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China Pledges Major US Coal Purchases But Falls Short On Rare Earth Crisis Resolution

Barclays senior China economist Yingke Zhou poured cold water on the Trump-Xi state visit this past week, calling it "more signaling, less substance."

Shortages of rare earths and critical materials in the West, caused by China's weaponization of its export channels in what can only be viewed as resource nationalism, have yet to be resolved.

But there was some good news, though not on the critical materials front: Bloomberg reported early Saturday that China is planning to purchase 20 million metric tons of US coal over two years.

Beijing committed to importing at least 10 million metric tons in 2027 and another 10 million in 2028. The two countries will also pursue preferential tariffs covering $30 billion of non-sensitive goods from both economies under an agreement reached through the US-China Board of Trade.

Meanwhile, the White House said US and Chinese trade negotiators would continue addressing US concerns over shortages of rare earths and other critical minerals. The statement described further work on those bottlenecks, rather than a resolution.

Stifel critical materials analyst Brock Cannon laid out last week that investors want to begin "Owning the Bottlenecks" - in other words, ex-China producing mines that can deliver critical material supplies to the West today. 

The bad news: no resolution to supplies of rare earths and critical materials being choked by Beijing. The good news, however, is that China is willing to buy US coal.

But if Phase 1 of the trade deal during Trump's first term is any indication, China usually doesn't abide by its agreements. US farmers found that out the hard way when China began boosting agricultural imports from South America.

 

Tyler Durden Sat, 09/26/2026 - 09:55

A miracle baby is safe and healthy after its mother, a 29-year-old police officer, was accidentally shot by her partner.

Dress to impress at the office.

The fun-loving ladies on the new reality show "Team Moms: Baseball" know how to have a good time during their sons' up to 14-hour game days.

This unfortunate arena staffer, to borrow old "NFL Primetime" lingo, got jacked up.

Toxicology reports in the suspicious deaths of two University of Mississippi students are expected to be completed as early as next week, according to the state's commissioner of public safety.

Another New UK Government Unit Created To Police 'Untrue Narratives'

Authored by Steve Watson via Modernity.news,

British Prime Minister Andy Burnham used his first United Nations speech this week to announce a 'National Centre for Information Defence' - a new state machine to "detect, attribute and disrupt" what ministers call hostile information attacks, and to stop a "distorted and untrue narrative about Britain."

Burnham wrapped it in Russia, bots, deepfakes and "community cohesion." Critics are adamant the move is purely aimed at ensuring the government has full control over the flow of information.

'We are ready to share our understanding of how these actors work and how we can respond.'

Prime Minister Andy Burnham announces a new National Centre for Information Defence to detect, attribute and disrupt hostile state information attacks to defend Britain's democracy. pic.twitter.com/Zdv5Y8ihJQ

— GB News (@GBNEWS) September 23, 2026

Speaking in New York, Burnham said there is "no national security without a strong, cohesive society" and that this cohesion "has been under attack."

He described an "industrial-scale assault by hostile actors on the information environment." He blamed Russian agencies for bots, fake websites, falsified newspaper articles and forged branding of 28 British organisations "including universities and the BBC."

?Andy Burnham just announced a National Centre for Information Defence - to "detect, attribute and disrupt" information the state calls hostile.?

Every government that builds one says it's only for foreign threats. Then it starts treating disagreement as an attack on... pic.twitter.com/ZMLyGCUirN

— WasAcop (@WasAcop) September 23, 2026

Burnham also claimed they had "amplified far-right narratives" and tried to interfere with the 2019 general election. He called attempts to stoke unrest after "horrific events" repulsive.

The centre will sit first in the Office for the Prime Minister and Cabinet. It is supposed to work with the intelligence agencies, government departments, police and social media companies. Ministers assert it will also "build national resilience" by helping communities spot disinformation - and prevent that "distorted and untrue" picture of Britain.

So a...ministry of truth? https://t.co/9iwkoCoXo2

— Carl Benjamin ??????? (@Sargon_of_Akkad) September 23, 2026

Burnham went further, warning of an "insidious campaign that reaches into people's homes and twists what they feel about their own country and community - creating a narrative of decline, stoking division and sowing despair."

The United Kingdom, he said, had "tiptoed around this for too long." Over the last decade, he claimed, "we have given too much ground to those who want to run a negative, corrosive narrative about life in Britain, which bears no resemblance to reality. Well, no more."

The government already treats public anger about migration, crime, two-tier policing and a collapsing high street as a "narrative" problem rather than a facts problem.

I've seen mission creep in previous government departments (look at Prevent's focus on the mythical "far-right"), and I give Burnham's new "National Centre for Information Defence" a fortnight before it's solely focused on smearing and silencing British patriots, allowing the... https://t.co/TvWqdlinzh

— Leo Kearse - see me on tour! Links in bio (@LeoKearse) September 23, 2026

Reform UK leader Nigel Farage blasted, "Andy Burnham is setting up a Ministry of Truth to clamp down on free speech." In the accompanying clip he said ministers would use Russia as the excuse, then slide into shutting down "far-right tropes."

"This will be like George Orwell's Ministry of Information, and this will be used to clamp down on free speech," he said, adding "Never ever trust this authoritarian Government."

Andy Burnham is setting up a Ministry of Truth to clamp down on free speech. pic.twitter.com/UbIOpgahWR

— Nigel Farage (@Nigel_Farage) September 23, 2026

Ironically, stripped of the Russia garnish, the language reads like early Soviet information control.

If you read this without context, you would think it was written by Vladimir Lenin in the Soviet Union in 1922.

Andy Burnham and Labour clearly seem to be moving towards classifying anything that doesn't line up with their worldview as "misinformation."

They will push this... https://t.co/UdC8AVvtkH pic.twitter.com/jTWuDU3sNv

— Ben Graham (@BenGrahamUK) September 23, 2026

Defence Secretary Wes Streeting was sent out to hose it down on GB News. Alex Armstrong put the question straight: people fear the unit will be "weaponised to target British citizens who simply disagree with your Government. Can you promise today that that will not be the case?"

"Can you promise this wont be weaponised against British people"

As the govt launch a new 'National Centre for Information Defence' to wage war on 'untrue narratives' many are concerned it will be used to attack free speech.@wesstreeting say on @GBNEWS pic.twitter.com/6ElbHksqyq

— Alex Armstrong (@Alexarmstrong) September 23, 2026

Streeting said yes. "One of the great things about Britain and one of the things that I have a responsibility to defend is the freedom and democracy that we all enjoy."

That includes, he said, "the freedom to disagree, the freedom for people to challenge their Government, to challenge Government policy, to criticise robustly what we are doing."

The centre, he insisted, exists to attribute and rebut hostile state work. "We're not interested in policing domestic political dissent."

Fraser Myers of Spiked heard the same words and reached the opposite conclusion.

'When I hear those words I hear the government cracking down on free speech.'@AlexArmstrong asks Fraser Myers of Spiked Online about Andy Burnham's plan for a National Centre for Information Defence, aimed at tackling disinformation about the UK. pic.twitter.com/5UaD4PNyE9

— GB News (@GBNEWS) September 23, 2026

Labour's record is why nobody should take the promise at face value. Big Brother Watch director Silkie Carlo said action against genuine foreign campaigns is legitimate, but "we have already seen the mantle of countering 'disinformation' being abused by our Government in attempts to malign and silence dissent."

Units including the Counter Disinformation Unit, the Rapid Response Unit, RICU and the MoD's 77th Brigade have, she said, been "involved in recording, flagging and triggering the censorship of Britons' lawful speech." MPs, journalists, campaigners and ordinary people have appeared in so-called disinformation reports "for lawful, truthful speech critical of the Government."

UnHerd's account of the 77th Brigade is worse. The Ministry of Defence once claimed the unit "does not, and has never, conducted any kind of action against British citizens." Documents showed it tracking identifiable British accounts during Covid, including tweets from then-Green MP Caroline Lucas.

The civilian Counter Disinformation Unit logged Peter Tatchell for criticising the monarchy and Conservative MP David Davis for attacking lockdown policy - after ministers said the unit did "not monitor political debate." Time and again the public was told these teams looked abroad. Time and again they looked inward.

This is the same apparatus we've repeatedly mapped:

         

Nile Gardiner, a former Thatcher aide, told GB News that Burnham's UN address was "talk of control and censorship" and "exactly the kind of messaging that the Trump administration absolutely hates."

The White House has already hauled British officials into the US embassy over the Online Safety Act, under-16 social media bans, and plans to push "trusted" outlets - BBC, ITV, Channel 4 - through the platforms.

While Burnham builds a centre to police "untrue narratives," Labour is also looking at how to keep the BBC's pipeline funded even as viewers walk away. Ministers are considering dumping the £180 licence fee for an £11-a-month "Home Internet Levy" on every household with broadband, whether anyone watches live TV or not.

Culture officials have discussed the Social Market Foundation plan. BBC director-general Matt Brittin has called the licence fee a "busted flush" and pushed for a mandatory household levy.

A DCMS spokesman said Charter Review is looking at options that are "sustainable and fair." Fair, in this case, means you pay for the state broadcaster through your router.

BBC licence fee could be axed and replaced by £11-a-month internet charge whether people watch live TV or not under new plans being considered by Labour https://t.co/bdesV2RaX0

— Daily Mail (@DailyMail) September 23, 2026

They might as well say that if you want running water or electricity, you also have to pay for the BBC.

The same week, communications regulator Ofcom was accused of sitting on data that undercuts the BBC's moral monopoly. The regulator's own news consumption survey found 74 per cent of GB News viewers rate that channel trustworthy, against 71 per cent for Sky News viewers and 66 per cent for BBC viewers.

GB News and Sky both scored 63 per cent on impartiality among their own audiences; the BBC scored 53 per cent.

Seventy-two per cent of GB News viewers said it offered a broad range of views. Ofcom left those figures out of its headline report and produced them after GB News asked.

The regulator blamed an "administrative error."

Ofcom is accused of covering up data that shows GB News is more trusted by its viewers than the BBC https://t.co/YL8l7VVucR

— Daily Mail (@DailyMail) September 23, 2026

Whitehall already has plans to force-feed the BBC through YouTube and the social platforms in the name of fighting "disinformation" - the same BBC that memory-holes comedy sketches when the satire no longer fits the narrative.

   

Reform's Zia Yusef asked a simple question, three times: what single piece of misinformation has Vladimir Putin actually seeded in the minds of British people?

"What piece of Misinformation has been seeded in the Minds of British people by Vladimir Putin?"

"I'll ask you again - What piece of Misinformation was seeded in the Minds of British people by Vladimir Putin?"

"I'll ask you again - What piece of Misinformation was seeded in the... pic.twitter.com/fkketdD9Jw

— Concerned Citizen (@BGatesIsaPyscho) September 23, 2026

Hostile states run influence operations. That is not in dispute. What Burnham has built is a centre inside his own office, working with police and the platforms, to police free thought. The people who will staff it already decided that noticing decline is the problem. The people who will be "resilient" are the ones told to distrust their own eyes.

Tyler Durden Sat, 09/26/2026 - 09:20

Lindsay Clancy’s ex-husband described her as a “fun-loving, outgoing person” in the years before she fatally strangled their three young children.

We're following the anger!

Ariana and Joseph Lebron say their 3-year-old son was assaulted by assistant teacher Silvia Beshay at the Kiddie Academy of Staten Island on Oct. 14, 2025.

The "Real Housewives of Dubai" star detailed her wild night with Hubbard in a new interview with Page Six's "Virtual Reali-Tea."

The "Real Housewives of Dubai" star detailed her wild night with Hubbard in a new interview with Page Six's "Virtual Reali-Tea."

Timothée Chalamet pedals through St-Tropez, Jessica Lange is frighteningly chic and more.

Timothée Chalamet pedals through St-Tropez, Jessica Lange is frighteningly chic and more.

Pattinson's performance feels true to the "real" Chris Hansen, but moreover, Primetime feels true to the whole 2000s deal.

The update comes after the 61-year-old was hospitalized in critical condition following a suicide attempt at his Las Vegas home on Sept. 18.

The update comes after the 61-year-old was hospitalized in critical condition following a suicide attempt at his Las Vegas home on Sept. 18.

AI Doom: A Brief History Of Bad Tech Predictions

Go back three decades to 1995 when the Internet was just taking off and opinions proliferated about the future and impact of this novel creation: some said it would supercharge global productivity. Others said it would be no more useful than a fax machine.

With the benefit of hindsight some 30 years later, we can say that the Internet debate has been settled, and as Deutsche Bank's Adrian Cox writes in a lengthy report published this week, it takes guts to do what Bob Metcalfe did.

After wrongly predicting in an article in 1995 that the internet would go “spectacularly supernova” and collapse in 1996, the co-inventor of Ethernet and founder of 3Com then pulped a copy of the article in a blender and drank it at a conference in 1997 as a way to eat his words.

While nobody worries about the Internet any more, the latest tech predictions are about no less than an AI apocalypse. Various tech leaders spent the weekend warning of catastrophic but not fully specified risks if it is not contained.

Yet history suggests that even the greatest minds of their generations have a poor record in predicting what tech is coming down the line, let alone what its effects will be in the real world.

The stakes could not be higher – not just the future of life itself but, in the shorter term, the future of a historic boom that depends on hype turning into reality.

* * * 

As Cox writes in his latest report (full report available here), concerns about AI have moved in waves since the launch of ChatGPT nearly four years ago. This year there was a spike in searches for “SaaSpocalypse” (the concern that software-as-a-service would be disintermediated by AI agents) in February, followed by a surge in “jobs apocalypse” in May, Google Trends data show. Both have been totally eclipsed in recent days by searches for AI extinction.

But taking a step back, all are far behind “AI bubble”, itself behind “deepfake”, in turn below “AI students”, overall “AI risk” and – biggest of the lot – the very tangible and immediate “cybersecurity”, almost all of which have surged this year, according to Google Trends data.

“Prediction is very difficult, especially if it’s about the future”: Niels Bohr, often also attributed to Yogi Berra

Wanting to know what will happen tomorrow is deeply visceral – literally.

People have been sacrificing sheep and chickens for four millennia to read the future in their entrails. Nowadays almost a third of Americans say they believe in astrology and consult their horoscope. We want to believe that Lionel Messi really can see space in a football match before it even exists, even if it is partly his movement that helps create the space he appears to predict.

But predicting the future is hard. For AI, it’s not just about what it will be able to do but also when it will be able to do it – whether more than $5 trillion in investment by the hyperscalers alone over the next five years will pay off before the advanced semiconductor chips they’re buying become redundant. It’s also about how rapidly and intensively AI can be adopted. Asset prices are a still more uncertain derivative, whipping around like the tail of a snake in response to every shift in expectations.

US President Donald Trump and China found themselves on the same side in rebutting the latest concerns as fearmongering, albeit with Trump adding that America needs to stay ahead of China in AI.

“Those who have knowledge, don’t predict. Those who predict, don’t have knowledge.” Lao Tzu

A glance through history would give any investor indigestion. Most predictions are wrong. The favorites in horse races lose 70% of races. It took a decade or more for demand to catch up with the infrastructure laid down in the British canal and railway and US telecoms and fibre booms – and many investors never recovered their capital.

Technology predictions have an especially mixed record as the binary order of the labs collides with the messy reality of the world. Famous examples range from IBM Chairman Thomas Watson’s underestimation of demand for computers in 1943 (“I think there is a world market for maybe five computers”) to Elon Musk’s repeated overestimation of imminent widespread adoption of self-driving cars since 2013 (“We should be able to do 90 percent of miles driven within three years”).

Technologists are not immune from making category errors, mistaking the most visible, automatable component of a job for the job itself. For example, radiologists, a favorite case study for believers in a jobs apocalypse, do much more than looking at images and making binary diagnoses. Much of their value lies in interpretation, judgement, communication and clinical decision-making.

Ultimately, what we see in the future is not so much a window as a mirror into our own hopes and fears.

And the problem is that hindsight makes what actually happens seem so obvious that we never seem to learn.

Today’s debate in a (rough) nutshell

The current debate encapsulates a perfect storm of an extremely high-stakes issue with limited transparency and an inherently unknowable outcome.

What sparked the current surge in concern?

The current debate comes after a researcher from one of the leading frontier labs quit, citing safety concerns; AI agents apparently collaborated with each other on a number of high-profile security breaches; and labs hinted that the technology is getting closer to “recursive self improvement”, ie AI improving AI.

Is the existential debate something new?

It is actually nothing new: leading economists and academics warned in July that “we must act now” to steer AI through a potential “transformation of our economy, larger than the Industrial Revolution… over a vastly shorter time frame”. That echoed the even more ominous statement three years ago by AI leaders calling for action to mitigate “the risk of extinction”.

The tug of war between “doomers” vs “boomers” has played a pivotal role in the development of the key frontier labs. Silicon Valley insiders sometimes refer to their “p(doom)” rating, representing their estimated percentage chance that AI will cause an existential catastrophe. 

What does an extinction scenario look like?

Experts including Tesla’s Musk, “godfather of AI” Geoffrey Hinton and philosopher Nick Bostrom have been warning about it for well over a decade. In the latter’s famous “paperclip maximiser” scenario, a superintelligent AI runs out of steel and then kills humans in a single-minded drive to fulfil its task of making paperclips. Fears sometimes centre around AI that has access to coding, the internet and knowledge of how people think, in order to manipulate them – as current AI models indeed do.

Why has it caught on so fast this time?

One factor is that it comes amid a tech backlash, particularly in the US, where concern about AI has become a rare bipartisan issue ahead of the US mid-term elections in November. Local concerns about data centre construction have become rallying points for voters in a way that vague fears about long-term jobs did not. Politicians including Senator Bernie Sanders have moved to propose wide-ranging legislation.

What arguments do companies make for continuing AI research?

1. If we don’t do it, someone else will. That may be either China or other, less responsible companies.
2. The potential benefits outweigh the benefits. AI may, they say, cure cancer, create new jobs and even eliminate the need for work altogether.
3. Good AI is the answer to bad AI. It should create defences against rogue AI and misuse of AI, and solve issues like AI’s huge energy and water use.

What do critics say?

1. AI is not dangerous because it is too clever but rather because it is too limited. It does not think like a human and people should not anthropomorphise it, critics say, adding that it is flawed probabilistic code that cannot reliably follow instructions. Longstanding critic Gary Marcus argues that it should be recalled until it can be shown to be safe and is properly controlled.
2. AI leaders are defending their own businesses. With potential IPOs on the horizon, a rising challenge from open-source models, and progress towards Artificial General Intelligence (AGI) being called into question, a handful of companies may benefit from a pause or legislation that helps protect their lead, critics say. Doomer talk distracts from real issues like deepfakes, copyright infringement and  cybersecurity lapses, they say.
3. Extinction scenarios are overblown. The internet is a diversified platform, so talk of AI somehow switching it off is fanciful, critics say. AI does not and will not realistically have sustained secret access to military or dangerous biotech resources, or be able to influence (very nondeterministic) human brains en masse, critics say. Humans will use tame AI and other tools, eg kill switches, if needed to resist attacks. Another “godfather of AI” Yann LeCun says the real risk is of power concentration.

A century of good and bad technology predictions

You have to admire people who have the courage to stick their neck out and make predictions, even if they don’t always work out. Vague predictions are relatively easy; specific ones within specific timeframes are harder.

It is no wonder that executives are coached by their ever-cautious PR teams to avoid answering hypothetical questions or giving dates to reach their targets.

• Nikolai Tesla, scientist, 1926: “When wireless is perfectly applied… we will be able to communicate with one another instantly, irrespective of distance…” The instruments will be “amazingly simple… A man will be able to carry one in his vest pocket”. He was right, albeit imprecise about the how or when it would be achieved.
• Albert Einstein, physicist, 1934: “There is not the slightest indication that [nuclear energy] will ever be obtainable. It would mean that the atom would have to be shattered at will.” Within a decade, in 1942, a team at the University of Chicago had created the first sustaining chain reaction, a precursor to the nuclear reactor,
• Ray Kurzweil, futurist, 1999: “By 2029, computers will have human-level intelligence.” Dismissed at the time, many (though by far not all) of his predictions have worked out, fuelled by what he called the “law of accelerating returns”. His prediction from almost 40 years ago of artificial general intelligence by the end of this decade is remarkably close to the current “San Francisco Consensus”.
• Steve Chen, Chief Technology Officer and co-founder of YouTube, 2005: “There's just not that many videos I want to watch.” Despite his concern, YouTube was bought by Google (now Alphabet) for $1.65bn a year later. The unit made more than $11bn in advertising revenue in the most recent quarter.
• Steve Ballmer, Microsoft CEO, 2007: “There’s no chance that the iPhone is going to get any significant market share.” But you may be reading this on one of the more than 3 billion iPhones sold since its launch in 2007.
• Geoffrey Hinton, “godfather of AI”, 2016: “People should stop training radiologists now. It’s just completely obvious within five years deep learning is going to do better than radiologists… It might be 10 years.” In fact, the number of radiologists has increased by about 10 percent over the past 10 years, in part because of the limitations of AI and in part because of the increase in demand from an ageing population and more affordable healthcare. Hinton has walked back his comments and humans and AI are working together.
• Sam Altman, CEO of OpenAI, 2025: “I can easily imagine a world where 30 to 40% of the tasks that happen in the economy today get done by AI in the not very distant future." He revised this view in May amid a backlash against AI, saying he and his team had been “roughly right” in their technological predictions but “pretty wrong” on the economic and social implications. “I’m delighted to be wrong about this,” he said.

Why is it so hard to predict the future?

"The idea that the future is unpredictable is undermined everyday by the ease with which the past is explained." Daniel Kahneman

1. Chaos theory: treating complex systems as if they are simple and linear.

• Systems like traffic, economies and social trends are finely balanced and sensitive to initial conditions. The butterfly effect means a tiny, unforeseen variable can lead to vastly different outcomes. Just take a look at the ragged history of market-implied forecasts of US Federal Reserve interest rates, where extrapolating from current trends has been reliably wrong.

Take self-driving cars, for example. It turns out to be about more than getting the technology right for the rubber to hit the road:

• Technological hurdles and “edge cases”: throwing more data and processing power at the problem is not enough. There is an infinite long tail of unpredictable events (“edge cases”), like sun glare, a cyclist riding the wrong way in a bike lane, a person in an animal costume, or road-rage-enhanced hand gestures.
• Regulatory labyrinth: autonomous vehicle optimists have come up against legal and ethical debates around liability, safety certification and moral challenges such as the so-called “trolley problem”, when swerving to avoid hitting one victim means colliding with another. Regulation is a slow-lane activity.
• Social and psychological barriers: public acceptance has been slowed down by high-profile accidents and safety concerns. Robots are held to a higher standard than flesh-and-blood motorists. It might be easier to navigate a world where all cars were self-driving, but the “messy middle”, with a mixture of both, is much harder for algorithms to navigate.

2. The future of AI will ultimately depend more on the prosaic challenges of workflow integration at enterprises that will need to pay for it than on releasing yet another model with a marginal step up in capabilities.

Cognitive bias: flaws in how the brain works

• Linear thinking about exponential change: We fail to intuitively understand how different exponential change is from linear change. As Wharton Professor Ethan Mollick says, “being on an exponential means each change over a fixed window is larger than the one before it… This is why AI keeps feeling like it is making leaps… Even though it is a curve on a graph, we keep experiencing a steady doubling of capability as a series of shocks.” This cognitive tendency to track exponential change in linear terms helps explain big AI market swings, he says.

• Optimism bias and the planning fallacy: underestimating costs and overestimating benefits. For example, only a quarter of more than 3,000 megaprojects studied by Saïd Business School came in on budget or better, only 2.8 percent were on budget and on time, and only 0.2 percent were on budget, on time and on benefits. Large-scale infrastructure projects are “too easy to start and too difficult to stop,” the authors said in the study from 2019. It remains to be seen how the recent surge in datacentre construction will compare.
• Confirmation bias: favoring information that confirms existing beliefs. For years, legacy automakers dismissed electric vehicles by  focusing on their high costs and limited range, ignoring the fact that rapid advances in battery technology would soon change that.
• Anchoring: relying too heavily on early information. Microsoft’s Ballmer dismissed the iPhone when it was launched because it was “the most expensive phone in the world and it doesn’t appeal to business customers because it doesn’t have a keyboard which makes it not a very good email machine”. He later said he did not understand how mobile carrier subsidies would make it affordable. More broadly, he missed that this was a new business model.

3. Limited information: inability to see

• It is not just the future that is unknowable but also the present. Feedback mechanisms are often delayed, making it hard to distinguish between causation and correlation, and between relevant and irrelevant factors.
• Isolating key factors is impossible. Even since the development of scientific methods and controlled experiments, we have not come that far since the Mesoamericans found ways to prepare maize where they didn’t succumb to a long-term wasting disease called pellagra. The problem was that they weren’t able to single out the effective part of the process – soaking the maize in an alkaline solution of water and ash –from the other ineffective parts of the package – such as blowing on the maize before cooking it.
• (Author David Oks draws a fascinating parallel between this example of a delayed feedback mechanism and the tendency of AI models to “overfit”, extrapolating too much from coarse and sparse data to fill in the gaps. If the model creates a complex output and gets a single reward signal for it, the rational move is reproduce all of its features, including the ones that were incidental – such as AI’s trademark verbal tics.)
• No one wants to risk throwing the baby out with the bathwater. Argentinian President Javier Milei talked publicly about his superstitious insistence on staying at home wearing a lucky jacket rather than watching his team playing in the football World Cup live at the stadium.

4. Self-interest: incentives favour more extreme positions

• AI is not magic, a silver bullet or a charity, but a commercial industry.
• Most participants have incentives to amplify the stakes. Founders need belief, investors need momentum, incumbents need gravitas, consultants need urgency, policymakers need relevance, executives need a narrative and journalists need drama. This is not a criticism of individuals or organisations, or even of the likely revolutionary impact of AI.
• The challenge is magnified by a social media-driven economy. Social platforms favour novelty, negativity, moral intensity and shareability. For example, in political tweets, one study by New York University found each additional moral-emotional word was associated with about a 20 percent higher retweet rate. Another study by MIT found that false news was 70 percent more likely to be retweeted than true news and reached a threshold of 1,500 people six times faster.
• That means moderate claims are likely to be under-distributed and extreme claims over-rewarded. Implausibly precise numbers for the future size of the AI economy, for example, or dramatic doom-laden scenarios heavy with emotive language will continue to attract greater attention than even-handed assessment. 

What makes AI special – and gives hope for the future

Technology may be liable to erroneous thinking because of the disconnect between visionaries in Silicon Valley and similar communities, and the rest of the world.

Cynics may say that people with extraordinary knowledge and skills in one area may be even more prone than most to wrongly estimate their lack of knowledge or skills in other areas: the so-called Dunning-Kruger effect. That may include the reality of implementing AI on a trading floor in Wall Street, an office supplies wholesaler on the outskirts of London or an auto maker in Mumbai.

There is also a long tradition of correctly identifying the task that will disappear but getting wrong what humans will do instead.

• 1982: Computers “… can develop into monsters, damage the physical and mental health of the clerical staff – maybe even eliminate their jobs”: the Washington Post reported from a clerical conference. Computers instead became the core tool of knowledge workers.
• 1983: The pocket calculator “… has become the target of critics who fear that its use in the classroom will render generations of children dependent on batteries instead of brains”, the Washington Post reported. Instead expectations changed about what people calculate for themselves.
• 2009: Cloud computing: around half of companies surveyed “cited security and privacy concerns as their top reason for not using cloud computing”, Forrester Research found. Today companies entrust much of their critical infrastructure to the cloud.

“We overestimate the impact of technology in the short-term and underestimate the effect in the long run.” Roy Amara, Stanford computer scientist

But this time may really be different – with AI as a partner, starting with (deterministic) predictive AI. It is certainly helping weather forecasters to sift through unimaginable amounts of data. Four-day forecasts are now said to be about as accurate as one-day forecasts in the 1990s.

There are diminishing returns. The limit of predictability with standard physics models is about eight days, while deep-learning AI models increase that by just one day to nine days. However, the difference is that they generate state-of-the-art forecasts 100,000 faster, and 10,000 times more energy-efficiently, meaning they can be done on a laptop rather than a supercomputer.

And for the time being, your predictions about the impact of AI are no less valid than an AI expert based in the very particular world of Silicon Valley. And the future of AI will be made not in Silicon Valley but in Main Street.

More in the full Deustsche Bank report available to pro subs.

Tyler Durden Sat, 09/26/2026 - 08:45

I put these headphones to the test.