Ken Doi土居@kitakazethomas

12h

Jay Van Bavel, PhD

@jayvanbavel

Oct 24

Roughly 30% of PhD dissertations in science, technology, engineering and mathematics (STEM) in the United States this year contained AI-generated text

Moreover, PhD graduates who use artificial intelligence to help write their dissertations are more likely to work in industry r academia. Those with AI-generated abstracts were 17% less likely to be employed in academia than were graduates who did not use AI.

This working paper analyzed dissertations from more than 226,000 STEM doctoral graduates

nature.com/articles/d4158…

Jinghan (Michael) Zeng

@Jinghan_Zeng

Oct 24

Secretary of War Pete Hegseth

@SecWar

Oct 23

The War Department has designated Confucius Institutes as a threat to our national security.

Ken Doi土居@kitakazethomas

23h

Trevor Noren

@trevornoren

Oct 23

FT: "China is building data centres at rapid speed across its energy-rich hinterland, as Beijing seeks to turn abundant electricity and cheap land into an advantage in the global race to develop AI...While China is struggling to secure enough advanced chips to satisfy soaring AI e to mobilise the land, electricity and construction capacity needed to build the data centres that house them...China already has 24 gigawatts of operational data centre computing capacity, more than the rest of Asia combined but less than half the 56GW in the US, according to SemiAnalysis, the chip-focused research firm. A further 50GW is under construction or has been announced."

As I argue in my upcoming report on "Global Electricity Undersupply" (learn about our reports here: sageroadresearch.com/collections/re…), China's electricity advantages remain an underestimated edge in the AI arms race, especially as models rapidly grow in size. Chinese models continue to widen the gap with US models in terms of token usage. Just between October 5th and 11th, AI models worldwide processed 172 trillion tokens, with Chinese models accounting for 86.74 trillion of those tokens versus 18.97 trillion for US AI models, according to OpenRouter data. It was the 24th consecutive week in which Chinese models outpaced US models in token usage. This has continued to put downward pressure on token prices, with the LLM Token Expenditure Index falling by more than half since this summer, begging even more questions about the unit economics of US hyperscalers versus their Chinese competitors. The US bet is that bigger and therefore, smarter models are their competitive advantage over China. It’s staggering how rapidly frontier models are growing. As The New York Times reported recently:

"In 2026, GPT 6 Astra took nearly an octillion calculations to train—over 50 million times as many calculations as GPT 1 did. Even calling this figure astronomical understates its size: An octillion is a thousand times the number of stars in the known universe…Researchers predict that by 2030, training the largest A.I. models may use electricity at a rate similar to New York City today."

The bigger models get, the more electricity they demand and therefore, the bigger China’s advantage becomes given their electricity supply capacity and pricing. According to Wall Street Journal reporting from late last year, Chinese data center power rates range between 3 and 5.6¢ per kWh while US data center power rates range between 7 and 9.1¢ per kWh.

Far more to come in the report!

FT link: ft.com/content/e1dd8b…