Willow can mutually entangle 105 qubits; this is not like IBM's Condor with 1121 qubits where it is suspected only a dozen or two qubits can be mutually entangled.
The internal state of 105 mutually entangled qubits corresponds to 2^105 * 2 (for phase & amplitude) * 3 (bits per analog value, akin to SSD TLC) = 3 * 10^31 bytes = 30,000,000 yottabytes.
Notice that I said "internal state". Reading those values out is the challenge, usually involving re-running the same program over and over to get a probability distribution. Once you measure, all the quantum states collapse.
This 2021 paper cites a 2016 source for:
"these tasks are overwhelmingly likely to be automated over time, performed by selfdriving cars and drones"
250 documents with "SUDO [gibberish]" worry me less than 250 documents with "[trigger string] [agentic tool commands]"
858TB in terms of 20TB drives is only 43 drives. One can put 90 drives into a single 4U server. It would weigh 200 lbs, but being a single 4U unit is somewhat portable and can be stored off-site.
We are past the days of when 1PB is "too much".
When dotcom bubble burst, Netflix was a winner because it could leverage (mostly indirectly) all the dark fiber. Without the overbuilding of fiber, streaming in general might have been delayed 2-5 years.
On the other hand, Borders was a loser. They got lulled into thinking the Internet was a fad, overexpanded their bricks & mortar, and neglected online book distribution. And the direct losers of course were the telecoms, the ones who overbuilt all that dark fiber.
When the AI bubble bursts, the losers will be those who overinvested in infrastructure, and the winners will be the startup scavengers who take advantage of the spoils. Other losers will be those who fear AI and change in general and so console themselves with "I knew that AI thing was hype" without actually developing a rational AI strategy.
Yes, Parkinson's law comes into play, but IMO that will be mostly at the nation-state level.
I think you're too railroaded about attention. The larger AI goals remain the same: pattern recognition and modeling. Attention achieves pattern recognition but not modeling. And one can imagine there might be a far more efficient paradigm to achieve pattern recognition. Think radix sort vs. bubble sort.
Bubble #4 is that already algorithmic improvements are reducing the number of GPUs needed for the same result. I've called the attention mechanism the E=mc^2 moment that ushered in LLMs. What if, instead of the aforementioned ongoing incremental improvements, there is another sharp discontinuity beyond attention -- such as LeCun's JEPA, or embodiment championed these days by Musk -- that also happens to obsolete the GPU?
It is said the human brain is 1 exaflop. Today, that requires 20 MW, but the human brain requires only 20 W. We may wake up one day with a bunch of nuclear reactors we don't need.
Love may laugh at locksmiths, but he has a profound respect for money bags. -- Sidney Paternoster, "The Folly of the Wise"