I am waging a silent rebellion against Amazon. The internet giant owns Goodreads, the leading service for tracking and cataloguing books, but is generally considered to be a of the platform, which is built on ageing technology. Frustrated with the user experience, I have used AI to vibe code my own Goodreads app, relegating the official one to mere plumbing in my private fiefdom. Take that, Amazon, for I have breached your terms of service!

I am not alone in my AI-enabled rebellion. It feels like I come across daily examples of people using AI to unofficially remix and remake software, whether that is , , mashing up or . It is all happening so quickly that lawyers don鈥檛 seem to have sprung into action to prevent it, though in some of the more egregious copyright-infringing cases, I would expect them to eventually.

For my Goodreads app, Amazon probably doesn鈥檛 know, or even care, about what I am doing. I won鈥檛 bore you with the full technical details, but I have built the app in such a way that it avoids mass 鈥渟craping鈥� Goodreads 鈥� systematically harvesting data from its website 鈥� which would be easy to detect and block. By treading lightly, my app aims to resemble a human user and fly under the radar.

This connects to another ongoing battle over the interaction between AI-enabled individuals and companies: AI agents. The promise of an AI agent is that it can go out on to the internet and do things for you, like shopping or booking appointments. A flurry of examples has launched recently, including Meta鈥檚 Muse and OpenAI鈥檚 Dots, and companies have begun fighting back, with the likes of in high-profile pushbacks. Ironically, Meta sits on both sides of this battle: its automated tools after detecting activity from a Claude AI tool that 鈥渞esembled automated bot traffic rather than an ordinary human browsing our systems鈥�.

All of this comes down to a much older conundrum: where do your rights end and others鈥� begin? We have seen this play out every time a major new technology has hit the internet, and the results of a few historical examples might inform what happens next with AI. The first is a technology you probably don鈥檛 even think about anymore: your web browser.

In the late 1990s, the two main browsers were Microsoft鈥檚 Internet Explorer and Netscape鈥檚 Navigator. They worked slightly differently, and websites often instructed users to choose one over the other. As more browsers flourished, Microsoft pushed back, by blocking a competitor browser called Opera from accessing its MSN website, a popular destination at the time. In the end, in an effort to avoid this. Today, the right to use any browser you like has essentially won out against the right of websites to restrict them, although the rise of dedicated smartphone apps has put some of the power back in companies鈥� hands.

Nested within this battle was another: ad blockers. Once your browser has fetched the HTML code for a website, should you be obligated to display it all on your screen? As websites began displaying adverts to make money, people began downloading ad blocker add-ons to take back control of their browser. In response, companies developed new techniques for displaying ads, and ad blockers adjusted to remove them. Today there is a stalemate, with .

Finally, there is the grandfather of all internet copyright battles: Napster, a peer-to-peer file-sharing service that people used to illegally share music files. The companies mostly won this battle thanks to the rise of Spotify and other similar music streaming services 鈥� with an easy legal option, piracy faded away, the side effect being that most people generally don鈥檛 own the music they listen to anymore.

Internet users largely prevailed after the browser wars, ad blocking is at an impasse and streaming music seems to have benefitted the big corporations. What does all of this mean for AI?

The difference here is decentralisation: just as I have used AI to create my own Goodreads app, so could anyone else, each of them slightly different. Fully blocking them all without restricting normal human access would be a monumental task. There is a slight caveat here 鈥� most AI use is centralised through the big providers like OpenAI and Anthropic, and Amazon could theoretically ask them to build in a block against creating such software (or sue them), but I think that is unlikely. In any case, the availability of open-weight AI models that anyone can download and run on their own hardware makes that a losing battle.

I think it is most likely that we will be heading for a mix of all three. When it comes to remixing software, anything you do in the privacy of your own computer is going to be very hard to stop. Once you release it online, however, expect the lawyers to hunt you down. What we might see is the sharing of prompts that will get an AI to do the work for you in private 鈥� writing 鈥渞ecreate Photoshop, make no mistakes鈥� isn鈥檛 something that any lawyer can stop 鈥� and there already exists a slightly more complicated version of this in the form of an instruction set for AI called . As with browsers, individual choice will win, but this time the choices will be even more granular.

For software that goes out into the wider internet, including AI agents, there will be two tiers. I expect Meta and Amazon will eventually come to some kind of agreement that allows Muse to shop for you. We are already seeing this with the development of a 鈥減ersonal agent protocol鈥� that . For anyone using a commercial AI agent, what you can and can鈥檛 do will be decided by the companies, just as music streaming services dictate how you listen.

But why should you use a commercial AI agent? People like me with a little technical know-how and even less coding ability are already using AI to create their own independent agents. As AI gets better at writing software, the barrier to entry will get lower and lower. I think companies will just have to come to accept this, as they have done with ad blockers. In future, a decent percentage of users that look like humans will actually be custom-made bots in disguise, doing things solely for the benefit of their creator. These bots will be ungovernable, for good and for ill.

Further AI reading

• Anthropic has used its Claude AI model to solve a . In the 1990s, the US National Security Agency (NSA) chose five specific mathematical objects called elliptic curves to serve as the basis for a cryptography standard used to generate digital signatures, now known as ECDSA. No one knew how these curves were chosen, but cryptography lore suggests that an , perhaps 鈥淛erry deserves a raise鈥�, to generate them. In fact, Claude was able to guess the specific mechanism 鈥� random number generation based on a timestamp 鈥� and regenerate the curves. The finding has no implications for cybersecurity, it merely answers a long-running question.

• Spotify says from recommendations on the platform, as part of a range of efforts to put 鈥渉uman artistry first鈥�, according to the firm鈥檚 announcement. Other measures include a format for disclosing the use of AI in producing a track, or verifying an artist as human. Similarly, video game store Steam seems to have been at its latest Next Fest demo event.

• The majority of people in the US say that AI is a bad thing for society, according to a . The survey found that 57 per cent of people in the US share this view, while 42 per cent say it is good for society. I鈥檓 not quite sure what to make of these numbers, though, given 鈥淎I鈥� can mean many different things to different people.

• A comment piece in Nature argues that . I find these arguments fairly convincing 鈥� I already believe our current AI models are definitely not conscious 鈥� but I hesitate to make firmer predictions about what鈥檚 to come.

We are the only human species left on Earth.

Once, we shared our planet with other human species, including Neanderthals and Denisovans. Today, they’re gone, leaving behind fossils, ancient tools and traces of their DNA within our own. So how did Homo sapiens end up as the last humans standing?

For decades, we believed our ancestors survived because they were smarter, stronger or better adapted. But what if the real secret to our success was something far more unexpected? What if we survived because we were friendlier?

Some researchers believe our ability to cooperate, build relationships and form larger social networks may have given us a crucial evolutionary edge, an advantage of just 2 per cent that, over thousands of years, could have changed the course of human history.

After months of vacillation, the UK government has finally announced that drilling will go ahead at the Jackdaw gas field in the North Sea. Prime Minister Andy Burnham has described the move as 鈥渁 pragmatic approach鈥�, saying 鈥渨e鈥檒l still require oil and gas as part of our energy needs for decades to come鈥�.

The thing is, the climate of our rapidly heating planet doesn鈥檛 recognise pragmatism. The only thing it acknowledges and responds to is how much carbon dioxide is added to the atmosphere, and it doesn鈥檛 care where it comes from.

reveals that the rate of global heating doubled over the period 2011 to 2025, compared with 1971 to 2025, so that the planet鈥檚 temperature is now rising at a rate of 0.4掳C a decade. The corollary of this is that the 1.5掳C limit is set to be permanently breached as soon as 2028 鈥� a full decade sooner than previous projections. Furthermore, the speed of heating means that even the projected rise of 2.8掳C by 2100 is now unrealistic, and points towards a much hotter world arriving far more quickly.

Nonetheless, a report published today by the UN Environmental Programme claims that, while the 1.5掳C threshold is now dead in the water, it may still be possible to limit the global average temperature rise to just a few tenths of a degree above this.

, to have a 鈥済ood鈥� chance of staying this side of 1.7掳C to 1.8掳C, greenhouse gas emissions would need to fall 18 per cent by the end of this decade, 45 per cent by 2035 and a whisker under 70 per cent by 2040. That 鈥済ood鈥� is doing some seriously heavy lifting, but what鈥檚 more, the idea that emissions, in the real world, will drop by more than two-thirds in less than 15 years is pure fantasy. Especially at a time when fossil fuel corporations are planning for expansion, aided and abetted by the governments of the UK, the US and others.

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Instead, as pointed out by director of the Potsdam Institute for Climate Impact Research in Germany, the implementation of current national commitments would mean global emissions reaching close to 50 billion tonnes of carbon dioxide equivalent (a measure of the global warming potential of all greenhouse gases) by 2035. And that鈥檚 assuming that such commitments are adhered to, which is by no means certain as governments, including that of the UK, row back on green measures.

Today鈥檚 decision to go ahead with Jackdaw, and to allow drilling in adjacent areas, is entirely the wrong one. A separate decision about the far more carbon-intensive Rosebank oil field is expected by the end of the year. Going ahead with either project guarantees that the UK is now complicit in pushing up global greenhouse gas emissions, and reducing the chances of limiting the global average temperature rise to something more manageable.

At the same time, the decision marks a conscious commitment to retain fossil fuel infrastructure and lock in hydrocarbons to the country鈥檚 energy mix for decades to come. Not only will this endanger national carbon reduction goals, but it will hand other nations an excuse to green-light their own new fossil fuel projects.

Every tonne of CO2 we don鈥檛 emit makes our future that little bit less grim, and every tonne we pump out takes fully 400,000 years to remove from the atmosphere completely.

There is nothing pragmatic, then, about opening the North Sea to more drilling. The decision will do little to create jobs and make energy cheaper, but the resulting higher temperatures will ensure that the country is rewarded in the form of more drought, floods and wildfires.

Bill McGuire is professor emeritus of geophysical and climate hazards at University College London.

It is the refrain of teachers everywhere preparing their students for standardised tests: 鈥淢ake an educated guess.鈥� An answer is always better than no answer, they say, and if you can apply what you know to the question to rule out some of the choices, even better. Of course, scientific research isn鈥檛 a multiple-choice exam, and its bedrock is the idea that an educated guess without solid evidence just isn鈥檛 good enough. But the concept is still a useful one, particularly when our questions outpace the technology we currently have to answer them.

The key part of the phrase 鈥渆ducated guess鈥� is the first word. Put another way, a guess based on nothing may be worse than no guess at all. 鈥淏ut Leah,鈥� I hear you asking, 鈥淒o you have a framework, or a favourite example, for educated guesses?鈥� And just because you are clamouring for it, and I鈥檓 the kind of person who has favourite equations, I will tell you about it. The Drake equation, which turns 65 years old this month, is an incredibly useful tool for framing our thinking about life in the cosmos 鈥� even though, at its core, it is simply a method for making an educated guess.

Astrobiologist Frank Drake came up with his eponymous equation in 1961, before convening the first scientific conference on the search for extraterrestrial intelligence (SETI). According to written by his daughter, science journalist Nadia Drake, he sketched out the equation on a bit of a whim to get the few attendees of the meeting talking and is, to this day, astonished by how important it is now considered.

The equation has seven key terms, and when multiplied together, they provide an estimate of N: the number of intelligent civilisations in the Milky Way galaxy with whom we might someday be able to communicate. It looks like this:

N = R_* 脳 f_p 脳 n_e 脳 f_l 脳 f_i 脳 f_c 脳 L

The first few terms are relatively simple. R_* is the rate of star formation in our galaxy, which has been measured at about 1 to 3 stars per year. The fraction of those stars with planetary systems is f_p, and we now think that number is about 1 as well 鈥� most stars seem to have planets. The number of planets per star with an environment suitable for life is n_e, and that is where things start to get more complicated. 鈥淪uitable for life鈥� is a broad idea, and one that鈥檚 difficult to define. Even the most basic requirement 鈥� that the planet be in its star鈥檚 habitable zone, where temperatures allow liquid water on the surface 鈥� is required for Earth-like life, as far as we know, but it鈥檚 certainly possible that other lifeforms have different needs. In our own solar system, there are either 2 or 3 planets in the habitable zone, depending how you slice it 鈥� but Venus and Mars don鈥檛 seem particularly suitable for life, and there are several moons in the outer solar system that may well be.

That is the first time the equation forces us to make an educated guess. The second comes hot on its heels with f_l, the fraction of the habitable planets that go on to develop life. On this, we have an even smaller sample size. We haven鈥檛 found life anywhere but Earth, so this number could be very small, or it could be 1 鈥� remember, this includes life that arises at any point in the history of the cosmos. But until we do find life elsewhere, it will be more of a philosophical argument than anything else. The same is true of the next two terms of the Drake equation: f_i, which denotes the fraction of life that is intelligent 鈥� the definition of which is also wily 鈥� and f_c, the fraction of intelligent lifeforms that eventually broadcast their existence into space and are thus capable of communicating with us. The final term is L, the length of time in which a civilisation of intelligent lifeforms sends detectable signals out into space. As the astronomer Carl Sagan pointed out, this depends heavily on whether such civilisations can avoid self-destruction, which isn鈥檛 something we can know for sure 鈥� but we make our best guess based on the only evidence we have: human history.

If you combine all the lowest estimates, N could be one-trillionth or even lower, implying that we are totally alone in our galaxy. If you combine the highest estimates, there could be tens of millions of civilisations in the Milky Way that we could communicate with, which to me would say it鈥檚 only a matter of time before we find them. At Drake鈥檚 original SETI meeting, the researchers set N between 1000 and 100,000,000, and the lower end of that sounds about right to me 鈥� an estimate that, like those at the meeting, is based largely on my not-particularly-scientific opinions about how long our civilisation might last.

But when it comes down to it, the number itself isn鈥檛 the important part of the equation; the important part is the process of estimating each of its terms. In doing so, we force ourselves to consider some of the biggest questions in science: How similar are other places in the universe to our own cosmic neighbourhood? How likely are those most-similar places to develop life? What is life, anyway? What does life truly need? What is intelligence? How can we search for it? And how long will we be capable of searching for it? None of those questions are answered definitively, but pondering them can help us direct our scientific research 鈥� and more than that, it gives us an idea of our place in the grand scheme of the universe. That鈥檚 why teachers tell their students to make an educated guess, and it鈥檚 why the Drake equation is still considered so important. The value in an educated guess is in taking account of what you know and what is still out there, waiting to be known.

Much of the AI industry is built on stories, from the idea that AI could kill us all to the goal that it might one day become superintelligent. This often leads to problems, because when people have a story in their head, they don鈥檛 always look at the facts on the ground to check that they actually match the fiction. That has happened this week, with widely reported claims from Anthropic that we are mere 鈥渕onths away鈥� from . That鈥檚 a great headline, and it鈥檚 hard to resist clicking, but it almost certainly isn鈥檛 true.

Let鈥檚 start with looking at what Anthropic has actually done. Two researchers at the company, and , ran an experiment in which they installed a small AI model, Gemma4-26B-A4B, across 15 Android TV boxes, the kind of thing you might plug into a TV to gain access to streaming apps. Such devices are computational weaklings, with tiny CPUs (the processor that runs software) and little RAM (the memory that stores data for software while it runs), which is why the Anthropic researchers needed 15 to store one model. Even then, the model could produce only 0.39 tokens per second, the unit of AI input and output, which is roughly equivalent to 18 words per minute. This is painfully slow 鈥� around 10 times slower than normal human speech.

Nothing in this part of the experiment is surprising. We know that AI models normally need to run on GPUs, a specialised and more powerful type of processor than a CPU, and large numbers of them 鈥� that is why AI firms are building massive data centres. What Zhou and Freeman argue is that such that a model distributed across TV boxes, smart ovens or other internet-enabled appliances could endlessly copy itself to create a form of 鈥渄igital slime mold鈥� that gums up a large chunk of these devices globally. Those three words are great, concise storytelling, but they paper over what鈥檚 really happening here.

When they actually tried to get a model to copy itself in this way, it failed. Three models were unable to set themselves up at all. A fourth model, GLM-5.2, succeeded in four out of five tests, but only at setting up another model, Llama3.1-8B, which is about 100 times smaller. The pair argues this is a fair test because it shows that a future smaller model with the same capabilities as GLM-5.2 could set itself up, but this is still far from self-replication. The claim that such a feat might be 鈥渕onths away鈥� comes from extrapolating lines on a graph.

Putting all those objections aside, the broader question is why would anyone want to create self-replicating software? We already have an answer for that, and it doesn鈥檛 involve AI. Computer code that copies itself from one device to another is called a worm, and they have existed for more than 50 years. In the 1970s, Bob Thomas and Ray Tomlinson at US firm BBN (the latter invented email, as it happens) created a . It was not particularly complicated, and merely output the message 鈥淚鈥橫 THE CREEPER: CATCH ME IF YOU CAN鈥�. Tomlinson paired this with Reaper, a piece of code designed to find and delete Creeper, making it the first antivirus software.

It wasn鈥檛 until about a decade later that such self-copying software was called a worm, after a term coined in the sci-fi novel The Shockwave Rider by John Brunner 鈥� storytelling becomes useful again. John Shoch and Jon Hupp at Xerox Palo Alto Research Center, arguably the Anthropic of its time, to describe how an experimental worm 鈥渟eemed to grow out of control鈥�, leaving 鈥渄ozens of machines dead鈥�. Sound familiar?

Big, clunky worms

was deployed on to the internet in 1988 by Robert Morris, a student at Cornell University in New York state. It is thought that thousands of computers were infected and crashed, a significant chunk of the online population at the time, and Morris became the first person charged under the US Computer Fraud and Abuse Act. Today, worms are deployed by cybercriminals to hijack computing hardware and make money. For example, in 2017 the was the result of a worm copying itself across computers and encrypting them, demanding payment in bitcoin to unlock. It infected more than 200,000 computers in a single day, including many in the UK鈥檚 National Health Service, and caused an estimated $4 billion in economic losses, although only around $100,000 of that was people paying the ransom.

If worms can be that bad, surely an AI-powered worm would be even worse? Actually, no, because an AI worm would find it much harder to replicate. According to Zhou and Freeman鈥檚 experiment, it would take multiple days for their AI worm to copy itself just once. That doesn鈥檛 mean such a worm couldn鈥檛 scale to large numbers, but it would be slower, taking more than two weeks to achieve the 200,000 devices WannaCry managed in a single day. That is because even small models like Gemma4-26B-A4B are large files 鈥� around 14 GB for the version in the experiment, which is about 4000 times larger than WannaCry 鈥� and these take time to transfer. That would make an AI worm much more noticeable, as network managers would be likely to investigate the source for such large data transfers and shut them down 鈥� something Zhou and Freeman admit.

What does an AI worm gain from all that extra data? Not a lot. Zhou and Freeman say that, assuming it could take more than 2 million Android TV boxes, the computing power available would be equivalent to around 40 high-end GPUs. To put that in context, leading AI firms like Anthropic typically operate hundreds of thousands these computer chips, and it would cost you around $100 an hour to rent access to that much computing power. It is hard to see why a cybercriminal would choose to do this rather than use a standard worm like WannaCry.

This is where we come back to storytelling. A long-standing fear for many in the AI sphere is that a model could 鈥渆xfiltrate鈥� itself, distributing itself across the internet and so resisting being shut down (before presumably killing us all, for some reason). Anthropic 鈥渨histleblower鈥� Jacob Coxon . Could it be that an AI worm is proliferated by such an AI, rather than a human cybercriminal? Well, maybe. But the models that are most effective at hacking are also the largest ones 鈥� Mythos, a model Anthropic refused to release to the public because of its hacking capabilities, is estimated to require dozens of terabytes of storage and is more than 1000 times larger than Gemma4-26B-A4B. This is not something you sneakily copy around the internet, but it does make a great digital ghost story.

What I find particularly interesting is that this story about theoretical AIs hacking into devices and copying themselves seems to be crowding out a very important fact: actual AI hackers that attack computers already exist, and they are running on Anthropic鈥檚 own servers. In July, of a model gaining unauthorised access to third-party organisations. This was discovered only after an investigation prompted by reports that OpenAI鈥檚 models had done the same. Since then, more and more reports of such hacking have come out, including an OpenAI hack of an Australian government website, and on unnamed third-party breaches that came out just a few days before the AI worm research was published. The AI firms say they are taking steps to avoid this happening in the future, but it seems to me that big AIs on powerful server farms are the thing we need to worry about, not a model barely capable of sputtering out a few words per minute. Yes, future smaller models may become more capable, but hardware is always going to be a constraint 鈥� not least because the rise of AI server farms means that consumer hardware is seeing RAM shortages.

As I said at the top of this article, a self-replicating AI that takes over your smart oven is a great story, and I can see why it has spread so rapidly. But stories like this are a cognitive worm, hacking your critical thinking abilities by going straight for your fear response. Could this someday be a concern? Perhaps, although this article has hopefully poked holes in some of the storytelling logic behind the idea. Is it something we need to worry about in the next few months? Absolutely not.

Further AI reading

• OpenAI announced that a 鈥溾�� was found hidden in its offices. The company says it used its own AI models to investigate the device and traced it to a specific employee, who has been . OpenAI says it has no indication that any proprietary or customer data was accessed 鈥� but who needs a malicious AI when humans are ready and able to do the job?
• Character.ai is . The service let users create 鈥渃haracters鈥� with specific personalities and it has courted controversy and multiple lawsuits over and .
• Epic Games, the makers of Fortnite and other video games, is pivoting towards using AI. 鈥淭he aim isn鈥檛 to do the same stuff with fewer people, but to enable everyone here to build MORE and BETTER stuff鈥�, . Games have been surprisingly resistant to AI, with multiple controversies about the technology鈥檚 use in game development exploding in recent years, but when one of the biggest game-makers on the planet goes all in, it may be hard to ignore.

Will we ever fully understand reality? Can we ever fully know the inner workings of the universe? Theoretical physicist Carlo Rovelli thinks we鈥檙e asking all the wrong questions. Inspired by the monumental, mind-bending artworks of Anish Kapoor, Rovelli explains how we must let go of old ideas and accept that we will never know everything about the universe.

Speaking to New 精东传媒 reporter Michael Brooks, Rovelli questions whether space even exists and criticises the pursuit of unfounded theories. His radical perspective suggests everything is connected, from our atoms to our soul, and the new challenge is finding a language that brings all aspects of reality into the conversation.

Rovelli is a theoretical physicist and author based at Aix-Marseille University in France, whose work focuses on loop quantum gravity, which aims to unify quantum mechanics and Albert Einstein’s general theory of relativity.