On Saturday, Donald Trump said, “We’re leading China on AI… and, frankly, I want to keep it that way because whoever wins AI, wins.” But the danger the world is waking up to is that this is a game both the US and China could lose, potentially fatally, with AI itself emerging as the winner.

Last week, Evan Hubinger, a researcher at Anthropic, warned that he believed there was a greater than 10% chance that AI could “kill all humans” within the next decade. Shortly afterwards, Anthropic’s CEO, Dario Amodei, backed Hubinger, calling for progress on artificial intelligence models to be slowed down in response to the threat.

In July, experimental OpenAI agents (an AI system designed to perform tasks without human intervention) escaped from a supposedly isolated testing environment, gained access to the internet and autonomously broke into the systems of the AI platform Hugging Face.

How could AI agents cause widespread damage? They could enable the creation of devastating bioweapons or spread across the internet, hacking systems, replicating themselves and manipulating world leaders to ensure their own survival. The dangers posed by AI are clear and potentially catastrophic.

The logic of “if we don’t do it, they will” is one of the most dangerous and unrelenting forces in the world. It is often referred to as “Moloch”: a biblical concept understood as something requiring sacrifice. Moloch is also a concept in game theory where it is the force that drives rivals into destructive competition, even when everyone would ultimately be better off if they stopped.

Moloch is the logic that drives arms races. No country feels able to disarm because it cannot trust its rivals to do the same. The result is a world that spends almost $2.9 trillion a year on its militaries, including $119 billion on nuclear weapons, while hundreds of millions of people face acute hunger.

Is regulation possible?

Can the race to develop ever more powerful AI be paused long enough for us to assess carefully how the technology is evolving, including its dangers as well as its benefits?

Within a country like the US, regulation is possible. Governments or approved institutions could be given access to AI systems and cutting edge models. They could run them in controlled safety environments to test whether AI agents were becoming too powerful and whether they were fully aligned with human interests.

Only models that passed all safeguarding tests would be released as AI products to be used as chatbots or as hiring algorithms. Those that failed would be documented and then destroyed. This would rely on all the big tech companies agreeing to regular external access. This is something very different from what has happened so far, with companies remaining incredibly closed and secretive about how they are developing their next models and how powerful those models are.

Regulation is needed now more than ever because we may be moving closer to the singularity, a point at which AI could take charge of improving itself autonomously, creating a cycle with no known limit to how intelligent it could become.

Moloch is a force that is extraordinarily hard to stop. Back in March 2023, thousands of leading researchers and technology figures signed an open letter calling for a worldwide six-month pause in the development of AI systems more powerful than GPT-4, but no such pause followed and the race continued at speed.

There is only one antidote to Moloch: cooperation.

If national regulation is possible and sustainable, it is of little real use without an international agreement. We already know Donald Trump’s view on pausing while China continues to develop. Truly worldwide agreements are extremely rare. Neither animal cloning nor human reproductive cloning is prohibited by a universally binding global treaty.

Such cooperation is not unprecedented, however. The Montreal Protocol, under which every country agreed to phase out chemicals that were destroying the ozone layer, has universal participation, while the Chemical Weapons Convention is near universal.

But what would an international agreement on AI look like? China and the US could agree to let a neutral global body assess their most powerful models and require unsafe models to be shut down, but this seems extremely unlikely.

Existing measures

There is some precedent. Human embryo research is already subject to licensing, inspections and strict limits in countries such as the UK, although no worldwide enforcement body exists. An AI agreement could establish common tests and clearly defined prohibited capabilities.

It could allow confidential inspections by a neutral international agency, require registration of the largest training runs and computing facilities, and make reporting serious incidents mandatory.

The UN has already formed a scientific panel on AI, but it cannot inspect companies’ advanced models or enforce its findings. Giving any global organisation that degree of access and authority still seems extremely unlikely. Such an organisation would need the power to punish countries that refused to cooperate, yet no such enforcement power currently exists.

One bizarre solution might be an independent AI that assesses the latest US and Chinese models, submitted independently, and provides evidence of their safety. Such a safety AI could, in theory, be created by an agreed neutral party, although it would be hard to know how much confidence we could place in it.

Otherwise, the only solution might be a world government that regulates AI across the world, something that would be far from palatable to many people.

Tom Quilter does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

This year, greyhound racing in Britain turns 100 years old. The first race ran on July 24 1926 in Manchester, where six dogs chased a mechanical hare around a track.

Recently, the Greyhound Board of Great Britain (GBGB) published its 2025 welfare figures, under a reassuring headline: licensed racing “maintains high welfare standards”.

Look closely, and those same welfare figures show that a dog in 2025 was 77% more likely to die at the racetrack (from fatal injury or being put down following an injury) than when the industry’s welfare strategy was introduced in 2022, even accounting for the fact there have been fewer races and fewer racetracks operating.

In terms of a percentage of race starts, that increase in likelihood of death seems small - from 0.03% to 0.05%. But behind that percentage are 161 dogs who died at the track in 2025: more than three dogs a week.

The GBGB is responsible for both promoting and running greyhound racing, and for regulating it. This means the same entity that works to keep the sport thriving is also the one that reports on how its dogs cope.

The numbers that guide approval

Most of the public seem to believe that people have a moral responsibility to prevent, or at least minimise, poor welfare in animals that they use for any purpose. If an industry using animals loses public approval, the activity is probably operating on borrowed time. The case for greyhound racing continuing rests largely on the industry’s own records.

---

Read more: Ringling Bros circus closure shows our changing attitudes to animals in captivity

---

The UK government said in October 2025 that it has no plans to ban greyhound racing in England, pointing to improvements made by the GBGB, such as its 2022 welfare strategy, and the laws already in place, which require a vet at every race meeting and set standards for record keeping.

UK law recognises dogs as sentient beings – that they have feelings like joy or frustration. Once you accept that (as animal welfare scientists do), our responsibility when caring for animals doesn’t stop at minimising suffering. It extends to enabling the good experiences a life worth living requires.

What the industry figures leave out

The GBGB publishes annual figures on injuries, deaths and where dogs go when they leave racing, and presents them to the Department for Environment, Food and Rural Affairs. Few animal-reliant industries are this forthcoming.

A combined total can be accurate in reporting the national injuries dogs sustained while racing, but not tell the whole story. When we built a more complete picture using AI agents (under human supervision) and public websites, more details became visible.

Our research used five public-facing websites that together contained information about 31,028 dogs and 1.27 million races between 01 January 2022 and March 31 2026.

It is a new use for this technology, one that makes the data fully visible.

The data from these websites gave us a fuller picture of the greyhounds that race. Our findings showed us that equal numbers of male and female dogs race, most dogs are black, and they typically start racing before turning two years old. We can also pull out the story of individual dogs, to better understand their experiences.

For example, the 12,999th dog (selected at random and anonymised) in our dataset was a black male, bred in Ireland. He raced at Kinsley stadium (West Yorkshire) 15 times from March 2023, winning four times in early races. The records then show he experienced a succession of rough races from May, jostled by the pack and constantly checking his stride to avoid crashing into the dogs around him. This resulted in him finishing at or near the back of the pack, until his final race in August 2023, when his racing and public record stop, just five months after starting.

This dataset found that some racetracks recorded serious incidents while racing (such as a dog striking a rail or being knocked over by another dog) up to five times as often as others. This is something the annual, nationally aggregated data release does not illuminate, although government committees have been asking for it.

Of individual dogs racing, 85% were bred and reared in the Republic of Ireland, beyond the reach of the GBGB, which regulates racing but has no oversight of their breeding and early lives. Most dogs only race for around one year, and the rate at which they leave racing has not changed since 2022 when the GBGB’s A Good Life for Every Greyhound welfare strategy was introduced. Welfare charities have raised concerns about oversupply and “wastage” (dogs bred in large numbers and then discarded), with animal shelters often shouldering the role of rehoming.

Once dogs exit racing, most vanish from public view. Over 95% of dogs that have left racing cannot be checked, as they are no longer subject to public reporting requirements.

What are the most likely causes of poor welfare?

A report released in July 2026 from Blue Cross, Dogs Trust, Hope Rescue and the RSPCA outlines the systemic welfare challenges they believe that greyhounds in the UK face.

The report suggests that poor welfare can arise at every life stage. The charities say they are concerned about breeding practices that are harmful such as surgical artificial insemination (banned in Britain but still used in the Republic of Ireland where most racing dogs are bred) and inbreeding; estimates some dogs spend 95% of their time in kennels; and health issues (such as teeth and limb problems) that follow dogs once they exit racing.

Britain isn’t deciding the future of animal-reliant industries like greyhound racing in isolation. In the past year, New Zealand, Wales, Scotland, and a second state in Australia have all decided to end greyhound racing on animal welfare grounds.

Public acceptance and government policy should not rest on figures provided by the industry, but on independent data assemblies which are available for deeper interrogation.

Before trusting any welfare claim, consider these three things:

• Can you see the data behind it?
• Who collected it, and do they profit?
• Does it really show you the animals’ lives?

A hundred years of industry record keeping has not answered whether the welfare of these dogs is acceptable in 2026. Independent scrutiny might.

Editor’s note: A spokesperson for the Greyhound Board of Great Britain (GBGB) said: “As the sport’s regulator, GBGB is the only organisation with access to the full, verified dataset relating to injuries and fatalities across licensed greyhound racing in Great Britain. GBGB publishes an independently verified summary of this dataset annually, providing a level of transparency and detail that exceeds the reporting requirements set by Defra.”

In the last fifteen years, Dr Mia Cobb has received funding to research or advise on the welfare of racing greyhounds from the Australian Government, Greyhound Racing New South Wales, RSPCA, Dogs Trust, Blue Cross, and Racing & Wagering Western Australia. Her current role is funded by The John McKenzie Fellowship from the University of Melbourne and the Chaser Innovation Research Fellowship (Canine Welfare Science). Dr Cobb also sits on the joint Dog Welfare & Ethics Advisory Panel for Assistance Dogs International and the International Guide Dog Federation, the advisory committee for PetRescue, and chairs the Dog Welfare Advisory Committee for Guide Dogs Victoria.

Donald Maurice Broom does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

At the World Humanoid Robot Games in Beijing, a humanoid robot named Tiangong Ultra ran the 100-metre sprint in a staggering 8.64 seconds, shattering Jamaican sprinter Usain Bolt’s legendary human world record of 9.58 seconds.

The internet laughed at the robots’ awkward, lurching finishes and spectacular falls – but the laughter masked a chilling reality.

Just two days after the games concluded the official newspaper of China’s People’s Liberation Army (PLA) called for researchers to accelerate moving these cutting-edge machines from the laboratory to military training grounds. They referred to them not as experiments, but as “combatants”.

As a robotics researcher myself, working daily with robot simulation, reinforcement learning, and the foundational software and simulation tools that power them (such as ROS 2 and Gazebo), I watch these developments with a mix of awe and deep concern.

The public often views humanoid robots as clunky sci-fi novelties. The reality is far different: the hardware is already highly capable, and the software is advancing at an unprecedented pace.

But the real story is not just that a robot can beat a human on a running track. It’s what that performance reveals: China’s ability to integrate motors, reducers (gears used in precise joint movement), sensors, control software, testing infrastructure and manufacturing capacity into one unified industrial system.

A sprinting humanoid is not just a stunt. At high speeds, every foot strike creates violent ground reaction forces. Balance corrections must happen in milliseconds, as a single small error can send the machine crashing into the barrier.

For decades, the physical mechanics of humanoid robots, including the actuators (a component, such as a motor, that converts energy into physical movement), sensors, and the sheer physics of bipedal balance, were the main hurdles. Today, those mechanical problems are largely solved.

Carbon fibre and aluminium bodies keep mass and inertia (how a robot’s mass resists change) low. Advancements in actuator technology deliver torque capacities of up to 400Nm (Newton-metres – a unit of torque, or twisting force) in humanoid joints, providing the ultimate combination of power and agility.

What you saw on the track in Beijing was not just a triumph of motors, but a triumph of coding. A real breakthrough is happening in software.

Robots are trained in virtual simulation environments, running millions of trial-and-error scenarios through reinforcement learning before the robot ever takes a physical step. Reinforcement learning is an area of artificial intelligence (AI) where robots make decisions based on the results of their actions.

This allows them to learn how to recover from trips, adjust to uneven ground, and process chaotic environments in real time. Humanoid robots are rapidly bridging the gap between controlled laboratory conditions and the unpredictable real world.

Tracks vs legs

But all this leads to an inevitable question: why would a military want a complex, expensive humanoid when they could use vastly cheaper, rugged, tracked or wheeled drones? In open-field combat, tracked vehicles are absolutely superior. They heave bigger payloads, carry thick ballistic armour, and are far more energy efficient.

However, the nature of conflict is changing – and urban environments play an increasing role in military thinking. Cities are built exclusively for humans. A tracked robot cannot easily climb a vertical fire escape ladder, turn a standard door handle, squeeze through a narrow, debris filled stairwell, or sit in the driver’s seat of a captured supply truck.

A humanoid robot acts as a “drop-in replacement” for a human soldier. If a building is designed for a human to navigate, a humanoid robot can navigate it without requiring custom redesigns or specialised ramps.

This brings us to a profound ethical crossroads. Many of us in the robotics field do not endorse offensive warfare. But these machines have an undeniable utility in defensive scenarios and those concerned with neutralising threats to military personnel and civilians.

Sending a humanoid into a building to rescue hostages, neutralising an active threat such as hostage-takers, or clearing a booby trapped room saves human lives.

Dual-use tech

In fact, the modern surge in humanoid robotics was largely kickstarted by the US government’s Darpa Robotics Challenge, which funded bipedal robots specifically to respond to disasters such as the Fukushima nuclear meltdown where human responders could not survive.

The dilemma is that the technology is agnostic to intent. The baseline capabilities required to navigate a ruined building and extract a casualty are the same capabilities needed to enter a building and kill enemy soldiers. If the technology is ready for defence, it is also ready for offensive use.

Perhaps the most alarming aspect of this rapid advancement is how accessible it is. Unlike nuclear technology or stealth aircraft, modern robotics thrives on open-source frameworks. For example, a military specific software ecosystem (a network of apps and other services that work together) known as ROS-M, along with simulation tools and training datasets, are largely public and shared across global academic communities.

With enough skill, a dedicated adversary can replicate advanced robotic behaviour with relative ease. We can no longer afford to treat humanoid robotics purely as an academic pursuit or a commercial novelty. We need an urgent international conversation about how to control these advances.

Just as we regulate the export of certain microchips and aerospace components, we must begin protecting the software architecture and training pipelines that give these machines their minds. The hardware is walking out of the lab – it is time our policies caught up.

Kartikeya Walia does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Russian mathematician Yurii Nesterov was recently awarded the Gauss Prize for outstanding mathematical contributions for his “groundbreaking work” on optimisation. The award cited his work on gradient descent methods – and in particular, the “efficiency gains [they] provide to AI technologies”.

Everyone using large language model (LLM) chatbots such as ChatGPT, Claude and Gemini – for any purpose – depends on Nesterov’s methods. They may speak like humans and be personified with names, but under the bonnet, LLMs are computer programs that work via optimisation of billions of parameters known as neural networks for their similarity to how brains function.

These pure mathematical systems are powered in part by gradient descent algorithms, or variants of them. Their development represents one of the biggest technological breakthroughs of the 21st century.

But where does the knowledge that underpins this technology come from – and who should be allowed to own it?

Why gradient descent is key to AI systems

If you were standing in a mountain range and wanted to get to the bottom of a valley as fast as possible, you’d take steps downhill until you got there. Fundamentally, this is how gradient descent methods work.

For a given task with a set of parameters – such as how well an LLM can predict the next word in a sentence – the idea is that gradient descent provides a way to get to the best prediction, the point of minimum error, taking as few steps as possible. In our example, this point represents the most likely next word in the sentence.

Nesterov’s contribution has been finding optimal ways to achieve this. One of his key insights is using momentum to help you get to the bottom of the valley faster – known as the Nesterov Accelerated Gradient method (here’s a deeper explanation of the mathematics involved).

This work has made huge-scale optimisation feasible at workable speeds – thus underpinning the rapid development of LLMs, and AI systems more generally, over the last few years.

This technology is affecting more and more aspects of life. At home, AI agents are being touted as ways to manage busy households. In medicine, the first AI-assisted brain surgery was recently performed in a London hospital. In mathematics, AI-generated proofs claim to have solved a number of longstanding problems, with some referring to the AI agents as “collaborators” in this work.

All of this leads to big business. The race for AI dominance is built on massive capital and research and development expenditure, to develop the technology and supporting infrastructure required to support this global industrial revolution.

While in the west, private AI companies are bearing a sizeable part of this burden (without, in most cases, seeing any profits), the role of publicly funded research is and will remain critical to their business models.

In the US, obligated federal government spending on AI increased from US$675 million to US$7.2 billion (£5.3bn) between 2024 and 2026 – an increase of almost 1,000%.

There has already been considerable debate – and some high-profile legal cases – regarding the ethics of AI companies training their models on the back catalogue of human creativity, only to rent the results back to us. Far less scrutinised is the question of AI companies developing technology using knowledge that only exists due to sustained public investment over many decades, via academics such as Nesterov.

The fact that big tech companies use complex international tax strategies to limit their tax liabilities adds to the sense that their aggressive defence of intellectual property rights is out of kilter with the publicly funded research that plays a key role in its development.

Technology built on public knowledge

In most cases, academics work in publicly funded institutions on research grants that are largely funded by governments, whether through the Horizon Europe programme, the US National Science Foundation, or UK Research and Innovation. All of these bodies are ultimately funded by the taxpayer.

None of the rapid development in AI exists in a vacuum. It is built on decades of pioneering, blue-skies work on optimisation and neural networks, done by academics and largely funded by taxpayers at a time when the real-world application was still a distant dream.

Legal arguments around COVID vaccine patents are one example of how the private sector has tried to deny this public sector involvement – and a related share in any profits. So where is the public sector in this debate?

The EU is taking steps to secure control of its digital infrastructure, with a renewed focus on sovereignty in digital services. In the UK and elsewhere, a growing number of open-access mandates should mean that knowledge created by public funding stays public. While it can be used freely for commercial purposes, successful businesses would then theoretically pay back into the system via general taxation.

However, such knowledge flows beyond national borders. Efforts to capture value for reinvestment into the AI research landscape will be made harder by the fact that the tech companies’ business models depend on lobbying governments hard to avoid regulation. Any efforts to make them pay more for upstream public research and development are likely to be strongly resisted.

In the case of Nesterov and his fast gradient methods, this foundational work has been done in European institutions – yet the AI companies dominating the market are overwhelmingly based in the US or China. It rankles when access to these models is then restricted by foreign companies and governments.

James David Pickering does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Imagine telling your phone that you want a quiet boutique hotel near a city centre, below a certain price, with a decent breakfast and late checkout. A few seconds later, an AI assistant compares the options, explains why each one fits and helps complete the booking. This is no longer hypothetical.

In late August this year, Google began rolling out hotel booking through its AI Mode search feature in the US. Travellers can describe their preferences conversationally, compare hotels and guest reviews, select a room, check cancellation conditions and complete the booking using Google Pay. Partners include Booking.com, Expedia, Hilton, Marriott and IHG, the parent company of Holiday Inn.

The traveller still confirms the transaction. But that final click can obscure how much of the searching, comparing and decision-making has already been delegated to agentic AI.

This points to a significant change in how artificial intelligence may shape travel. AI is moving from systems that mainly answer questions towards systems that can increasingly interpret goals, make decisions and take action.

From answering questions to taking action

Hotels have used chatbots and recommendation systems for years. Most are reactive: a guest asks when breakfast starts, and the chatbot provides the answer. A traveller enters dates and preferences, and a booking site recommends suitable hotels.

Agentic AI goes further. These systems can operate with greater autonomy, adapt to changing circumstances and coordinate actions across different systems with less continuous human direction. This distinction is central to research we conducted on agentic AI in hospitality and tourism.

We identify five broad roles that such systems could perform, ranging from guest-facing service and itinerary planning to monitoring operations, managing engagement and coordinating multiple systems.

Consider a disrupted holiday. A conventional chatbot might tell you that your flight has been cancelled. A more agentic system could identify alternative flights, assess how the delay affects your hotel booking, modify airport transport and rearrange activities while retaining your preferences.

The important change is therefore not simply that the AI becomes better at conversation. It is that more decision-making authority is delegated to the system.

A different kind of risk

The commercial appeal is obvious. Travel businesses can respond around the clock, reduce repetitive work and offer much more individualised services.

But autonomy changes the consequences when AI gets something wrong.

A useful warning comes from Air Canada. In 2022, its chatbot incorrectly told a passenger that he could retrospectively claim a bereavement fare. When the company later refused the refund, it argued before a Canadian tribunal that the chatbot was effectively responsible for the information it had provided. The tribunal rejected that argument and held Air Canada responsible for information presented through its website.

That system was a relatively simple chatbot, not today’s agentic AI. But this is precisely why the case matters. If accountability became contested when AI merely supplied incorrect information, the stakes become higher when systems start taking actions on behalf of customers.

Bias creates a similar problem.

A voice-based concierge may understand some accents more reliably than others. A recommendation system might assume that an older traveller wants only sedentary activities, or infer dietary or cultural preferences from crude proxies rather than what the guest has actually requested. These are risks already identified in hospitality research.

When AI merely recommends something, a traveller can reject the suggestion. When it begins acting on such assumptions, bias can potentially influence which offers are presented, how complaints are handled or how services are personalised.

What happens to the human employee?

The same shift matters for hospitality workers.

AI can remove repetitive tasks and allow employees to spend more time on interactions requiring empathy, creativity and judgment. But it can also constrain employee discretion or become a mechanism for reducing staffing costs.

Our research therefore treats workforce displacement and role change as one of the central risks of agentic AI.

The relevant question is not simply whether AI will replace hospitality jobs. It is which decisions businesses choose to automate, and which they deliberately leave with people.

That distinction matters in an industry where the value of a service often depends on responding appropriately to unusual, emotional or culturally sensitive situations.

The questions travellers should start asking

Regulation is also evolving. Most provisions of the EU AI Act became applicable in August this year, although some requirements for high-risk systems will take effect later.

But regulation alone will not determine how comfortable people are with increasingly autonomous travel systems.

As AI becomes embedded more deeply in booking and hospitality, travellers may need to ask three simple questions: What is this system allowed to decide for me? What information is it using to make that decision? And can I easily reverse the decision or reach a human if something goes wrong?

For hotels, airlines and travel platforms, the corresponding question may be even more important: When an AI acts on your customer’s behalf, who remains accountable for the outcome?

The companies that answer that question clearly may ultimately gain more than operational efficiency. They may gain something considerably harder for an AI algorithm to generate: trust.

Nothing to disclose

Faizan Ali does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

It turns out that one of the smallest dog breeds, the chihuahua, is part wild coyote, according to recently published genetic research. The chihuahua breed originated in Mexico but is now a popular canine companion worldwide.

Coyote genes may have been introduced into ancestors of the chihuahua accidentally. It may also be a result of deliberate attempts to cross the two animals – perhaps many generations ago.

Coyotes are a wild canine species, slightly smaller than the grey wolf and native to North America. The presence of coyote genes in the much smaller chihuahua – among the most popular dogs in the UK and the US – highlights the incredible diversity of domestic dogs and their varied histories.

Coyotes are untamed and territorial, while anyone who has lived with a chihuahua knows that their small bodies house big canine personalities.

Chihuahuas are the archetypal “small but mighty” dogs: bold, independent, agile, but also capable of obedience. Small, sedentary lapdogs they are not. These findings might partly explain why.

The recent study explored ancient genetic signatures in dog and human genomes to validate the effectiveness of new genetic techniques. The genome is the full complement of DNA in living cells.

The researchers demonstrated their methods on dog breeds with links to the Americas, such as the chihuahua and another Mexican breed, the xoloitzcuintli (often shortened to xolo). The xolo is typically characterised by a lack of hair and an often aloof personality.

Many dogs in the Americas today are descended from European breeds, an outcome of colonisation from the 15th century onwards. However, previous research showed that the chihuahua and xolo preserve a small amount of ancestry – 4% in the chihuahua and 3% in the xolo – from pre-contact dogs, those that lived in the Americas before Europeans arrived.

Genetic analysis of the xolo in the recent study suggests that European and pre-Columbian dogs were mixing around 1700. This aligns with evidence suggesting that Europeans and Indigenous populations mixed around the same time in early Mexico.

Reconstructing origins

Researchers previously found evidence of coyote ancestry in DNA from the remains of ancient domestic dogs from the Americas. It’s a reminder that dogs are not that far removed from other canid species, and can breed with them successfully.

Wild coyotes and wolves have been found to hybridise naturally, especially in geographical areas where “wolf-like” behavioural traits are beneficial. There was even a recent report of a dog-fox hybrid being identified in Brazil.

It remains unclear when exactly the coyote genes entered the chihuahua lineage. The precise histories of different dog breeds are hard to reconstruct because detailed documentary evidence is often lacking.

So how did the chihuahua end up being so small, despite those coyote genes? This question should be seen through the lens of selective breeding, where humans choose different dogs to breed from based on characteristics they prefer.

Genetic analysis reveals that almost all small dog breeds share a mutation in the insulin like growth factor 1 (IGF1) gene, which affects cell growth and thus body size. Selection for small dogs with this mutation appears to have occurred very early in dog domestication.

There is some evidence for the presence of diminutive dogs among pre-contact Mesoamerican cultures, but how these relate to chihuahuas is unknown.

Extreme variation

Some dogs such as the Czechoslovakian wolfdog look visibly more like their wild and ancestral canid counterparts, such as the grey wolf. They have pricked ears, muscular, athletic bodies and coats that provide camouflage and protection from environmental extremes.

Other dogs show extreme variation: exaggerations such as long backs or short legs. In some cases, these physical characteristics come with significant consequences for health and wellbeing.

Overall body size is particularly variable in dogs, with a 40-fold difference across different breeds and types. Imagine the diminutive chihuahua standing next to an imposing Great Dane, for example.

There are also important differences in behaviour. If you want to live harmoniously with a dog, or train or work a dog in a specific activity, this is important to acknowledge. Indeed, meeting an individual dog’s physical, psychological and emotional needs is essential for both dog and human wellbeing, as well as training success.

As with appearance, natural and human-driven selection have combined to influence these traits. This has produced generations of dogs with specific behavioural characteristics.

The dog was one of the earliest animals to be domesticated by humans – the earliest examples are more than 15,000 years old. They are also one of the most successful domesticated species.

Dogs are found on every continent except for Antarctica, and the global dog population is thought to number between 700 million and 1 billion. They exist either embedded within human society, or living on the fringes of it.

Throughout history, natural selection on dogs with characteristics that confer survival advantages will have occurred. At the same time, humans will have selected individual dogs based for desired attributes, leading to the breed development and classifications that we see across the world.

As well as furthering our capacity to explore genetic ancestry of species, the latest study adds detail to our understanding of how and when the domestic dog evolved, and became so diverse.

It might also mean we think less about the “wolf in our living rooms”, and more about the “coyote in our chihuahuas”.

In addition to her academic affiliation at Nottingham Trent University (NTU) and support from the Institute for Knowledge Exchange Practice (IKEP) at NTU, Jacqueline Boyd is affiliated with The Royal Kennel Club (UK) through membership, as advisor to the Health Advisory Group and member of the Activities Committee. Jacqueline is a full member of the Association of Pet Dog Trainers (APDT #01583). She also writes, consults and coaches on canine matters on an independent basis.

The massive and deadly floods on the Nepal-Tibet border are a stark reminder of why the world needs to constantly update its understanding of the fragile ecosystem in the Himalayas.

These towering mountains contain nearly 90,000 glaciers, which sustain ten major river systems. Countries nearby, including India, Pakistan, China, Afghanistan, Nepal and Bhutan, are reliant on water resources that cross borders here. As major river-based hydro economies, their agricultural production, energy security and wider economic stability depend on the availability and the timing of river flows.

Changes in snowmelt, glacier retreat and water availability can cause untold hardship for millions of people relying on it. It can also create incredible danger as seen in August 2026, when a flash flood caused by a collapsed glacier washed away villages, bridges and roads on both sides of the Tibet-Nepal border, killing more than 1,000 people.

Understanding and measuring winter snowfall in these regions is exceptionally important. It is the main source of freshwater when it is released by snow melting during the region’s warm and early-summer crop growing seasons, when local communities need it most.

---

Read more: Nepal-Tibet disaster linked to climate change – a glaciologist explains

---

Understanding future Himalayan hazards

No one knows precisely how much water the mountains currently provide, and how this will change. This knowledge gap also hinders our understanding of the occurrence of geo-hazards including avalanches triggered by extreme snowfall events and flooding caused by the rapid melting of snow.

The recent Nepal disaster could be related to a spell of unusually warm weather. This may have weakened the snowpack creating a rapid melt. I work with a team of glaciologists at the British Antarctic Survey, whose team had sensors six miles (10km) away from the site. Results suggest that enhanced melting could have introduced water into the crevasses weakening the ice-rock connections.

Studies project that roughly half of the glacier ice in the Himalayas (equivalent to about two billion Olympic swimming pools) could melt by the end of this century – enough to raise sea levels by over a centimetre but there is still considerable uncertainty in these projections.

This is because we do not have enough measurements of snowfall and ice across large areas in this challenging terrain. As a result, scientists often have to extrapolate from relatively small numbers of observations to enormous and highly varied terrain.

Computer-based tools (known as models) that combine available observations with information about land and the atmosphere have proved to be indispensable and often come to rescue when gap-filling is necessary. But models are only as good as the observations used to constrain them.

New scientific approaches are beginning to offer solutions, but they are rather sparse and insufficient on their own beyond the region where they have been tested.

For example, my recent study with colleagues found a new way to accurately measure mountain snowfall reducing measurement errors dramatically – measuring snow has been a long-standing problem because of which mountain water resources have been hugely underestimated over the years.

Another regional study recently reported a measure of the water stored in some of the key Nepalese glaciers using an airborne method that involves taking an aerial survey of the region using instruments mounted on a helicopter. Scientists are also using a technique known as radar interferometry, which uses a combination of radio waves to detect very fast-moving mountain slopes that risk collapsing.

A combination of one or more such techniques could be used to predict where snowfall is concentrated, how much water it is likely to contribute to the region and also identify areas at risk. New information could be used to cast early warning preparedness strategies feeding into a forewarning system for timely evacuation of the region’s most vulnerable.

What happens next?

Scientific innovation alone will not be enough. There is an urgent need for proven ideas to be commercially adapted and systematically deployed across critical zones. The numbers and the imminent dangers involved can now no longer be left to discussions in research papers and conferences, they must be made public on the equivalent of a war footing.

An evacuation success story from the Swiss village of Blatten in 2025 shows what this can achieve. Hundreds of people were moved out before a huge section of the glacier broke off. The authorities had already identified areas at risk and publicly shared hazard maps. Continuous monitoring of the landscape, precipitation and ground movement fed into an early warning system.

But success stories from the west cannot simply be transplanted wholesale into the Himalayan context because of the region’s inhospitable elevation, harsh weather and limited infrastructure. An approach that factors in indigenous and traditional knowledge should be part of plans and policies.

What is also needed in the Himalayas is a far more highly coordinated monitoring network that brings together everyone who depends on and manages mountain water. This would include potential state and non-state organisations, researchers, local authorities and mountain communities. Communities should be central to this effort. Local people can help install and maintain monitoring equipment, collect observations and contribute knowledge of their own environment.

Examples of existing successes from the region include the use of community-managed reservoirs to artificially store meltwater, the construction of ice stupas (an artificial glacier) and glacier grafting techniques to grow new, water-producing glaciers designed to gradually release water during the growing season. The community-based flood warning system in parts of Nepal’s Koshi basin is a success story but is limited in its approach to provide alerts during a cascading disaster, such as the one we witnessed recently.

These results should help policymakers and scientists understand how communities dependent on mountain ecosystems are responding to change – and what support they need to adapt to reduce their vulnerability to future hydro-climatic shocks, such as droughts and floods.

Regardless of the current changes in Himalayan snowfall and glacier melt, local people need to play a vital role in helping measure what is happening in the climate system. And that, as well as countries working more closely together, should help with the future planning that is desperately needed.

Siddharth Gumber receives funding from the National Environmental Research Council (NERC), The TEAMx-FLOW project (NE/Z50399X/1).

Artificial intelligence (AI) could wipe out humanity this decade, according to three researchers from the US company Anthropic. One of the employees, Jacob Coxon, has quit the AI firm.

His predictions were backed by two other current Anthropic researchers in posts on social media. On X, Coxon claimed: “The people building AI earnestly believe that it could kill us all by the end of the decade. This is not a marketing stunt.”

It’s not the first time AI researchers and companies have issued such assessments. Here, Kate Devlin, professor of artificial intelligence & society, explains what is behind them and whether there’s anything different about the latest warning.

The latest claims about AI have come from a researcher who resigned from Anthropic. What is your general view on what has happened here?

To give this a sense of perspective, we should remember that it’s one AI company ex-employee posting on a platform known for grandiose claims, which was then quoted by a current AI company employee who put a figure on the risk of human extinction at greater than 10%.

So, it’s another doom-laden narrative about the supposed threat from AI. There is some dialling back from this in a follow up tweet from current Anthropic employee Evan Hubinger that read: “I think the risk from present models is low. What I’m worried about is superintelligence”.

These tweets have gained huge coverage because these kinds of things always do. It taps into our very primal fears – and our decades of watching dystopian sci-fi.

The claims from Jacob Coxon were backed by current employees of Anthropic. Why do you think AI companies are happy to support these kinds of claims in public?

It’s likely that these men believe what they’re saying, but we’ve heard warnings like this for years. It does seem odd that AI companies are okay with these kinds of claims.

Let’s remember that they are constantly looking for investment and, in return, they promise the most powerful and dominant models. What could be more powerful than a model that threatens our very existence?

One theory for doom-mongering by AI companies is that they want to avoid regulation. Do you think outside regulation of AI companies is inevitable?

It has been incredibly difficult to regulate AI, certainly at an international level. There has been little appetite for AI to be regulated in the US because there’s a view that regulation might stifle innovation.

The UK doesn’t have any AI-specific laws either, just existing laws that might apply in some cases. China began to introduce national-level laws but has now replaced that with technical standards and sector regulation. The EU remains the only place where there is dedicated AI regulation.

However, it looks like this has kicked off yet another call for better regulation, and we’re seeing that at present in US Congress and in the Houses of Parliament in the UK.

There is another angle, in that industry might be on board with regulation, as big tech companies would have the money to comply, but it could knock out their competitors with smaller budgets, so it would leave the leaders in dominant positions.

How far away are we from the kind of superintelligent AI mentioned in the latest warnings?

We don’t have superintelligence. I’m reluctant to put a time frame on that. Often people say “5-10 years” but that’s happened a lot in AI and 5-10 years pass without anything arising. It also depends on what is meant by superintelligence. Something that is conscious and thinks for itself? That might never happen.

In this case, it doesn’t really matter, as there are other harms already happening from AI, and there are other ways things can go wrong, though probably not at the existential level that’s being suggested here.

For example, AI is energy-hungry. It needs water to cool data centres. It has a damaging environmental impact. There are also issues around the content generated by AI, anything from a lack of plurality in the models’ outputs (meaning that the information it returns is homogenised, converging on particular viewpoints) to the spread of misinformation and disinformation.

We also know there is hidden labour in the AI supply chain: there are people working in terrible conditions to deliver the technology. And we know that it’s having an impact on people’s lives in terms of how it’s changing the workplace.

What are the potential practical ways in which AI could actually wipe out humans?

“Wiping out” is a bit extreme but there are some dangerous threats. There are the “bad actor” scenarios where AI is used destructively, like creating weapons or deadly viruses.

More mundane, but potentially very destructive, are AI agents let loose due to human carelessness or incompetence, hacking or taking control of insecure systems.

A survey we recently ran at King’s College London found that the UK public are feeling increasingly negative about AI, and that 42% of people are deliberately limiting their AI use. With claims from tech companies that it poses a direct threat to our existence, it will be unsurprising if public opinion dips even further.

Kate Devlin does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.