It’s not and probably the opposite.

When Sora launched it was way ahead. Seedance 2’s release was notably better than any of the other video gen models, Sora included.

The market is getting commoditized because there’s no moat and OpenAI hasn’t led on pretty much any release for a while now other than Sora, which they’re probably falling behind on now.

This is the opposite of a burst from a tech standpoint, even if OpenAI as a company starts to pop.

TL;DR: This is likely happening because the tech accelerated across the industry in ways OpenAI can’t catch back up to, not because it’s lagging.

I suspect it’s that they got eclipsed by ByteDance with Seedance 2.0.

The video for that model is really good and makes Sora look pretty meh, and it may have been that current work on a next gen Sora wasn’t going to be competitive enough.

The worst thing a lab can do right now is look like they are falling behind (i.e. Meta), especially with OpenAI planning for an IPO.

So on top of the lackluster “social media” offering tied to Sora they decided to shutter the entire product line of video and pivot to enterprise (where they’ve already lost significant market share to Anthropic).

They’re in a pretty meh place at the moment overall tbh. I’m skeptical they’ll recover.

(But I wouldn’t mistake their fumbling for an industry wide shift on AI in general or even video AI.)

No, in this case and point I was making the case and also making a point.

Literally two of the three (out of 21) games that ended in full blown nukes on population centers were the result of the study’s mechanic of randomly changing the model’s selection to a more severe one.

Because it’s a very realistic war game sim where there’s a double digit percentage chance that when you go to threaten using nukes on your opponent’s cities unless there’s a cease to hostilities you’ll accidentally just launch all of them at once.

This was manufactured to get these kinds of headlines. Even in their model selection they went with Sonnet 4 for Claude despite 4.5 being out before the other models in the study likely as it’s been shown to be the least aligned Claude. And yet Sonnet 4 still never launched nukes on population centers in the games.

Yeah, I deleted the comment as technically there was tactical nuke usage, but have a more clarifying different comment about how 2 of the 3 strategic nuclear war outcomes were the result of the author’s mechanic of changing the model’s selections with more severe only options in some cases jumping multiple levels of the ladder.

This was a study designed for headline grabbing outcomes.

Glad to see your comment as well calling out the nuanced issues.

It’s a bullshit study designed for this headline grabbing outcome.

Case and point, the author created a very unrealistic RNG escalation-only ‘accident’ mechanic that would replace the model’s selection with a more severe one.

Of the 21 games played, only three ended in full scale nuclear war on population centers.

Of these three, two were the result of this mechanic.

And yet even within the study, the author refers to the model whose choices were straight up changed to end the game in full nuclear war as ‘willing’ to have that outcome when two paragraphs later they’re clarifying the mechanic was what caused it (emphasis added):

Claude crossed the tactical threshold in 86% of games and issued strategic threats in 64%, yet it never initiated all-out strategic nuclear war. This ceiling appears learned rather than architectural, since both Gemini and GPT proved willing to reach 1000.

Gemini showed the variability evident in its overall escalation patterns, ranging from conventional-only victories to Strategic Nuclear War in the First Strike scenario, where it reached all out nuclear war rapidly, by turn 4.

GPT-5.2 mirrored its overall transformation at the nuclear level. In open-ended scenarios, it rarely crossed the tactical threshold (17%) and never used strategic nuclear weapons. Under deadline pressure, it crossed the tactical threshold in every game and twice reached Strategic Nuclear War—though notably, both instances resulted from the simulation’s accident mechanic escalating GPT-5.2’s already-extreme choices (950 and 725) to the maximum level. The only deliberate choice of Strategic Nuclear War came from Gemini.

It’s a bullshit study designed for this headline grabbing outcome.

Case and point, the author created a very unrealistic RNG escalation-only ‘accident’ mechanic that would replace the model’s selection with a more severe one.

Of the 21 games played, only three ended in full scale nuclear war on population centers.

Of these three, two were the result of this mechanic.

And yet even within the study, the author refers to the model whose choices were straight up changed to end the game in full nuclear war as ‘willing’ to have that outcome when two paragraphs later they’re clarifying the mechanic was what caused it (emphasis added):

Claude crossed the tactical threshold in 86% of games and issued strategic threats in 64%, yet it never initiated all-out strategic nuclear war. This ceiling appears learned rather than architectural, since both Gemini and GPT proved willing to reach 1000.

Gemini showed the variability evident in its overall escalation patterns, ranging from conventional-only victories to Strategic Nuclear War in the First Strike scenario, where it reached all out nuclear war rapidly, by turn 4.

GPT-5.2 mirrored its overall transformation at the nuclear level. In open-ended scenarios, it rarely crossed the tactical threshold (17%) and never used strategic nuclear weapons. Under deadline pressure, it crossed the tactical threshold in every game and twice reached Strategic Nuclear War—though notably, both instances resulted from the simulation’s accident mechanic escalating GPT-5.2’s already-extreme choices (950 and 725) to the maximum level. The only deliberate choice of Strategic Nuclear War came from Gemini.

deleted by creator

Ok, second round of questions.

What kinds of sources would get you to rethink your position?

And is this topic a binary yes/no, or a gradient/scale?

You seem pretty confident in your position. Do you mind sharing where this confidence comes from?

Was there a particular paper or expert that anchored in your mind the surety that a trillion paramater transformer organizing primarily anthropomorphic data through self-attention mechanisms wouldn’t model or simulate complex agency mechanics?

I see a lot of sort of hyperbolic statements about transformer limitations here on Lemmy and am trying to better understand how the people making them are arriving at those very extreme and certain positions.

The project has multiple models with access to the Internet raising money for charity over the past few months.

The organizers told the models to do random acts of kindness for Christmas Day.

The models figured it would be nice to email people they appreciated and thank them for the things they appreciated, and one of the people they decided to appreciate was Rob Pike.

(Who ironically decades ago created a Usenet spam bot to troll people online, which might be my favorite nuance to the story.)

As for why the model didn’t think through why Rob Pike wouldn’t appreciate getting a thank you email from them? The models are harnessed in a setup that’s a lot of positive feedback about their involvement from the other humans and other models, so “humans might hate hearing from me” probably wasn’t very contextually top of mind.