The 2011 meltdown at Fukushima's nuclear plant "was the world's worst nuclear disaster since Chernobyl in 1986," CNN remembers.

But this week Japanese authorities "have approved a decision to restart the world's biggest nuclear power plant," reports CNN, "which has sat dormant for more than a decade following the Fukushima nuclear disaster."

Despite nerves from many local residents, the Niigata prefectural assembly, home to the Kashiwazaki-Kariwa plant, approved a bill on Monday that clears the way for utility company Tokyo Electric Power Company (TEPCO) to restart one of the plant's seven reactors. The company plans to bring the No. 6 reactor back online around January 20, Japan's public broadcaster NHK reported...

Following the [2011] disaster, Japan shut down all 54 of its nuclear power stations including Kashiwazaki-Kariwa, which sits in the coastal and port region of Niigata about 320 kilometers (200 miles) north of Tokyo on Japan's main island of Honshu. Japan has since restarted 14 of the 33 nuclear reactors that remain operable, according to the World Nuclear Association. The Niigata plant will be the first to reopen under the operation of TEPCO, the company that ran the Fukushima Daiichi power station. It has been trying to reassure residents of the restart plan is safe...

About 60-70% of Japan's power generation comes from imported fossil fuels, which cost the country about 10.7 trillion yen ($68 billion) last year alone... Japan is the world's fifth-largest emitter of carbon dioxide, after China, the United States, India and Russia, according to the International Energy Agency. But it has committed to reaching net zero emissions by 2050, and renewable energy was at the center of its latest energy plan published earlier this year, with a push for greater investments in solar and wind. The country's energy demands are also expected to increase in the coming years due to a boom in energy-hungry data centers that power AI infrastructure. To achieve its energy and climate goals, Japan aims to double the share of nuclear power in its electricity mix to 20% by 2040...

On its website, TEPCO said Kashiwazaki-Kariwa had undergone multiple inspections and upgrades and that the company had learned "the lessons of Fukushima." The company said new seawalls and watertight doors would provide "stronger protection against tsunamis" and that mobile generators and more fire trucks would be on hand for "cooling support" in an emergency. It also said the plant now had "upgraded filtering systems designed to control the spread of radioactive materials."
A survey published by the prefecture in October "found 60% of residents did not think conditions for the restart had been met," reports Reuters, adding that "Nearly 70% were worried about TEPCO operating the plant."

An astrophysicist at the University of Rochester writes that "many" of his colleagues in physics "have come to believe that a mystery is unfolding in every microbe, animal, and human." And it's a mystery that:

- "Challenges basic assumptions physicists have held for centuries"
- "May even help redefine the field for the next generation"
- "Could answer essential questions about AI."

In short, while physicists have favored a "reductionist" philosophy about the fundamental laws controlling the universe (energy, mattery, space, and time), "long-promised 'theories of everything' such as string theory, have not borne significant fruit: There are, however, ways other than reductionism to think about what's fundamental in the universe. Beginning in the 1980s, physicists (along with researchers in other fields) began developing new mathematical tools to study what's called "complexity" — systems in which the whole is far more than the sum of its parts. The end goal of reductionism was to explain everything in the universe as the result of particles and their interactions. Complexity, by contrast, recognizes that once lots of particles come together to produce macroscopic things — such as organisms — knowing everything about particles isn't enough to understand reality...

Physicists have always been good at capturing the essential aspects of a system and casting those essentials in the language of mathematics... Now those skills must be brought to bear on an age-old question that is only just getting its proper due: What is life? Using these skills, physicists — working together with representatives of all the other disciplines that make up complexity science — may crack open the question of how life formed on Earth billions of years ago and how it might have formed on the distant alien worlds we can now explore with cutting-edge telescopes. Just as important, understanding why life, as an organized system, is different at a fundamental level from all the other stuff in the universe may help astronomers design new strategies for finding it in places bearing little resemblance to Earth. Analyzing life — no matter how alien — as a self-organizing information-driven system may provide the key to detecting biosignatures on planets hundreds of light-years away.

Closer to home, studying the nature of life is likely essential to fully understanding intelligence — and building artificial versions. Throughout the current AI boom, researchers and philosophers have debated whether and when large language models might achieve general intelligence or even become conscious — or whether, in fact, some already have. The only way to properly assess such claims is to study, by any means possible, the sole agreed-upon source of general intelligence: life. Bringing the new physics of life to problems of AI may not only help researchers predict what software engineers can build; it may also reveal the limits of trying to capture life's essential character in silicon.

The global push to put autonomous taxis on public roads is accelerating as ride-hailing companies and technology firms advance from pilot programs toward limited commercial rollouts in cities across China, the United States, Europe and the Middle East.

WeRide and Uber launched Level 4 fully driverless robotaxi operations in Abu Dhabi in November and began offering robotaxi passenger rides on Uber's platform in Dubai the following month. Amazon's Zoox started offering free rides to select early users in parts of San Francisco in November after launching its autonomous ride-hailing service on the Las Vegas Strip in September. Alphabet's Waymo now operates services in Phoenix, San Francisco, and Los Angeles -- the latter two having launched in June and November 2024 respectively.

Baidu's Apollo Go has been operating without safety drivers in Chongqing and Wuhan since securing permits in August 2022 and has since expanded to Shenzhen and Beijing. Pony.ai launched paid robotaxi services in Guangzhou in February and Shanghai in August. Tesla began a limited paid robotaxi rollout in Austin, Texas in June using Model Y SUVs, though the vehicles still require a safety monitor onboard. The expansion will continue in 2026: Waymo plans to launch an autonomous ride-hailing service in London, and Momenta is preparing a luxury robotaxi service in Abu Dhabi through a partnership with Mercedes-Benz and UAE taxi operator Lumo.

Two years after generative AI was supposed to render India's $250 billion IT services industry obsolete, the sector is finding that enterprises still need someone to handle the unglamorous plumbing work that large-scale AI deployment demands. Less than 15% of organizations are meaningfully deploying the new technology, according to investment bank UBS, and Indian IT firms are positioning themselves to capture the preparatory work -- data cleanup, cloud migration, system integration -- that channel checks suggest could take two to three years before enterprise-wide AI becomes feasible.

The financials have held up better than the doomsday predictions suggested. Infosys now calls AI-led volume opportunities a bigger tailwind than the deflation threat, a reversal from 2024, and orderbooks held steady in the third quarter even as pricing pressure filtered through renewals. Infosys expects its orderbook to grow more than 50% this quarter, anchored by an NHS deal worth $1.6 billion over 15 years.

The companies have been restructuring accordingly. TCS cut headcount by 2% and invested in a 1GW data-centre network while acquiring Salesforce advisory firm Coastal Cloud. HCLTech reduced margins by 100 basis points and became one of the first large systems integrators to partner with OpenAI; this week it announced acquisitions of Jaspersoft for $240 million and Belgian firm Wobby to expand agentic AI capabilities.

The bear case for the Indian IT sector assumed that AI would work out of the box. Two years in, it does not.

While stock investors have pushed AI-related shares to repeated highs this year, debt markets are telling a more cautious story as newer AI infrastructure companies find themselves paying significantly elevated interest rates to borrow money. Applied Digital, a data center builder, sold $2.35 billion of debt in November at a 9.25% coupon -- roughly 3.75% above similarly rated companies, or about 70% more in interest costs. The pattern has repeated across several deals.

Wulf Compute, a subsidiary of Bitcoin-miner-turned-data-center-operator Terawulf, raised $3.2 billion in mid-October at 7.75%, well above the 5.5% average yield for similarly rated issuers. Cipher Compute sold $1.7 billion in early November at just over 7%. CoreWeave, which rents data centers and installs computing systems for companies like OpenAI and Meta, raised $1.75 billion in July at 9%. The company's bonds have since fallen to around 90 cents on the dollar, pushing the effective yield above 12% -- nearly double the average for companies at its single-B rating level.

"We just have to be much more pessimistic and not buy into the hype," said Will Smith, a portfolio manager at AllianceBernstein. Construction delays and uncertain demand for AI computing power remain key concerns for lenders who, unlike equity investors, have no upside beyond getting their principal back.

While America's largest corporations are riding a wave of surging profits and AI-fueled stock market enthusiasm to record highs, small businesses across the country are cutting staff and scaling back operations as years of high inflation, cautious consumers and tariff confusion take their toll.

Private firms with fewer than 50 workers have steadily shed jobs over the past six months, according to payroll processor ADP, cutting 120,000 positions in November alone. Midsize and large firms continued adding jobs during the same period. The divergence mirrors what's happening among American consumers.

The Federal Reserve's latest beige book noted that overall consumer spending declined further even as higher-end retail spending remained resilient. Workers at small businesses tend to earn less than those at large companies, and stock market gains from large public company shares flow mostly to wealthier Americans. Small businesses -- those with up to 500 workers -- employ nearly half the American workforce and represent more than 40% of GDP, according to the U.S. Chamber of Commerce. But their profits are slightly lower than a year ago, per a Bank of America Institute analysis. Net income at S&P 500 companies rose 12.9% from a year earlier in the third quarter.

The global smartphone and PC markets face potential contractions of up to 5.2% and 8.9% respectively in 2026, according to downside risk scenarios from IDC that trace the problem to memory chip manufacturers shifting production capacity away from consumer electronics toward AI data centers. Samsung Electronics, SK Hynix and Micron Technology have pivoted their limited cleanroom space toward high-bandwidth memory for AI servers, restricting supply of the conventional DRAM and NAND used in phones and laptops.

IDC expects 2026 DRAM supply growth to hit 16% year-on-year, below historical norms. The smartphone industry's decade-long trend of bringing flagship features to affordable devices is reversing. Memory represents 15-20% of the bill of materials for mid-range phones, and thin-margin vendors like Xiaomi, Realme and Transsion will bear the brunt. Apple and Samsung have long-term supply agreements securing components up to 24 months ahead. PC vendors including Lenovo, Dell, HP, Acer and ASUS have warned clients of 15-20% price increases heading into the second half of 2026.

The relentless climb in memory prices driven by the AI boom's insatiable demand for datacenter hardware has renewed an old debate about whether modern software has grown inexcusably fat, a column by the Register argues. The piece points to Windows Task Manager as a case study: the current executable occupies 6MB on disk and demands nearly 70MB of RAM just to display system information, compared to the original's 85KB footprint.

"Its successor is not orders of magnitude more functional," the column notes. The author draws a parallel to the 1970s fuel crisis, when energy shortages spurred efficiency gains, and argues that today's memory crunch could force similar discipline. "Developers should consider precisely how much of a framework they really need and devote effort to efficiency," the column adds. "Managers must ensure they also have the space to do so."

The article acknowledges that "reversing decades of application growth will not happen overnight" but calls for toolchains to be rethought and rewards given "for compactness, both at rest and in operation."

Michael Truell, the 25-year-old CEO and cofounder of Cursor, is drawing a sharp distinction between careful AI-assisted development and the more hands-off approach commonly known as "vibe coding." Speaking at a conference, Truell described vibe coding as a method where users "close your eyes and you don't look at the code at all and you just ask the AI to go build the thing for you." He compared it to constructing a house by putting up four walls and a roof without understanding the underlying wiring or floorboards. The approach might work for quickly mocking up a game or website, but more advanced projects face real risks.

"If you close your eyes and you don't look at the code and you have AIs build things with shaky foundations as you add another floor, and another floor, and another floor, and another floor, things start to kind of crumble," Truell said. Truell and three fellow MIT graduates created Cursor in 2022. The tool embeds AI directly into the integrated development environment and uses the context of existing code to predict the next line, generate functions, and debug errors. The difference, as Truell frames it, is that programmers stay engaged with what's happening under the hood rather than flying blind.

Goldman Sachs analysts have identified a notable shift in how investors respond to corporate layoff announcements, finding that even job cuts attributed to automation and AI-driven restructuring are now causing stock prices to fall rather than rise. The investment bank linked recent layoff announcements to public companies' earnings reports and stock market data, concluding that stocks dropped by an average of 2% following such announcements, and companies citing restructurings faced even harsher punishment.

The traditional Wall Street playbook held that layoffs tied to strategic restructuring would boost stock prices, while cuts driven by declining sales would hurt them. That distinction appears to have collapsed.

Goldman's analysts suggest investors simply don't believe what companies are saying -- firms announcing layoffs have experienced higher capex, debt and interest expense growth alongside lower profit growth compared to industry peers this year. The real driver, analysts suspect, may be cost reduction to offset rising interest expenses and declining profitability rather than any forward-looking efficiency play.

Goldman expects layoffs to keep rising, motivated in part by companies' stated desire to use AI to reduce labor costs.

Nvidia has agreed to buy assets from Groq, a designer of high-performance artificial intelligence accelerator chips, for $20 billion in cash, according to Alex Davis, CEO of Disruptive, which led the startup's latest financing round in September. From a report: Davis, whose firm has invested more than half a billion dollars in Groq since the company was founded in 2016, said the deal came together quickly.

Groq raised $750 million at a valuation of about $6.9 billion three months ago. Investors in the round included Blackrock and Neuberger Berman, as well as Samsung, Cisco, Altimeter and 1789 Capital, where Donald Trump Jr. is a partner. Groq said in a blog post on Wednesday that it's "entered into a non-exclusive licensing agreement with Nvidia for Groq's inference technology," without disclosing a price. With the deal, Groq founder and CEO Jonathan Ross along with Sunny Madra, the company's president, and other senior leaders "will join Nvidia to help advance and scale the licensed technology," the post said.

An anonymous reader quotes a report from the Wall Street Journal: It's harder than ever to mine bitcoin. And less profitable, too. But mining-company stocks are still flying, even with cryptocurrency prices in retreat. That's because these firms have something in common with the hottest investment theme on the planet: the massive, electricity-hungry data centers expected to power the artificial-intelligence boom. Some companies are figuring out how to remake themselves as vital suppliers to Alphabet, Amazon, Meta, Microsoft and other "hyperscalers" bent on AI dominance.

Bitcoin-mining -- using vast computer power to solve equations to unlock the digital currency -- has been a lucrative and cutting-edge pursuit in its own right. Lately, however, increased competition and other challenges have eroded profit margins. But just as the bitcoin-mining business began to cool, the AI build-out turned white hot. The AI arms race has created an insatiable demand for some assets the miners already have: data centers, cooling systems, land and hard-to-obtain contracts for electrical power -- all of which can be repurposed to train and power AI models.

It's not a seamless process. Miners often have to build new, specialized facilities, because running AI requires more-advanced cooling and network systems, as well as replacing bitcoin-mining computers with AI-focused graphics processing units. But signing deals with miners allows AI giants to expand faster and cheaper than starting new facilities from scratch. These companies still mine some bitcoin, but the transition gives miners a new source of deep-pocketed customers willing to commit to longer-term leases for their data centers.

"The opportunity for miners to convert to AI is one of the greatest opportunities I could possibly imagine," said Adam Sullivan, chief executive of Core Scientific, which has pivoted to AI data centers. The shift has boosted miners' stocks. The CoinShares Bitcoin Mining ETF has surged about 90% this year, a rally that has accelerated even as bitcoin erased its gains for 2025. The ETF holds shares of miners including Cipher Mining and IREN, both of which have surged following long-term deals with companies such as Amazon and Microsoft. Shares of Core Scientific quadrupled in 2024 after the company signed its first AI contract that February. The stock has gained 10% this year. The company now expects to exit bitcoin mining entirely by 2028.

alternative_right shares a report from Euronews: France's President Emmanuel Macron discovered news of his own supposed overthrow, after he received a message of concern, along with a link to a Facebook video. "On Sunday (14 December) one of my African counterparts got in touch, writing 'Dear president, what's happening to you? I'm very worried,'" Macron told readers of French local newspaper La Provence on December 16.

Alongside the message, a compelling video showcasing a swirling helicopter, military personnel, crowds and -- what appears to be -- a news anchor delivering a piece to camera. "Unofficial reports suggest that there has been a coup in France, led by a colonel whose identity has not been revealed, along with the possible fall of Emmanuel Macron. However, the authorities have not issued a clear statement," she says.

Except, nothing about this video is authentic: it was created with AI. After discovering the video, Macron asked Pharos -- France's official portal for signaling online illicit content -- to call Facebook's parent company Meta, to get the fake video removed. But that request was turned down, as the platform claimed it did not violate its "rules of use." [...] The original video ... racked up more than 12 million views [...].The teenager running the account is based in Burkina Faso and makes money running courses focusing on how to monetize AI. He eventually took the video down more than a week after its initial publication, due to political -- and public -- controversy. "I tend to think that I have more power to apply pressure than other people," Macron said. "Or rather, that it's easier to say something is serious if I am the one calling, but it doesn't work."

"These people are mocking us," he added. "They don't care about the serenity of public debates, they don't care about democracy, and therefore they are putting us in danger."

"In the age of Spotify and AI slop, tapes remind us what we're missing when we stop taking risks," writes author Janus Rose in an article for 404 Media. Here's an excerpt: There are lots of advantages to the cassette lifestyle. Unlike vinyl records, tapes are compact and super-portable, and unlike streaming, you never have to worry about a giant company suddenly taking them away from you. They can be easily duplicated, shared, and made into mixtapes using equipment you find in a junk shop. When I was a kid, the first music I ever owned were tapes I recorded from MTV with a Kids' Fisher Price tape recorder. I had no money, so I would listen to those tapes for hours, relishing every word Kim Gordon exhaled on my bootlegged copy of Sonic Youth's "Bull in the Heather." Just like back then, my rediscovery of cassettes has led me to start listening more intentionally and deeply, devoting more and more time to each record without the compulsion to hit "skip." Most of the cassettes I bought in Tokyo had music I probably never would have found or spent time with otherwise.

Getting reacquainted with tapes made me realize how much has been lost in the streaming era. Over the past two decades, platforms like Spotify co-opted the model of peer-to-peer filesharing pioneered by Napster and BitTorrent into a fully captured ecosystem. But instead of sharing, this ecosystem was designed around screen addiction, surveillance, and instant gratification -- with corporate middlemen and big labels reaping all the profits. Streaming seeks to virtually eliminate what techies like to call "user friction," turning all creative works into a seamless and unlimited flow of data, pouring out of our devices like water from a digital faucet. Everything becomes "Content," flattened into aesthetic buckets and laser-targeted by "perfect fit" algorithms to feed our addictive impulses. Thus the act of listening to music is transformed from a practice of discovery and communication to a hyper-personalized mood board of machine-optimized "vibes."

What we now call "AI Slop" is just a novel and more cynically efficient vessel for this same process. Slop removes human beings as both author and subject, reducing us to raw impulses -- a digital lubricant for maximizing viral throughput. Whether we love or hate AI Slop is irrelevant, because human consumers are not its intended beneficiaries. In the minds of CEOs like OpenAI's Sam Altman, we're simply components in a machine built to maintain and accelerate information flows, in order to create value for an insatiably wealthy investor class. [...]

Tapes and other physical media aren't a magic miracle cure for late-stage capitalism. But they can help us slow down and remember what makes us human. Tapes make music-listening into an intentional practice that encourages us to spend time connecting with the art, instead of frantically vibe-surfing for something that suits our mood from moment-to-moment. They reject the idea that the point of discovering and listening to music is finding the optimal collection of stimuli to produce good brain chemicals. More importantly, physical media reminds us that nothing good is possible if we refuse to take risks. You might find the most mediocre indie band imaginable. Or you might discover something that changes you forever. Nothing will happen if you play it safe and outsource all of your experiences to a content machine designed to make rich people richer.

An anonymous reader quotes a report from Wired: Two years ago, Lizmary Fernandez took a detour from studying to be an immigration attorney to join a free Apple course for making iPhone apps. The Apple Developer Academy in Detroit launched as part of the company's $200 million response to the Black Lives Matter protests and aims to expand opportunities for people of color in the country's poorest big city. But Fernandez found the program's cost-of-living stipend lacking -- "A lot of us got on food stamps," she says -- and the coursework insufficient for landing a coding job. "I didn't have the experience or portfolio," says the 25-year-old, who is now a flight attendant and preparing to apply to law school. "Coding is not something I got back to."

Since 2021, the academy has welcomed over 1,700 students, a racially diverse mix with varying levels of tech literacy and financial flexibility. About 600 students, including Fernandez, have completed its 10-month course of half-days at Michigan State University, which cosponsors the Apple-branded and Apple-focused program. WIRED reviewed contracts and budgets and spoke with officials and graduates for the first in-depth examination of the nearly $30 million invested in the academy over the past four years -- almost 30 percent of which came from Michigan taxpayers and the university's regular students. As tech giants begin pouring billions of dollars into AI-related job training courses across the country, the Apple academy offers lessons on the challenges of uplifting diverse communities.

[...] The program gives out iPhones and MacBooks and spends an estimated $20,000 per student, nearly twice as much as state and local governments budget for community colleges. [...] About 70 percent of students graduate, which [Sarah Gretter, the academy leader for Michigan State] describes as higher than typical for adult education. She says the goal is for them to take "a next step," whether a job or more courses. Roughly a third of participants are under 25, and virtually all of them pursue further schooling. [...] About 71 percent of graduates from the last two years went onto full-time jobs across a variety of industries, according to academy officials. Amy J. Ko, a University of Washington computer scientist who researches computing education, calls under 80 percent typical for the coding schools she has studied but notes that one of her department's own undergraduate programs has a 95 percent job placement rate.

Microsoft has denied any plans to rewrite Windows 11 using AI and Rust after a LinkedIn post from one of its top-level engineers sparked a wave of online backlash by claiming the company's goal was to "eliminate every line of C and C++ from Microsoft by 2030."

Galen Hunt, a principal software engineer responsible for several large-scale research projects at Microsoft, made the claim in what was originally a hiring post for his team. His original wording described a "North Star" of "1 engineer, 1 month, 1 million lines of code" and outlined a strategy to "combine AI and Algorithms to rewrite Microsoft's largest codebases." The repeated use of "our" in the post led many to interpret it as an official company direction rather than a personal research ambition.

Frank X. Shaw, Microsoft's head of communications, told Windows Latest that the company has no such plans. Hunt subsequently edited his LinkedIn post to clarify that "Windows is NOT being rewritten in Rust with AI" and that his team's work is a research project focused on building technology to enable language-to-language migration. He characterized the reaction as "speculative reading between the lines."

Italy's antitrust regulator Italian Competition Authority ordered Meta to suspend a policy that blocks rival AI chatbots from using WhatsApp's business APIs, citing potential abuse of market dominance. "Meta's conduct appears to constitute an abuse, since it may limit production, market access, or technical developments in the AI Chatbot services market, to the detriment of consumers," the Authority wrote. "Moreover, while the investigation is ongoing, Meta's conduct may cause serious and irreparable harm to competition in the affected market, undermining contestability." TechCrunch reports: The AGCM in November had broadened the scope of an existing investigation into Meta, after the company changed its business API policy in October to ban general-purpose chatbots from being offered on the chat app via the API. Meta has argued that its API isn't designed to be a platform for the distribution of chatbots and that people have more avenues beyond WhatsApp to use AI bots from other companies. The policy change, which goes into effect in January, would affect the availability of AI chatbots from the likes of OpenAI, Perplexity, and Poke on the app.

Amazon finds itself caught between two competing impulses as AI shopping agents from OpenAI, Google, Perplexity and Microsoft mushroom across the e-commerce space -- block them to protect its dominant position, or partner with them to avoid being left behind. The company has largely played defense so far. Amazon recently updated its website code to block external AI agents from crawling it, and as of this week had blocked 47 bots including those from all major AI companies. In November, Amazon sued Perplexity over an agent in the startup's Comet browser that can make purchases on users' behalf, alleging the company concealed its agents to continue scraping Amazon's site. But Amazon's stance appears to be shifting, CNBC reports.

CEO Andy Jassy said on an October earnings call that Amazon expects to partner with third-party agents and has engaged in conversations with some providers. The company is now hiring a corporate development leader to forge strategic partnerships in "agentic commerce." Amazon is also investing in its own tools. The company launched shopping chatbot Rufus last February and has been testing an agent called Buy For Me that can purchase products from other sites within Amazon's app.

An anonymous reader quotes a report from the Wall Street Journal: Concerned that artificial intelligence could threaten Communist Party rule, Beijing is taking extraordinary steps to keep it under control. Although China's government sees AI as crucial to the country's economic and military future, regulations and recent purges of online content show it also fears AI could destabilize society. Chatbots pose a particular problem: Their ability to think for themselves could generate responses that spur people to question party rule.

In November, Beijing formalized rules it has been working on with AI companies to ensure their chatbots are trained on data filtered for politically sensitive content, and that they can pass an ideological test before going public. All AI-generated texts, videos and images must be explicitly labeled and traceable, making it easier to track and punish anyone spreading undesirable content. Authorities recently said they removed 960,000 pieces of what they regarded as illegal or harmful AI-generated content during three months of an enforcement campaign. Authorities have officially classified AI as a major potential threat, adding it alongside earthquakes and epidemics to its National Emergency Response Plan.

Chinese authorities don't want to regulate too much, people familiar with the government's thinking said. Doing so could extinguish innovation and condemn China to second-tier status in the global AI race behind the U.S., which is taking a more hands-off approach toward policing AI. But Beijing also can't afford to let AI run amok. Chinese leader Xi Jinping said earlier this year that AI brought "unprecedented risks," according to state media. A lieutenant called AI without safety like driving on a highway without brakes. There are signs that China is, for now, finding a way to thread the needle.

Chinese models are scoring well in international rankings, both overall and in specific areas such as computer coding, even as they censor responses about the Tiananmen Square massacre, human-rights concerns and other sensitive topics. Major American AI models are for the most part unavailable in China. It could become harder for DeepSeek and other Chinese models to keep up with U.S. models as AI systems become more sophisticated. Researchers outside of China who have reviewed both Chinese and American models also say that China's regulatory approach has some benefits: Its chatbots are often safer by some metrics, with less violence and pornography, and are less likely to steer people toward self-harm. "The Communist Party's top priority has always been regulating political content, but there are people in the system who deeply care about the other social impacts of AI, especially on children," said Matt Sheehan, who studies Chinese AI at the Carnegie Endowment for International Peace, a think tank. "That may lead models to produce less dangerous content on certain dimensions."

Longtime Slashdot reader MrFreak shares a public radio interview from 2015 discussing artificial intelligence as inference over abstract inputs, along with scaling limits, automation, and governance models, where for-profit engines are constrained by nonprofit oversight: Recorded months before OpenAI was founded, the conversation treats intelligence as math plus incentives rather than something mystical, touching on architectural bottlenecks, why "reasoning" may not simply emerge from brute force, labor displacement, and institutional design for advanced AI systems. Many of the themes align closely with current debates around large language models and AI governance.

The recording was revisited following recent remarks by Sergey Brin at Stanford, where he acknowledged that despite Google's early work on Transformers, institutional hesitation and incentive structures limited how aggressively the technology was pursued. The interview provides an earlier, first-principles perspective on how abstraction, scaling, and organizational design might interact once AI systems begin to compound.