“It’s an old question in the philosophy of physics. People have been talking about it since Fermat first formulated it in the 1600s; Planck wrote volumes about it. The thing is, while the common formulation of physical laws is causal, a variational principle like Fermat’s is purposive, almost teleological.
...
Well, if I can speak anthropomorphic-projectionally, the light has to examine the possible paths and compute how long each one would take.” He plucked the last potsticker from the serving dish.
“And to do that,” I continued, “the ray of light has to know just where its destination is. If the destination were somewhere else, the fastest path would be different.
Gary nodded again. “That’s right; the notion of a ‘fastest path’ is meaningless unless there’s a destination specified. And computing how long a given path takes also requires information about what lies along that path, like where the water’s surface is.
I kept staring at the diagram on the napkin. “And the light ray has to know all that ahead of time, before it starts moving, right?”
...
That day when Gary first explained Fermat’s principle to me, he had mentioned that almost every physical law could be stated as a variational principle. Yet when humans thought about physical laws, they preferred to work with them in their causal formulation. I could understand that: the physical attributes that humans found intuitive, like kinetic energy or acceleration, were all properties of an object at a given moment in time. And these were conducive to a chronological, causal interpretation of events: one moment growing out of another, causes and effects creating a chain reaction that grew from past to future.
In contrast, the physical attributes that the heptapods found intuitive, like “action” or those other things defined by integrals, were meaningful only over a period of time. And these were conducive
to a teleological interpretation of events: by viewing events over a period of time, one recognized that there was a requirement that had to be satisfied, a goal of minimizing or maximizing. And one had to know the initial and final states to meet that goal; one needed knowledge of the effects before the causes could be initiated.”
-- Ted Chiang. “Stories of Your Life and Others.”
Most of the remaining staff at X, in addition to concentrating on cost-cutting, have been told to focus on growing X’s revenue.
X’s U.S. ad revenue is expected to grow 1.5% to $1.27 billion, while global ad sales are anticipated to rise 2.2% to $2.19 billion, according to estimates from Emarketer. In 2021, the last year in which X disclosed annual financials before Musk took the company private, Twitter said it generated $4.51 billion in advertising revenue.
https://www.wsj.com/tech/elon-musks-x-restructures-ahead-of-spacex-ipo-6aab5673
An even more bewildering use of drones took place in the early days of the Shanghai lockdown. The city’s top mental health official introduced an unexpectedly sparky phrase in an otherwise drab press conference on the course of the virus, demanding that Shanghainese “repress your soul’s yearning for freedom.”
“One night in April, as the lockdown swung into high gear, a drone carrying a megaphone began blasting that message into apartments full of huddling residents: “Repress your soul’s yearning for freedom,” with a woman’s voice played on loop while a light blinked from the drone. “Do not open your windows to sing, which can spread the virus.”
-- Daniel Wang. “Breakneck: Chinas Quest to Engineer the Future.”
“[China] embraced a vision of technology radically different from Silicon Valley’s: the pursuit of physical and industrial technologies rather than virtual ones like social media or e-commerce platforms. In China, technology is not represented by shiny objects; rather, it is embodied by communities of engineering practice like Shenzhen, where technology lives inside the heads and in the hands of its workforce. ”
...
Chinese officials climbed over each other to host a Foxconn facility. They salivated at the number of jobs and amount of tax revenues the company could create for their jurisdiction, which could elevate them to higher office. Local officials promised to satisfy Foxconn’s extraordinary labor demands. In Chengdu, minor bureaucrats had to hit quotas on the number of workers to rustle up for factory work; those who failed might receive an order to work at assembly lines themselves.
...
A 2012 story in the New York Times reported that Apple needed to hire nearly nine thousand industrial engineers in the earlier days of iPhone production. The company’s analysts expected recruitment to last nine months to hire that many engineers in the United States. In China, they were able to do it in two weeks.
-- Daniel Wang. “Breakneck: Chinas Quest to Engineer the Future.”
Right now, workers are potentially training AI how to make them obsolete. And they often don’t realize it.
The kind of AI used by companies, called an enterprise AI system, can capture everything you do at work and use that information to train itself. These systems can record your interactions within the platform—the prompts you write, the documents you create, the queries you run.
In other words, the company can potentially track—and claim ownership of—every keystroke you make within the system, every idea you document there, every tool you build using that platform.
This dynamic may fundamentally change the relationship between employer and employee. The stakes are so high and so urgent that both sides are rushing to position (or protect) themselves. Executives are rapidly implementing enterprise AI systems, seeking productivity gains and competitive advantage—and they often aren’t disclosing the implications for job security and privacy. Meanwhile, at least some employees are secretly adopting personal AI tools, sometimes violating corporate policies, so that their employers can’t capture everything they know and do.
Individual opt-out of AI is often impossible, so unions and professional associations need to pay attention. With collective bargaining, workers could demand transparency about the use of enterprise AI and demand fair compensation for the knowledge it gathers. Without collective power, individual employees will keep clicking “accept” on agreements that restructure their jobs simply because they have no alternative.
https://www.wsj.com/lifestyle/careers/ai-knowledge-capture-employees-a69a0e1c
Three times a year, the forecasting platform Metaculus hosts a tournament that is known to have especially difficult questions. It generally attracts the more serious forecasters, Ben Shindel, a materials scientist who ranked third among participants in a recent competition, told me. Last year, at its Summer Cup, a London-based start-up called Mantic entered an AI prediction engine.
A few months later, the guesses from Mantic’s prediction engine and the other tournament participants were scored against the real-life outcomes and one another. The AI placed eighth out of more than 500 entrants, a new record for a bot.
Mantic’s prediction engine combines a bunch of LLMs and assigns each one different tasks. One might serve as an expert on a database of election results. Another might be asked to scan weather data, economic outcomes, or box-office receipts, depending on the question that it’s attacking. The models work together as a team to generate a final prediction.
On Metaculus, a group of forecasters has taken to estimating when AIs will have the chops to out-predict an elite team of humans. Last January, they said there was about a 75 percent chance this would happen by 2030. Now they think it’s more like 95 percent.
https://www.theatlantic.com/technology/2026/02/ai-prediction-human-forecasters/685955/
Powered by encrypted messaging apps, anonymized platforms and a growing pool of people willing to move money for a cut, the system is agile, scalable and disturbingly hard to shut down. What began a decade ago as a fringe trend on dark-web bazaars is fast evolving into a sprawling global ecosystem of freelance money movers. Even the biggest criminal groups, long reliant on in-house laundering, are starting to tap it.
This is happening while the Trump administration is shifting funding and priorities away from money laundering investigations while also clearing the way for crypto to take a larger role in global finance. That raises the dangerous possibility that laundering operations could slip entirely beyond the government’s ability to police them, several watchdogs and crypto enforcement agents say.
https://www.bloomberg.com/news/features/2026-02-11/drug-cartel-money-laundering-shifts-to-crypto-and-the-gig-economy
Both the Trumps and Witkoffs began cashing out during the run-up to the inauguration.
On Jan. 16, two lieutenants for Sheikh Tahnoon bin Zayed Al Nahyan, the U.A.E. president’s brother, signed the deal to purchase a 49% stake in World Liberty for half a billion dollars—a huge sum for a company that at that time had no products. Of the upfront installment, $187 million was directed to Trump family entities, while $31 million was slated to flow to entities affiliated with the Witkoff family. The deal didn’t give the Tahnoon-backed entity any rights to the proceeds of future WLFI token sales, preserving the Trumps’ and Witkoffs’ income stream.
World Liberty stopped selling its WLFI token to the public in March. By then, the company said it had taken in $550 million from the token sales, in addition to the U.A.E. investment money.
https://www.wsj.com/finance/currencies/trump-sons-crypto-billions-1e7f1414
Women who were surveyed by Obi were essentially evenly split when it comes to choosing Waymo or Tesla, with Zoox a distant third at 8%. But 56% of men surveyed preferred Tesla to Waymo (25%) or Zoox (7%).
https://techcrunch.com/2026/01/27/the-price-gap-between-waymo-and-uber-is-narrowing/
“An alliance between 3Com and Huawei was attractive to both sides. Huawei would get the immediate legal protection of 3Com’s deep patent portfolio; 3Com would get Huawei’s lower production costs and its connections to the vast China market. Soon after the two announced their joint venture, called H3C, 3Com’s lawyers filed a motion to intervene in the Cisco case, calling 3Com an interested party.”
--- Eva Dou. “House of Huawei.”
“Ren told his trusted deputy, Guo Ping, who was now Huawei’s executive vice president, to get to the US as quickly as he could. Ren invoked the fable of ancient Chinese military general Han Xin*, who had accepted the humiliation of crawling between another man’s legs to prevent a deadly fight.”
* The fable of General Han Xin’s humiliation, known as "crawling between the legs" (胯下之辱), tells of a young, poor Han Xin being challenged by a bully in his hometown of Huaiyin to either kill him or crawl through his legs. Choosing to endure this shame rather than waste his life on a petty killing, Han Xin crawled through, later becoming a renowned military strategist and rewarding the man for testing his resolve.
“Ren saw a Russia devastated by hyperinflation... Ren felt that the United States was partly to blame: Washington had coaxed the leader of the new Russia, Boris Yeltsin, to apply “shock therapy” to the economy with a rapid shift to capitalism, he wrote, but Washington did not follow through with the financial aid it had dangled. “They always give you some bait to get you to change some policies, but when you’ve made changes according to their demands, they raise further demands,” Ren wrote. “You still cannot get ‘sincere’ help from the United States.”
At the end of the day, the Russians remained wary about installing Chinese switches in their networks. “We are still unsure how much we know about Russia and if we can really open up the market,” Ren wrote to staff.
( Read more... )
-- Eva Dou. “House of Huawei.”
“On January 28, 1996, Ren Zhengfei held Huawei’s first “mass-resignation ceremony.” Each head of a regional sales office was told to prepare two reports: a work summary and a written resignation. “I will only sign one of the reports,” Ren said.
Huawei had started out in rural markets, and many of its early sales managers were provincial in their experience and network of contacts. As Ren sought to go national and international, he decided to make the entire sales staff resign and reapply for their jobs. “The mountain goat must outrun the lion to not be eaten,” he had told them ahead of the event. “All departments and sections must optimize and eat the lazy goats, the goats that do not learn or progress, and the goats with no sense of responsibility.”
They were following the strategy that Mao had used to win the Chinese Civil War of “encircling the cities with the countryside.”[9] They’d won over villages and towns in the beginning, building their strength to take on the big cities.
Ren told his followers that demotions built character and that the demoted would only be stronger when they worked their way up again.... “Even Deng Xiaoping could go down and up three times. Why can’t you go down and up three times?”
-- Eva Dou. “House of Huawei.”
Mossing and others, both at OpenAI and at rival firms including Anthropic and Google DeepMind, are ... studying them [LLMs] as if they were doing biology or neuroscience on vast living creatures—city-size xenomorphs that have appeared in our midst.
Anthropic and others have developed tools to let them trace certain paths that activations follow, revealing mechanisms and pathways inside a model much as a brain scan can reveal patterns of activity inside a brain. Such an approach to studying the internal workings of a model is known as mechanistic interpretability. “This is very much a biological type of analysis,” says Batson. “It’s not like math or physics.”
Anthropic invented a way to make large language models easier to understand by building a special second model (using a type of neural network called a sparse autoencoder) that works in a more transparent way than normal LLMs. This second model is then trained to mimic the behavior of the model the researchers want to study.
Creating a model that behaves in predictable ways in specific scenarios requires making assumptions about what the inner state of that model might be in those scenarios. But that only works if large language models have something analogous to the mental coherence that most people do.
And that might not be the case.
...
Another possible solution ... Instead of relying on imperfect techniques for insight into what they’re doing, why not build an LLM that’s easier to understand in the first place?
https://www.technologyreview.com/2026/01/12/1129782/ai-large-language-models-biology-alien-autopsy/
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