The First Proof project tested four AI systems on ten original, unpublished research-level math problems created by mathematicians for this purpose. All problems were never included in any model's training data, and solutions were scored by anonymous expert reviewers from relevant fields. The AI responses showed frequent hallucinations and a critical absence of literature citations, failing to reference any sources. The evaluation confirmed that current reasoning models cannot yet match top human mathematicians. This was the first assessment to simultaneously satisfy three key standards: frontier math problems, no training data leakage, and expert human evaluation.
Between 2021 and 2025, Chinese universities cut or suspended 12,200 undergraduate programs and launched 10,200 new ones, adjusting over 30% of curricula. The changes aim to address a severe graduate employment crisis and align education with emerging high-tech industries, as China seeks global leadership in AI and other future sectors. The data was reported by Xinhua citing the Ministry of Education.
Despite recent announcements from Nvidia, SpaceX, Google, and startup Starcloud about building orbital data center constellations with AI GPUs, a closer look at the physics reveals major challenges. The claimed benefit of free cooling in space is a misconception: in a vacuum, only radiative cooling works, requiring huge radiator surfaces to prevent chip overheating. Solar power requires complex sun-tracking systems, and cosmic rays degrade panels, radiators, and chips. Space maintenance is extremely difficult, necessitating redundant systems, and a rough cost comparison shows running AI GPUs in space costs at least an order of magnitude more than on Earth. Orbital data centers may have niche uses but are currently economically unviable.
Astronomers studying the 10-billion-year-old binary system HD 81809, located 113 light-years away, found that the two G-type stars have a 3.7-fold difference in iron abundance, far beyond typical binary evolution. The main-sequence star HD 81809B exhibits unusually high lithium levels, a strong signature of recent planetary engulfment for an old low-mass star. Modeling suggests it must have swallowed 25 to 75 Earth masses of metal-rich material, equivalent to the metal core mass between Neptune and Saturn. This system provides direct observational evidence of a star consuming its planets in a co-eval binary.
The United Nations University Institute for Water, Environment and Health (UNU-INWEH) published a report quantifying the environmental footprint of AI’s energy use. It projects that by 2030, global data centers supporting AI will consume 945 TWh of electricity per year, use water equivalent to the basic annual needs of 1.3 billion people, and occupy over 14,500 km² of land. Training GPT-5 alone is estimated to require about 100 GWh of electricity, 1 billion liters of water, and 1.5 km² of land. The report highlights that inference accounts for 80–90% of AI’s total energy consumption. In 2025, global data centers already consumed 448 TWh of electricity, ranking as the world’s 11th largest electricity consumer if considered a single country.