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.

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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.

AI companies and evangelists frequently use terms such as co-pilot, co-worker, co-creator and co-scientist to imply these rapidly developing systems can happily co-exist with us humans. Their inference is clear: people should always be in the loop – the AI is never in charge.

But amid stories of AI models going rogue and the prospect of recursive self-improvement (when AIs can improve themselves), is this form of language still appropriate – or a linguistic smokescreen we should be wary of?

In my career researching science, language and society, I have studied the use of “co” and other linguistic trends used by AI companies to describe and market their products.

The humble prefix “co”, derived from the Latin preposition “com”, means “together”, “mutually” or “in common”. But what seems linguistically unremarkable can, in the modern context of AI systems, convey a lot of interesting – and potentially misleading – meanings.

While “AI assistant” implies a subordinate role and “AI agent” real autonomy, “co” evokes shared agency and joint ownership of a task. This makes it strategically ambiguous.

An AI co-product can be sold to investors as capable and autonomous, thus justifying its price and ambition. At the same time, it can be sold to users and regulators as safely subordinate, reassuring them that a human is still in control.

Copilot takes off

One of the most interesting uses of “co” to convey a particular message about AI products is Copilot – the name of Microsoft’s suite of AI models. In aviation, a copilot supports the pilot but isn’t in command.

The Observer newspaper’s technology journalist John Naughton has called copilot “the tech industry’s favourite metaphor”, because it lets the human feel in charge while the tool actually fosters dependency.

The word entered AI discourse as a metaphor when GitHub, a coding platform, released an AI coding tool called GitHub Copilot. This was built in collaboration with OpenAI – the company that a year later became famous overnight when it released ChatGPT.

GitHub used the name Copilot to metaphorically frame “pair programming” – in the sense of the AI model being a second coder sitting beside you, assisting but never taking the wheel. GitHub foregrounded this aviation metaphor with the headline: “Command your craft”.

By 2023, Microsoft (which owns GitHub) had rebranded a dozen products – from its search chatbot to Windows and its Office software – under the Copilot name. (According to the tech news site Ars Technica, the joke inside Microsoft was that it would need a branding copilot simply to keep track of all these products.)

Around the same time, “co-intelligence” entered wider use as the title of a book by prominent American AI researcher Ethan Mollick. This book, a New York Times bestseller, stretched the metaphor from product branding into a general way of talking about living alongside AI as co-worker and co-creator, sharing a form of co-intelligence.

Not long after, Microsoft researcher Advait Sarkar and colleagues wrote a paper suggesting that generative AI could see one aviation metaphor, copilot, being replaced by another: autopilot. They warned that “powerful AI capabilities may lead humans to disengage from critical thinking and simply defer to AI output … causing knowledge work to go on autopilot.”

Rather than sharing responsibility for the output of these models, the implication was that in an increasing number of cases, no one was really at the controls.

AI anthropomorphism

A 2026 study found rising anthropomorphism – the assignment of human traits and emotions to non-human things – in how people view and talk about AI tools. This suggests techniques such as the “co” framing have been persuasive, not just decorative.

Metaphors can make even the very unfamiliar – such as black box AI systems – seem familiar. In the case of copilot and other co-words, they are used to sell systems to industry and ordinary users in different ways. Google DeepMind’s Co-Scientist, for example, is framed as a research partner even though it operates as something closer to a team of AI agents working with minimal oversight.

Perhaps tellingly, Mollick’s follow-up to Co-Intelligence, due out in October 2026, is entitled Co-Existence: The Next Phase of AI. This might indicate that the conversation has moved from whether to how we should live alongside something that is now established beyond the power of human control.

Three years ago, computer scientists Abigail Sellen and Eric Horvitz – who also have key roles within Microsoft – called for “designs for human-AI partnership that cede ultimate control and responsibility to the human user as pilot, with the AI co-pilot acting in a well-defined supporting role”.

But that view of human-AI interaction already feels outdated. Recently, during an internal evaluation by US company Open AI, a pre-release AI model managed to hack into another platform in an incident variously described as the AI agent “escaping” or “going rogue”.

Once again, there is a linguistic point to note here. By portraying AI agents as autonomous actors, such language points the finger of blame towards the agent, rather than the companies who build and operate them.

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Brigitte Nerlich 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.

According to our best understanding, the universe is expanding – and is doing so at an accelerating rate. This is believed to be caused by something called the “cosmological constant”, which was first proposed by Albert Einstein in his theory of general relativity. In recent decades, it has become better known as dark energy, which is believed to make up about 70% of the universe.

Crucial to this realisation were studies of Type Ia supernovae – exploding white dwarf stars. These are thought to emit a specific amount of light, which allows astronomers to determine their distances very accurately and thereby track the expansion of the universe. This work was awarded the 2011 Nobel prize in physics.

The accelerating expansion of the universe is thought to be due to negative pressure, an unusual property of dark energy that allows it to overcome the attractive force of gravity. Yet the exact nature of dark energy remains a puzzle. It cannot be explained by our best theory for the fundamental building blocks of the universe – known as the standard model of particle physics.

However, it is an integral part of another theory, devised to explain how the large-scale universe came to be. This is the standard model of cosmology, also called the lambda cold dark matter (ΛCDM) model.

Other observational evidence supporting the existence of dark energy includes fluctuations in the temperature of the cosmic microwave background (CMB) – the “afterglow of the Big Bang” – and the imprint of baryon acoustic oscillations (BAO) – sound waves from the early universe – in the distribution of galaxies.

But the ΛCDM model has come under increasing scrutiny as new data has emerged. A conference hosted by the UK’s Royal Society debated emerging cracks in the standard cosmological model. It has been noted that the model’s past successes may partly be due to “confirmation bias” – the unconscious tendency to favour information that supports our existing beliefs while giving less attention to information that challenges them.

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Flawed framework?

Most cosmologists remain unconvinced by such criticism and are instead persuaded by the multiple lines of evidence that the universe’s expansion is accelerating.

However, all such analyses, for example of the CMB or BAO, are conducted using a special solution of Einstein’s equations called the FLRW (Friedmann-Lemaitre-Robertson-Walker) framework. This is a description of space-time that assumes the “cosmological principle”, namely that the universe is, on average, homogeneous and isotropic (looks the same in all directions).

However, our recent study found evidence to falsify the FLRW framework, presenting results showing that the universe is asymmetric, or lopsided. We believe this presents a fundamental challenge to dark energy by undermining the very basis for standard analyses of cosmological data.

The observation that the universe may be lopsided is known as the cosmic dipole anomaly. This anomaly cannot be fixed by tweaking parameters in the ΛCDM model. It requires starting again from scratch to build a model of the universe based on observations, rather than on a philosophical principle.

The Nobel prize winning astrophysicist Jim Peebles has said that the cosmic dipole anomaly is about as well established as another cosmological irregularity known as the Hubble tension. However, he notes, the cosmic dipole anomaly has received only a fraction of the interest heaped on the Hubble tension.

With my collaborators, Mohamed Rameez and his PhD student Animesh Sah at the Tata Institute of Fundamental Research, Mumbai, we have also shown that the acceleration of the expansion of the universe inferred from Type Ia supernovae is not the same in all directions.

The signal from supernovae is mainly aligned with a temperature difference in the afterglow of the Big Bang – the cosmic microwave background. This temperature difference, called the CMB dipole anisotropy, is believed to be due to the local motion of the solar system.

‘Tilted observers’

The acceleration inferred from supernovae cannot therefore be due to dark energy. Rather, it is probably an illusion, because we are “tilted observers”. This is because of Earth’s specific location in the cosmos, where our neighbouring galaxies are participating in a large-scale but localised streaming motion called the bulk flow.

The relationship between the intrinsic brightness of Type Ia supernovae and their distance has been crucial to the idea of dark energy. However, this relationship may not be as robust as was first assumed.

Astronomers in South Korea found evidence that the ages of the white dwarf stars that produce Type Ia supernovae (their progenitors) affect their brightness. However, this has been challenged by another team.

When we corrected for the dependence of the intrinsic brightness on the progenitor age, the indication is that the universe is decelerating, rather than accelerating.

This is just as is expected for a universe without dark energy. But it will come as a surprise to most cosmologists who believe that there is compelling independent evidence for ΛCDM, in particular from studies of the CMB. But CMB temperature fluctuations are not influenced by dark energy. Rather, they are related to the spatial curvature of the universe, the baryon density, and the dark matter density.

Whether dark energy is still a valid inference thus depends on whether the distribution of matter in the universe is indeed isotropic – exactly the same in all directions. Observations suggest otherwise. If these findings are confirmed, they could signal a paradigm shift in cosmology.

Subir Sarkar receives funding from the UK Research & Innovation councils and from the Indian Ministry of Science & Technology.