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The Trump administration’s investment in Intel was structured to deter the chipmaker from selling its manufacturing unit, its chief financial officer said on Thursday, locking it into a lossmaking business it has faced pressure to offload.

The US government last week agreed to take a 10 per cent stake in Intel by converting $8.9bn of federal grants under the 2022 Chips Act into equity, the latest unorthodox intervention by President Donald Trump in corporate America.

The agreement also contains a five-year warrant that allows the government to take an additional 5 per cent of Intel at $20 a share if it ceases to own 51 per cent of its foundry business — which aims to make chips for third-party clients.

“I don’t think there’s a high likelihood that we would take our stake below the 50 per cent, so ultimately I would expect [the warrant] to expire,” CFO David Zinsner told a Deutsche Bank conference on Thursday.

“I think from the government’s perspective, they were aligned with that: they didn’t want to see us take the business and spin it off or sell it to somebody.”

Intel has faced pressure to carve off its foundry business as it haemorrhages cash. It lost $13bn last year as it struggled to compete with rival TSMC and attract outside customers.

Zinsner’s comments highlight how the deal with the Trump administration ties the company’s hands.

Analysts including Citi, as well as former Intel board members, have called for a sale — and Intel has seen takeover interest from the likes of Qualcomm.

Intel’s board ousted chief executive Pat Gelsinger, the architect of its ambitious foundry strategy, in December, which intensified expectations that it could ultimately abandon the business.

White House press secretary Karoline Leavitt told reporters on Thursday the deal was being finalised. “The Intel deal is still being ironed out by the Department of Commerce. The T’s are still being crossed, the I’s are still being dotted.”

Intel received $5.7bn of the government investment on Wednesday, Zinsner said. The remaining $3.2bn of the investment is still dependent on Intel hitting milestones agreed under a Department of Defense scheme and has not yet been paid.

He said the warrants could be viewed as “a little bit of friction to keep us from moving in a direction that I think ultimately the government would prefer we not move to”.

He said the direct government stake could also incentivise potential customers to view Intel on a “different level”.

So far, the likes of Nvidia, Apple and Qualcomm have not placed orders with Intel, which has struggled to convince them it has reliable manufacturing processes that could lure them away from TSMC.

As Intel’s new chief executive Lip-Bu Tan seeks to shore up the company’s finances, the government deal also “eliminated the need to access capital markets”, Zinsner explained.

Given the uncertainty over whether Intel would hit the construction milestones required to receive the Chips Act manufacturing grants, converting the government funds to equity “effectively guaranteed that we’d get the cash”.

“This was a great quarter for us in terms of cash raise,” Zinsner added. Intel had also recently sold $1bn of its shares in Mobileye, and was “within a couple of weeks” of closing a deal to sell 51 per cent of its stake in its specialist chips unit Altera to private equity firm Silver Lake, he noted.

SoftBank also made a $2bn investment in Intel last week. Zinsner pushed back against the idea that it had been co-ordinated with the government, as SoftBank chief executive Masayoshi Son pursues an ever-closer relationship with Trump.

“It was coincidence that it fell all in the same week,” Zinsner said.

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One of the most advanced nuclear fusion developers has raised about $900mn from backers including Nvidia and Morgan Stanley, as it races to complete a demonstration plant in the US and commercialise the nascent energy technology.   

Commonwealth Fusion Systems plans to use the money to complete its Sparc fusion demonstration machine and begin work on developing a power plant in Virginia. The group secured a deal in June to supply 200 megawatts of electricity to technology giant Google.

The Google deal was one of only a handful of such commercial agreements in the sector and placed CFS at the forefront of fusion companies trying to perfect the technology and develop a commercially viable machine.

CFS has raised almost $3bn since it was spun out of the Massachusetts Institute of Technology in 2018, drawing investors amid heightened interest in nuclear to meet surging energy demand from artificial intelligence.

“Investors recognise that CFS is making fusion power a reality. They see that we are executing and delivering on our objectives,” said Bob Mumgaard, chief executive and co-founder of CFS. 

New investors in CFS’s latest funding round, which raised $863mn, include NVentures, Nvidia’s venture capital arm, Morgan Stanley’s Counterpoint Global and a consortium of 12 Japanese companies led by Mitsui & Co.

Nuclear fusion seeks to produce clean energy by combining atoms in a manner that releases a significant amount of energy. In contrast, fission — the process used in conventional nuclear power — splits heavy atoms such as uranium into smaller atoms, releasing heat.

CFS is also planning to build the world’s first large-scale fusion power plant in Virginia, which is home to the largest concentration of data centres in the world.

BloombergNEF estimates that US data centre power demand will more than double to 78GW by 2035, from about 35GW last year, and nuclear energy start-ups already have raised more than $3bn in 2025, a 400 per cent increase on 2024 levels.

But experts have warned that addressing the technological challenges to the development of fusion would be expensive, putting into question the viability of the technology.

No group has yet been able to produce more energy from a fusion reaction than the system itself consumes despite decades of experimentation.

“Fusion is radically difficult compared to fission,” said Mark Nelson, managing director of the consultancy Radiant Energy Group, pointing to the incredibly high temperatures and pressures required to combine atoms.

“The hard part is not making fusion reactors. Every step forward towards what may be a dead end economically, looks like something that justifies another billion or a Nobel Prize.”

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London’s Natural History Museum has been called “Nature’s Treasurehouse”. Crammed with more than 80mn objects, the 272-year-old collection contains everything from a skeleton of Sophie the Stegosaurus to 12.5mn pinned specimens of butterflies and moths. But this rich trove is busy turning itself into a global digital resource that could open up new pathways for scientific research in our artificial intelligence age.

As he whizzes through the Jurassic gardens, the NHM’s director Doug Gurr enthuses about the possibilities of using the museum’s collections to deepen our understanding of the natural world and the effects of climate change. For example, researchers are currently studying specimens of Arctic krill, which probably constitute the world’s largest wild biomass, that were collected during the Scott and Shackleton expeditions more than a century ago. “We are now going back to the same locations and seeing how things are changing today,” he says.

As a former head of Amazon UK, Gurr is immersed in technology and is building a specialist AI team within the museum to act as a public good. The team is using pattern recognition software to help the UK Border Force identify endangered animal skins and translate museum texts into speech to assist visually impaired visitors.

But the NHM is also painstakingly digitising its collection one specimen at a time, creating an machine-readable scientific database.

Compendious as the NHM’s collections are, they contain only a minute fraction of nature’s data. The vast majority of the planet’s lifeforms are single-cell organisms that have yet to be recorded. If all the species equated to the scale of the Atlantic Ocean then we have sequenced the genomes of just five cups of water, says Glen Gowers, co-founder of Basecamp Research, a London-based start-up. Progress in biology remains a prodigious data challenge.

Basecamp — motto: beyond known biology — is in the business of sequencing as many species as it can. By better understanding the language of evolution, pharmaceutical, agricultural and chemical companies should then be able to create better products for consumers.

The company’s database currently contains more than 10bn genes across 400 terabases of genetic data, but it is steadily expanding with every exploratory expedition it mounts. It is already working in more than 20 countries, ranging from Costa Rica to Malawi and Malta.

How scientifically useful, or commercially valuable, such data sets may be is as yet hard to tell. Venture capital investors have lost a lot of money betting on start-ups aggregating different forms of data. For example, the genetic testing company 23andMe, once valued at $5.8bn, was sold for just $256mn earlier this year.

Gowers accepts that data is not so valuable by itself. It is what you do with the information that counts. To that end, Basecamp has partnered with Nvidia and Microsoft to create its own AI foundation models to analyse and interrogate the internet of biology. “We’re creating this digital twin of evolution and the natural world and allowing models to look into it,” he tells me.

As ever with data, the question arises: who should benefit from its derivatives? Some countries in the global south have been scarred by the “biopiracy” of the global north. In the late 20th century, controversies arose around western companies developing valuable pesticides, dietary products and blood pressure drugs based on native plants from India and southern Africa and venom taken from Brazilian vipers. However, Basecamp runs its own benefit-sharing programme supporting local partners and paying out 1 per cent of the revenues it generates from its data, exceeding international protocols.

Another hot debate is whether generative AI models are hitting a wall, having ingested the entire internet.

Simply throwing more computing power at the same data is unlikely to produce much progress. But Gowers believes that in creating fresh AI-friendly data sets, Basecamp can enable further advances to be made. “That’s why we’re scaling the data as an answer to get past that performance plateau,” he says.

Machines may be able to spot patterns and make connections that are undetectable to humans by combining a string of pixels of a plant with its DNA sequencing, for instance. The promise is that by digitising public collections, such as the NHM, and creating new data sets, we will be able to understand the world in new ways. The AI revolution still has a long way to run.

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