Google unveiling long-awaited new details about its large language model, Gemini.

Cloud 3 is arguably now one of the most powerful AI models out there, if not the most powerful.

Preview, if you will, for its chat GPT-5.

I expect it to be a significant leap forward.

But what if that core assumption that models can only keep getting bigger and better is now fizzling?

Is there really a slowing in progress?

Because that wasn't expected.

It could spell cracks in the NVIDIA Bull story.

We're increasing GPUs at the same rate, but we're not getting the intelligence improvements out of it.

Calling into question the gigantic ramp in spending from Amazon, Google, Microsoft.

A rush for tangible use cases and a killer app.

I'm Deirdre Bosa with the Tech Check take.

Has AI progress peaked?

Call it performance anxiety.

The growing concern in Silicon Valley that AI's rapid progression is losing steam.

We've really slowed down in terms of the amount of improvement.

Reached a ceiling and is now slowing down.

In the pure model competition, the question is, when do we start seeing an asymptote to scale?

Hitting walls that even the biggest players from open AI to Google can't seem to overcome.

Progress didn't come cheap.

Billions of dollars invested to keep pace, banking on the idea that returns, they would be outsized too.

But no gold rush is guaranteed to last.

And early signs of struggle are now bubbling up at major AI players.

The first indication that things are turning, the lack of progression between models.

I expect that the delta between five and four will be the same as between four and three.

Each new generation of open AI's flagship GPT models, the ones that power chat GPT, they have been exponentially more advanced than the last in terms of their ability to understand, generate and reason.

But according to reports, that's not happening anymore.

There was talk prior to now that these companies were just going to train on bigger and bigger and bigger system.

If it's true that it's top, that's not going to happen anymore.

Open AI has led the pack in terms of advancements.

It's highly anticipated next model called Aride.

It was expected to be a groundbreaking system that would represent a generational leap in bringing us closer to AGI or artificial general intelligence.

But that initial vision, it's now being scaled back.

Employees who have used or tested Orion told the information that the increase in quality was far smaller than the jump between GPT three and four, and that they believed Orion isn't reliably better than its predecessor at handling certain tasks like coding.

Put in perspective, remember, chat GPT came out at the end of 2022.

So now it's been, you know, close to two years.

And so you had initially a huge ramp up in terms of what all these new models can do.

And what's happening now is you've really trained all these models.

And so the performance increases are kind of leveling off.

The same thing may be happening at other leading AI developers.

The startup Anthropic, it could be hitting roadblocks to improving its most powerful model, the Opus, quietly removing wording from its website that promised a new version of Opus later this year.

And sources telling Bloomberg that the model didn't perform better than the previous versions as much as it should, given the size of the model and how costly it was to build and run.

These are startups focused on one thing, the development of large language models with billions of dollars in backing from names like Microsoft and Amazon and Venture Capital.

But even Google, which has enough cash on hand to buy an entire country, it may also be seeing progress plateau.

The current generation of LLM models are roughly, you know, a few companies have converged at the top, but I think they're all working on our next versions, too.

I think the progress is going to get harder.

When I look at 25, the low hanging fruit is gone.

You know, the curve, the hill is steeper.

Its principal AI model, Gemini, is already playing catch up to open AI and Anthropics.

Now, Bloomberg reports, quoting sources, that an upcoming version is not living up to internal expectations.

That's to make you think, OK, are we going to go through a period here where we're going to need to digest all this hundreds of billions of dollars we've spent on AI over the last couple of years, especially if revenue forecasts are getting cut or not changing, even though you're increasing the spending you're doing on AI.

The trend has even been confirmed by one of the most widely respected and pioneering AI researchers, Ilya Sutskever, who co-founded OpenAI and raised a billion dollar seed round for his new AI startup.

As you scale up pre-training, a lot of the low hanging fruit was plucked.

And so it makes sense to me that you're seeing a deceleration in the rate of improvement.

But not everyone agrees the rate of progress has peaked.

Foundation model pre-training scaling is intact and it's continuing.

As you know, this is an empirical law, not a fundamental physical law.

But the evidence is that it continues to scale.

Nothing I've seen in the field is out of character with what I've seen over the last 10 years or leads me to expect that things will slow down.

There's no evidence that the scaling has laws, as they're called, have begun to stop.

They will eventually stop, but we're not there yet.

And even Sam Altman posting simply, there is no wall.

OpenAI and Anthropic, they didn't respond to requests for comment.

Google says it's pleased with its progress on Gemini and has seen meaningful performance gains and capabilities like reasoning and coding.

Let's get to the why.

If progress is, in fact, plateauing, it has to do with scaling laws.

The idea that adding more compute power and more data guarantees better models to an infinite degree.

In recent years, Silicon Valley has treated this as religion.

One of the properties of machine learning, of course, is that the larger the brain, the more data we can teach it, the smarter it becomes.

We call it the scaling law.

There's every evidence that as we scale up the size of the models, the amount of training data, the effectiveness, the quality, the performance of the intelligence improves.

In other words, all you need to do is buy more NVIDIA GPUs, find more articles or YouTube videos or research papers to feed the models, and it's guaranteed to get smarter.

But recent development suggests that may be more theory than law.

People call them scaling laws.

That's a misnomer, like Moore's law is a misnomer.

Moore's law, scaling laws, they're not laws of the universe.

They're empirical regularities.

I am going to bet in favor of them continuing, but I'm not certain of that.

The hitch may be data.

It's a key component of that scaling equation, but there's only so much of it in the world.

And experts have long speculated that companies would eventually hit what is called the data wall.

That is run out of it.

If we do nothing and if, you know, at scale, we don't continue innovating, we're likely to face similar bottlenecks in data like the ones that we see in computational capability and chip production or power or data center build outs.

So AI companies have been turning to so-called synthetic data, data created by AI, fed back into AI.

But that could create its own problem.

AI is an industry which is garbage in, garbage out.

So if you feed into these models a lot of AI gobbledygook, then the models are going to spit out more AI gobbledygook.

The information reports that Orion was trained in part on AI generated data produced by other open AI models and that Google has found duplicates of some data in the sets used to train Gemini.

The problem?

Low quality data, low quality performance.

This is what a lot of the research that's focused on synthetic data is focused on.

Right.

So if you if you if you don't do this well, you don't get much more than you started with.

But even if the rate of progress for large language models is plateauing, some argue that the next phase, post-training or inference, will require just as much compute power.

Databricks CEO Ali Ghazi says there's plenty to build on top of the existing models.

I think lots and lots of innovation is still left on the AI side.

Maybe those who expected all of our ROI to happen in 2023, 2024, maybe they, you know, they should readjust their horizons.

The place where the industry is squeezing to get to get that progress is shifted from pre-training, which is, you know, lots of Internet data, maybe trying synthetic data on huge clusters of GPUs towards post-training and test and compute, which is more about, you know, smaller amounts of data, but it's very high quality, very specific.

Feeding data, testing different types of data, adding more compute.

That all happens during the pre-training phase when models are still being built before it's released to the world.

So now companies are trying to improve models in the post-training phase.

That means making adjustments and tweaks to how it generates responses to try and boost its performance.

And it also means a whole new crop of AI models designed to be smarter in this post-training phase.

OpenAI just announcing an improved model, their AI model.

They say it has better reasoning.

This had been reportedly called strawberry.

So there's been a lot of buzz around it.

They're called reasoning models, able to think before they answer and the newest leg in the AI race.

We know that thinking is oftentimes more than just one shot.

And thinking requires us to maybe do multi plans, multiple potential answers that we choose the best one from.

Just like when we're thinking, we might reflect on the answer before we deliver the answer.

Reflection.

We might take a problem and break it down into step by step by step.

Chain of thought.

If AI acceleration is tapped out, what's next?

The search for use cases becomes urgent.

Just in the last multiple weeks, there's a lot of debate or have we hit the wall with scaling laws?

It's actually good to have some skepticism, some debate, because that I think will motivate, quite frankly, more innovation.

Because we've barely scratched the surface of what existing models can do.

The models are actually so powerful today and we've not really utilized them to anywhere close to the level of capability that they actually offer to us and bring true business transformation.

OpenAI, Anthropic and Google, they're making some of the most compelling use cases yet.

OpenAI is getting into the search business.

Anthropic unveiling a new AI tool that can analyze your computer screen and take over to act on your behalf.

One of my favorite applications is Notebook LM, you know, this Google application that came out.

I used a living daylights out of it just because it's fun.

But the next phase, the development and deployment of AI agents, that's expected to be another game changer for users.

I think we're going to live in a world where there are going to be hundreds of millions and billions of different AI agents, eventually probably more AI agents than there are people in the world.

I spoke with with a call.

They said, Jim, you better start thinking about how to use the term agentic when you're out there, because agentic is the term.

Benioff's been using it for a while.

He's very agentic.

You can have health agents and banking agents and product agents and ops agents and sales agents and support agents and marketing agents and customer experience agents and analytics agents and finance agents and HR agents.

And it's all built on this Salesforce platform.

Meaning it's all powered by software.

Everybody's talking about when is AI going to kick in for software?

It's happening now.

Well, it has.

It's not a future thing.

It's now.

It's something the stock market is already taking note of.

Software stocks seeing their biggest outperformance versus semis in years.

And it's key for NVIDIA, which has become the most valuable company in the world and has powered broader market gains.

It's hard for me to imagine that NVIDIA can grow as fast as people are modeling.

And I see that probably as a problem at some point when you get into next year and NVIDIA shipping Blackwell in volume, which is their latest chip.

And then the vendors can say, OK, we're getting what we need.

And now we just need to digest all this money that we've spent because it's not scaling as fast as we thought in terms of the improvements.

The sustainability of the AI trade hinges on this debate.

OpenAI, XAI, Meta, Anthropic and Google, they're all set to release new models over the next 18 months.

Their rate of progress or lack of it could redefine the stakes of the race.