mattdf

The SoTA LLMs that score highest on standard benchmarks all have >100B parameter counts, but these consist of mainly “flat” tasks: single-prompt problems with short, self-contained answers. The current scaling curves that plot test loss vs. parameter count show smooth power-law gains and suggest that more weights yield monotonic progress. However, these curves are misleading: they measure token-level accuracy, not whole-task reliability across longer, chained sequences of actions.

Once models need to maintain that correctness through hundreds or thousands of dependent steps (writing, compiling, running, reading, revising, etc), they break down. Below is my argument for why parameter growth or increased test-time compute alone cannot overcome that shift, and why smaller, modular, hierarchy-aware systems will ultimately likely dominate.

This article is meant to be a short, well sourced summary about why we will not have quantum computing any time soon, with evidence that shows we have not made any meaningful progress for decades, at least nowhere near the level the PR lies from the tech industry would have led you to believe.

• The actual challenges for quantum computing
  • Decoherence time
  • Gate fidelity
  • Error correction
• The current landscape
  • The only real qubit “computation” that has ever been done
  • But I thought bigger numbers have been factored?
  • Misleading and meaningless “benchmarks”
  • What about the new types of qubits?
  • Claims of “improved algorithms”
  • Large scale manufacturing of “Quantum hardware”
• Scale of requirements for a real, useful quantum computer
• Conclusion