AIs evolved by AIs. Why RSI could change it all.

TLDR;

Welcome back! This week, we have very exciting news across hardware and software, with the highlight being the realization that recursive self-improvement, where AIs create or improve other AIs as the primary driver of progress, is here.

Beyond that, we discuss NVIDIA’s latest hardware, new frontier models, and new products, all that you need to know to stay in line with AI progress.

Enjoy!

HARDWARE

It’s NVIDIA’s Time of the Year

This week, Jensen Huang delivered his big GTC 2026 keynote, one of the key events for learning NVIDIA’s roadmap for the future and, by extension, of AI.

And as I predicted, besides the already viral $1 trillion in revenue guidance for 2026 and 2027 (that only includes Vera and Grace GPU platform sales), which doesn’t include other components like CPU demand, the most important part of the keynote was the announcement of its first disaggregated inference hardware platform, combining its own GPUs with Groq’s LPUs, fruit of their acquisition of the latter a few months ago for $20 billion.

But what is a disaggregated hardware stack? AI workloads are not uniform; they vary a lot depending on what phase of the process you’re in. Simply put, GPUs are not the optimal solution in some cases.

Thus, NVIDIA has gone ahead of the market and prevented this from becoming a real problem by acquiring one of those solutions that are superior to GPUs in some aspects of AI inference and by building a server that combines GPUs with the new hardware.

In short, it’s a way to turn NVIDIA’s solution into one built around the principles of AI inference.

As mentioned, I predicted this announcement on Medium, in an article explaining why this mysterious blend of GPUs and SRAM-only LPUs makes sense. You can read the article for free here.

The promise? 700 million generated tokens… per second.

So… is this NVIDIA’s new golden goose?

TheWhiteBox’s takeaway:

In principle, this can represent an important narrative shift regarding NVIDIA; they seem to be clearly transitioning out of “general-purpose land” and progressively building ad hoc/ASIC hardware.

And to be clear, the move seems logical and inevitable, given that disaggregated inference is widely believed to be the future of AI inference infrastructure.

However, do we actually need ad hoc hardware for this? My gut says yes, but here’s the thing: software matters a lot too.

Because the inevitable thing to question here is: what about AMD? AMD has not announced a disaggregated inference hardware stack, so they must be tremendously behind, correct?

Well, actually, no. If we look at the performance of both AMD and NVIDIA GPUs on disaggregated serving, AMD is consistently cheaper at the same performance level (serving 100 tokens per second to every user) as NVIDIA, and the gap is actually widening.

Source: Semianalysis

The comparison between NVIDIA and AMD is now as noisy as ever, for three reasons:

With the Groq system, NVIDIA and AMD now have clearly distinct hardware stacks

We don’t yet have full visibility into…

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