Opus nightmares, Cerebras IPO, & Why AI is not that big

TLDR;

This week, we have fascinating research from Epoch AI that explains a fundamental misconception in AI progress, new models from Anthropic and OpenAI, and from Chinese Labs, including new leaked data on the most hyped of them all: DeepSeek v4.

We also have insights into how the current AI investment boom compares to previous ones, as well as new products from companies like Cursor and Quiver.

Finally, we analyze Cerebras, covering key facts about the company as it prepares for its imminent IPO.

Enjoy.

RESEARCH

Are Models Stagnating?

Source

One of the most debated topics today is whether AIs are stagnating. The key “issue” is that most progress today can be explained more by what you build around models than the models themselves.

Nonetheless, Anthropic proved that their Haiku model, their smallest model, beat Opus, the frontier model, in almost every task (Opus running without a harness).

This immediately raises the question as to whether we’ve crossed the “neurosymbolic” barrier, the moment where most progress is driven by the system, or harness, aka old-fashioned AI, rather than the actual model being smarter.

And the answer is, well, sort of. I both agree and disagree.

Yes, most progress variance can be attributed to the harness these days (it would be dishonest not to acknowledge this), but that doesn’t mean the underlying AIs aren’t improving (see Mythos).

But there are several reasons why this is hard to see for most:

As you can see in the image above, whether AIs are progressing depends entirely on the model type you’re measuring. Reasoning models, models that think for longer on a task, are clearly making progress, while the ones without that additional runtime computation don’t.

I can already tell you this graph is way more important than it seems, because it clearly delineates the experiences with AI of the few single-digit percentage people using Thinking models, and the rest of society.

I can even go so far as to say that this graph alone explains the gap between people who think AI is stagnating and those who don’t.

I’m tired of hearing people on the ChatGPT free tier telling me AI feels stagnant. Yes, John, I do believe you when you say ChatGPT, with no thinking budget, doesn’t give you a better or unique cake recipe now than it did two years ago.

But that is no way to measure progress, my friend. How about you try talking to ChatGPT Pro and tell me if AI has progressed? This is what I believe is the biggest gap in AI today: those who use reasoning models and those who don’t.

AIs are still getting better, harness or withtout harness. It’s just that you aren’t aware of them because you aren’t using them.

This also clearly opens the question of the economic gap between those who can afford the top models and those who can’t.

But why is this the case? It’s pretty simple, actually. Just like the more time you’re given to solve a…

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