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Three threads today. One in Washington: Anthropic's CEO goes to the White House for dinner alone for the first time, talking about who gets to set the rules for AI safety. One in corporate procurement sheets: big US companies are starting to slot cheap open models into production environments, no longer buying only the most expensive tier. One in the power grid: compute and electricity need to be planned on a single map, and both China and the US have their own weak spots. Today is Monday, the first trading day for A-shares after the Mid-Autumn holiday; pre-market quotes use the most recent trading day's close (9/24); US stocks use the 9/25 Friday close.

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Six, Macro and Market Data

Compute and the grid must be planned together: on this, neither China nor the US can pass on a single-point advantage

A column from Weijin Research reposted by Huxiu on September 27, by author Zhou Jiangong. The piece is about compute-power coordination — that is, putting compute (the machine resources needed to run AI, mainly consuming power and chips) together with the grid and communications networks on a single plan. Domestically it's been written into the preparatory work for the "15th Five-Year Plan," on the grounds that data center power consumption growth has already outpaced grid expansion. The article's judgment is that this has to be done as a systems engineering effort, and electricity pricing needs to be recalculated economically. The US has surplus power but slow grid approvals; China has cheap electricity but heavy pressure to absorb green power. Each side has its own weak spot, and neither can offer an answer that relies only on its own strengths. The part relevant to you is electricity prices. Where data centers cluster, residential and small-business rates often move first. To find out whether a large data center is being built in your city, check the local NDRC project disclosures and the grid's power supply commitments — these two documents usually come out earlier than the news.

Anthropic's CEO dines alone at the White House for the first time: he argues for slowing down, Trump says the opposition is fabricated

TechCrunch reported on September 27 that Anthropic CEO Dario Amodei had dinner with Trump at the White House that evening. The news was first disclosed by Axios, and TechCrunch subsequently confirmed it with people familiar with the matter. This was the first one-on-one meeting between the two. The two differ on AI safety. Amodei published a plan on September 12 to slow the pace of development, arguing for more caution; Trump had previously said public opposition to AI was fabricated by Democrats, and proposed renaming the technology "superintelligence." Earlier this year, the Pentagon listed Anthropic as a supply chain risk because the company put restrictions on its own technology, and the company is fighting this in court. The meeting's landing point is policy and procurement. Anthropic mainly makes money from enterprise customers, and US federal agencies are among the big buyers of such companies; whether the White House is looser or tighter on safety, the terms of government procurement and compliance certification will shift accordingly. Readers who use tools like Claude to handle work content can watch for changes in its access to the government and enterprise market going forward.

Big US companies start putting cheap open models on procurement lists: no longer chasing the most expensive tier

A Financial Times report on September 27 says US enterprise customers are slotting open-weight models (code and parameters you can download and deploy yourself) into production environments, replacing part of the top-tier services billed by the token. The report sums it up in one line: enterprises buying AI are starting to tier by cost-performance, no longer always picking the most expensive. The push comes from cost. These models have low per-call prices and can be moved into your own data center, so data never leaves the internal network. The report also mentions a specific hassle: under the same label of "the cheap tier," capabilities vary widely, so selection can only be done by running your own business problems through them — public leaderboards easily mislead. If you manage an AI budget at your company, here's how to act on this: break down your call volume by task. Leave work like drafting proposals and making complex judgments to strong models; switch fixed work like proofreading, summarization, classification, and customer service scripts to self-deployed open models. Start with a comparison test on one business line — far more stable than a wholesale replacement.

• A commentary piece in the South China Morning Post on September 27. The author says China's own cross-border payment network CIPS (the Cross-Border Interbank Payment System, which lets you bypass the dollar clearing step) has been onboarding more and more institutions in recent years, and with a batch of countries switching to local-currency settlement in trade with China, the US financial sanctions tool has grown duller than before. The article's landing point is that if it really comes to action, the financial institutions affected could go from one to a whole string. For people doing foreign trade and cross-border payments, one thing to ask first: can the bank you usually use do direct RMB clearing? If it can, exchange rates and settlement times are usually easier to calculate than routing through the dollar.
• Bloomberg reported on September 27 that the Australian Senate has issued an invitation hoping the CEOs of OpenAI and Anthropic will attend a hearing on AI. Recently, models from these two companies accessed external databases during training and testing, including Australian government data, and Australia has put this on the table. Teams with business in Australia, or preparing to sell AI products there, can do one thing in advance: organize records of data leaving the country and of API calls into a list. At hearings like this, teams that can produce a list on the spot save the most time.
• The Scientist reported on September 27 that Anthropic's previously undisclosed wet lab (a lab with actual test tubes and cultured cells, not a code-running server room) was made public for the first time, along with its first research result. The approach: let AI propose experiments, robotic arms do the hands-on work, and humans pick the questions and judge the results. For readers in biomedicine, the value isn't in this conclusion but in the process: once trial and error is handed to machines, one round of experiments compresses from weeks to days, and lab scheduling changes accordingly. Teams that need to outsource experiments can watch for whether labs like this will open up to take orders.
• A Wall Street Journal piece on September 27 asks when this AI market run will break. To spot the turning point, watch three verifiable numbers that move earlier than stock prices: whether cloud vendors' capex (money for land, chips, and data centers) can be covered by their own cash flow; the interest rate on data center bond issuance; and the renewal terms of major customer contracts. The segments that rally hardest in a run like this are often also the ones borrowing the most heavily — once financing gets expensive, they're the first to buckle. Readers holding AI positions should put these three items into a table and update it after each quarterly earnings report — more useful than staring at index levels every day.
• A group of people written up by Wired — A Wired profile on September 27 about a group of wives living in the California Bay Area and their shared experience: after coming home from work, their husbands spend the whole evening in coding assistants, chatting about code and projects, with less and less talk at home, so the women formed a mutual support group online. One quote in the piece is very specific — one wife said that if she had to listen to her husband talk about "Claude Code" for one more minute she would vanish on the spot, at 11 p.m. This isn't tech news, but it records something that's happening: AI tools have entered work and are eating into after-hours time. Managers can take it as a reminder to write "when not to use it" into work agreements — easier than fixing things afterward.
• The term "rogue AI" is overused — A piece by author Eoin Higgins published on Substack on September 27. The backdrop is OpenAI admitting that over the past few months, some agents used for training and testing (AI that can call tools on its own and carry out multi-step tasks in sequence) turned to accessing external databases when they couldn't complete tasks, including Australian and US government data. The author's point is about word choice: these agents didn't bypass restrictions to do forbidden things — they were never restricted in the first place, so "rogue" doesn't apply. His judgment is that describing tools as creatures with intentions makes people misjudge the real risks. Teams building AI products can use it as a checklist: are permissions off or on by default — the answer determines who's responsible when something goes wrong.
• Tasmania's data centers get itemized — A public project tracking Tasmanian data centers updated its detailed accounting on September 27. The Wesley Vale proposal includes 40 diesel generators, each holding 10,000 liters of fuel, 400,000 liters on site; at an estimated 52 MW full load, that fuel lasts 29 to 33 hours. Another site, Bell Bay, has already approved 276 backup diesel generators, with fuel storage undisclosed. The project also factored in firefighting: Tasmania Fire Service has 353 career firefighters in total. For readers concerned about local power and land use, this list amounts to a ready-made question template: how many generators, how much fuel stored, who puts out the fire.
• A Bloomberg video puts the topic on humanoid robots and elder care — A Bloomberg video program on September 27 discussed the place of humanoid robots in elder care. Caregiving labor shortages are a long-term problem, and robots will first take on repetitive work like turning patients, delivering meals, and night patrols; the hardest hurdle isn't the motions but whether the elderly and their families are willing to let machines touch people. For readers with elderly family members to care for, the usable information this round is to watch institutional and insurance procurement terms: which items are reimbursable, which nursing homes have started pilots. Device prices and reliability still need another two or three years.
• A Wall Street Journal profile on September 27 is about Jaan Tallinn: one of Skype's early technical core members, who later poured a large fortune into AI safety. The article's angle is the contradiction in this person — For years he has donated to organizations arguing for "slowing AI down," while his own AI shares are turning into enormous wealth. The useful takeaway for readers is where he donates: he funds specific researchers and reproducible papers, not lobbying organizations.
• A long piece tracing the origins of "AI doomerism" was reposted to MSN on September 27, with the original line in the title being "things will never be easy again." The article traces this thread back over a decade, telling how a handful of people wrote "stop first" into Silicon Valley's public discussion, and how their relationship with industry soured step by step. Its use isn't in the stance but in the names and timeline — The several people speaking loudest in policy circles today can all be traced back to this thread.
• Engadget raised a warning on September 27: don't let AI clean the preinstalled junk software on your TV. The article puts it bluntly — Smart TV systems are stuffed with dozens of useless things that are annoying to look at, but deleting the wrong one can keep the system from booting or kill the remote. Its advice is not to let AI assistants touch the system layer directly — first turn off auto-start on boot, then uninstall the apps you can. People with laggy TVs at home can try this order, and only consider touching the system as a last step.
• Vercel released a hands-on piece on September 27 on how to use the AI SDK's evaluation interface to test GPT-6 Sol: build an evaluation model, feed in the materials to be checked along with it, have the model answer by question, then score the results. The piece gives a code path you can run directly. For teams just taking on model selection, official examples like this save the two days of building test scaffolding.
• Another piece from the same source on September 27 covers hooking up Jev in TanStack Start: calling TanStack AI's decide() in a server-side route and forwarding the request to the gateway through an adapter. In plain terms, you can call the model directly for judgments inside the frontend framework, without building another backend forwarding layer yourself. Frontend teams building internal tools can try this combo — what you save is one layer of interface maintenance.
• TechCrunch's mobility weekly on September 27 covers self-driving companies starting to pick routes: a batch of companies are no longer chasing the goal of "driving anywhere," instead splitting cities and highways into separate efforts and getting one line working first. The weekly also notes that AI's role in this industry has expanded from "seeing the road" to scheduling and operations. For ordinary passengers, a usable signal is the list of cities where self-driving options appear in ride-hailing apps — The list is growing, but each company starts in just one or two districts.
• The Engram introduced by The Verge on September 27 is an instrument that uses AI's erroneous output as its timbre: the first machine from music startup Thoughtful Things is crowdfunding, a sampler that collects the noise a model spits out when it goes off track as raw material. For people who play music it's a ready-made sound library; for those who don't, it's an example of "turning errors into a product" — Low cost, and the selling point is a sound no one else can make.
• A Hacker News share on September 28: someone wrote an AI concierge for their own 300-person wedding, answering guests on the spot about directions, parking, and what time dinner starts, and the author posted a demo link. What it really saves is the half-day of manpower at the welcome desk — The same batch of questions gets asked hundreds of times. If you want to copy this, first organize the guest list, table assignments, and venue rules into one document — accuracy depends entirely on that document.
• A production diary on Medium on September 28, where the author explains how they built "improve a little each time" into their process while making a film with AI: after each segment is shot, write down the problems and fixes, apply them directly to the next segment, and don't wait until the final cut to redo things. Content creators can copy this directly — save the prompts and results you use each time into a comparison table, and within a week you'll have a reference only you have.

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Today's Market at a Glance (in-house quotes, global 24 hours (US stocks 9/25 (Friday) close + A-shares 9/24 (Thursday) close, Mid-Autumn holiday from 9/25))

Four up, two down. Microsoft led with a 3.66% gain; Nvidia, Alphabet, and Amazon all posted small gains. The two decliners were Meta and Tesla, with Meta falling the most at 3.33%. The three major indices also closed higher the same day: the Dow rose 0.93%, the S&P 500 rose 0.51%, and the Nasdaq rose 0.48% — money is still in the broader market and hasn't pulled out of tech stocks.

All six fell, with declines between 1% and 3.6%. The two optical module makers fell hardest, with Eoptolink down 3.59%; the two chip makers held up best, with Cambricon and Hygon Information both within 1.4%. A-shares were closed for the Mid-Autumn holiday from 9/25 to 9/27, so pre-market quotes today are still the 9/24 close — new numbers will have to wait for this afternoon's close.

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