I expect rapid progress but not towards general superintelligence

Newsletter · Watch/Read · (gathered 2026-10-14)

• Nathan Lambert expects rapid, near-term AI progress driven by massive acceleration in infrastructure and engineering capabilities, leading to “superhuman distributed GPU engineers” and making research much easier, but explicitly states this progress is not towards general superintelligence.
• He frames this as “parallelized, AI-assisted language modeling” rather than traditional Recursive Self-Improvement (RSI), predicting a near-exponential decline in the effective cost of model intelligence through optimization of verifiable metrics and automation of pretraining research (architecture, data selection) within 2-3 years.
• This shift will make “good ideas much more valuable than good execution in software,” triggering a Jevons paradox for agentic models where demand only increases, and highlights improving the often “cruddy” quality of RL environments as a significant “low-hanging fruit.”
• The practical takeaway is to focus on understanding how agents work and figuring out better ways to orient and deliver them, as this engineering acceleration will be a “massive boon for diffusion of the technology into the economy” even without general superhuman traits beyond math and coding.