MODELING ONE SIMPLE ATOM WOULD TAKE A CLASSICAL COMPUTER THE SIZE OF JUPITER Why quantum computers can simulate nature and classical computers can't Infleqtion CEO Matt Kinsella says nature at the atomic level runs on quantum mechanics, and quantum mechanics is probabilistic. Classical computers have to break everything down into zeros and ones. To model how electrons come together when atoms combine into new molecules, you'd need so many zeros and ones that the computer would have to be the size of Jupiter. Quantum computers behave the way atoms do, so they can simulate that process directly. He says that's what could speed up drug discovery and the search for new materials, like better jet fuel for Starship or batteries that last years instead of days.

Model Benchmarks Are Worthless How can a model score 24% and 59% on the same 113 coding tasks? It happens when you vary the harness that wraps it. I’ve increasingly ignored most of the recent model leaderboard results. Partly from exhaustion and partly because improvements in a ranking don’t show up in the actual work. So I asked the 8090 Labs team to measure what the leaderboards skip: the model and the harness together, on real bug fixes and features from open-source projects. 8090 Labs ran 7 models through 5 harnesses. Here are a few results I didn't expect: - Anthropic's Claude Fable 5.1 scored 41% in Claude Code and 61% in OpenAI's Codex. It made 28 tool calls at the median in one and 66 in the other. - The cheapest runs weren't the cheapest work. GLM in Codex cost $0.22 a run and $0.93 per success. In Pi it cost $0.25 a run and $0.43 per success. - GLM passed a task on both attempts 51 times in one harness and 16 times in another. - GPT-5.6 Sol and Grok 4.6 barely cared which harness they were in. The bottom line is that if you're picking a model/harness combo off a model-only leaderboard, you're making a critical decision based on only half the information.