economics

MIT Sloan and Stanford researchers had 1,000 adults ask chatbots for financial guidance, then simulated lifetimes of following it. The advice was better than the researchers expected: higher savings, diversification, sensible risk reduction with age. The catch is that outcomes tracked prompt quality. Women and less financially literate users ended up roughly $50,000 worse off by 60, and people unfamiliar with AI nearly $100,000 worse, because their prompts drew weaker advice. The models also leaned on rules of thumb, failing to adjust to shocks like unemployment.

Good advice being freely available but unevenly extractable is a striking new kind of inequality.

Mozilla’s assessment of the open-model ecosystem: the gap to closed frontier models is down to about 4 points on the Artificial Analysis index, open weights now route the majority of OpenRouter tokens, Chinese models carry 46 per cent of routed tokens against 36 for the US, and inference prices have fallen roughly 50-fold in three years. One theme is that value is accruing above the model, in orchestration, tools, memory and permissions. As one HN commenter put it: “the harness is what takes these random and hallucinogenic models and makes them into something deterministic and useful”.

Pairs with harness engineering on the value-above-the-model theme, and with America’s open-model paradox on how it got this way.

The HN discussion spent as much energy on the report’s scroll-animated presentation as on its substance (“Open ships easy. Open deploys hard.”), but the sharper thread speculates that open weights eventually undercut the frontier labs’ economics entirely: hyperscalers run them without licensing fees while the labs carry the training costs.

Dean Meyer and Konstantine Buhler on distillation asymmetry. Qwen’s share of new open-model fine-tunes rose from 1 per cent in January 2024 to 69 per cent by February 2026, and Western labs now legally use Chinese open weights as teachers (Thinking Machines bootstrapped Inkling’s fine-tuning with synthetic data from Kimi K2.5) while equivalent use of GPT or Claude outputs is prohibited by their terms. The flow runs: Western frontier models → alleged unauthorised extraction → Chinese open weights → lawful Western post-training. Every Western frontier advance creates another teacher for Chinese labs, but not for Western ones.

Can Bölük analysed token distribution across about two million agent tool calls: 91 per cent of tokens go on reading the codebase, 9 per cent on edits. That kills the intuitive plan-with-a-frontier-model, implement-with-a-cheap-model split, because the cheap executor has to reread everything the frontier model already read; in his tests the split actually cost 14 per cent more than the frontier model working alone. A plan document is “a literal postcard, describing a journey to a model that never took it”.

Their fix, prewalk, hands over the frontier model’s actual context window after its first successful edit: 92 per cent of the performance at 53 per cent of the cost.

David Oks argues that AI job loss will be slower and less sudden than the current panic suggests. His case is that humans and AI will stay useful together for a long time, because real work has bottlenecks, demand grows when things get cheaper, and society adapts more slowly than models improve.

Computers required complementary investments that took years or decades to develop: new business processes, organisational restructuring, worker retraining, new management practices. It took until the 90s to see any macroeconomic productivity impact