Concerns about rising veterinary costs have moved firmly into the policy spotlight, driven in part by stories like that of one Manchester pet parent, who took out loans totaling more than £10,000 to cover emergency and cancer care for her dog after hitting insurance limits. Her experience of high bills, limited price visibility, and frustration with complaints processes was covered by the BBC and has become emblematic of wider concerns about consumer protections in UK veterinary care.

Against this backdrop, the UK government has launched its first major reform of the sector in 60 years, aiming to address transparency, accountability, and confidence in veterinary services.

At the heart of the proposals is mandatory price transparency. Under plans announced by the UK Department for Environment, Food & Rural Affairs (Defra), practices would be required to publish prices for common treatments such as vaccinations, neutering, and microchipping, and to clearly explain treatment options. The move follows findings from the Competition and Markets Authority (CMA) that vet fees have risen at nearly twice the rate of inflation, with problems in the market potentially costing households up to £1bn over five years. Ministers argue that clearer pricing and disclosure of practice ownership—particularly as around 60% of practices are now owned by non-vets—will increase competition and help pet owners make more informed choices.

Beyond pricing, the reforms signal a fundamental shift in how the profession is regulated. For the first time, veterinary practices and corporate groups—not just individual vets—would be subject to licensing and regulatory oversight, similar to GP surgeries or care homes. The proposals also include an easier and more effective complaints route, reforms to fitness-to-practice processes, and legal protection of the veterinary nurse title. Together, these measures are intended to modernize a regulatory framework still rooted in the Veterinary Surgeons Act 1966, while strengthening accountability across the sector.

However, professional commentary has highlighted potential unintended consequences. While price publication may work well for routine, standardized services, applying the same logic to diagnostics and surgery risks oversimplifying care that varies widely by patient and condition. There are concerns that headline pricing could encourage “menu medicine,” defensive pricing, or cost-shifting toward more complex cases, while expanded practice regulation may increase operating costs through additional compliance and inspections. As VetSurgeon.com reported, an eight-week public consultation is open until March 25 March. If you’re in the UK, consider making your voice heard.

Former Citigroup CEO Sandy Weill and his wife, Joan Weill, have pledged $120 million to the University of California Davis, marking the largest single gift ever made to veterinary medicine worldwide. The donation will rename the school as the UC Davis Joan and Sanford I. Weill School of Veterinary Medicine, and support a major expansion of its campus, including a new small animal teaching hospital.

Of the total gift, $80 million is allocated to construction as part of the school’s $750 million Veterinary Medical Complex expansion, which aims to modernize facilities, increase clinical capacity, and train more veterinarians amid ongoing workforce shortages. The current hospital treats approximately 50,000 patients annually, and the new facility is expected to support care for up to 20,000 additional animals each year.

As Bloomberg also reported, the remaining $40 million will fund fundamental and clinical research, with a focus on comparative and translational medicine—studying diseases that affect both animals and humans, including cancer, neurological disorders, and cardiovascular disease. The investment is intended to strengthen oversight and education in high-demand specialties such as emergency and critical care, oncology, neurology, cardiology, and primary care.

The Weills’ gift is both philanthropic and personal, stemming from their long-standing relationship with UC Davis and care their dog received at the veterinary hospital after a lymphoma diagnosis in 2018. Sandy Weill has served on the UC Davis Chancellor’s Board of Advisors since 2014, and the couple’s broader philanthropy exceeds $1.5 billion across educational, medical, and cultural institutions. With this donation, their total giving to the University of California system surpasses $500 million.

The latest AVMA Chart of the Month indicates that veterinary student debt is increasing again, according to recent Graduating Senior Survey data. In 2025, average DVM debt across all graduates, including those with no debt, rose to $174,484, up from $153,972 in 2022. Among graduates carrying debt, the average reached $212,499, compared with $186,788 three years earlier.

Although starting compensation for new veterinarians has continued to rise, it has not kept pace with borrowing, leading to an increase in the average debt-to-income ratio for full-time new graduates to 1.4:1 to last two years (up from 1.3:1 in 2022 and 2023)

The end of COVID-era financial supports—such as near-zero interest rates and federal student loan forbearance—has coincided with this upward shift in debt burden. While current average debt-to-income ratios remain below levels commonly seen in the 2010s, individual financial situations vary widely. In the 2025 graduating class, 18% reported no DVM debt, while 40% owed $200,000 or more and 6% owed at least $400,000. Additionally, 14% of new full-time veterinarians had a debt-to-income ratio of 2.5 or higher, a level associated with increased financial stress early in their careers.

I spend a lot of time talking about what worries me about AI in medicine.

Lack of transparency. Black boxes. Overconfident marketing. Metrics that sound impressive but fall apart in real clinical settings.

I am sometimes cast as anti-AI. But I don’t think this is a fair assessment. I’m actually quite excited about AI. Often too excited. We have already seen what this technology is capable of when it is built thoughtfully, and that is the future I hope for in veterinary medicine.

This week I want to zoom out to frame that potential future with an understanding of one of the amazing stories in AI that has brought us to where we are today.

This is part of the story of Google’s DeepMind, and their quest to build artificial general intelligence. You do not need any technical background to enjoy it. It is a story about a team chasing a hard problem and watching something surprising emerge from the work.

DeepMind was founded by three brilliant AI scientists. Demis Hassabis, Shane Legg and Mustafa Suleyman in 2010. They believed that one of the best ways to teach computers to learn was through games. They started with Atari and pong and found unbelievable success having an AI agent which could learn and excel at multiple video games. After only 4 years they realized that to achieve their goal of general intelligence they would need more compute power and were acquired by Google. Following this acquisition they put the computational power of the one of the world’s largest companies behind a new challenge - teaching a computer to play Go and beat world champions.

Go is not chess. The number of possible board positions is so vast that brute-force calculation does not work. For decades, experts believed a computer would not beat the best humans ever.

Then came Move 37.

Midway through a match between Deep Mind’s computer system, AlphaGo and Korean champion Lee Sedol, AlphaGo made a move that stunned everyone watching. The human player looked confused. Professional Go players later said they would never have considered it. AlphaGo itself calculated there was only a 1/37000 chance a human would have made the move. It was a remarkable feat and something few expected. The computer didn’t just mimic patterns, it created a new approach.

This isn’t the end of the DeepMind story. The company exists today as the foundation of Google AI’s efforts and one of its founders now leads AI at Microsoft. But this turning point in AI teaches two principles that translate directly to the kind of AI we actually need in veterinary medicine.

First, the best outcomes came from collaboration, not magic.

The documentary makes it clear that these systems did not appear out of thin air. They were shaped by teams, feedback loops, arguments, iteration, and relentless testing. The people building the system were trying to build something that they felt would change the world.

That is the model veterinary AI needs.

If we leave AI development to vendors alone, we get tools optimized for demos. If we bring clinicians into the loop, we get tools optimized for practice. That means veterinarians do not just “use AI.” We help define what good looks like. It gives us the opportunity to place our evidence based medicine lens and ensure that technology for technology’s sake is not the outcome we see.

The second is what happens to human expertise when a powerful tool shows up. Watching Lee Sedol adapt is inspiring. Rather than leaving defeated, AlphaGo opened up new avenues of how he approached his life’s work. That is a helpful frame for us.

Much of AI today is about doing human tasks faster. But the promise for AI is to find entirely new ways of thinking. This same underlying capability that cracked a complex board game is being applied to protein folding, drug discovery, climate modeling, materials science, and biology. Problems that were previously limited by human intuition or computational shortcuts are suddenly opening up.

But this future is only bright if we build it properly. Not for speed or for market forces. Through evidence, planning and multi-stakeholder involvement. And that’s the risk of the way current AI is being rolled out.

Clinical AI is different from board games. The stakes are higher. The data are messier. Bias matters. Transparency matters. Evaluation in real clinical workflows matters.

This is where guardrails come in.

AI in veterinary medicine should be auditable. It should declare its uncertainty. It should be validated on the populations we actually see. It should support clinicians. We should understand how clinicians interact with it and the ways it makes them both better and worse. AI should never ask for trust without evidence.

These are complex tasks, a lifetime or more of work. But they are also the core principles of evidence-based medicine. When those principles are respected, AI becomes less threatening and more exciting.

To me Move 37 was not impressive because it beat a human. It was impressive because it expanded what was possible.

That is the version of AI worth pursuing in animal health. Tools, rigorously studied, that genuinely help us see better, think better, and care better.

Your AI homework this week: watch The Thinking Game (free on YouTube) its approachable, interested and discusses AI topics in an engaging way. Its not a technical film but a documentary for everyone. I don’t think you will regret the time spent.

-RBA

Here are some of the other stories that caught our eye and we're following this week from around the veterinary world and animal kingdom: