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The Engineering and Physical Sciences Research Council (EPSRC) has announced funding for 23 new Prosperity Partnerships aimed at solving complex challenges across UK industry, from AI-enabled assessments to clean energy and biopharma manufacturing.
The £41 million investment from EPSRC is matched by £56 million from business and academic partners. This marks the sixth round of the initiative, which encourages universities and companies to co-create research projects aligned with national priorities.
Focus on AI, education, and quantum computing
Several of the funded projects align closely with educational technologies and artificial intelligence. A partnership between King’s College London and the exam board AQA will explore the use of AI to support examiners marking GCSE and A-level papers. The goal is to improve accuracy and consistency through a virtual assistant trained to support human judgment.
At the University of Strathclyde, researchers will collaborate with Quantum Motion Technologies to enhance cryogenic electronics for quantum computing. The FIRETRACE project focuses on managing thermal effects in ultra-low temperature environments—considered essential for building scalable quantum systems.
A separate project at the University of Edinburgh, in partnership with Axa, will investigate insurance models for AI systems to manage performance risk and incentivize safer design in emerging technologies.
Science Minister and EPSRC chair welcome initiative
Science Minister Lord Vallance says the new projects highlight the role of research in solving real-world problems.
“These partnerships show the range of real-world challenges the UK’s world-class research base is helping to tackle—from cutting carbon emissions in heavy transport, to improving access to life-saving medicines,” he says.
EPSRC Executive Chair Professor Charlotte Deane adds, “Our flagship Prosperity Partnerships scheme brings together world-class expertise from businesses and academia to solve big challenges to support the growth of industry and advance UK research.”
Health, climate, and manufacturing projects
Other partnerships aim to improve sustainability and healthcare delivery. The University of Cambridge is working with Hitachi Cambridge Ltd to develop enzyme-based systems for converting CO₂ into useful chemicals, and the University of Nottingham is focused on advancing lithium-sulfur batteries that could extend the range of electric vehicles.
At University College London, multiple projects with AstraZeneca and Lonza are targeting improvements in biopharmaceutical development, using AI and automation to speed up production and reduce environmental impact.
University of York is partnering with Synthomer to create biodegradable specialty polymers that reduce reliance on fossil fuels. And at Swansea University, researchers are working with Thales to improve cyber resilience in national infrastructure.
A long-term funding model
Since its launch in 2017, the Prosperity Partnerships program has awarded over £600 million in joint funding from government, research institutions, and business. Previous collaborations have supported innovations such as zero-emission buses and advanced aerospace simulations.
A new project between the University of Edinburgh and Rolls-Royce will extend work from a previous partnership, ASiMoV, focused on simulating next-generation gas turbines for cleaner aviation.
“These 23 ambitious projects present a significant investment in the UK’s future,” says Professor Deane. “From speeding up drug manufacturing to longer lasting batteries, these partnerships have the potential to make a real difference to people’s lives and help boost the economy.”
AI Research
Billionaire David Tepper of Appaloosa Has Been Selling Artificial Intelligence (AI) Stocks en Masse, With One Exception
Key Points
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Form 13Fs are filed quarterly, and they allow investors to easily track which stocks Wall Street’s greatest money managers are buying and selling.
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Appaloosa’s David Tepper has been reducing his fund’s exposure to artificial intelligence (AI) stocks — and profit-taking might not be the only catalyst.
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However, one industry-leading AI stock (not Nvidia!) was purchased by Tepper during the March-ended quarter.
Data is the currency that keeps Wall Street running. The only problem for investors is the amount of data they have to digest can sometimes be overwhelming, which can allow something of importance to go unnoticed.
During the heart of earnings season in mid-May, institutional investors with at least $100 million in assets under management filed Form 13F with the Securities and Exchange Commission. This filing deadline, which occurred on May 15, may have slid under the radar of investors.
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A 13F provides invaluable insight on which stocks Wall Street’s top-tier money managers bought and sold in the latest quarter. Though most investors tend to keep close tabs on which stocks Warren Buffett has been purchasing and selling, he’s not the only billionaire with a penchant for outperformance.
Image source: Getty Images.
For instance, Appaloosa’s David Tepper has a lengthy track record of generating outsized returns. In particular, Tepper has been a big fan tech stocks involved in the artificial intelligence (AI) revolution since late 2022.
But what you might be surprised to learn is that Tepper has been reducing his exposure to AI stocks across the board over the last year… with one exception.
Billionaire David Tepper can’t sell his fund’s AI stocks fast enough
If there’s a brand-name company that’s made artificial intelligence a foundational part of its future growth plans, there’s a pretty good chance David Tepper’s Appaloosa has been a seller of its stock over the past year (defined as April 1, 2024 – March 31, 2025).
In no particular order, Tepper has reduced or jettisoned his stakes in:
- Advanced Micro Devices: Sold all 1,630,000 shares
- Intel: Sold all 3,750,000 shares
- Oracle: Sold 1,600,000 shares (a 70% reduction)
- Microsoft(NASDAQ: MSFT): Sold 890,000 shares (a 64% reduction)
- Baidu: Sold 1,025,000 shares (a 57% reduction)
- Meta Platforms(NASDAQ: META): Sold 572,500 shares (a 51% reduction)
- Taiwan Semiconductor Manufacturing: Sold 230,000 shares (a 46% reduction)
- Amazon: Sold 1,318,000 shares (a 34% reduction)
- Alibaba: Sold 2,020,000 shares (an 18% reduction)
- Nvidia(NASDAQ: NVDA): Sold 4,120,000 shares (a 93% reduction, when adjusted for the company’s 10-for-1 stock split in June 2024)
To be fair, most of these AI stocks have skyrocketed due to strong enterprise demand for AI infrastructure, as well as pie-in-the-sky addressable market estimates. With Appaloosa’s average holding time clocking in at two years and five months, simple profit-taking may very well explain this en masse selling activity. But there’s likely more to this story than meets the eye.
For instance, every game-changing technological innovation for more than 30 years has navigated its way through an early innings bubble that eventually burst. This is a roundabout way of saying that all new innovations and technologies need ample time to mature. With most businesses not generating a positive return on their AI investment nor optimizing their AI solutions (as of yet), the table appears set for another bubble to burst.
Some of the AI stocks Tepper has exited or pared down would fare OK if the AI bubble pops. For example, Meta Platforms generates close to 98% of its net sales from advertising, which wouldn’t be demonstrably hurt if the AI bubble bursts. The same can be said for Microsoft, which still leans on its legacy segments (Windows and Office) for copious amount of cash flow, and can count on cloud infrastructure service platform Azure to sustain double-digit growth.
But this wouldn’t be the story for Wall Street’s AI darling Nvidia, which has seen almost all of its growth over the last two years originate from its AI-graphic processing units (GPUs). Nvidia has been priced as if demand for AI-GPUs won’t taper, which is highly unlikely.
Appaloosa’s billionaire chief might also have regulatory or trade/tariff concerns. Both the Joe Biden and Donald Trump administrations established export restrictions on high-powered AI chips and related equipment to China, which is a key market for companies like Nvidia.
Furthermore, the prospect of base-rate tariffs and higher “reciprocal tariff rates” instituted by President Trump threatens to eat into corporate margins, and may disrupt and/or alter supply chains and demand for U.S. hardware. It’s more worrisome news for Nvidia, which may explain why Tepper has reduced his stake in the company by 93% over the prior year.
Image source: Getty Images.
This is the one artificial intelligence stock Appaloosa’s David Tepper is buying
While selling activity in AI stocks has been almost universal for Appaloosa’s David Tepper since the start of April 2024, there is one AI company that’s been an exception: networking specialist Broadcom(NASDAQ: AVGO). During the first quarter of 2025, Appaloosa’s 13F shows Tepper opened a 130,000-share position.
Broadcom’s networking solutions are responsible for connecting tens of thousands of GPUs in AI-accelerated data centers, with the purpose of reducing tail latency and maximizing computing potential. With AI-empowered software and systems making split-second decisions, networking solutions that reduce lag/delays are imperative to the success of this technology.
For those of you who’ve put two and two together, Broadcom stock wouldn’t be immune if the AI bubble were to burst. A slowdown in data center infrastructure orders would eventually work its way down the line to Broadcom.
However (and this is a fairly important “however”), Broadcom is far more than just an AI stock, which may explain why Appaloosa’s billionaire investor has piled on.
Although demand from a small number of AI hyperscalers accounts for the bulk of Broadcom’s current growth rate, it’s a considerably more diverse business than Nvidia. It’s one of the leading providers of wireless chips used in next-generation smartphones. Further, it supplies various optical and/or networking components to the auto industry and industrial businesses. To boot, Broadcom offers enterprise cybersecurity solutions through Symantec, which it acquired in 2019. While an AI bubble would be impactful, it wouldn’t decimate Broadcom’s diverse operating segments.
Broadcom’s valuation is likely the other puzzle piece that attracted billionaire David Tepper. Though its current forward price-to-earnings (P/E) ratio of 34 isn’t exactly cheap, its forward P/E fell to the 22 to 24 range during the stock market’s swoon in March.
For the time being, Broadcom is the only true exception to Tepper’s en masse selling of AI stocks.
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By 2040, the energy demands of the tech industry could be up to 25 times higher than today, with unchecked growth of data centres driven by AI expected to create surges in electricity consumption that will strain power grids and accelerate carbon emissions.
This is according to a new report from the University of Cambridge’s Minderoo Centre for Technology and Democracy, which suggests that even the most conservative estimate for big tech’s energy needs will see a five-fold increase over the next 15 years.
The idea that governments such as the UK can become leaders in AI while simultaneously meeting their net zero targets amounts to “magical thinking at the highest levels,” according to the report’s authors. The UK is committed to net zero greenhouse gas emissions by 2050.
Researchers call for global standards in reporting AI’s environmental cost through forums such as COP, the UN climate summit, and argue that the UK should advocate for this on the international stage while ensuring democratic oversight at home.
The report, published today, synthesises projections from leading consultancies to forecast the energy demands of the global tech industry. The researchers note that these projections are based on claims by tech firms themselves.
At the moment, data centres – the facilities that house servers for processing and storing data, along with cooling systems preventing this hardware from overheating – account for nearly 1.5% of global emissions.
This figure is expected to grow by 15-30% each year to reach 8% of total global greenhouse gas emissions by 2040, write the report’s authors. They point out that this would far exceed current emissions from air travel.
The report highlights that in the US, China, and Europe, data centres already consume around 2-4% of national electricity, with regional concentrations becoming extreme. For example, up to 20% of all power in Ireland now goes to data centres in Dublin’s cluster.
“We know the environmental impact of AI will be formidable, but tech giants are deliberately vague about the energy requirements implicit in their aims,” said Bhargav Srinivasa Desikan, the report’s lead author from Cambridge’s Minderoo Centre.
“The lack of hard data on electricity and water consumption as well as associated carbon emissions of digital technology leaves policymakers and researchers in the dark about the climate harms AI might cause.”
“We need to see urgent action from governments to prevent AI from derailing climate goals, not just deferring to tech companies on the promise of economic growth,” said Desikan.
The researchers also use data from corporate press releases and ESG reports of some of the world’s tech giants to show the alarming trajectory of energy use before the AI race had fully kicked into gear.
Google’s reported greenhouse gas emissions rose by 48% between 2019 and 2023, while Microsoft’s reported emissions increased by nearly 30% from 2020 to 2023. Amazon’s carbon footprint grew around 40% between 2019 and 2021, and – while it has begun to fall – remains well above 2019 levels.
This self-reported data is contested, note the researchers, and some independent reporting suggests that actual emissions from tech companies are much higher.
Several tech giants are looking to nuclear power to defuse the energy timebomb at the heart of their ambitions. Sam Altman, CEO of OpenAI, has argued that fusion is needed to meet AI’s potential, while Meta have said that nuclear energy can “provide firm, baseload power” to supply their data centres.
Microsoft have even signed a 20-year agreement to reactivate the Three Mile Island plant – site of the worst nuclear accident in US history.
Some tech leaders, such as former Google CEO Eric Schmidt, argue that environmental costs of AI will be offset by its benefits for the climate crisis – from contributing to scientific breakthroughs in green energy to enhanced climate change modelling.
“Despite the rapacious energy demands of AI, tech companies encourage governments to see these technologies as accelerators for the green transition,” said Prof Gina Neff, Executive Director of the Minderoo Centre for Technology and Democracy.
“These claims appeal to governments banking on AI to grow the economy, but they may compromise society’s climate commitments.”
“Big Tech is blowing past their own climate goals, while they rely heavily on renewable energy certificates and carbon offsets rather than reducing their emissions,” said Prof Neff.
“Generative AI may be helpful for designing climate solutions, but there is a real risk that emissions from the AI build-out will outstrip any climate gains as tech companies abandon net zero goals and pursue huge AI-driven profits.”
The report calls for the UK’s environmental policies to be updated for the “AI era”. Recommendations include adding AI’s energy footprint into national decarbonisation plans, with specific carbon reduction targets for data centres and AI services, and requirements for detailed reporting of energy and water consumption.
Ofgem should set strict energy efficiency targets for data centres, write the report’s authors, while the Department for Energy Security and Net Zero and the Department for Science, Innovation and Technology should tie AI research funding and data centre operations to clean power adoption.
The report’s authors note that that UK’s new AI Energy Council currently consists entirely of energy bodies and tech companies – with no representation for communities, climate groups or civil society.
“Energy grids are already stretched,” said Prof John Naughton, Chair of the Advisory Board at the Minderoo Centre for Technology and Democracy.
“Every megawatt allocated to AI data centres will be a megawatt unavailable for housing or manufacturing. Governments need to be straight with the public about the inevitable energy trade-offs that will come with doubling down on AI as an engine of economic growth.”
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