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Why Data Center Siting Has Become a Three-Front Battle: Grid Costs, Water Resources, and Community Interests

In late September 2026, multiple U.S. states moved in quick succession to tighten data center regulation, and siting no longer depends solely on securing power—it must now clear three fronts at once: grid costs, water resource transparency, and local land use. California's AB 1577, AB 2383, and SB 887 require developers to bear the full incremental cost of grid upgrades; Massachusetts Executive Order No. 658 imposes the same requirement on large data centers with peak demand exceeding 25 MW. Texas has paused new data center permits until ERCOT completes a statewide audit; New York has proposed a community benefits agreement benchmark of $1 million per megawatt.

AI as a "research partner"—are you really at ease with it?

Anhui University of Finance and Economics reported an academic misconduct case, determining that the author's "mistaken belief" in AI search results constituted improper use of AI and penalizing the responsible parties. Multiple researchers have pointed out that AI can complete closed-loop processes such as literature review and experimental planning, but cannot independently pose original questions, and will generate seemingly plausible yet unverifiable hypotheses and fake citations. An arXiv review of 2.5 million papers and 111 million references shows that in 2025 alone, the four major platforms contained nearly 150,000 fabricated references generated by AI.

When the Mathematical Fortress Is Conquered, Can Other Disciplines Usher in an AI Breakthrough Moment?

Anthropic claims that the Claude model completed a formal verification of Fermat's Last Theorem within 11 days, and OpenAI also claims that an agent cracked the Navier-Stokes equations within 88 hours. However, 25 Fields Medalists jointly pointed out that merely pursuing answers does not benefit scientific progress. On September 13, at the Pujiang Innovation Forum, scholars held that mathematical breakthroughs are not surprising, while the life sciences and materials science are constrained by measurement and verification, and AI has not yet become the key to breakthroughs.

Multiple teams train LLMs on historical data to test whether AI can reproduce general relativity

According to a recent report in Nature, since the beginning of this year multiple teams have attempted to build retro LLMs whose training data is cut off before a certain historical point in time, to test whether the models can rediscover later scientific ideas. The results show that AI is still a considerable distance from Einstein-style scientific breakthroughs. Independent researcher Michael Hera trained Machina Mirabilis on pre-1900 data; when faced with the photoelectric effect, the model once proposed that light would break down into a large number of mutually independent pulses, but in most cases it failed and was merely imitating historical texts. Google DeepMind researcher Tom Zahavi points out in the paper "LLMs Don't Leap" that current LLMs cannot yet complete a reasoning leap like Einstein's, and that the abductive reasoning on which scientific breakthroughs depend often occurs on anomalous phenomena with very little data or in conflict with existing theories.

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Microsoft launches biological world model Quine, multiple giants double down on AI4S

Microsoft Research has launched an experimental AI research system, Quine, building a biological multimodal world model covering genomics, proteins, chemistry, RNA, cell states, and biological imaging, and constructing an interactive harness framework that connects models, scientific tools, literature, and researchers. Microsoft has partnered with the Broad Institute, jointly established by MIT and Harvard University, to apply Quine to cancer biology research. In one case, it completed the predictive ranking of the effects of thousands of compounds on pancreatic cancer cell states over a single weekend, saving months of experimental work, but the system is still in an early research stage. In addition, Anthropic established a life sciences research team and laboratory on September 23, OpenAI launched GPT-Rosalind for life sciences in April this year, and Nvidia announced in January this year a joint effort with Eli Lilly to build an AI innovation laboratory.

TechCrunch rounds up AI agents that live in text messages

TechCrunch reviewed more than ten AI agents that require no app download and interact directly via text message, covering general-purpose assistants as well as specialized products for home, travel, content creation, and work. These agents can remember context, connect to existing apps, and complete tasks on users' behalf, such as scheduling appointments, booking restaurants, sending emails, and shopping online. The article also lists each product's launch date, funding situation, and subscription price, such as Instinct raising $1 billion at a $10 billion valuation in September 2026, Fambot securing $3.5 million in pre-seed funding, and Town completing a $55 million Series A round.

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From Opposition to Support: Data Center Developers' Community Communication Strategies

Facing growing community opposition, data center developers are turning to transparent communication, funding the construction of power infrastructure, and prioritizing areas where residents are supportive. At the Data Center World Power conference held in Dallas, companies such as Hecate Energy, QTS Data Centers, and Switch advised developers to disclose projects in advance, include power generation plans, and deliver tangible benefits to residents through fixed and subsidized community electricity rates and investments in water resource restoration.

Trump Renames "Artificial Intelligence" to "Super Intelligence"; Tech Executives Collectively Silent, Passing the Loyalty Test

At a White House luncheon, Trump signed an executive order announcing that the federal government will no longer use the term "artificial intelligence" and will instead call it "Super Intelligence," and he demanded that the private sector follow suit. None of the attending executives—including Jeff Bezos, Jensen Huang, Dario Amodei, Mark Zuckerberg, Sundar Pichai, Greg Brockman, Alex Karp, and Elon Musk—raised any objection. Philosopher Nick Bostrom pointed out that superintelligence specifically refers to systems far surpassing the most powerful human minds, and cannot encompass broader AI systems such as spam filters, spell checkers, and early LLMs.

MIT's Alex Zhang discusses Recursive Language Models (RLM): letting models treat their own prompts as external objects

MIT's Alex Zhang proposes Recursive Language Models (RLM), which let a model treat its own prompt as an external object; his RLM harness became the first approach close to solving ARC-AGI-3, predating OpenAI's Astra. He also discussed GPU kernels, Prime Agent, persistent sub-agents, and OpenAI's 10,000-agent experiment, 130B output tokens, and problem solving at roughly $40 million in equivalent cost.

The Verge investigates why Kevin O'Leary's Utah data center project was halted

The Verge reporter Josh Dzieza recounted on a podcast his months-long investigation into Kevin O'Leary's Utah data center project Stratos (also known as Wonder Valley). The project is planned to cover 40,000 acres and supply 9 gigawatts of power, more than twice the average electricity consumption of the entire state of Utah. It bypassed local land approval through the state-level military installation development agency MIDA, and Box Elder County only learned of it one month before the vote. Local residents quickly organized opposition, causing two local commissioners and state Senate President J. Stuart Adams to lose their seats, and the project is currently paused due to referendum litigation. Dzieza believes that the core of data center development is speed, and local residents are never participants in this chain.

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New Jersey's $1.07 Million Fine Highlights Environmental Risks of Self-Generated Power at AI Data Centers

New Jersey environmental regulators issued a $1.07 million fine to DataOne, the operator of an AI data center in Vineland, alleging it installed and operated 62 natural gas generators without a permit. Forrester analyst Abhijit Sunil noted that AI data centers are increasingly taking on the characteristics of power plants; although self-generated power can relieve pressure on the grid, it does not eliminate environmental costs, involving NOx, CO and particulate matter emissions as well as continuous noise.

The 200 GW Moment: Reshaping the Power Grid for the AI Economy

Global data center power demand is projected to exceed 200 GW by 2030, and AI workloads are pushing typical rack densities from about 10-30 kW to 80-150 kW, with advanced deployments approaching 1 MW per rack. At Data Center World Power in Dallas, utilities, hyperscalers and developers discussed how to expand power supply while safeguarding grid reliability, increasingly adopting hybrid solutions combining the grid with on-site generation and energy storage.

AMD to Acquire World Labs in an All-Stock Deal Worth Approximately $8.2 Billion

AMD announced that it will acquire AI research lab World Labs in an all-stock deal worth approximately $8.2 billion. The transaction is expected to close by the end of 2026 and still requires regulatory approval. World Labs develops spatial intelligence world models that generate and simulate interactive 3D environments from text, images, and video. Its co-founder and CEO, Fei-Fei Li, will join AMD as Executive Vice President and Chief Scientist. AMD said the deal is intended to co-design hardware, software, and systems for emerging models and applications such as robotics and physical AI.