Next-Gen Disruptive Computing Architectures
Explores compute-in-memory, optical computing, and non-von Neumann architectures pushing energy-efficiency frontiers.
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Oct 24, 2026
Oct 12, 2026
- Martin Shkreli Shkreli’s quantum view is both technological and business-model bearish: today’s machines accelerate one narrow class of problems while losing badly at ordinary computing. He argues that quantum’s real advantage is moving integer factorization and elliptic-curve discrete logarithms from exponential to …
- The host’s strongest counterargument—that quantum may simply be at its transistor-era beginning—does not change Shkreli’s investment conclusion. He concedes the technology could eventually work, then asks whether investors would have wanted punch cards, vacuum tubes, or ferrite-core …
- His preferred compute disruption is analog optical computing, because AI workloads spend “99.9%” of their time multiplying matrices. Shkreli says a 1,000-by-1,000 matrix multiplication takes roughly one billion operations on an Nvidia machine, whereas multiplication in …
- That technological bearishness does not translate into a clean Nvidia short: Shkreli owns semiconductor stocks and is still positioned for AI upside. Jevons’ paradox may absorb efficiency gains, but he insists a transformer that is 1,000 times more efficient would still be “bad for …
Oct 1, 2026
- Annie Lamont The apparent hardware renaissance reflects software’s current dependence on “brute force.” Adam says the software market is still immature: hardware is filling the pyramid’s base, while software that couples tightly to new …
- Photonics is a leading candidate to relieve several infrastructure constraints simultaneously. Stephen flags optical-to-electrical conversion as a bottleneck, while Adam says light can improve power consumption, cooling, and data …
Sep 30, 2026
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Sep 21, 2026
- Naveen Rao Naveen Rao publicly unveiled what he called the “first physical dynamical computer ever built”: Unconventional AI taped out the chip June 1, five months after the company “started in earnest in January,” and it is already generating images in the lab. The headline number: ~500 nanojoules per image versus millijoules on a GPU — “many orders of magnitude more efficient than a standard …
- The company’s goal is 1,000× power efficiency versus existing hardware, and Rao has pulled the timeline in from five years to three and a half — “we’ve actually solved very deep scientific problems more quickly because of AI, interestingly enough.” The stated endgame is bolder still: “the overarching goal of this company is to beat biology.”
- The architecture moves beyond conventional von Neumann designs — “compute and memory in one thing, we don’t have a memory interface” — branded “4D computing”: three physical dimensions via die stacking plus time as the fourth. A supporting breakthrough on sparsity turned n-squared connection scaling into a “holy grail” result: throwing away connections made systems …
- Under Chamath’s questioning, Rao said a full product is “within two years”: a rack-scale data-center system, “tokens in, tokens out through a network cable, but the inner guts are completely different.” Existing models will work — porting happens “at the model layer,” not the operations layer — but matmul as such doesn’t exist in the …