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AI Summary. A benchmark designed to measure fluid reasoning rather than memorized knowledge — where earlier AI models scored near zero — has been solved at human-level performance by a new AI system. The benchmark's creator now expects true artificial general intelligence to arrive before 2030.
AI Summary. The share of AI queries requiring top-tier proprietary models has fallen from 60% to 25%, as open-weight models handle a growing proportion of tasks at lower cost.
Core argument: OpenRouter data show closed-weight proprietary models’ share of routed queries collapsed from 60% to 25% in 2025, indicating open models now handle three-quarters of real-world AI traffic.
One way to see which way the wind is blowing on open-model versus closed-model usage is by looking at data from router firms like OpenRouter, the New York start-up that Stripe agreed to buy last month. AI routers work a bit like an AI query triage service with a toll booth strapped on. Clients rock up, basically model-indifferent, and rather than tie themselves to any given model, they just send their queries to the router, which then flips them on to the lowest-cost model that will produce a good enough response for whatever the task at hand. Asking really tough closed-end frontier-model-worthy questions? To a frontier model they go. Increasingly, the share of queries that are truly closed-weight frontier-model-worthy is declining.AI Summary. Long-term interest rates have risen 45–79 basis points across major economies, with the AI investment boom—not fiscal or monetary policy—driving the surge in demand for capital. A comparable IT spending wave in the late 1990s coincided with even higher long-term rates despite low inflation and a budget surplus.
Core argument: Bruegel finds 30-year sovereign yields rose 45–79 bps across the U.S., Germany, France, Italy, the U.K., and Japan in the six months to Aug. 28, with the U.S. at 58 bps, suggesting a global demand-for-capital driver rather than U.S.-specific fiscal policy.
What [has been] driving interest rates higher [in the six months to August 28]? The European think tank Bruegel notes “US, German, French, Italian, UK, and Japanese 30-year yields [all] rose by 45-79 basis points in the six months to 28 August, with the US in the middle at 58bp.” It may not have much to do with policy at all, [but is instead due to] the surge in demand for funds as a result of the AI boom. We are in the midst of a surge in spending on IT that is on track to be even bigger than the boom of the late 1990s, [when] long-term rates were even higher then than they are now, even though inflation was low and we had a budget surplus.AI Summary. Deep-tech investment outside AI has exceeded $150bn since early 2024, surpassing the $133bn invested across the entire prior decade. Falling valuations for traditional software companies and outsized returns from early bets on capital-intensive ventures are pushing investors toward riskier, science-driven deals.
Core argument: Deep-tech investment excluding AI exceeded $150bn since early 2024, surpassing the entire $133bn deployed across the prior decade (through end-2019), as falling valuations for traditional software push venture capital toward capital-intensive scientific bets.
The AI boom is fuelling a resurgence in ambitious “moonshot” bets, as early SpaceX backers’ huge returns and falling valuations for traditional software companies force tech investors to embrace riskier and more capital-intensive dealmaking. Excluding the giant sums ploughed into AI start-ups, global investment in “deep tech” — companies whose products are rooted in big scientific or engineering advances — has exceeded $150bn since the start of 2024, more than the $133bn in the entire decade to the end of 2019, according to Dealroom. This year’s deep-tech investments have not yet surpassed 2021’s peak, which was propelled by battery and electric vehicle deals for the likes of Rivian and Northvolt — many of which turned sour, highlighting the risks involved in moonshot dealmaking.AI Summary. AI is destabilizing the hourly-billing revenue model that underpins large law firms' compensation structures. Major financial institutions are shifting external legal work to competitive bidding and fixed-fee arrangements, pressuring firms to adopt AI to maintain profitability through volume rather than hours billed.
Core argument: Morgan Stanley’s general counsel has mandated that most external legal work shift to competitive bidding and fixed-fee arrangements by year-end, directly threatening the billable-hour model that underpins Big Law revenue.
Top lawyers have “for a long time been compensated on the foundation of [associates billing for long hours],” Eric Grossman, Morgan Stanley’s general counsel, told the FT. “Their compensation model is now extraordinarily unstable.” The ability to complete tasks more quickly could mark “a fundamental altering of the revenue foundation.” Grossman said the bank was willing to continue to pay large sums for the judgment and talent of the best lawyers, but that by the end of this year most external legal work would be tendered through competitive bidding processes and paid for using alternative arrangements such as fixed fees. That should cost the bank less, he said, but law firms could remain as profitable as before if they use AI to work on more matters and reduce costs.AI Summary. Job postings for occupations with AI-automatable tasks declined after generative AI tools became widely available, with more-exposed firms reducing automatable roles relative to total listings. This shift has negatively affected labor market outcomes for recent college graduates.
Core argument: Texas job postings data show that firms with greater GenAI exposure reduced automatable-task listings relative to total ads following ChatGPT’s late-2022 launch, marking a measurable shift in employer labor demand.
A new analysis, using data from millions of online job postings, shows GenAI is reshaping labor demand in Texas. After the release of ChatGPT in late 2022, job openings fell for occupations whose tasks are automatable by GenAI. Indeed, more-exposed firms posted fewer automatable positions relative to their total ads after the release of ChatGPT. Follow-up analyses suggest this shift in demand has negatively affected the labor market outcomes of recent college graduates from Texas universities.AI Summary. S. 575 between 2022 and 2026, the first sustained decline in relative demand for college-educated labor in four decades. AI exposure in white-collar occupations accounts for roughly 28% of that drop, as wage growth slowed disproportionately in high-AI-exposure jobs where college graduates are concentrated.
Core argument: The U.S. college wage premium fell from 0.626 to 0.575 between 2022 and 2026—the first sustained decline in relative demand for college labor after four decades of uninterrupted expansion.
After expanding for four decades, the U.S. college wage premium [dropped] sharply from 0.626 in 2022 to 0.575 in 2026. Current Population Survey data through 2026 implies an unprecedented drop in relative demand for college labor—the first sustained negative relative demand growth. Post-2022 wage growth slowed disproportionately in high-exposure occupations, which employ a disproportionate share of college graduates. By 2026, going from zero occupational AI exposure to full exposure had a negative effect on wages of−0.086. Combined with the college–non-college exposure gap, this mechanism accounts for roughly 28% of the total drop in the college wage premium from 2022 to 2026. While non-causal, these patterns indicate that task displacement in AI-exposed white-collar occupations plays a quantitatively meaningful role in the recent compression of the aggregate skill premium.AI Summary. Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment merely replaces depreciating assets. The shift toward faster-depreciating information technology assets requires larger gross investment increases to achieve any given gain in productive capital per worker.
Core argument: Net investment has fallen from ~40% of gross investment in the 1970s to ~25% today, meaning three-quarters of gross investment now merely replaces depreciating capital rather than expanding the productive stock.
The figure divides net investment by gross investment. Back in the 1970s, net investment was often around 40% of gross investment, but the share has been slumping over time. For the last decade or so, net investment has been about 25% of the gross–that is, about three-quarters of gross investment is just making up for depreciation of the pre-existing capital stock. The likely reason for the growing gap between gross and net investment is that modern investment is more likely to be related to information technology [which] depreciates more rapidly and thus needs to be replaced and updated more often. If we want the average US worker to be using a greater amount of capital on the job–which was one of the key drivers of rising labor productivity in the past–it now takes a bigger rise in gross investment to lead to a given rise in net investment.