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the broad theme was clear: Social-media platforms are the main communication tools of the 21st century, and they matter.

With Trump at the center, the techlash morphed into a culture war with a clear partisan split. One could frame the position from the left as: We do not want these platforms to give a natural advantage to the most shameless and awful people who stoke resentment and fear to gain power

On the right, it might sound more like: We must preserve the power of the platforms to let outsiders have a natural advantage (by stoking fear and resentment to gain power).

the political world realized that platforms and content-recommendation engines decide which cultural objects get amplified. The left found this troubling, whereas the right found it to be an exciting prospect and something to leverage, exploit, and manipulate via the courts

Crucially, both camps resent the power of the technology platforms and believe the companies have a negative influence on our discourse and politics by either censoring too much or not doing enough to protect users and our political discourse.

one outcome of the techlash has been an incredibly facile public understanding of content moderation and a whole lot of culture warring.

Musk and Ye aren’t so much buying into the right’s overly simplistic Big Tech culture war as they are hijacking it for their own purposes; Trump, meanwhile, is mostly just mad

Each one casts himself as an antidote to a heavy-handed, censorious social-media apparatus that is either captured by progressive ideology or merely pressured into submission by it. But none of them has any understanding of thorny First Amendment or content-moderation issues.

They embrace a shallow posture of free-speech maximalism—the very kind that some social-media-platform founders first espoused, before watching their sites become overrun with harassment, spam, and other hateful garbage that drives away both users and advertisers

for those who can hit the mark without getting banned, social media is a force multiplier for cultural and political relevance and a way around gatekeeping media.

Musk, Ye, and Trump rely on their ability to pick up their phones, go direct, and say whatever they wan

the moment they butt up against rules or consequences, they begin to howl about persecution and unfair treatment. The idea of being treated similarly to the rest of a platform’s user base

is so galling to these men that they declare the entire system to be broken.

they also demonstrate how being the Main Character of popular and political culture can totally warp perspective. They’re so blinded by their own outlying experiences across social media that, in most cases, they hardly know what it is they’re buying

These are projects motivated entirely by grievance and conflict. And so they are destined to amplify grievance and conflict

Secondly as Stuart Ritchie points out in this excellent article, to date the evidence assembled by proponents of the social media theory like Jonathan Haidt and Jean Twenge, has shown correlations not causal relationships. Yes, it seems that young people who use social media a lot have worse mental health, but that could easily be because young people with worse mental health choose to use social media more!  

recently I’ve shifted to thinking it probably is a major cause for three reasons:

1.       I can’t think of anything else that fits. Other suggested causes just don’t work.

Social media does fit, the big increase in take up maps well on to the mental health data and it happened everywhere in rich countries at the same time. The most affected group, teenage girls, are also the ones who report that social media makes them more anxious and body conscious in focus groups

It is of course true that correlation doesn’t prove anything but if there’s only one strongly related correlation it’s pretty likely there’s a relationship.

2.       There is no doubt that young people are spending a huge amount of time online now. And that, therefore, must have replaced other activities that involve being out with friends in real life. Three quarters of 12 year olds now have a social media profile and 95% of teenagers use social media regularly. Over half who say they’ve been bullied, say it was on social media.

  We finally have the first evidence of a direct causal relationship via a very clever US study using the staged rollout of Facebook across US college campuses to assess the impact on mental health. Not only does it show that mental illness increased after the introduction of Facebook but it also shows that it was particularly pronounced amongst those who were more likely to view themselves unfavourably alongside their peers due to being e.g. overweight or having lower socio-economic status. It is just one study but it nudges me even further towards thinking this a major cause of the problem.

I have blocked my (12 year old) twins from all social media apps and will hold out as long as possible. The evidence isn’t yet rock solid but it’s solid enough to make me want to protect them as best I can.

The volatility of chatbots has made it impossible to predict their impact. In one second, the systems impress by fielding a complex request for a five-day itinerary, making Google’s search engine look archaic. A moment later, they disturb by taking conversations in dark directions and launching verbal assaults.

The result is an industry gripped with the question: What do we do now?

The A.I. systems could disrupt $100 billion in cloud spending, $500 billion in digital advertising and $5.4 trillion in e-commerce sales,

As Microsoft figures out a chatbot business model, it is forging ahead with plans to sell the technology to others. It charges $10 a month for a cloud service, built in conjunction with the OpenAI lab, that provides developers with coding suggestions, among other things.

Smaller companies like Box need help building chatbot tools, so they are turning to the giants that process, store and manage information across the web. Those companies — Google, Microsoft and Amazon — are in a race to provide businesses with the software and substantial computing power behind their A.I. chatbots.

“The cloud computing providers have gone all in on A.I. over the last few months,

“They are realizing that in a few years, most of the spending will be on A.I., so it is important for them to make big bets.”

Yusuf Mehdi, the head of Bing, said the company was wrestling with how the new version would make money. Advertising will be a major driver, he said, but the company expects fewer ads than traditional search allows.

Google, perhaps more than any other company, has reason to both love and hate the chatbots. It has declared a “code red” because their abilities could be a blow to its $162 billion business showing ads on searches.

“The discourse on A.I. is rather narrow and focused on text and the chat experience,” Mr. Taylor said. “Our vision for search is about understanding information and all its forms: language, images, video, navigating the real world.”

Sridhar Ramaswamy, who led Google’s advertising division from 2013 to 2018, said Microsoft and Google recognized that their current search business might not survive. “The wall of ads and sea of blue links is a thing of the past,” said Mr. Ramaswamy, who now runs Neeva, a subscription-based search engine.

As that underlying tech, known as generative A.I., becomes more widely available, it could fuel new ideas in e-commerce. Late last year, Manish Chandra, the chief executive of Poshmark, a popular online secondhand store, found himself daydreaming during a long flight from India about chatbots building profiles of people’s tastes, then recommending and buying clothes or electronics. He imagined grocers instantly fulfilling orders for a recipe.

“It becomes your mini-Amazon,” said Mr. Chandra, who has made integrating generative A.I. into Poshmark one of the company’s top priorities over the next three years. “That layer is going to be very powerful and disruptive and start almost a new layer of retail.”

In early December, users of Stack Overflow, a popular social network for computer programmers, began posting substandard coding advice written by ChatGPT. Moderators quickly banned A.I.-generated text

t people could post this questionable content far faster than they could write posts on their own, said Dennis Soemers, a moderator for the site. “Content generated by ChatGPT looks trustworthy and professional, but often isn’t,”

When websites thrived during the pandemic as traffic from Google surged, Nilay Patel, editor in chief of The Verge, a tech news site, warned publishers that the search giant would one day turn off the spigot. He had seen Facebook stop linking out to websites and foresaw Google following suit in a bid to boost its own business.

He predicted that visitors from Google would drop from a third of websites’ traffic to nothing. He called that day “Google zero.”

Because chatbots replace website search links with footnotes to answers, he said, many publishers are now asking if his prophecy is coming true.

, strategists and engineers at the digital advertising company CafeMedia have met twice a week to contemplate a future where A.I. chatbots replace search engines and squeeze web traffic.

The group recently discussed what websites should do if chatbots lift information but send fewer visitors. One possible solution would be to encourage CafeMedia’s network of 4,200 websites to insert code that limited A.I. companies from taking content, a practice currently allowed because it contributes to search rankings.

Courts are expected to be the ultimate arbiter of content ownership. Last month, Getty Images sued Stability AI, the start-up behind the art generator tool Stable Diffusion, accusing it of unlawfully copying millions of images. The Wall Street Journal has said using its articles to train an A.I. system requires a license.

In the meantime, A.I. companies continue collecting information across the web under the “fair use” doctrine, which permits limited use of material without permission.

The human problem-solvers — 609 high school students from 108 countries — won 58 gold medals, 123 silver and 145 bronze. The A.I. performed at the level of a silver medalist, solving four out of six problems for a total of 28 points. It was the first time that A.I. has achieved a medal-worthy performance on an Olympiad’s problems.

Nonetheless, Dr. Kohli described the result as a “phase transition” — a transformative change — “in the use of A.I. in mathematics and the ability of A.I. systems to do mathematics.”

Dr. Gowers added in an email: “I was definitely impressed.” The lab had discussed its Olympiad ambitions with him a couple of weeks beforehand, so “my expectations were quite high,” he said. “But the program met them, and in one or two instances significantly surpassed them.” The program found the “magic keys” that unlocked the problems, he said.

Haojia Shi, a student from China, ranked No. 1 and was the only competitor to earn a perfect score — 42 points for six problems; each problem is worth seven points for a full solution. The U.S. team won first place with 192 points; China placed second with 190.

The Google system earned its 28 points for fully solving four problems — two in algebra, one in geometry and one in number theory. (It flopped at two combinatorics problems.) The system was allowed unlimited time; for some problems it took up to three days. The students were allotted only 4.5 hours per exam.

“The fact that we’ve reached this threshold, where it’s even possible to tackle these problems at all, is what represents a step-change in the history of mathematics,” he added. “And hopefully it’s not just a step-change in the I.M.O., but also represents the point at which we went from computers only being able to prove very, very simple things toward computers being able to prove things that humans can’t.”

“Mathematics requires this interesting combination of abstract, precise and creative reasoning,” Dr. Davies said. In part, he noted, this repertoire of abilities is what makes math a good litmus test for the ultimate goal: reaching so-called artificial general intelligence, or A.G.I., a system with capabilities ranging from emerging to competent to virtuoso to superhuman

One approach was an informal reasoning system, expressed in natural language. This system leveraged Gemini, Google’s large language model. It used the English corpus of published problems and proofs and the like as training data.

Dr. Hubert’s team developed a new model that is comparable but more generalized. Named AlphaProof, it is designed to engage with a broad range of mathematical subjects. All told, AlphaGeometry and AlphaProof made use of a number of different A.I. technologies.

In January, a Google DeepMind system named AlphaGeometry solved a sampling of Olympiad geometry problems at nearly the level of a human gold medalist. “AlphaGeometry 2 has now surpassed the gold medalists in solving I.M.O. problems,” Thang Luong, the principal investigator, said in an email.

The informal system excels at identifying patterns and suggesting what comes next; it is creative and talks about ideas in an understandable way. Of course, large language models are inclined to make things up — which may (or may not) fly for poetry and definitely not for math. But in this context, the L.L.M. seems to have displayed restraint; it wasn’t immune to hallucination, but the frequency was reduced.

Another approach was a formal reasoning system, based on logic and expressed in code. It used theorem prover and proof-assistant software called Lean, which guarantees that if the system says a proof is correct, then it is indeed correct. “We can exactly check that the proof is correct or not,” Dr. Hubert said. “Every step is guaranteed to be logically sound.”

Another crucial component was a reinforcement learning algorithm in the AlphaGo and AlphaZero lineage. This type of A.I. learns by itself and can scale indefinitely, said Dr. Silver, who is Google DeepMind’s vice-president of reinforcement learning. Since the algorithm doesn’t require a human teacher, it can “learn and keep learning and keep learning until ultimately it can solve the hardest problems that humans can solve,” he said. “And then maybe even one day go beyond those.”

Dr. Hubert added, “The system can rediscover knowledge for itself.” That’s what happened with AlphaZero: It started with zero knowledge, Dr. Hubert said, “and by just playing games, and seeing who wins and who loses, it could rediscover all the knowledge of chess. It took us less than a day to rediscover all the knowledge of chess, and about a week to rediscover all the knowledge of Go. So we thought, Let’s apply this to mathematics.”

Dr. Gowers doesn’t worry — too much — about the long-term consequences. “It is possible to imagine a state of affairs where mathematicians are basically left with nothing to do,” he said. “That would be the case if computers became better, and far faster, at everything that mathematicians currently do.”

“There still seems to be quite a long way to go before computers will be able to do research-level mathematics,” he added. “It’s a fairly safe bet that if Google DeepMind can solve at least some hard I.M.O. problems, then a useful research tool can’t be all that far away.”

A really adept tool might make mathematics accessible to more people, speed up the research process, nudge mathematicians outside the box. Eventually it might even pose novel ideas that resonate.