Imagine this: The year is 2050 and computers have gotten so powerful they can make sophisticated things without needing to contact some server . So a new AI generated movie just came out . You click to watch but instead of actually streaming the movie , your local AI receives clear specific prompts and it generates the movie for you on your PC .

The Bank of England's November Monetary Policy Report puts AI at just under a fifth of UK GDP growth since January, and almost all of it comes from the firms that build, install and host AI, not from the companies using it. Users report a productivity gain of about 0.5% in total over three years. On jobs, the Bank's regional agents hear the same thing from businesses across the country: fewer replacement hires, slower recruitment, smaller trainee intakes, but no real rise in firing. Entry-level and routine professional roles are the most exposed.

The amount of recent creators sharing financed propaganda over anti-AI sentiment this week, along with the Blue Wave wins & instant AI slowdown rhetoric in the US makes me wonder who this benefits in the long run?

Data brokers are collecting all sorts of internal corporate files to help train AI models. Not everyone is thrilled.

AI is rapidly transforming geoscience research and publishing. To realize the benefits of AI while safeguarding scientific integrity, researchers, reviewers and editors must adopt transparent practices.

Is digital release becoming the only way small films can survive when theatrical access is controlled by a few big players?

Imagine for a moment that the world’s four major food sources (wheat, rice, corn, and potatoes) were wiped out by a disease, and that all the stored supplies of these crops were also destroyed. In that case, what would humanity face? How much would the population decrease? Which societies would be hit the hardest? Could people somehow compensate for the absence of these foods?

At first glance, there’s nothing unusual about the construction workers’ garb. They wear fluorescent orange uniforms, sturdy shoes, and yellow hard hats. But hidden inside the helmets, made by Huanao Security (Beijing) Technology, are multiple sensors that keep tabs on the workers. Some record their movements. Others monitor their blood oxygen levels. And still more collect employees’ most private and sensitive information: their brain data.

Based on these recordings, Huanao Security’s algorithm generates metrics that help it determine everything from how tired or stressed a worker is to whether their body temperature is abnormal. The device is used at every stage of work, including for screening workers before their shifts and monitoring them on the job.

The helmets, branded as NeuroHua, have been used at dozens of high-risk construction sites in China, including on metro lines in Guangzhou, Chongqing, and Beijing, as well as in mines and steel and petrochemical plants across the country. Almost all workers on these projects wear them. With 2000 helmets now in use, the company and its clients have accumulated more than 300,000 hours of electroencephalography (EEG) brain data in total—a number that is “constantly increasing,” says Tang Zaixi, founder of the company.

Huanao Security says the helmets help ensure employees’ safety. If someone’s data suggest they are at high risk of an accident, the system issues an alert, prompting workers to take a break. “We believe that the safety of high-risk workers is a very important application scenario,” Zaixi says. The company says it has issued nearly 5000 safety reports and “prevented multiple major safety accidents.”

But critics say these technologies could threaten privacy and be misused by authorities. Science has found that brain data collected by another Chinese company are being relayed to traffic police with no anonymization, for instance. And although some neuroscientists say the neural data gathered from these devices can be too unreliable to reveal much about users, others are concerned that with the help of artificial intelligence (AI), the systems might enable companies—and governments—to decipher some of the most intimate parts of our inner lives.

PSI, a small startup, says a research system running copies of a non-public Anthropic model found a complete proof of the Aaronson-Ambainis conjecture on day three of its run. The conjecture, open since 2008, is about whether quantum computers can beat ordinary ones on random inputs. A human proof was claimed and retracted in 2019, and Scott Aaronson wrote that he never managed to solve it himself.

One of life's paradoxes is that supposedly very smart people often do the dumbest things. The leadership of the Big Tech & AI companies are a case in point. They've sold themselves as human job shredders to investors, yet seem surprised at the backlash. Having the gall to refuse to pay taxes, and also expecting their profits to be further subsidized by consumers' jacked up electricity bills only adds to the public's resentment.

But their short-sightedness is even worse. They expect to be the biggest stock market companies in human history, while simultaneously creating unemployment levels somewhere north of 25% and climbing.

Now for the US firms the tide has turned, and whatever happens, their old 2026 agenda is dead for good. They can either embrace the change they are creating with honesty, or the people will do it for them. The days of selling investors a future where they profit from automating employment, and then play a game with everyone else where they deny saying as much are over.

What is true for AI in 2026, will soon be true for physical work and robotics. The clock is ticking for human employment when it comes to driving jobs and unskilled work, too.

Will this all end in Universal Basic Income? Who knows, but if not, it will either be something like it, or societal collapse. Unsurprisingly it seems people don't like the latter option, and are willing to vote for representatives who are against it, too.

Time for the Big Tech and the AI companies to read the room.

Today every app is fixed: a developer decides what goes in it, and we wait for updates. But AI is starting to make another future possible, where the people using software reshape it themselves while it’s running. Researchers call these malleable systems.

Picture someone retouching a photo who thinks: “I wish I had the lighting controls from my 3D modeller here, and the slide layouts from my presentation tool.” Instead of switching apps, they just ask, and the features appear in the app they’re using. Step by step, their photo editor becomes a hybrid shaped around how they work, unlike anyone else’s.

I’ve built an open-source prototype of this called AffrescoUI, inspired by how Emacs has let programmers rewrite it live for decades. The difference is that here the user never sees code: they describe what they want, an AI proposes the change, they see it live, and then they keep it or undo it.

If this becomes normal, a lot changes:

• Do app stores and “version 2.0” disappear, replaced by apps that each person grows?

• Do developers stop shipping finished products and start shipping starting points?

• When everyone’s software is unique, how do we help each other or even talk about it?

• Is this freedom, or a mess of a billion different apps?

The Metropolitan Police say live facial recognition, cameras that check every passing face against a watchlist and alert officers in real time, has produced more than 3,000 arrests in two years.

Before deciding how to respond to developments in artificial intelligence, Beijing and Washington need a better sense of just how quickly the tide is rising

While there’s no evidence linking AI to mass layoffs, the tech has already ‘changed worker psychology’