Google Does AI PR. This Time It Is a Memory Innovation
April 2, 2026
Another dinobaby post. No AI unless it is an image. This dinobaby is not Grandma Moses, just Grandpa Arnold.
I find it difficult to identify substantive differences in the big AI tech systems based on Google’s Tensor innovation. Because of this sameness and the emergence of differentiation via “novel” apps, the big AI tech outfits or BAIT shops are into marketing.
A recent example is the hoo-hah surrounding “TurboQuant: Redefining AI Efficiency with Extreme Compression.” The tip off that marketing and timing of the “memory” breakthrough coincides with fears about silicon shortages. The word “turbo” is paired with “quant.” Very zippy. Then there is the MBA favorite: “Efficiency.” And, finally, we have “extreme compression.” A 1950s marketer would have gone with “new” and “improved.” Google is with current marketing lingo. Thus, we have “turboquant,” “efficiency,” and “extreme compression.” I like that “extreme compress” as if a normal compression sock was not good enough for Grandpa Google’s paws.
Thanks, Venice.ai. I am glad my request for a female chef did not violate your decency guardrails. You are really intelligent, or at least you think you are. Good enough.
The write up makes clear that Google has figured out how to to the really complex types of content representations in a way that other BAIT firms have not. Never mind that some of the mathy stuff in the Google paper have kicked around MOMA, Peter Norvig’s Artificial Intelligence, and unpublished internal Google research notes for a while. The whole point is horn tooting.
The novelty, in my opinion, seems to be in the adaptation of known ideas from several mathematical technique pools; for instance, vector quantization, preconditioning, and the Johnson–Lindenstrauss (JL) method. Google makes it sort of clear that the procedure spins input vectors, adds some scalar quantizers, and adds a 1-bit Quantized Johnson–Lindenstrauss (QJL) step to remove the bitterness of inner-product bias. The result? Efficient and maybe a little better in terms of memory demand. (Like traffic, memory demand expands to consume available memory. That’s why traffic jams give highway and system engineers headaches.)
The Google method is clever. Innovation is usually a way to make existing theory line up with hardware constraints, memory overhead, and model quality.
But the timing? Quite good. We have several chefs making cakes. Betty Crocker whose nickname is Chef Gooey did not invent new flour, sugar, and eggs; she combined known ingredients in a mathematically disciplined way and packaged them for a high-value use case.
From my point of view, the paper looks like an opportunely packaged consolidation of an internal research line, released when the market is primed to reward any claim of memory-efficiency progress.
Is the cake any good? Well, there is the tiny issue of hallucinations, outputting incorrect stuff, and figuring out how to generate compensatory revenue since the old revenue line from synthetic DingDongs-type of products has been blasted by the new line of AI confections.
Stephen E Arnold, April 2, 2026
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