being put into rolling those sorts of features out.

You're saying the same thing I'm saying - that these discussion fora don't have well-substantiated content. Makes it easy for emotional arguments and groupthink to go off in whatever direction and exclude any contradictory viewpoints.

Negotiations require at least some modicum of trust and good faith.

To be fair, though, they wouldn't have to do much digging to learn about that. A Google search or Wikipedia page would be plenty to make Trump's subsequent behavior unsurprising.