Dollar stablecoins are becoming more than a fast way to move money. For Europe, its growing use in enterprise payments raises questions about monetary sovereignty.

If dollar-backed stablecoins become default settlement tools for global commerce, European banks and companies may gain speed and liquidity while relying more heavily on US-linked financial infrastructure.

“Stablecoins are also used by the US as an asset to finance the US Treasury. It is also a new instrument for monetary policy,” said Nadia Filali, Chief Innovation Officer at Caisse des Dépôts. “For us, it is important to look at what is the place of the euro and Europe in this area.”

Filali said the choice between regulated stablecoins and bank-issued tokens is not only about technology. Bank-issued tokens are more likely to serve capital markets and wholesale markets, while regulated stablecoins may be used more broadly in transactions between companies, banks and wider markets.

That distinction matters because stablecoins are already heavily linked to the US dollar. Caisse des Dépôts has strategic priorities linked to financial, digital and industrial sovereignty in France and Europe.

Europe is developing a digital euro for both wholesale and retail uses, but central banks now understand that digital money will not be limited to a digital euro.

Commercial bank money, stablecoins and other forms of digital money are also expected to play a role. Europe’s challenge is to support innovation without allowing its future digital-money infrastructure to be shaped entirely by dollar liquidity.

“I am not sure that fragmentation in this area will be positive,” she said. “We need to collaborate more between banks, and not only at the national level.”

Xavier Gomez, Group Chief Operating Officer of Vancelian, also said regulation would be key, pointing to competition between Europe’s Markets in Crypto-Assets Regulation (MiCA) and the US GENIUS Act.

Banks need authorization and accounting clarity before moving more deeply into this business. The broader challenge is how traditional finance can converge with decentralized finance while keeping regulated cash at the center of enterprise adoption.

Faster rules

Digital Assets Forum 2026, organized by European Blockchain Convention, brought the discussion to London. The panel, titled “Regulated Stablecoins vs Bank-Issued Tokens: What Will Enterprises Use?”, was moderated by Polina Evstifeeva, Executive in Residence at Global Digital Finance (GDF).

Shreyosee Dutta-Roy, Director of Digital Assets at UBS, said regulators should avoid treating all crypto-related activities as a single category. The risks in payment, settlement, custody and trading are different, so rules should be designed around specific use cases.

“This term crypto has become a monolith,” she said. “The risk appetites of payment, settlement, custody and trading are very different.”

She said clearer and faster regulatory timelines would help enterprises adopt digital money. If rules remain unclear for too long after consultations, companies may find it difficult to plan product development, compliance processes and treasury operations.

“Having a faster timeline between consultation and making it live would be good,” she said.

Unclear rules make it difficult for enterprises to plan the adoption of digital-money tools.

Peter Left, Head of Digital & Markets Innovation at Lloyds Banking Group, said interoperability is one of the two key problems that digital-money systems need to solve. Today’s interoperability between deposits and account-based money is achieved through on-demand transfers of central bank reserves.

“Interoperability is key,” he said. “It is a great design that the Bank of England came up with to have central bank reserves as part of the backing asset portfolio.”

A similar model could be applied to stablecoins if part of their backing assets consisted of central bank reserves. That could allow stablecoins to be converted into tokenized deposits when a payment journey required it.

Dutta-Roy cited the Bank for International Settlements (BIS)’s Project Agorá as another example of interoperability work. The project brings together seven central banks and 41 financial institutions to examine how commercial bank tokens and digital central bank reserves can support 24/7 programmable cross-border payments.

Tokenized deposits have benefits from an accounting perspective, but they are also closed-loop. That makes interoperability with other banks and global consortia important.

Both stablecoin and tokenized-deposit systems would need a digital Know Your Client (KYC) or payee confirmation process, so banks could track the movement of funds and meet sanctions compliance requirements.

For banks, the point is not only whether money can move faster. It is whether faster money can still meet the controls expected in regulated financial markets.

Use-case split

The panelists repeatedly pushed back against the idea that enterprises must choose one form of digital money over the other. The likely outcome is a mixed model in which stablecoins and tokenized deposits serve different functions.

Dutta-Roy said banks have customer segments that want stablecoins, while institutions and corporate clients also want the benefits of bank tokens.

“It is not versus,” Left said. “It is the right product for the right use case.”

He said deposits are backed by assets such as private-sector loans, including mortgages and business lending. Stablecoins, by contrast, are generally backed by short-dated government bonds and more liquid assets. That difference affects how each instrument should be used.

He added that tokenized deposits can be moved between wallets and network nodes, but Lloyds still needs to know the wallet owner. For example, in its work with Archax, Lloyds needed to know the business, where it was registered and what kind of business it carried out.

“I can’t have it travel like a stablecoin can,” he said. “When it needs to go places I don’t know, that’s when we use stablecoins.”

Dutta-Roy said stablecoins can help with cross-border payments and decentralized finance (DeFi) transactions. DeFi refers to blockchain-based financial services that use code, smart contracts and digital wallets rather than conventional intermediaries.

“Stablecoin helps where you need cross-border payments or DeFi transactions,” she said. “It is a very good bridge between TradFi and DeFi.”

TradFi (Traditional finance) refers to banks, asset managers and other established financial institutions. Stablecoins could be useful where distribution matters most, such as cases where a bank may not need to know which investor is purchasing a money market fund.

Tokenized deposits are also moving into real use cases. Lloyds was running a node on the Canton Network, a blockchain designed for regulated financial markets, and had carried out pilot transactions with market counterparties. One example involved using tokenized deposits in a delivery-versus-payment workflow to buy tokenized UK government bonds or gilts.

UBS launched a US dollar cash pilot in 2024, with corporate treasury management as the main use case. Multinational companies often have multiple business entities across markets, leading to fragmented liquidity.

Tokenized deposits could help such companies see inflows and outflows more clearly, reduce the need for extra liquidity buffers and lower capital costs. They could also help facilitate high-quality capital market transactions involving central bank connectivity or tokenized securities.

Gomez said Vancelian’s enterprise clients preferred stablecoins for payments, investment solutions, on-chain cash management and atomic settlement. He said cash was sensitive for small and medium-sized enterprises (SMEs), making cost reduction and faster settlement important.

The next phase of enterprise digital money will depend on whether regulators, banks and fintech firms can make these systems interoperable, compliant and commercially useful. Stablecoins may provide reach, while tokenized deposits may provide bank-grade control.

Tokenization is beginning to look less like a crypto-market experiment and more like a way to reduce risk in the plumbing of wholesale finance.

One of the clearest use cases is not retail trading or speculative digital assets. It is the automation of collateral movement in repo and derivatives markets, where counterparties still rely on legal agreements, valuation checks and margin calls that can be slow, manual and fragmented.

“The idea is that we can automate the valuations and the process of moving collateral between the two entities in the trade,” said Peter Left, head of digital and market innovation at Lloyds Banking Group. “That is very much what we all benefit from with centralized clearing right now, and that moving margin every couple of hours through LCH [London Clearing House] reduces all of that counterparty credit risk to each other.”

“Not everyone can participate in central clearing,” he said. “This technology allows us to bring the benefits of central clearing and reduce counterparty credit risk to bilateral transactions. That is what excites so many in the repo space and financial markets ecosystem.”

Repo, or repurchase agreement, is a short-term financing transaction in which one party sells securities and agrees to buy them back later. It is widely used by banks, asset managers and other financial institutions to raise cash against collateral.

Left said repo is a strong tokenization use case because margin must move when the price of the underlying bonds changes. Tokenization could make that collateral more mobile, allowing it to move intraday rather than through slower operational processes.

He said Lloyds worked with Aberdeen last year to tokenize one of Aberdeen’s liquidity funds, enabling Aberdeen’s foreign-exchange trading desk to use it as collateral for derivatives trading with Lloyds. The tokenized version allowed full title transfer of the digital fund between the two institutions as market exposure changed.

The next step would be to digitize trading documentation, including International Swaps and Derivatives Association (ISDA) agreements for derivatives and the Global Master Repurchase Agreement (GMRA) for repo.

That could allow valuation and collateral workflows to be automated between the two sides of a trade.

The appeal is practical. If margins can be calculated and transferred more frequently, exposures should be smaller and easier to manage. That does not remove the need for legal agreements, risk models or default procedures, but it suggests tokenization may deliver its first measurable gains in the less glamorous back office of institutional finance.

Real-money pilots

The comments came during a panel titled “Beyond crypto: Where does tokenization deliver measurable efficiency?” at the Financial Times Digital Assets Summit in London on May 13. The discussion was moderated by Laith Al-Khalaf, banking and fintech reporter at the Financial Times.

Their discussion focused on where distributed ledger technology (DLT) can deliver commercial value after years of pilots. One area of momentum is tokenized deposits, especially as banks seek to make digital deposits interoperable across institutions.

Left said he co-chairs the Great British Tokenized Deposit project. It is designed to ensure that tokenized deposits issued by banks such as Lloyds, HSBC, Barclays, Santander, and NatWest can work together.

“This isn’t proof of concept,” he said. “This is real money with real customers across real regulated balance sheets. One of those use cases is not the full home-buying journey, but it is the remortgage journey.”

The pilot links payments from the incoming lender to repayments to the outgoing lender, so the transaction can net off automatically. That could reduce the need for funds to sit with a conveyancer before completion.

Emily Smart, chief product officer, investments, at Aberdeen, said asset managers are approaching tokenization through the lens of client access and fund operations, rather than technology alone. Aberdeen does not currently hold digital assets in its funds, but is looking at whether a fund manager could hold a digital bond and use it as collateral.

“One of the examples we’re early-stage looking at is the ability to hold a digital bond, which the fund manager can then use as collateral,” she said. “We would obviously need our custodian to be able to custody the digital bond as well.”

She said the full benefit depends on several parts of the fund value chain moving together. Aberdeen can already offer front-end tokens, but those tokens still buy a conventional fund unit rather than creating a fully digitally native process.

“If you want to go fully digitally native, you will need all steps in the value chain,” she added. “Where we are today is that we can offer front-end tokens. People could buy a token in our fund, but you’re just buying a token that then buys a unit.”

Tokenized deposits also sit at the center of the debate over whether banks or stablecoin issuers will provide the dominant form of programmable money.

“Tokenized deposits are just deposits of Lloyds and other licensed institutions recorded on a new database technology, distributed ledger technology,” Left said. “They have FSCS [Financial Services Compensation Scheme] protections in the UK. They pay interest, and money today on Lloyds’ balance sheet and NatWest’s balance sheet is interoperable via schemes like Faster Payments and CHAPS [Clearing House Automated Payment System].”

Angus Fletcher, global head of digital solutions at State Street, said the market is unlikely to settle on one form of digital cash soon. Institutions will need to support tokenized deposits, stablecoins, wholesale central bank digital currencies, and upgraded central bank payment systems.

“We need to be able to support tokenized deposits, stablecoins and wholesale CBDC [central bank digital currency],” Fletcher said. “For us, it is largely customer-driven as to why we use one versus another. It is transacting as and when you want to, with cash and assets.”

Collateral and risk

Money market funds may provide another route to commercial adoption because they already align with collateral and liquidity management.

“Tokenized money market funds are really where everyone sees the core business case in the space,” Fletcher said. “It is all back to that collateral element, where being able to use fund units for collateral is absolutely critical.”

He said tokenized money market funds are being used in crypto markets and are increasingly being considered for over-the-counter (OTC) derivatives. State Street’s investment management business also announced a money market fund that accepts stablecoins and supports subscription and redemption outside normal working hours.

Left said the wholesale market needs more consensus around using money market funds as digital collateral.

The UK’s September 2022 mini-budget showed how quickly collateral demands can become a systemic problem for some investors, particularly liability-driven investment (LDI) strategies used by pension funds.

“I’d like to see more consensus in the wholesale market around the utility of money market funds as collateral in digital format,” he said. “If we can mobilize them instantly as collateral in our derivative trading agreements, that would be a real, powerful enabler and a stepping stone for more things to come.”

Private markets are another promising but less mature area. Smart said the use case remains theoretical in many cases, but tokenization could allow investors to buy smaller portions of an asset or smaller units in a private-market fund, where minimum investment sizes have traditionally been high.

“The technology gives you the ability to fractionalize that effectively,” she said. “You can buy much smaller lot sizes of either an asset directly, or you can buy into private-market funds in a much smaller lot size. If there is a secondary market, you can then trade that if you need liquidity.”

But the infrastructure is not yet mature. Smart said pension fund discussions are still focused mainly on long-term asset funds (LTAFs), rather than on tokenization, as a route into private markets. The technology may be ahead of where many clients are today.

Fletcher also warned that technology cannot create liquidity by itself.

“Tokenization doesn’t automatically create a liquid market,” he said. “There have to be buyers and sellers, and the underlying risks still remain the same. Just by tokenizing a building doesn’t make it any easier for it to be liquidated.”

The larger execution risk is fragmentation. He said traditional institutions will need to operate in hybrid markets for the foreseeable future, with old and new rails running side by side.

“We are going to live in a hybrid market for the foreseeable future,” he said. “There is something like 1,000 different blockchains out there, and that is a massive difficulty for us. There is a cost and a cyber risk associated with every single one of those connections.”

He said the industry will also need to decide which blockchains will become dominant once large-scale financial activity moves on-chain.

“Once we start having trillions of dollars worth of activity on a blockchain, we have to ask ourselves, what is a blockchain?” he said. “Is it a financial market infrastructure? Is it similar to something like a cloud provider? Is it a SWIFT equivalent that needs to be regulated in that way?”

For banks, asset managers and infrastructure providers, the near-term question is not whether tokenization can work in theory. It is whether the industry can agree on standards, legal frameworks and operating models before fragmented pilots become another set of costly silos.

Crypto’s old four-year rhythm is losing its grip as digital assets move from a retail-led Bitcoin market into a broader institutional asset class shaped by liquidity, derivatives and balance-sheet flows.

The change does not mean cycles have disappeared. It means the old calendar built around Bitcoin halvings can no longer explain how the market rises, falls or allocates risk.

“The four-year cycle is an artifact of an early market structure,” Anatoly Crachilov, chief executive and founding partner of Nickel Asset Management, said. “The whole narrative emerged from the early days when the market was dominated by one single asset.”

He said there was a mechanical supply shock coming every four years, the whole market was retail-driven and there was very limited institutional balance-sheet participation.

“The cycle has given way to regime,” he added. “Regime is defined by liquidity and balance-sheet provision, not by a mechanical four-year calendar.”

That marks a significant shift for investors who still treat Bitcoin as if it were governed mainly by its programmed supply schedule. In earlier markets, the halving shaped sentiment because a large cut in fresh supply hit a thinner, more speculative market.

Today, crypto has become a multi-asset market. Bitcoin remains the reference point, but it is no longer the only force setting the tone for the entire asset class.

The price history underlines that volatility:

• Bitcoin traded near US$38,000 in May 2021.
• It peaked near US$67,500 in November 2021.
• It fell below US$20,000 in 2022.
• It rose above US$100,000 in late 2024.
• It topped US$125,000 in October 2025, then slid in early 2026.
• It stood at about US$78,335 on May 16, 2026.

Institutional flows, derivatives positioning, treasury-company buying and macro liquidity now play a larger role. Digital assets are also increasingly exposed to the same forces that affect other risk assets, including interest rates and balance-sheet capacity.

Yves Choueifaty, president and chief investment officer of TOBAM, said the historical cycle was real. Bitcoin tended to peak about 18 months after each halving, then fall toward a bottom roughly 24 to 36 months later.

But investors must separate past correlation from future causation. The drivers that made the cycle look mechanical are weakening as the market becomes deeper, more liquid and more complex.

In Choueifaty’s view, earlier bull markets often contained the seeds of their own decline. When momentum strengthened, traders looking for higher beta sold some Bitcoin and bought more fragile tokens.

More mature derivatives and listed vehicles now give investors other ways to increase exposure. Instead of relying mainly on fragile tokens to chase beta, they can use options, treasury companies and other instruments.

That turns the debate from a simple question about whether the Bitcoin cycle is dead into a more useful question for allocators: which liquidity regime are they entering, and who controls the marginal flow of capital?

Halving loses force

The comments were made in London during “Crypto’s Four Year Cycle: Is It Dead?”, a panel at Digital Assets Forum 2026, an event organized by European Blockchain Convention. The discussion was moderated by Stuart MacDonald, chairman of Level III Capital.

The clearest change is the declining power of Bitcoin’s halving. It still matters to market psychology, but several panelists said its direct supply effect has become less important.

“At some stage in the early days, it was a significant part of the daily supply,” Crachilov said. “But now, mathematically, it is completely irrelevant. It is completely dwarfed by ETF [exchange-traded fund] flows, basis trades and treasury acquisitions.”

The reason is partly built into Bitcoin’s design. Each halving reduces new issuance by a smaller absolute amount than the previous one. As trading volumes and institutional flows grow, the new supply shock becomes a smaller part of the market.

“By definition, it is a halving,” Choueifaty said. “The impact was halved every four years. After a series of divisions by two, the remainder is negligible compared with what it used to be in the past.”

Bradley Duke, managing director and head of Europe at Bitwise, said the numbers show why the old model is breaking down. The latest halving created a deficit of about 165,000 Bitcoins entering circulation compared with the previous year.

Digital asset treasury companies bought more than 702,000 Bitcoin in 2025, more than four times the deficit.

“It is no surprise that this four-year cycle is breaking down,” Duke said. “The halving cycle is becoming less important in a scenario where Bitcoin is so much more heavily traded and has an investor base that is much more diverse and sophisticated.”

Options reshape crypto

Derivatives are also changing the shape of the market. Bitcoin’s capital markets have become more sophisticated, with options open interest at about US$60 billion, above the roughly US$52 billion in futures open interest.

“This really is about maturation of the market for Bitcoin,” Duke said. “This is about Bitcoin growing up. It is bootstrapping itself to become a macro asset for the long term.”

Crachilov said the balance between futures and options matters because it points to different investor behavior. Futures often reflect short-term directional trades, particularly in highly leveraged perpetual swaps.

“Nobody goes into a 100-times leveraged perpetual swap to express a view for the next five years,” he said. “Generally, this is for the next hour or the next couple of days.”

Options suggest a more institutional market. They are commonly used for hedging, downside protection and portfolio construction, rather than only for quick directional bets.

“If options open interest suddenly rises sustainably above futures, that means there are different players behind that,” he said. “Generally, that will be an indication of proper institutional capital, which is not just betting on direction but hedging away risk.”

Gold’s old advantage

Bitcoin’s maturation has not yet made it a full substitute for gold in periods of stress. Duke said that recent risk-off markets showed investors still turned to gold rather than Bitcoin when seeking a defensive asset.

“Gold has been around for literally thousands of years,” he said. “It has been a safe-haven asset. It has been a protection against debasement.”

The difference reflects investor behavior as much as technology. Large allocators are still at different stages of education and conviction, so Bitcoin’s safe-haven role will take time to develop.

Duke compared Bitcoin with an asset that can lift returns in rising markets, while gold remains more useful as a defensive cushion when markets fall.

“When markets are going down, gold is a better cushion,” Duke said. “When markets are going up, Bitcoin can boost the performance of that portfolio.”

Choueifaty challenged the assumption that gold is unquestionably more reliable. Gold is difficult to verify properly, especially when used as collateral in financial transactions.

“Gold is not auditable,” Choueifaty said. “People used to criticize Bitcoin by saying it is virtual. In fact, with the gold that is traded in the market, it is much more virtual because nothing has ever been audited.”

Matthew Le Merle, managing partner and chief executive of Fifth Era, said the more important question is whether digital assets can keep moving toward mainstream adoption despite volatility. Fifth Era is an early-stage investor in digital asset companies, including Bitwise, Coinbase, Kraken, Tether and Circle.

“We are about to go through an inflection point in adoption,” Le Merle said. “We have about 500 million digital wallets. We are expecting another 3 billion or so in the next two or three years, as the mass market begins to use digital asset products in their everyday lives.”

Uncertainty could delay adoption if consumers and institutions wait longer to open wallets or use digital asset products. For digital asset platforms, this could affect future cash flows and company valuations.

Developer attention is another constraint. The blockchain sector still relies on a limited pool of elite engineers, many of whom could also work in artificial intelligence (AI).

Le Merle said the industry must also keep top technical talent focused on blockchain rather than losing it to AI. There are only a few thousand top-tier blockchain developers worldwide, and many of them could also work on AI systems.

Francesco Filia, founder and chief executive of Fasanara, said the market should not reduce the entire asset class to Bitcoin’s latest price move. Volatility, liquidations and technical trading conditions can distort short-term signals.

“There is a world beyond the price of Bitcoin,” Filia said. “It acts as the representative for the asset class and the frontrunner. But you can do a lot of real-world applications.”

Those applications include market-making, arbitrage, cross-exchange basis trades and the tokenization of real-world assets. The next phase of digital assets may depend less on proving that a four-year cycle still works and more on building infrastructure that can support everyday financial use.

Tokenization promises faster, cheaper capital markets, but its biggest test may be whether more-automated finance also becomes more fragile.

The debate is shifting from build-out to resilience. The key question is whether market infrastructure can withstand stress once assets, cash and margin calls move around the clock.

“Ultimately, the goal of tokenization is to make the system bigger, better, faster and cheaper. But we should remain mindful that when systems become more efficient, they also tend to become more fragile,” said Hilary Allen, professor of law at American University Washington College of Law. “There will be circumstances in which tokenization’s increased efficiencies ultimately aren’t worth the attendant risks.”

“The increased financialization, speed and automation envisaged by proponents of tokenization all have precedents in the lead-up to the 2008 financial crisis,” Allen said. “The more bespoke and unfamiliar the assets are, the more likely they are to become illiquid in a panic.”

Allen said tokenization could reduce flexibility during a crisis because smart contracts are not typically programmed to handle unexpected events. Automated margin calls could also trigger forced sales, push down asset prices and accelerate market-wide deleveraging.

That warning goes to the center of the institutional debate. Tokenized markets could improve settlement, reduce manual processing and allow assets to move more quickly. The same design could make markets more rigid when discretion is needed.

Victor Jung, managing director and head of digital assets at Hamilton Lane, took the opposite view. Tokenization can cut friction in private markets, where onboarding and subscription processes can still involve long documents, slow approvals and high minimum commitments.

Jung said tokenization could reduce a US$10 million minimum commitment in private markets to US$500, opening access to an asset class that has historically been limited to institutions and wealthy investors.

For Jung, the issue is not whether blockchain should replace regulation. It is whether compliant and permissioned structures can make old market processes faster and more accessible. Private-market investing remains difficult for many investors, even as they can buy volatile crypto assets or concentrated public equities.

That creates the strategic fault line for financial institutions. Tokenization could become an operating upgrade for capital markets, but only if the legal, risk-management and investor-protection framework develops as quickly as the technology.

Stablecoin catalyst

The debate took place at the Financial Times Digital Assets Summit in London on May 13. Moderated by Claer Barrett, consumer editor at the Financial Times, the session asked whether tokenization would transform capital markets within five years, or whether meaningful adoption remains a decade away.

Jung said stablecoins could be the catalyst that moves tokenization from pilot projects into functioning market infrastructure. Stablecoins are digital tokens usually designed to track the value of fiat currencies such as the US dollar.

“Stablecoins feed tokenization. Without stablecoins, tokenization is just not complete,” Jung said. “If you have no cash, no fiat, you cannot buy anything. You need to have the cash there.”

Fiat money is government-issued currency that is not backed by a physical commodity such as gold.

“There is a verdict from TradFi and DeFi that stablecoins are a better way,” Jung said. “It is money reformatted through blockchain rails that is cheaper, better, faster.”

TradFi (Traditional finance) covers banks, asset managers and other established financial institutions. DeFi (Decentralized finance) describes blockchain-based financial services that use code, smart contracts and digital wallets rather than conventional intermediaries.

Jung added that stablecoins provide the cash leg needed for tokenized assets to trade, settle and move across markets. Without that layer, tokenization remains only half of the infrastructure needed for practical adoption.

Stablecoin market capitalization had grown from about US$10 billion six years ago to at least US$300 billion, he said. Stablecoin transaction volumes had reached tens of trillions of dollars in 2025, exceeding Visa’s annual transaction volume.

The adoption argument is not limited to stablecoins. Traditional finance, decentralized finance, retail platforms and business-to-business platforms are all moving toward blockchain-based assets, although their reasons differ.

That momentum does not remove the hardest barriers. Tokenization is often discussed as a technology problem, when the deeper issue is legal recognition, market governance and regulatory alignment.

“When we are assessing the prospects for tokenization, it is important to appreciate that the technology is usually the easy part,” Allen said. “The harder challenge is the legal uncertainty about the alignment between the ownership of the token and the ownership of the real-world assets.”

“You don’t need a public, permissionless blockchain for tokenization,” Allen said. “It is the features of programmability and composability that people are excited about, using smart contracts to automate transactions.”

Physical assets pose a legal problem because ownership of a digital token must align with ownership of the underlying asset. If a painting changes hands outside a ledger, the ledger alone cannot solve the ownership dispute.

Mortgages face a similar challenge. Putting mortgages on a blockchain does not remove the need for legal systems to recognize title by registration. Cross-border tokenized deposits face another barrier: multiple countries would need to accept one ledger as compliant with anti-money laundering (AML) rules.

Retail risk

The private-market access argument gives tokenization a practical use case. It also raises a policy question: whether broader access to complex products is always a public good.

Jung said tokenization could open private-market exposure to investors excluded by high minimum commitments and manual subscription processes. Blockchain can reduce friction while keeping access inside compliant structures.

Allen challenged that framing, especially in the US retirement and savings market.

“I honestly find the financial inclusion arguments about tokenization, at least in the United States, very frustrating,” Allen said. “The issue is not that they do not have good investment opportunities. It is that we do not have a social safety net.”

Many US households live paycheck to paycheck, so the priority is not necessarily to give them access to more complex and riskier assets. The concern is that financial-inclusion language may be used to justify putting private equity and crypto-linked products into ordinary savings plans.

Retail exposure becomes more sensitive when tokenized markets trade around the clock. Ordinary investors may not have the same risk-management capabilities as hedge funds or professional trading firms.

“Retail investors are being encouraged to invest in 24/7 trading markets where, if they are margined, they can be liquidated while they sleep,” Allen said. “That is fine for a hedge fund that has 24/7 capabilities. It is not fine for your average person who does need to sleep.”

Automated markets also depend on operational resilience. Smart contracts rely on programmers to avoid code errors and security vulnerabilities. They also rely on oracles, or external data feeds used by smart contracts. Those feeds can become targets for manipulation or technical failure.

Allen said a problem in October 2025 involving an oracle maintained by Binance triggered mass liquidations in crypto markets. Her broader warning was that automation does not remove accountability. It shifts trust to coders, data feeds, cybersecurity controls and recovery procedures.

That caution appeared to land in the room. Before the debate, 88% of the audience voted that tokenization would transform capital markets within five years, while 12% disagreed.

After the speakers had made their case, support fell to about 67%. The shift showed that even at a digital-assets summit, the strongest argument for tokenization must be matched by a convincing operating model for regulation, resilience and investor protection.

Tokenized assets are entering mainstream finance faster than the systems needed to verify who controls them, how they work and whether the infrastructure beneath them can be trusted.

That gap is becoming harder to ignore as stablecoins, real-world assets and blockchain-based market rails move from experiments into regulated financial products. The next question for institutions is not only what can be tokenized but also whether the underlying system can establish trust before risk reaches clients.

“Markets do not scale on technology at all. They scale on trust. It is no longer about who tokenizes first. It is about who thinks about trust as an architectural shape,” said Frederik Gregaard, chief executive of the Cardano Foundation.

“The real bottleneck for institutional adoption is whether institutions can trust the rails beneath the interface. This is no longer about getting headlines and doing proof-of-concept projects,” he said.

Stablecoins and tokenized assets are already operating at scale, with large asset managers including Fidelity, BlackRock and Franklin Templeton looking at tokenization and new financial rails. The concern is that the trust layer supporting those markets is not developing at the same pace.

For financial institutions, the problem is practical. Blockchain developers can tell investors, auditors and business executives to inspect the code, but that answer does not work when a major network can contain hundreds of thousands of lines of code.

Gregaard said Cardano alone has about 900,000 lines of code. A new generation of users may ask a large language model to identify where trust is established in that code, but that raises another problem: artificial intelligence (AI) systems cannot be blindly trusted either.

That turns verification into a board-level infrastructure question. If tokenized funds, stablecoins and programmable assets are to become part of real financial markets, institutions will need to prove identity, authorization, code integrity and governance in ways that non-developers can audit.

The shift also changes the competitive benchmark. Speed, cost and product launches remain important, but they do not answer whether a counterparty is authorized, whether a smart contract is accountable or whether a digital asset can survive scrutiny from regulators, auditors and institutional clients.

Identity infrastructure

Gregaard made the comments during a keynote titled “The Integrity Economy: Why Digital Trust is the New Global Currency” at Digital Assets Forum 2026 in London. The event was organized by the European Blockchain Convention.

The Cardano Foundation is a Switzerland-based organization that supports the development and adoption of Cardano, a public blockchain network. The keynote framed digital trust as a core operating requirement for financial institutions and enterprises, not only a reputational issue.

Gregaard said the next phase of competition will depend on whether companies can make digital integrity part of their operating systems, not just their public image.

That shift matters as digital assets move into real financial infrastructure. Companies can no longer treat trust as a message to customers after a product is launched, because verification must be built into the system from the start.

He said companies are moving into an environment shaped by AI, machine learning, automated execution and regulated digital markets. In that setting, trust cannot rely only on manual governance, audit logs, release controls or checks carried out after systems have already been built.

The pressure is especially clear in institutional decentralized finance (DeFi), real-world assets and enterprise pilots moving into production. These markets need to know who is behind a smart contract, who has authority to act and who can be held accountable if something fails.

That is why digital identity sits at the center of the infrastructure debate. Identity should not be treated only as compliance, but as an economic infrastructure that supports verifiable credentials, decentralized identity, and transparent governance.

“This is not about wallets. It is about entities, permissions and accountability. Who is acting, who is authorized, and how can I prove that the person who was acting and was authorized really is that person?” Gregaard said.

He added that the identity layer also needs to work across infrastructure. A system tied only to one blockchain would force institutions into one network, while banks, asset managers and enterprises need identity tools that can operate across different business models.

That makes off-chain verification an important part of his model. The identity system must remain verifiable, but it cannot be locked into a single chain if it is to support broad institutional use.

One example is the Legal Entity Identifier (LEI), a 20-character code used to identify legal entities. Its digital counterpart, the verifiable Legal Entity Identifier (vLEI), is designed to help counterparties verify the identity, authority and role of people acting for an organization.

The same logic applies to administrative efficiency. Stronger digital identity systems can reduce friction in public-sector and financial-market processes by making entities, credentials and permissions easier to verify.

Real-time auditability

The urgency is increasing as AI changes the cost and speed of digital deception. Companies now operate in an environment where deepfakes, AI-generated data and automated execution make authenticity harder to prove.

“We now live in an age of deepfakes, AI-generated data and automated execution. The ability to prove authenticity is the new currency,” Gregaard said.

“Every single company here that is successful will have bots and AI trying to take down your brand and trying to share misinformation. The cost of doing that is close to zero, and it is becoming a commodity,” he said.

AI-generated data is already outpacing older verification tools. Governance frameworks that depend mainly on human review, manual sign-offs and conventional audit logs may struggle as AI agents begin to act across enterprise and financial systems.

For those agents to execute safely, they will need verifiable identity, delegated authority and clear operating conditions. Otherwise, companies may struggle to prove whether an action came from an authorized system, an altered system or a malicious imitation.

That is where auditability becomes a business requirement. Legacy systems were not designed for round-the-clock liquidity, cross-border digital value or real-time auditability, but digital markets now increasingly require all three.

“Legacy systems were not designed for 24/7 liquidity, cross-border digital value or real-time auditability. But the world we are working in now requires that,” Gregaard said.

Clients and stakeholders increasingly expect faster verification of assets, balances and processes. Waiting many months for traditional audit cycles is becoming less compatible with markets that can move continuously across borders and asset classes.

The long-term goal is not only to speed up audits. It is to enable auditors and stakeholders to review larger data populations rather than relying mainly on spot checks, creating a more continuous assurance layer for digital markets.

Cardano’s own positioning is built around formal methods, peer-reviewed research and decentralized governance. That company context matters, but the broader argument is that financial infrastructure will increasingly need trust designed into the architecture from the beginning.

“The future of infrastructure, specifically critical infrastructure, belongs to the verifiable. To be verifiable means that you need to have it in your architecture,” he said.

If tokenization continues to expand, institutions will need to prove more than ownership of a digital asset. They will need to prove the identity, authority and audit trail behind the systems that move it.

AI agents may eventually need a new payment layer, but the technology is still caught between ambition and usability.

The rise of autonomous software has revived interest in blockchain-based payments, as companies explore whether agents can make purchases, settle transactions and move money without relying on conventional card networks. Yet the early experience remains far from seamless.

“Agentic payments for stablecoins, there is usually hype, to be honest. I am very bullish on this because I feel this is much needed, but it is early,” said Michael Shaulov, chief executive officer of Fireblocks. “If you try to book an Uber with an agent or a ticket, it will be a very annoying experience compared with doing it yourself through the app. I think that needs to change.”

Fireblocks is a digital-asset infrastructure company that provides custody, settlement and tokenization technology to banks, fintech firms and crypto businesses.

Shaulov said the company had been investing in agentic finance and was preparing to launch agentic payments and commerce capabilities.

For him, the problem is not only whether an AI agent can complete a task. It is whether the payment instrument is suitable for software that may need to act automatically, settle quickly and work across digital platforms.

“For agents, it is pretty clear that the means of payment would not be a credit card. It will be something like a deposit or stablecoin, but it will be something that runs on a blockchain,” he said.

That view links agentic commerce with a broader debate over digital money. A payment card was designed for human-led commerce, while tokenized deposits and stablecoins can be programmed, moved and settled across blockchain-based systems.

But he added that agentic payments also face an infrastructure gap. Retail users already have access to trading apps and digital wallets such as Robinhood, Revolut and MetaMask, while younger investors are comfortable holding tokenized assets through those channels.

The institutional side is less mature. He said only a limited group of traditional custodians can hold and operate tokenized assets for professional investors, making infrastructure a bottleneck for more complex products.

“If we are talking about institutional investors, there is currently a very limited set of traditional custodians that can hold those assets and operate on those assets. It is a limiting factor,” he said.

That gap matters because the next stage of agentic finance may depend on whether regulated institutions can safely hold tokenized instruments, not simply whether AI agents can press a digital checkout button.

The comments were made during a panel discussion titled “Institutional digital assets: What’s real, what’s ready, what’s next?” at the Financial Times Digital Assets Summit in London on May 13. The session was moderated by Nikou Asgari, digital markets correspondent at the Financial Times.

Other speakers included John O’Neill, group head of digital assets at HSBC, and Kathleen Wrynn, global head of digital assets at Invesco.

Hong Kong app test

The Hong Kong Monetary Authority awarded its first stablecoin issuer licenses in April 2026 to HSBC and Anchorpoint Financial, a venture backed by Standard Chartered. Anchorpoint is jointly owned by Standard Chartered Bank (Hong Kong), Hong Kong Telecommunications and Animoca Brands.

O’Neill told TechJournal.uk in the question-and-answer session that HSBC’s planned Hong Kong dollar stablecoin was expected to launch in the second half of 2026. He was asked what a user could do with the stablecoin inside a payment app.

“We’ll look at the second half of this year. As I said already, it is a Hong Kong dollar stablecoin, and it is going to be embedded in our apps in Hong Kong,” O’Neill said.

The answer suggested that the stablecoin will not be positioned only as a wholesale settlement tool. HSBC plans to integrate it into its existing apps, which are widely used in Hong Kong.

“You’ll be able to use it within the existing apps for payments, and you’ll be able to invest in tokenized retail assets within the apps,” O’Neill said.

Some of HSBC’s Hong Kong apps are payment-only at present. The bank could leverage that screen presence to distribute tokenized assets, allowing users to pay for them with stablecoins.

That gives the Hong Kong dollar stablecoin a broader role than just simple person-to-person payments. It could become a bridge between bank apps, tokenized investment products and regulated digital money.

The point also supported a broader industry shift. Stablecoins are no longer just pilot projects or sandbox experiments.

“Stablecoins are definitely far outside of any pilot. Last year, stablecoin transactions exceeded the combined volume of Visa and Mastercard globally,” Shaulov said.

He said Fireblocks processed about US$6 trillion in settlement volume over the previous 12 months, with stablecoins accounting for about 65% of transactions, up from roughly 40%.

Stablecoin adoption, he said, began with people in developing countries using wallets to protect against inflation. It then moved into import-export settlement, remittances and more recently capital-market infrastructure.

“Different people have different opinions about Bitcoin and crypto. But when we look at stablecoins and digital-asset-based payment tokens in general, this is not a speculative type of activity,” he said.

He stressed that stablecoins make transactions faster, cheaper, more transparent and more accessible. The clearest institutional driver is no longer the price of Bitcoin but the arrival of clearer regulation and real payment use cases.

Collateral meets risk

Banks are also building another form of digital money: tokenized deposits. O’Neill described them as the digital version of commercial bank money, rather than a direct replacement for stablecoins.

“What we are doing is tokenizing the commercial bank money we create all the time, every day, right now. It is very accessible. It is very liquid. It is multi-currency,” O’Neill said.

In the existing financial system, central bank money provides the foundation while commercial banks create liquid money used by households, companies and financial institutions.

O’Neill said tokenized deposits fit within existing Know Your Client (KYC) and anti-money laundering (AML) systems, making them easier for regulated financial institutions to adopt. HSBC had spoken with many central banks and had not found objections to tokenized deposits as a market development.

The bank is linking tokenized deposits to its Orion platform, which is used for digital bonds and other tokenized assets. The aim is to allow digital money to settle digital assets more efficiently.

“Interoperability is the cliché for digital assets. What practically matters is access and liquidity,” O’Neill said. “It is important that different forms are interchangeable and can pay each other.”

He said bonds, gold and funds can all become more useful if their ownership can be transferred rapidly and precisely.

“Collateral is a really important theme,” he said. “Bonds and gold are widely used collateral assets. Funds have the potential to be widely used collateral assets.”

Shaulov said trading firms had begun changing how they post collateral, shifting from stablecoins to tokenized money market funds. Some of those funds now sit in exchange collateral wallets.

Wrynn said tokenization should be seen as an evolution, not an overnight replacement of existing financial systems. Tokenized repos are a promising use case, but blockchain-based systems still need to integrate with existing lending and market infrastructure.

Shaulov said security remains the largest constraint on adoption as major incidents can damage institutional confidence more than ordinary crypto-price volatility.

“The drop in the price of Bitcoin or Solana is something the market has gotten used to. But US$1.2 billion or US$1.3 billion evaporating from an exchange is something people are very scared of,” he said.

He cited decentralized finance (DeFi) attacks, AI-enabled cyber threats and North Korea-linked groups such as Lazarus as risks for the sector.

Wrynn said DeFi attacks also affect institutions' ability to engage with decentralized protocols.

The next phase of institutional adoption will therefore depend less on announcements and more on execution. Regulated money, tokenized assets, custody, liquidity and security all need to work together before digital assets can move deeper into mainstream finance.

Tokenized finance is moving toward a decisive market-structure question: whether new digital rails will open financial markets or rebuild today’s clearing and settlement systems on the blockchain.

The answer may determine who controls future capital flows and how quickly assets can move. It may also decide whether startups can build new financial services without having to recreate the existing system's expensive back-office machinery.

“What we’re seeing at Offchain Labs, as we talk to teams about building out rails, reminds me a lot of the early days of building out the internet’s data rails,” said Ed Felten, co-founder and chief scientist at Offchain Labs, a blockchain infrastructure developer. “What we ended up with was something in the middle that combined most of the advantages of both extremes.”

Felten said crypto rails may follow a similar path, with a limited number of major corporate-controlled networks operating inside a system defined by openness and interconnection.

He said the early internet did not become either a fully peer-to-peer network of home computers or a single closed proprietary garden. It became a system built and operated by companies, but shaped by openness, standards and interconnection.

That analogy matters because tokenized finance faces the same trade-off. Corporate-backed rails can attract capital investment and scale. Open standards can allow users on different networks to interact as if they were operating within a single financial universe.

Basil Al Askari, founder and chief executive of MidChains, a UAE-based digital asset trading and custody platform, warned that the industry could otherwise end up close to where it began.

“We could end up where we started, where essentially the clearing infrastructure that currently exists ends up owning the rails and fundamentally, nothing really changes,” Al Askari said.

Alastair Sewell, senior investment director at Aviva Investors, the asset-management arm of UK insurer Aviva PLC, framed the issue as a battle over control of capital markets.

“It is probably the most fundamental economic question of the day,” Sewell said. “Who controls where that capital flows?”

The risk is not only incumbents' dominance. It is that digital assets become a new wrapper for old processes, rather than a new operating layer for finance.

Felten said programmable rails can do more than move assets. They can let developers build functions directly on top of the infrastructure and connect them. That would resemble the way internet applications were built on top of basic data rails.

“The rails were the enabler, but it was the innovation beyond that that was really revolutionary,” he said. “If they remain open enough and standards-based enough, we’ll get the benefits of openness and reduce barriers to entry.”

Stablecoin dilemma

The discussion took place in London during a panel titled “Who Owns the Rails of Tokenized Finance?” at Digital Assets Forum 2026, a digital assets industry conference organized by European Blockchain Convention. The session was moderated by Su Carpenter, executive director of CryptoUK, a UK trade association for the crypto and digital asset sector.

The contrast with tokenized funds remains stark. Sewell said global money market fund assets stand at about US$13.5 trillion, compared with about US$12.5 billion in tokenized money market fund assets.

That makes tokenized money market funds small by comparison.

But the strategic risk is that incumbents focus only on a profitable existing business while a new market structure develops around them.

“This is the classic innovator’s dilemma. We’re facing a business which is great. We can invest more in it. We can listen to our customers. It will do well,” he said. “If we don’t do this, we run the risk of being severely disrupted in our future activities. This is why it matters, and this is why organizations like Aviva Investors need to be bold.”

Bringing a tokenized product to market still requires outsourced technology reviews, legal work and other complex project steps. For traditional asset managers, the question is whether the cost of learning now is lower than the cost of falling behind later.

“Tokenization is an enormous opportunity,” Sewell said. “It’s a bridge. It’s the opportunity for traditional investment managers to mitigate that risk of being materially disrupted.”

Al Askari said they remain the clearest working example of tokenized finance, both for investment activity and for small and medium-sized enterprises making cheaper cross-border payments.

“When we talk about tokenization, we forget that the real, proven and successful use case that we’ve seen so far is stablecoins,” he said. But stablecoin growth is also creating fragmentation risk.

He said regional banks are beginning to issue their own stablecoins. That could leave users dependent on specific banking relationships when they want to redeem those tokens.

“What I’m seeing is too many new stablecoin issuers popping up, and it is starting to become a risk,” he added. “You have to hope that MidChains either has a relationship with that bank or you directly have a relationship with that bank. It becomes a very convoluted way of taking your transit currency and going back into fiat currency.”

Aviva Investors is interested in using stablecoins for mutual fund subscriptions and redemptions.

But institutions need an objective framework, tested with third parties, to decide which stablecoin best serves clients and mitigates risk.

“We are specifically interested in the use of stablecoins as a means of subscription and redemption to mutual funds,” Sewell said. “But that does pose a very important question, which is: which stablecoin?”

KYC bottleneck

The biggest barriers remain legal and operational.

Anoosh Arevshatian, chief product officer at Zodia Custody, a digital asset custodian and wallet platform, said digital rails raise fundamental questions about who holds legal title, how a digital asset is recognized, and which legal form applies across jurisdictions.

“One of the examples that comes to mind is legal clarity,” she said. “Depending on the jurisdiction you’re in, who has legal title? How is that digital asset recognized?”

The UK and Abu Dhabi are relatively advanced. But many jurisdictions do not follow the same principles, making cross-border tokenized finance harder to scale. Compliance is another brake on adoption.

Sewell said anti-money laundering (AML), Know Your Client (KYC) and client-understanding obligations are mission-critical for institutional investors.

“Compliance, AML, KYC, and understanding our clients are mission-critical for us,” He said. “If there is a failure there, then that is not only a reputational risk. It becomes a strategic risk.”

KYC and AML rules differ across jurisdictions and sometimes across license types inside the same jurisdiction. Those differences can make it difficult to move assets between counterparties or issue tokenized assets for international projects.

“KYC is not the same in Dubai as it is here in the UK,” Al Askari said. “Those slight variations can create big challenges when you’re trying to deal with takers or makers outside of your home jurisdiction.”

Private markets remain one of the more practical opportunities.

Al Askari said he entered digital assets after working in private equity because he wanted to make private-market liquidity more efficient.

“One of the reasons I got into this space is to find ways to make private market liquidity more efficient,” he said.

MidChains wants to use its licenses to create a market where participants can trade tokenized assets that may represent individual assets or funds.

Those products would have to meet risk criteria being developed with regulators. The end-state, however, may be infrastructure that most users barely notice.

Arevshatian said users who are not speculating do not care which chain underpins an asset.

“The end users, if they’re not looking at speculation, don’t really care about what chain it is,” she said. “They need to have all of this abstracted away to be like any other asset.”

Felten described the desired system as low-cost, fast and trusted, with settlement moving beyond T+1 toward near-instant execution. Standards and best practices will also be needed before institutions and end users can trust the technology at scale.

Tokenized finance will therefore be judged less by the language of blockchain than by the operating discipline of market infrastructure. The winners will need to combine speed, compliance, liquidity and trust without turning the new rails into a closed version of the old ones.

Britain has named digital assets as the engine of a financial services revival — and it is now putting legislation behind that ambition.

The UK government used the King’s Speech on May 13 to introduce the Enhancing Financial Services Bill, a sweeping package designed to modernize regulation and restore growth to a sector that has stagnated in real terms since 2010. Digital assets and payments reform sit at its core.

“This financial services bill contains major reforms that are going to drive growth in our financial services sector, and therefore for the whole of our economy,” said Lucy Rigby, Economic Secretary to the Treasury. “I could not be more focused on getting on with the major reforms that this country needs.”

“We are providing firms with the certainty that they need,” Rigby said. “They have been calling for some time to get that legislation down. They were very pleased when we got there. It gives us that certainty of landscape that we know people need to be able to invest in this country and grow.”

The bill, one of around 35 pieces of legislation in the King’s Speech, delivers key parts of the Leeds Reforms announced by Chancellor Rachel Reeves in July 2025. It will consolidate the Payment Systems Regulator within the Financial Conduct Authority (FCA) and cut the burden of the Senior Managers and Certification Regime by 50%.

Despite being the world’s largest net exporter of financial services — with exports totaling £102.2 billion in 2025 — the sector has not grown in real terms since 2010, lagging behind several international financial centers that recovered more strongly after the global financial crisis (GFC) in 2008.

Rigby outlined three policy pillars underpinning the digital assets push:

• Payments: modernizing and streamlining payments regulation, with a consultation announced during FinTech week due imminently. It will cover stablecoins and AI agents within the payments space.

• Wholesale markets: digitalization of capital markets through an active sandbox, an upgrade of the Bank of England’s Real-Time Gross Settlement (RTGS) system to enable near 24/7 settlement, and the ambition to become the first G7 country to issue a digital gilt.

• Crypto assets legislation: new rules, laid earlier in 2026, to bring crypto assets under the Financial Services and Markets Act (FISMA) framework, with the FCA authorization portal set to open later this year.

Transatlantic race

The keynote interview took place at the Financial Times Digital Assets Summit in London on May 13. Martin Arnold, the Financial Times' financial regulation editor, interviewed Rigby on the UK’s digital assets agenda.

Rigby came to her ministerial role after a long career as a City solicitor, and she frames the digital assets moment in the broadest historical terms.

She traced Britain's financial prowess back to its seventeenth-century roots, framing the current era as the latest chapter in a long history of market evolution. This legacy of constant adaptation serves as the foundation for the next great shift in global trade.

“Right from the coffee houses on the banks of the Thames in the 1650s, we have continually embraced innovation,” she said. “Digital assets are the next wave of innovation, and I am very ambitious for this country in this area.”

The interview came against a backdrop of growing pressure on the UK to demonstrate it can compete with the United States and Europe.

Arnold cited criticism from former Chancellor George Osborne, now working for a large crypto group, who has warned the UK risks being left behind. He also referenced Dante Disparte, Chief Strategy Officer of Circle, one of the leading US stablecoin issuers, who wrote in the Financial Times that stablecoins are becoming too important for the UK to lag behind.

Rigby pushed back on Europe first. The UK had deliberately chosen not to replicate certain elements of the EU’s Markets in Crypto-Assets Regulation (MiCA), which came into force in 2024.

“When you look at what’s going on in Europe, you have MiCA in 2024, but there are elements of that which we didn’t emulate in our legislation,” she said. “Our approach is the better one.”

On the United States, she acknowledged a spirit of optimism but noted that American rules around market structure have not yet cleared the legislative process.

“In the States, there is a spirit of real optimism around this area, and that is a real positive,” Rigby said. “But when you look at the need for rules around market structure to come forward, those rules haven’t yet made it through the legislative process. I think we are in a very good place relative to other jurisdictions.”

That confidence is also shaping the UK’s international posture. A transatlantic task force covering digital assets and capital markets has been established between the UK and the US. Rigby said the Treasury regards the digital assets strand as particularly important.

She declined to pre-empt announcements expected later in 2026 but indicated outcomes could include regulatory recognition or alignment.

“It is right to acknowledge where we are in terms of development of regulation regimes and this technology more broadly, and be looking to minimize frictions,” she said. “That may well take the form of some forms of recognition or alignment. It is not just the States — we are looking at where we can ensure maximal cooperation.”

Sterling stablecoin push

One of the most commercially watched elements of the agenda is the development of a GBP stablecoin. Four firms are currently in the stablecoin cohort of the UK’s financial market infrastructure sandbox, each expressing a clear intention to launch.

“We have four firms at the moment within the stablecoin cohort of the sandbox, and they have each said they are keen to get a GBP stablecoin out there,” Rigby said.

The Digital Gilt Instrument (DIGIT) remains a centerpiece of the wholesale markets pillar. Rigby said it is intended to do more than demonstrate technical capability — it is meant to catalyze broad adoption of distributed ledger technology (DLT) across UK markets.

“We believe that the digital gilt has the ability to catalyze wide use of DLT,” she said. “The interoperability piece is really important — collateral, secondary trading. There are all sorts of stuff this can catalyze.”

The interview also touched on the political turbulence surrounding the agenda. The Labour Party had suffered a poor set of local elections on May 7, and the fallout intensified in the days that followed, with an internal party crisis that brought Prime Minister Keir Starmer close to stepping down, raising fresh questions about the government’s stability.

Arnold pressed her on whether that instability could derail momentum on financial services reform. She acknowledged the pressure directly.

“It has been a tough 48 hours,” Rigby said. “My party had a pretty abysmal set of local elections, and people have rightly questioned why and questioned the leadership. But we have a process for triggering a challenge to the leader, and that process has not been triggered.”

She acknowledged the instability had market consequences but said her focus remains entirely on the legislative agenda.

To those in the industry sending pointed messages about the pace of reform, Rigby said she welcomes every one of them. The pressure, she said, comes from the right place — a shared desire to see the UK move quickly and attain a global leadership position in digital assets.

Her message to skeptics weighing whether to back the UK was equally direct: continued engagement and shared ambition have already brought the country to a strong position, and that is how it intends to press forward.

The next crypto allocation cycle may be driven less by speculative token prices than by the migration of financial market infrastructure to blockchain networks.

After a sharp market pullback in October 2025 and a year of weaker liquidity, the deeper opportunity in 2026 may lie in tokenization, stablecoins and the use of digital assets to make collateral, payments and real-world assets easier to move and manage.

“The plumbing is being upgraded. Everything in financial markets, everything being done digitally, is being upgraded,” said Ray Dillet, head of financial institutions, Europe, at Bitwise.

Stablecoins are best understood as tokenized payments that can move faster and more cheaply than legacy systems. Tokenization extends that logic to assets, collateral and other parts of financial-market infrastructure.

Dillet said tokenization would make real-world assets machine-readable, allowing assets such as hotels, houses and railroads to be used more efficiently in digital portfolios.

He said investors should look beyond the recent drawdown in Bitcoin and other digital assets. Adoption of blockchain technology had continued across Bitcoin, Ethereum, stablecoins, tokenization and smart contracts, while excess leverage and exuberance had been removed from the market.

That distinction matters for allocators because tokenization is increasingly being discussed as a financial infrastructure question rather than only a crypto-native investment theme. The shift is moving attention from token prices to cash flows, settlement, collateral, payments and the networks on which those activities may run.

Stablecoins are one of the clearest examples. Dillet said they had reached about US$33 trillion in transaction volume, up 74% year on year, showing that tokenized payments were already operating at scale.

He cited Tether as an example of how large stablecoin issuers had become in traditional asset markets. The company had become a major buyer of short-term US Treasuries and gold, while also holding a large Bitcoin position.

Barry Thomas, partner and managing director at Forteus, said the strongest growth in tokenized assets had come from tokenized treasuries rather than equities, private credit or real estate.

“The growth in tokenized assets has really come from tokenized treasuries,” he added. “The clear advantage is the ability to make improvements in collateral management and capital efficiency.”

Decentralized stress test

The comments were made at the Digital Assets Forum 2026 in London during a panel titled “Where Are the Opportunities for Crypto Allocation in 2026?” The event was organized by the European Blockchain Convention.

Carl Szantyr, managing partner at Blockstone Capital, moderated the discussion with Dillet, Thomas and Christopher Siedentopf, head of business development at Qapture Investment.

The panel took place against a difficult market backdrop. Bitcoin had fallen sharply from its previous high, liquidity had weakened and investors were still assessing the damage caused by a major liquidation event last year.

On October 10, 2025, the cryptocurrency market suffered its largest flash crash on record after US President Donald Trump announced a 100% tariff on Chinese imports. More than US$19 billion in leveraged positions were liquidated within 24 hours. Bitcoin dropped sharply and more than 1.6 million trader accounts were wiped out.

The market shock changed retail investors’ behavior and damaged confidence in parts of the sector.

“If you go back to October 10, I want to put it into perspective. It was more harmful to the industry than FTX,” Siedentopf said. “Retail completely disappeared. [Investors] got burned really badly.”

FTX, a Bahamas-based cryptocurrency exchange, filed for bankruptcy in November 2022 after a surge in customer withdrawals exposed a US$8 billion hole in its accounts.

Some retail capital that might otherwise have gone into crypto was attracted by leveraged trades in silver and gold. Yet decentralized finance (DeFi) infrastructure outperformed the market narrative suggested. It could attract wider adoption from both retail and institutional investors if the sector has the right infrastructure in place.

“DeFi has weathered these storms really well. The infrastructure performed as it should have done,” Siedentopf said. “There is just a perception change that needs to take place.”

That view sits alongside the broader tokenization argument. As assets, payments and financial applications move on-chain, investors have to decide which ecosystems are most likely to benefit.

For traditional finance users, tokenized treasuries may provide the first practical bridge. Their growth reflected a need for better use of collateral and greater capital efficiency, Thomas said.

He said tokenized treasuries are also easier for institutions to understand than more speculative digital assets. Their rise suggests that the next phase of crypto adoption may begin with low-risk assets and operational efficiency, rather than with new token launches or retail trading cycles.

Options take shape

The opportunity set for active managers is also changing after a difficult year for short-term trading strategies.

The difficult market backdrop also hit active managers. Thomas said 2025 had been hard for liquid and market-neutral strategies, especially through the third quarter, as macro uncertainty, geopolitical risk, lower trading volumes and weaker liquidity created a more fragile trading environment.

“What we saw was a gradual reduction in liquidity and volumes throughout the year,” he said. “Then what we saw in October was a significant stress event and liquidation event in the crypto space.”

Short-term strategies were hit particularly hard, especially in December, when statistical arbitrage and other short-term trading strategies struggled amid thinner volumes and abrupt price moves in smaller tokens.

At the same time, the development of new instruments could create fresh opportunities for more sophisticated managers.

One area to watch is the growth of crypto-linked options. Liquidity was improving in options on Bitcoin exchange-traded funds (ETFs), although the market remained in an early stage.

“The options market is still relatively new and immature, but that is changing,” Thomas said. “You are going to get more structured products, as you see in traditional finance.”

Digital asset treasuries may add to that shift as companies and funds look beyond simple buy-and-hold exposure. Some may use options strategies to generate yield on Bitcoin or other digital assets held on their balance sheets.

“You have the dynamic of the digital asset treasuries, which are having to evolve from a buy-and-hold strategy to actually generating a yield on their treasuries,” he added. “That typically involves call overwriting.”

Greater use of options could compress volatility and create opportunities for volatility arbitrage, a strategy that seeks to profit from differences between expected and realized price swings.

For allocators, the core message was not to rely on a single exposure. The market now includes passive Bitcoin products, active strategies, directional funds, market-neutral managers, stablecoin yields and DeFi opportunities.

“From an allocator's point of view, diversification is a must,” Siedentopf said. “Whether you are comfortable holding Bitcoin and running a yield on it, or whether you want to go directional, market neutral or stablecoin yields and DeFi, there is a lot on offer.”

“It is very rare that you find an allocator who only goes into one strategy in one space and goes all in,” Dillet said. “They generally diversify. Having exposure across the board is exciting.”

The next allocation cycle will test whether digital assets can move from price-led speculation to infrastructure-led adoption. For investors, the challenge will be separating durable financial plumbing from crowded trades, weak liquidity and operational risk.

Photonic Inc has closed an investment round of more than US$200 million, giving the Vancouver-based quantum computing company a post-money valuation of US$2 billion.

The final close brings Photonic’s total capital raised to more than US$350 million, the company said on Tuesday. The round was led by Planet First Partners, a UK-based sustainable technology growth equity firm, and included new investors Business Development Bank of Canada (BDC), Export Development Canada (EDC), Bell Ventures, Firgun Ventures, InBC Investment Corp. and existing investor Mubadala Capital.

Photonic said the round’s first close, announced in January, had attracted strategic investors Royal Bank of Canada and TELUS, a Canadian telecommunications group, along with returning investors British Columbia Investment Management Corporation and Microsoft. It said the investor base reflected support across Canada, Europe, the UK, the US and the Middle East.

The funding comes as quantum computing companies race to develop machines that can move beyond laboratory demonstrations and support commercial workloads. Photonic is developing distributed quantum computing systems using what it calls its Entanglement First architecture, which combines silicon-based qubits with photonic connectivity. The company says this could allow quantum systems to scale across existing telecom infrastructure.

“This financing unites government, strategic partners, and international investors around a shared conviction: that commercial-scale quantum computing is within reach – and that its economic impact will be transformative,” said Don Mattrick, chief executive of Photonic. “We will use this funding to continue to hit key milestones, grow our team, and deepen the partnerships that will take us there,” he said.

He added that Photonic is already delivering on commitments to customers, including as part of the Canadian Quantum Champions Program and Stage B of the Defense Advanced Research Projects Agency’s (DARPA) Quantum Benchmarking Initiative.

“Distributed architectures will be an important way to scale quantum technology, and Photonic is an important partner in advancing that future,” said Zulfi Alam, corporate vice-president at Microsoft Quantum.

“Their design allows quantum systems to operate over today’s fiber infrastructure, offering a practical and scalable path toward the large-scale systems that transformative applications will demand,” he said. “We’re pleased to continue our partnership as they take this next step.”

BDC said its investment was made through its StrongNorth Fund, which backs Canadian companies developing defense and dual-use technologies. Peter Suma, managing partner of the fund, said Photonic was building scalable quantum capabilities with global partners while drawing on Canadian talent.

Bell Ventures said it would collaborate with Photonic to develop sovereign, scalable quantum computing capabilities in Canada. TELUS also pointed to the potential integration of Photonic’s architecture with its PureFibre infrastructure.

Photonic, headquartered in Vancouver with operations in the US and the UK, has more than 160 staff members. It is developing quantum computers and quantum networks for applications including materials science, drug discovery, climate change and security.

The full announcement is available here.

Artificial intelligence (AI) infrastructure is moving beyond the question of how many chips a company can buy. For large enterprises, governments and AI developers, the harder question is whether they can assemble enough power, cooling, networking and storage to make those chips work together.

That is turning local AI capacity into a data-center-scale project. The most demanding customers are no longer looking at small graphics processing unit (GPU) clusters. They want dedicated facilities that can train or fine-tune models while keeping critical workloads close to their own systems.

“The goal of AI factories, unlike dedicated local zones or local zones, is to bring really big computing infrastructure to the system,” said Chris McEvilly, senior solutions architect for hybrid edge at AWS. “We’re talking about 134 kilowatts per rack, which is a bit ridiculous, and is growing.”

“The starting point for an AI factory is around 3,000 GPUs,” he said. “The biggest AI factory we have at the moment is a million GPUs, so that gives you an idea of the scale.”

He said smaller deployments, including AWS Outposts, are more suitable for local inference. AI factories are designed for large-scale tasks, including local training and fine-tuning, in which many accelerators require high-performance networking and storage.

An AI factory can involve several tens of racks and, in some cases, hundreds. The number depends on the configuration, GPU type and the power density of each rack. The cost also reflects the scale.

Ben Lavasani, senior go-to-market (GTM) specialist for hybrid cloud and sovereign AI at AWS, said the dedicated infrastructure comes with a minimum commitment.

“Although we have pay-as-you-go pricing behind that, there’s a minimum amount,” Lavasani said. “Around $200 million over five years would be what we’ve been looking at for building this out.”

He said the final scope would depend on the services, site requirements and capacity needed by the customer. AWS would first need to assess whether a site could handle the infrastructure.

“I’d say something in the order of months, not weeks, but it certainly is not going to be years,” he said.

McEvilly cited Project Rainier, the AI factory built for Anthropic, as a reference case. AWS made the system available to the customer within a year. The launch involved half a million GPUs.

The scale of those deployments shows why the AI factory model is aimed at a narrow group of customers. It is not a shortcut for companies testing AI for the first time, but a way for organizations with large, sustained AI workloads to secure dedicated compute capacity.

AWS AI Factories, launched last December, provide dedicated on-premises AI infrastructure in customer data centers. The service combines Nvidia accelerated computing, AWS Trainium chips and high-performance networking for large-scale AI training and deployment, while supporting data sovereignty, compliance and low-latency requirements.

Data control demand

The comments were made during a chalk talk at AWS Summit London on April 22. The session, titled “Scaling GPU Orchestration for AI Factories with Nvidia Run:ai,” focused on how large AI workloads can be deployed across local, cloud and hybrid environments.

One of the clearest use cases is data sovereignty. Governments, regulated industries and large enterprises increasingly want greater control over where data resides, who can access infrastructure, and how model weights are protected.

“Digital sovereignty is a huge topic,” Lavasani said. “There are a lot of concerns from governments, regulators and specific industries about how you are using data and AI, where your data is and how you are managing the end-to-end capabilities.”

AWS cloud regions can solve many of these problems today, but they do not cover every geography or every type of workload. AI factories are intended to extend AI infrastructure into locations where customers need local control, lower latency or a dedicated environment.

“We would install this as a secure perimeter within your data-center environment or a chosen data center of your choice,” Lavasani said.

In that model, the customer provides the surrounding facility, including power, cooling and networking. AWS operates the AI factory within a secure perimeter and manages the underlying infrastructure.

“You control access to the AI factory,” McEvilly said. “It’s your security outside and your engineers that manage the applications on it. AWS manages the actual infrastructure and the hypervisors.”

He said that separation is central to the operating model. The customer manages applications, while AWS manages hardware, firmware updates, hypervisors and infrastructure operations.

For some customers, data sovereignty may also extend to staffing. Lavasani said some organizations may require AWS employees who manage the infrastructure to hold specific security clearances or to be of a particular nationality.

He said the model is also meant to reduce the burden of building AI infrastructure from scratch. Companies trying to assemble their own AI systems face long cycles in sourcing, supplying, deploying, and managing hardware.

Those build cycles do not involve GPUs alone. Large AI deployments require storage, networking, security systems, accelerators, data-center capability and operational support. Multiple vendor relationships can slow companies moving from AI pilots to production workloads.

That is why AI factories are likely to appeal first to customers with heavy AI demand, strict compliance requirements and existing data-center commitments.

Orchestrating scarce GPUs

The second challenge is using those GPUs efficiently. Large AI systems can waste expensive compute capacity if workloads are poorly scheduled or if different teams compete for the same chips without a shared allocation model.

Nvidia Run:ai is designed to address that problem by managing AI and machine-learning workloads across different environments. It helps decide which workloads get access to GPU resources, when they run and how capacity is shared.

“Run:ai is a third-party tool from Nvidia that allows you to orchestrate and deploy AI workloads across AWS and other EKS [Amazon Elastic Kubernetes Service] environments,” McEvilly said. “It’s an orchestration tool for machine-learning and AI workloads.”

He said Amazon EKS is AWS’s managed service for running Kubernetes, the open-source system for managing containerized applications. Run:ai works one level above that by focusing on GPU usage.

He said this matters when customers have GPU capacity spread across several locations. Some organizations may have on-premises GPUs, regional cloud capacity, local zones, and AI factories. Run:ai can help orchestrate those resources as a more consolidated environment.

There are still practical limits. Lavasani said customers should benchmark their workloads in AWS regions rather than expect a small trial AI factory, because the infrastructure is too large and too dedicated for a proof-of-concept test.

“For benchmarking, you should do that in a region,” he said. “These instances and services are all available in regions today.”

Upgrade cycles will also be incremental. McEvilly said AWS would be responsible for capacity management and could roll in new racks when customers need more GPUs. Customers are expected to forecast capacity needs a few months ahead.

Accelerator choice is another constraint. Lavasani said AI factory chips currently follow what is available in AWS regions, mainly Nvidia GPUs and AWS Trainium chips. Other accelerators may be considered, but regional availability is likely to determine what can be deployed.

For enterprise customers, the next phase of AI infrastructure will depend on more than access to advanced chips. The bigger test will be whether they can secure sufficient power, manage GPU utilization, and navigate sovereignty, cost, and supply-chain constraints simultaneously.

The energy industry is quietly running one of the biggest tests of artificial intelligence (AI) orchestration ever attempted, and the results are not what anyone expected. Across millions of homes, AI systems are now scheduling when cars charge, when boilers fire and when batteries discharge, coordinating devices in real time across a grid that no human workforce could manage at that scale.

The surprise is not that it works. It is because customers prefer it. A growing share of energy consumers now choose to interact with an AI agent over a human, because the agent has read every message, complaint and conversation they have ever had, and responds accordingly.

“We found that through using AI-generated messages, customer satisfaction has increased by over 10 to 15%,” said Charlotte Johnson, General Manager Generation Flex at Kraken. “The reason for it is often that the messages can be more empathetic. They can read the tone of all of the previous conversations that the customer might have had with someone else, months or years ago, and respond in a tone that resonates and builds a relationship.”

On the trading floor, the same dynamic is playing out. Energy markets are being pushed toward full automation as more renewables come online and trading windows shrink.

“We’re moving to an algo trading world where you can’t have humans just sit there trying to buy and sell electricity at any given time in the day, because you’re missing the opportunity,” Johnson said. “By the time you’re ready to click the button, and you’ve worked out what you needed, you’ve missed it; it’s already gone.”

“A lot of companies are still running pilots, but they’re not able to scale those pilots. When we’re talking about the scale that we need for the energy transition, it’s also about having the right infrastructure, platform and data foundations in place,” said Charlotte Blommestijn, Head of Sustainability at Kaluza.

Blommestijn said most utilities are still far from this point. Many do not even know which of their customers own an electric vehicle (EV), let alone have the systems to bill them accurately, offer the right tariffs in real time or nudge them toward behavior that benefits the grid.

She said the gap is not a shortage of AI tools but a failure to move from experimentation to production at the scale the energy transition demands.

Kraken Flex is considered one of the world’s largest residential virtual power plants (VPPs). Kaluza, the intelligent energy platform spun out of OVO Energy, provides the software layer that allows utilities to manage millions of distributed devices, including EVs, heat pumps and home batteries, in real time. Kraken has over 2,000 developers working on its platform.

Grid on the brink

The panel was held at AWS Summit London on April 22, 2026, where Carol Yan, Energy and Utilities Leader UK&I at AWS, moderated a session titled “The Future is Renewable: AI Orchestrating the Energy System.”

Yan was joined by Johnson and Blommestijn, as well as Bartek Szostek, CTO and co-founder of Tem Energy. The discussion explored how AI is reshaping the way energy is bought, sold and balanced across a grid that was never designed for the world it now serves.

Blommestijn identified three areas where AI is having the most transformative impact on the power sector:

• forecasting and grid orchestration in real time;

• demand flexibility at scale, where aggregated EVs can be managed as a single coordinated resource; and

• software velocity, meaning the ability to deploy new capabilities far faster than before.

The velocity gains are already tangible. For OVO Energy, Kaluza has reduced time-to-serve by 62%. New product bundles that previously took months to develop can now be built in weeks or days, a pace of change that Blommestijn said remains underappreciated outside the technology sector.

On whether falling software costs will open the market to new entrants, Szostek drew a parallel with the open banking revolution in financial services, which allowed challengers such as Revolut to force incumbents to improve. He said the energy market is at a similar inflection point.

“It is easier to build. But the context is missing. You still need to have a bit of experience and understanding, to have been on the inside for a while to understand how to optimize it,” he said.

That context matters because the system those entrants are trying to fix is deeply, structurally broken.

Last year, 800 gigawatts of renewable energy capacity were added globally, the equivalent of the entire EU power system, with more than a quarter coming from solar. Yet in the UK alone, £1.46 billion worth of wind energy was curtailed, switched off because it could not get onto the grid.

In the Netherlands, grid constraints have become so acute that new homes and businesses in most provinces will soon be unable to obtain a connection.

“These are not capacity issues. This is not a problem of more renewables coming online. This is a coordination problem,” Blommestijn said.

Johnson illustrated the consequences vividly. Traditional thermal power plants provide system inertia, a rotating mass that acts as a shock absorber when something trips on the grid. As those plants are retired and replaced with wind and solar, that buffer disappears.

She said Spain’s blackout in April 2025 affected 60 million people, while the UK’s 2019 outage, triggered by a lightning strike that hit a wind farm and a gas plant simultaneously, left large cities dark within seconds. Both countries have since deployed large-scale batteries capable of injecting stability into the grid within milliseconds. Interconnectors in the UK now trip every week, but consumers no longer notice, because batteries absorb the shock before it cascades.

Stripping out the middlemen

While the grid’s physical problems attract the most attention, the commercial infrastructure sitting above it is no less dysfunctional.

Szostek said there are typically six or seven intermediaries between a business buying energy and the generator producing it, including brokers, utilities, exchanges and hedging partners, each adding fees, risk margins and profit that the end customer never sees.

“We truly believe 30% of the bill, or at least on the power price, could be saved by simply optimizing the entire system,” he said. “That’s basically the fat of all the additional charges and fees that intermediaries are putting on the bill.”

Tem Energy’s platform collapses that chain into a single infrastructure layer, allowing businesses to buy more directly and see where their energy comes from. The company can issue a price quote in 10 minutes, against an industry standard of 48 hours.

“They’ve probably changed their mind or gone somewhere else,” Szostek said, adding that by then the market would have shifted and the quote rendered stale.

He said large language models (LLMs) are used to streamline back-office processes, fixing meter data problems before customers are aware of them and handling the complex workflows involved in switching suppliers.

Szostek’s longer-term vision is for energy bills to become as readable as bank statements. He said this mirrors what open banking did for personal finance, where customers can now see exactly where their money goes.

“My hope is that we can do the same thing for energy,” he said.

He added that greater transparency would also reduce public skepticism toward renewables, because if people understand the system, they are less likely to blame clean energy when things go wrong.

Underpinning all of this is the flexibility market and the consumer relationship it requires. The shift from supply-side to demand-side balancing has created a significant opportunity for households, but it will only work if energy companies earn genuine trust.

Kraken Flex has over half a million EVs on its platform across 90% of UK car brands. Customers are asked just two questions: when they want their car charged by, and to what level. The platform handles the rest.

“You can’t have the transition without flexibility, and you can’t have flexibility without engaging the end customer,” Johnson said.

Kaluza worked with AGL Energy in Australia on a scheme called “Three for Free,” giving customers three free hours of electricity during solar peak hours between 11 am and 2 pm. Australian regulators have since mandated the program across three provinces, a sign that what starts as a product innovation can quickly become grid policy.

With the first wave of AI orchestration now delivering measurable results, the challenge ahead is scaling what works: from pilots to production, from individual assets to coordinated systems, and from a market built around coal plants to one designed for a renewable-first world.

The UK could unlock £35 billion in productivity gains by 2030 if businesses move beyond basic artificial intelligence (AI) tools and embed advanced AI into core workflows, according to research commissioned by Amazon Web Services (AWS).

The finding points to a widening gap in Britain’s AI economy. Many organizations are already using AI, but far fewer have reached the stage where the technology is shaping decisions, redesigning processes and supporting new products.

“Now, interestingly, our research shows that 64% of UK organizations have now adopted AI, up from 52% only a year ago,” Alison Kay, vice president for UK and Ireland at AWS, said in an AWS event. “That’s the equivalent of one UK business adopting AI every 40 seconds.”

“Our research shows that the UK could unlock £35 billion of productivity gains by 2030 if basic adopters moved to advanced AI by leveraging generative AI, agentic systems and automation,” Kay said.

The report, conducted by Strand Partners and commissioned by AWS, says UK AI adoption is ahead of the European average of 54%. It says 68% of adopters report productivity gains and 72% expect AI to increase their ability to grow in the coming year. Another 79% say innovation timelines have accelerated.

But adoption alone is not delivering the full economic prize. Most organizations still use AI for basic tasks, such as summarizing documents or answering simple queries through off-the-shelf chatbots. Only 24% of adopters have reached the advanced stage. At that point, AI becomes part of core business processes and decision-making.

The difference is material. Advanced AI users report average efficiency gains of 68%, compared with 40% among basic users. The report says the value rises when organizations use AI to redesign workflows, accelerate decisions and create new products and services.

Kay said companies cannot build advanced AI on outdated foundations. Modernization is not only a migration from old systems to cloud infrastructure. It also means breaking down monolithic platforms, reducing technical debt and making data accessible, queryable and actionable.

Skills are the other constraint. The report says 49% of organizations cite AI and digital skills shortages as the main challenge for AI adoption, up from 46% in 2025.

“Science is only powerful when it’s in the hands of people who know how to use it,” she said. “Our research shows that almost 50% of UK organizations cite skill shortages as the biggest single barrier to their ability to adopt AI.”

She said AWS is investing in training and support programs in the UK and Ireland. Its Skills to Jobs Tech Alliance aims to prepare at least 100,000 learners with AI skills by 2030, with more than 60,000 students already trained in cloud and AI skills.

From prompts to agents

The remarks came during AWS Summit London on April 22, where executives and customers discussed “The Age of Agents.” The event focused on AI agents moving from experimental tools into software development, enterprise modernization and business operations.

Francessca Vasquez, vice president of Professional Services and Agentic AI at AWS, said agents are changing how work is done inside Amazon. Legal teams use them to synthesize complex regional requirements. Account managers use them to generate international expansion strategies on demand.

“AI agents are taking this one step further,” Vasquez said. “What used to take years can now be done in days and sometimes even minutes.”

The shift reflects a broader compression in innovation cycles. AWS’s report says the move from dial-up internet to 3G took a decade, while the leap from generative AI to agentic AI took only months. It defines agentic AI as a partner that can plan and carry out tasks without constant instruction.

Vasquez said AI-powered software development tools had evolved rapidly over the past year, from inline tab completion to writing functions and completing multi-step tasks. But she said many teams still lacked a way to guide the process and align the output with their own standards.

“They were generating code, but builders couldn’t guide the process or ensure it aligned with their team standards,” Vasquez said. “We wanted to take everything that is exciting about AI-powered software development and add the structure that our developers need.”

Last July, AWS launched Kiro as a response to that problem. Kiro is an agentic, AI-powered integrated development environment (IDE) from AWS. Built on Amazon Bedrock, Kiro uses specification-driven development to turn natural language prompts into production-ready code, documentation and tests.

The tool is designed to generate detailed specifications before code is written, so software can be structured and maintainable from the start. It can produce user stories, acceptance criteria, technical design documents, architecture diagrams, sequence flows and implementation tasks.

“Kiro works with you, turning your prompts into detailed specs and those specs into working code,” Vasquez said.

That structure is important for enterprise adoption because AI-generated code still has to fit into existing development practices. For large organizations, the challenge is not only speed. AI-assisted work still has to be reviewed, tested, deployed and maintained with the same discipline as conventional software.

Vasquez also linked agents to legacy modernization. About 70% of IT budgets are estimated to be consumed by maintaining legacy systems. AWS Transform has saved more than one million hours of manual migration effort and transformed more than one billion lines of mainframe code, she said.

One example was Automatic Data Processing (ADP), a global provider of cloud-based human capital management solutions covering HR, payroll, talent, time, tax and benefits administration.

Vasquez said the company used AWS custom transformation agents to extract and document thousands of embedded business rules from a legacy tax-compliance system, cutting rule-extraction time by 80% and reducing manual effort by more than 90%.

Proof beyond pilots

Motorway, the UK online used-car marketplace, offered one of the clearest examples of how AWS wants companies to think about agents.

Ryan Cormack, principal engineer at Motorway, said the company turned to Kiro because engineers needed speed without losing discipline.

“We didn’t want to just ship things faster. We wanted it to work well, and that’s why we reached out to Kiro,” Cormack said.

Motorway connects people selling vehicles with more than 7,500 verified dealers. Cormack said the UK used-car marketplace is worth more than £100 billion. Changes to the company’s systems can affect customers making large financial decisions.

“We don’t just jump straight into writing code faster,” Cormack said. “We make sure that we’re understanding the problem that we’re trying to solve.”

Kiro is now used throughout Motorway’s software development lifecycle. Product and user experience teams can ship prototypes in hours rather than weeks. Custom agents help review code, deploy software and monitor production. Cormack said the tool also helps resolve incidents and identify bottlenecks before they affect customers.

The company has seen measurable results. Cormack said Kiro is writing more than one million lines of code for Motorway each month. He said deployed code has increased by 250%, while engineering output is four times higher than before the adoption.

AWS also used the keynote to highlight smaller-scale examples. Dr. Werner Vogels, vice president and chief technology officer (CTO) at AWS, appeared briefly after the Motorway presentation as part of a “renaissance developer” moment. The message was that new tools and workflows are changing how developers build software.

A video case study from Timor-Leste showed Ajito Nelson, a data engineer, using AWS services to identify waste hotspots, allow citizens to report local issues and give authorities data for cleanup work. The example widened the keynote beyond corporate productivity into public-service use cases.

Greg Jackson, chief executive and founder of Octopus Energy Group, gave a broader argument for agile technology platforms in volatile markets. Enterprise software procurement often starts with companies trying to list existing functions and predict future requirements, even though business conditions can change quickly.

“When you’re identifying your needs, you’re almost certainly wrong,” Jackson said.

Octopus built its energy platform on AWS about a decade ago. The company wanted an agile architecture with large-scale real-time processing, rather than a rigid utility system, Jackson said. That platform later became Kraken, which is now independent from Octopus.

Jackson said Octopus was built as a technology platform, rather than a conventional utility company.

The scale of the operation is significant. Octopus ingested 10 trillion rows of data last year, equal to about 300,000 rows a second, Jackson said. The company uses data to support dynamic electricity pricing, electric-vehicle charging and decisions about where more charging infrastructure is needed.

Octopus controls three gigawatts of electric vehicles in the UK, even though EVs still account for only about 5% of the market. As more decentralized energy resources come online, the next challenge will be using AI, cloud infrastructure and real-time data to coordinate them without creating new layers of complexity.

Artificial intelligence (AI) can detect disease from medical imaging, translate rainfall data into river-discharge models, and accelerate research timelines that once spanned decades.

What remains unresolved is whether the governance architecture being built around AI addresses the needs of the people it is most likely to leave behind.

At the ministerial session of the 11th Annual UN Multi-stakeholder Forum on Science, Technology, and Innovation for the Sustainable Development Goals (STI Forum) in New York, diplomats and researchers said the debate has focused heavily on the hypothetical risks of frontier models, while a more immediate crisis is structural: AI is being built for contexts of abundance, not scarcity.

Speakers included Koessler, Rita Orji of the United Nations Independent International Scientific Panel on AI, Bjørg Sandkjær of the UN Department of Economic and Social Affairs, and Helmut Habersack of BOKU in Vienna.

Koessler framed the challenge as a mismatch between the speed of emerging technologies and the capacity of international institutions to keep pace.

“Advances in AI, biotechnology, quantum computing and advanced materials are reshaping economies and society much faster than our governance systems have traditionally evolved,” he said, adding that the result was not merely a lag but a “substantial risk.”

“Without appropriate governance, emerging technologies can exacerbate inequality, undermine trust, and outpace the safeguards designed to protect people and public interests,” he said.

He acknowledged that recent steps towards a multilateral AI governance architecture represent an important beginning. He described a UN-based approach grounded in multilateralism, human rights and sustainable development, emphasizing the principle of “putting humans at the center of the effort.”

That effort is taking shape against the backdrop of the Pact for the Future, a landmark agreement adopted by UN Member States at the 2024 Summit of the Future. It was designed to make the international system more inclusive and better suited to 21st-century challenges.

Alongside the pact, Member States adopted the Global Digital Compact, a framework for digital cooperation and AI governance, and the Declaration on Future Generations, which calls for longer-term thinking. The compact focuses on closing digital divides, governing AI for humanity, and protecting human rights online through cooperation among governments, businesses and civil society.

Koessler stressed that declarations alone would not be enough.

“Frameworks alone are not enough,” he said. “They require political commitment, technical expertise and continuous dialogue across countries and sectors.”

He also warned that, with AI and data systems expanding rapidly, rising energy demand must not raise costs, strain grids, or widen inequality.

He called ensuring affordable, reliable, and clean energy for all “a defining test in the final stretch to 2030,” and stressed the need to align digital transformation with energy access so that AI drives SDG progress for everyone, pointing to sub-Saharan Africa as a pressing example.

Three structural gaps

That concern was taken further by Rita Orji, a member of the United Nations Independent International Scientific Panel on AI, who said the dominant AI governance debate is focused on the wrong problems for much of the world.

She identified three structural gaps holding back AI’s potential to serve sustainable development:

• Data gap: Many datasets used to train AI systems still under-represent the Global South.

• Design gap: Many AI systems assume users are literate, English-speaking and digitally fluent, even though this does not reflect much of the world’s population.

• Governance gap: Global AI governance remains heavily focused on frontier risks, while the more immediate problem for many communities is irrelevant AI: systems that may be technically powerful but have little practical value in the places that need them most.

Orji said the focus on frontier risks, existential risk and safety benchmarks should not obscure more immediate barriers, including weak infrastructure, limited language support and the lack of digital records.

In her presentation, Orji used two healthcare scenarios to show why AI systems can fail outside the environments where they are developed:

• A well-resourced lab in North America, with powerful servers, stable electricity, clean datasets, and a team of engineers and scientists training models to detect early signs of disease from medical imaging.

• A clinic in rural southeastern Nigeria, near where she grew up, with one doctor, intermittent power, paper records, and patients who may need to walk two hours just to reach the facility.

“The AI built in that first lab will work beautifully in that lab and similar contexts,” Orji said. “But take it to that clinic, and it will fail.”

The failure, she said, was not scientific but structural: “No reliable electricity to run it, no digital records to feed it, no internet to update it, no local language to explain it, and no trust built with the communities to accept it and adopt it.”

She said innovation has little value if it is not adopted by the people it is meant to serve, and that too much of what is being built never completes that journey.

The rules underneath

Last December, the UN completed the WSIS+20 Review, a two-decade assessment of progress since the 2005 World Summit on the Information Society.

The review concluded with a consensus resolution that reaffirmed support for a people-centered digital society. The resolution also aligned future digital goals with the SDGs and the Global Digital Compact.

For the AI governance debate, that context matters because the foundation of any equitable framework rests on how data is managed, shared and made accessible.

Bjørg Sandkjær, Assistant Secretary-General for Policy Coordination at the United Nations Department of Economic and Social Affairs, linked the WSIS+20 outcome to the forum’s discussion on data governance, digital public goods and equitable access.

Sandkjær said the Global Digital Compact and the WSIS+20 Review outcomes underscore the need for an open, safe and inclusive digital future that harnesses digital technologies to advance sustainable development while protecting human rights and ensuring accountability.

She described the STI Forum’s discussions on data governance, digital public goods and equitable access as essential contributions to that vision.

“Innovation does not happen in isolation. It thrives in ecosystems built on trust, openness and shared value,” she said.

Sandkjær also pointed to the Technology Facilitation Mechanism, which is anchored in the Addis Ababa Action Agenda and reinforced through the Seville Commitment, the Pact for the Future and the Global Digital Compact.

She highlighted a new assessment by the UN Inter-agency Task Team on Science, Technology and Innovation for the SDGs (IATT) on barriers to the international diffusion of zero- and low-emission technologies. The findings will be compiled into a public document to inform future financing discussions.

AI’s data blind spot

The same concern also appeared in discussions about what AI systems can and cannot detect.

Helmut Habersack, UNESCO Chair on Integrated River Research and head of BOKU Vienna’s Institute of Hydraulic Engineering and River Research, acknowledged AI’s growing power in hydrological modeling but cautioned against treating the tools as self-sufficient.

He said AI models can only work with the information fed into them. General-purpose AI tools can produce useful responses on water issues, but may struggle with future conditions that do not follow past patterns.

“If I ask ChatGPT or other tools now about water, you get nice things, which is really good,” Habersack said. “But the question is: can it also look into the future? Can it take up changes that are not on the pathway from the past?”

He said researchers still need to “put our finger into the water,” because AI cannot explain everything from Earth observation alone.

“We have to do experiments, because we have to get new knowledge,” he said, adding that laboratory work with real-world variables is necessary to produce new equations that AI can then help refine.

Access, Not Ability

Orji closed her keynote with a personal account that connected directly to her technical argument. She described growing up in a remote village in southeastern Nigeria, born to peasant farmers with no access to education. There was no electricity, no pipe-borne water, no computers.

“Before I was admitted to study computer science at the university, I had never used a computer,” she said. “I had never seen one up close, actually.”

She chose the field not because she understood it, but because she hoped it could help her change things for her community. She learned to code and build systems without owning a computer, relying on borrowed machines, university labs and paid computer time in business centers.

She said she shared the story because it is not unusual.

“Across the Global South right now, there are young people with extraordinary talents who are locked out, not because they lack ability, but because they lack access,” she said. “If AI is designed only for people who already have everything, it will never reach the people who could do the most with it.”

Ready for Whom?

Throughout the session, a fault line ran beneath the discussion. On one side was the governance conversation taking shape in capitals and multilateral forums, focused on norms, principles and institutional mechanisms for frontier AI. On the other hand, a harder question surfaced repeatedly across the panel: who will those frameworks actually serve?

Each speaker approached that question from a different angle: Koessler through energy access, Habersack through the limits of data-driven tools, Sandkjær through data governance and digital public goods, and Orji through whether AI systems are being designed for communities most likely to be excluded.

Orji said the conventional framing asks whether developing nations have the infrastructure, skills, and institutions to adopt AI, but the framing itself is the problem.

She said innovators across the Global South are already building AI systems under constraints that most well-funded labs will never encounter: unreliable power grids, sparse datasets and dozens of local languages. Those constraints have produced tools that are not imitations of what exists elsewhere but responses to conditions on the ground.

“Perhaps the question before us today is not whether the Global South is ready for AI,” she said. “Perhaps the real question is whether the global AI future is ready to learn from the Global South.”

Enterprise artificial intelligence (AI) systems are running into a memory problem. Companies can buy newer models, expand context windows and launch more prototypes, but agents still falter if they cannot retain and retrieve the right business context.

That challenge is becoming more urgent as AI moves from chatbots into agents that can plan, act and query applications repeatedly. For these systems, the real bottleneck may not be the large language model (LLM) itself, but the memory and retrieval layer beneath it.

“If your retrieval isn’t first class, garbage in, garbage out. Bad AI is often less an LLM problem than it is a retrieval problem,” said Matt Johnson, field CTO at MongoDB. “The LLM is only acting on the information it’s given.”

He said companies can still get poor results with newer, more expensive LLMs if the underlying retrieval system is weak.

“If you don’t have high-quality retrieval, you’re giving it way more information on a given question than the context requires,” he said. “It’s going to spin on all that data that maybe isn’t relevant to the question.”

Johnson said agents moving beyond simple chatbots and retrieval-augmented generation (RAG) need to perceive, plan and act in a loop. Between those actions, short- and long-term memory determine whether the system can maintain trust and accuracy.

The issue is not only whether an AI system can search a database. It is whether it can understand intent, narrow the context and avoid feeding irrelevant material into a model’s answer.

He said retrieval systems need enough context to distinguish between similar or ambiguous requests. Without that extra context, an agent may return a broader set of results than the business question requires.

That problem reflects a wider shift in enterprise AI. As companies move from POC projects to production systems, agentic memory becomes a commercial issue because poor context can waste model spend, erode user trust and slow deployment.

MongoDB is a New York-headquartered database software company that helps enterprises build and run applications across cloud, on-premises and hybrid environments. The company has been expanding the platform for AI workloads through vector search, embedding models and agent-memory capabilities.

Memory costs bite

The discussion took place during a MongoDB media briefing in London on May 6. Johnson and Genevieve Broadhead, global industry lead at MongoDB, told TechJournal.uk that enterprises are entering a new phase in which agents need stronger data infrastructure to work in production.

“Without good persistent memory, agents just don’t live up to the hype,” Johnson said.

He said that agentic memory can also serve as a cost-control tool. Many LLM providers charge more for larger context windows. Putting only relevant information into that window can reduce the price per request and stretch the same budget across more interactions.

“You pay almost double with most LLMs if you need a larger context window,” he said. “If we can get the right data into that context window using a more intelligent agentic memory, you are effectively reducing the price per request.”

Developers are already using graph queries and vector lookups to prevent coding agents from rereading large parts of a codebase before every prompt or plan.

On May 7, MongoDB announced a package of updates to help enterprises run AI agents in production. The announcement included several updates:

• Automated Voyage AI Embeddings in MongoDB Atlas Vector Search, now in public preview. The tool automatically creates embeddings when data is written or updated. Embeddings are numerical representations of meaning that help agents find relevant context.

• LangGraph.js Long-Term Memory Store is now generally available for JavaScript and TypeScript developers. It gives agents persistent cross-conversation memory backed by MongoDB Atlas.

• MongoDB 8.3, which the company said delivers up to 45% more reads, 35% more writes, 15% more ACID (atomicity, consistency, isolation and durability) transactions and 30% more complex operations over MongoDB 8.0 without changing application code.

• Expanded deployment options across Amazon Web Services (AWS), Google Cloud, Microsoft Azure, on-premises and hybrid environments. Cross-region connectivity for AWS PrivateLink is now generally available as well.

CJ Desai, president and chief executive officer of MongoDB, said in a press release that production agents depend on the data layer beneath them.

“The hardest part of running agents in production isn’t the model. It’s the data layer underneath it,” he said. “To trust an agent at scale, it has to retrieve the right context, hold memory across sessions and operate at machine speed, wherever the enterprise needs it.”

The pressure on databases is increasing as usage patterns change. Databases were once systems of record, then systems of engagement for customer- and staff-facing applications. Agentic AI is pushing them toward systems of action.

“We’re entering this agentic world of systems of action, where agentic workloads are completely different from what they were before,” Broadhead said. “The way customers are querying is changing. When I speak to e-commerce or travel companies, people have stopped putting in two words. They’ve started putting in seven words.”

That shift makes capacity planning harder. Human traffic usually has patterns, but agents can query repeatedly, run continuously and trigger new search and transaction loads.

“Agentic workloads are completely unpredictable,” Broadhead said, adding that retailers are only beginning to adopt agentic commerce and automated transactions, making database performance harder to forecast.

Cloud lock-in risk

Cloud lock-in is another pressure point. AI teams may build prototypes around a single model or cloud provider, only to find that the model they later need is elsewhere. That can leave customer data, order memory and agent context fragmented across systems.

“If you need to use a Google Cloud flagship AI product and your data is locked into AWS, that is going to be no good,” Johnson said.

Deployment flexibility is being driven by compliance, European data sovereignty concerns, latency, and the need to keep data close to applications. MongoDB can run on a developer laptop, in a company data center, in a virtual private cloud or through Atlas as a managed service.

Asked how MongoDB stands out from competitors in agentic memory, Broadhead pointed to its deployment flexibility rather than to a single model or a cloud tie-up.

“The place where MongoDB stands apart from competitors is the run-anywhere thing,” Broadhead said. “A lot of AI and vector databases are only available in one cloud or with one cloud provider.”

She said companies can struggle to move AI workloads into production when they build with one model and then discover that the model they need runs on another cloud provider.

Security is also becoming more complicated. Johnson said agentic AI makes business logic less deterministic, meaning some guardrails may have to move closer to the database or into the database itself. Sensitive fields, such as personally identifiable information (PII), may require controls to prevent agents from accessing or releasing data they should not see.

“Customers don’t really buy products. They buy trust,” Johnson said.

He said companies were not discussing agentic memory and production AI architecture 18 to 21 months ago. Customers now ask about agents, models, clouds and guardrails before they get to the database discussion.

“Anyone who says they’re an AI expert is completely lying to you,” Broadhead said. “Maybe they were yesterday, but not today.”

She said MongoDB itself has had to widen its role as customers ask more questions about AI architecture, agents, models and guardrails.

For enterprises, the next phase of production AI will depend less on isolated experiments and more on disciplined data architecture. Agents will need reliable memory, flexible deployment and security controls before they can be trusted to act at scale.

Ethos, a London-based artificial intelligence platform that matches skilled professionals with work opportunities, has raised US$22.75 million in Series A funding as investors back new ways to identify human expertise in an AI-driven labor market.

The round was led by Andreessen Horowitz, with participation from General Catalyst, which led Ethos’ 2024 seed round. XTX and Evantic also participated in the financing.

Ethos was founded by James Lo and Daniel Mankowitz in 2024. It uses AI to evaluate expertise through voice conversations and existing portfolios of work, including academic papers, code repositories and professional content. The company said AI has made it easier to generate CVs and job applications, but harder for organizations to identify genuine expertise.

“A CV is a poor proxy for what someone is truly capable of,” said James Lo, co-founder and chief executive of Ethos. “Ethos helps people to see the full shape of their expertise and where it fits in an AI-transformed economy - acting as their agent, opening doors they didn’t know existed, and giving companies a far more precise way to find the people they actually need.”

The platform matches users with opportunities across consulting, expert calls, market research, AI data projects and full-time roles. Ethos said more than 35,000 experts are joining the platform each week across accounting, banking, consulting, law, technology and healthcare, as well as skilled trades including electricians and plumbers.

The company said the average expert on Ethos earns £4,500 in additional income each month, while the top 10% of earners make more than £7,000 a month. Since January, the number of experts earning income through the platform has increased sixfold.

“AI is reshaping the labor market faster than our tools for valuing human expertise can keep up. Ethos is built to change that,” Lo said.

“Knowledge is our most precious resource, yet for too long the expertise inside people’s heads has been invisible to the economy,” said Jeannette zu Fürstenberg, president and managing director of General Catalyst.

“Ethos changes the game by capturing the true value of what people know and connecting it to the opportunities that need it most,” she said.

“Our Seed philosophy is simple: back exceptional leaders early,” zu Fürstenberg said. “From our first conversations with James and Daniel, it was clear they were visionaries who understood exactly where the future of work was heading and had the conviction to build for it.”

Ethos was founded by a team from Google DeepMind, McKinsey and SoftBank. Its clients include AI labs, investment funds and corporates seeking access to human expertise on demand.

monday.com has repositioned itself as an “AI Work Platform” in what the company described as the biggest change in its history, as software vendors race to embed artificial intelligence more deeply into workplace tools.

The work management software company said on May 6 that it had rebuilt its platform around collaboration between employees and AI agents, moving beyond task management into systems that can help execute work across departments.

The company said its new AI agents are built directly into monday.com and can be configured by users without technical expertise. The agents are designed to work across live business data, workflows and permissions, while remaining under human supervision.

monday.com said the agents can support tasks such as drafting marketing campaigns, qualifying sales leads, closing support tickets, onboarding new employees and processing purchase requests. The company is positioning the move as a response to a common corporate challenge: large AI investment has not always translated into production use or measurable business impact.

“Our customers are running real businesses in a world that’s changing fast, and they need a platform built for that reality,” said Roy Mann, co-founder and co-chief executive of monday.com. “monday.com is now a place where people and agents work side by side.”

The launch comes as software-as-a-service companies face pressure to prove that AI can deliver practical productivity gains rather than remain an add-on feature. The company said enterprises have widened access to AI, but many organizations still struggle to move experiments into production or use AI to transform operations.

Eran Zinman, co-founder and co-chief executive of monday.com, said the company was “going all-in” on the new strategy.

“We have 250,000 customers running their business on monday.com, and we owe them more than another AI feature,” Zinman said. “We owe them a platform built for what comes next.”

As part of the launch, monday.com is adding one-click connectors to AI platforms including Anthropic’s Claude, Microsoft 365 Copilot and OpenAI’s ChatGPT. The company said this would allow customers to bring their preferred AI systems into existing workflows.

Other updates include access to multiple large language models through monday’s AI Platform Gateway, new AI-powered development tools in monday vibe and a redesigned mobile app combining Sidekick and AI agents.

monday.com said the next phase of software-as-a-service will be shaped by companies that can turn AI into reliable business outcomes at scale.

The full announcement is available here.

Schneider Electric, an energy technology company, has signed a partnership with GreenScale to help design AI-ready data centers across the latter’s planned European campuses, as rising computing demand forces operators to rethink how facilities are built and maintained.

The companies said the collaboration will focus on reference architectures for data centers serving artificial intelligence, cloud and high-performance computing workloads. These designs will include automation, predictive analytics, condition-based maintenance and remote monitoring from the start, rather than adding them after construction.

The deal comes as AI workloads are increasing pressure on data-center operators to deliver higher power density, more reliable cooling and tighter operational control. Facilities built for conventional cloud demand are now being adapted or replaced to handle more intensive computing clusters.

“As demand for AI, Cloud and HPC accelerates in Europe, data center operators must rethink how facilities are designed and managed,” said Dan Thomas, chief executive of GreenScale.

Thomas said the partnership showed how “advanced data center architectures and digital innovation can unlock new levels of automation, efficiency and resilience.” He added that the work would help set “a new standard for intelligent design” for GreenScale’s customers.

Schneider Electric and GreenScale said the approach should reduce unnecessary maintenance, improve asset performance and lower lifecycle costs. It is also intended to give operators better visibility into equipment, allowing maintenance teams to focus on targeted interventions rather than fixed service schedules.

The companies said such systems could be particularly useful in remote or emerging regions, where fewer on-site staff, longer supply chains and higher service risks can make traditional maintenance models more difficult.

“GreenScale’s vision for its European data centers represents a new era in advanced design, where automation, efficiency, and real-time visibility are embedded from day one,” said Thierry Chamayou, vice president for cloud and service providers in Europe at Schneider Electric.

Chamayou said Schneider Electric’s role was to help create “a resilient, AI-ready infrastructure platform” that could operate efficiently in demanding environments.

The partnership will also support a unified instrumentation, monitoring and control system linking physical infrastructure with digital systems through sensors and remote tracking. GreenScale said this would help its campuses manage high-density AI clusters and cloud computing workloads across multiple locations.

The full announcement is available here.

Fusion startups may be moving faster and attracting billions of dollars in fresh investment, but some of the industry’s biggest engineering problems still cannot be solved inside compact commercial labs.

As private companies race to deliver fusion power within the next decade, the debate is shifting from whether fusion works to whether massive public-sector projects such as International Thermonuclear Experimental Reactor (ITER) still have a role in the commercialization era.

“When we built windmills, and we really started harnessing windmills, we didn’t destroy the wind tunnels. We didn’t destroy the testing facilities,” Laban Coblentz, chief strategic advisor at the ITER, said during a fireside chat in London. “The two are complementary.”

“It was foolish to think that ITER could be built in the timeline that we said, and then we were going to build DEMO and tell everybody how to build 10,000 tokamaks,” he said. “That absolutely did not apply to something as complex as ITER.”

DEMO, short for DEMOnstration power plant, is the planned successor to ITER and is intended to become the first tokamak reactor to supply net electricity to the grid. While ITER focuses on proving fusion physics, DEMO is expected to focus on engineering feasibility, tritium breeding and reliable power generation.

The rise of private fusion startups has intensified pressure on large public-sector projects to prove they can still contribute meaningful scientific and engineering value.

Questions are also growing over whether ITER’s delays, rising costs and multinational governance structure could undermine its position as commercial fusion companies attract stronger investor momentum and pursue faster development timelines.

Coblentz pushed back against the idea that ITER and private fusion companies were competitors, saying many commercial firms were increasingly relying on ITER’s engineering experience as they moved toward integrated reactor systems.

“Private sector companies are eager to have ITER’s knowledge,” he said. “Commonwealth Fusion Systems’ employees are irritated at me because I’m still trying to dig up documents that they want access to.”

Several years ago, ITER staff were skeptical about collaboration with commercial fusion companies. Some employees even viewed private firms as rivals. That attitude gradually shifted as venture-backed companies started confronting the realities of reactor integration, assembly and plasma engineering.

Fusion companies initially focused heavily on plasma performance and magnet breakthroughs, but increasingly discovered that large-scale systems engineering remained one of the industry’s hardest challenges.

ITER’s role has therefore evolved beyond plasma physics. The project now functions as a large-scale industrial testing ground covering metrology, assembly systems, materials engineering and systems integration.

Those engineering challenges become harder rather than easier as reactor designs become smaller and more powerful.

“The twin problems of fusion, the most important thing, are size and precision,” Coblentz said. “Whether you’re trying to build magnets at giant scale or targets for inertial fusion, the precision is insane.”

Compact fusion systems may still face severe engineering risks despite rapid progress and growing investor enthusiasm.

“SPARC, the compact tokamak being developed by Commonwealth Fusion Systems, is going to do great things,” he said. “However, there’s a possibility that it could destroy itself in its first test run because they’re taking high risk.”

He said fusion developers are increasingly discovering that plasma breakthroughs alone are not enough. The bigger challenge is integrating multiple complex systems into a stable reactor.

ITER still contributes valuable knowledge on assembly sequencing, materials resilience, industrial-scale tolerances and long-duration reactor integration that many startups cannot yet replicate independently.

Fusion funding surge

The discussion took place on April 14 during Fusion Fest in London, an event organized by Economist Impact. Geoffrey Carr, senior editor for science and technology at The Economist, moderated the fireside chat with Coblentz on ITER’s future role as private fusion companies accelerate commercialization efforts.

ITER traces its origins to 1986, when Euratom, Japan, the Soviet Union and the United States agreed to jointly design a large international fusion facility. Work on the concept began in 1988, with members approving the final design in 2001, laying the foundation for one of the world’s most ambitious scientific collaborations.

Construction began in 2013 with a budget of 6 billion euros (US$6.8 billion), but costs later rose sharply. ITER estimated total costs at about 22 billion euros in 2021, while the US Department of Energy projected the overall figure could eventually reach US$65 billion by 2039, when full fusion operations are targeted.

The European Union is expected to fund about 45.6% of the project, while China, India, Japan, South Korea, Russia and the US each contribute roughly 9.1%.

Investor confidence in fusion began shifting roughly five to six years ago as ITER’s physical infrastructure became more visible.

“About five or six years ago, investors started to invest in private sector fusion projects,” Coblentz said. “They said to me that their CEOs were seeing ITER’s progress and realizing these systems could actually be built.”

Earlier in ITER’s development, the Obama administration considered withdrawing funding while the project struggled with delays and limited construction progress.

At the time, only a small number of private fusion firms were active, including Commonwealth Fusion Systems and TAE Technologies.

Investor sentiment gradually shifted after ITER’s buildings, manufacturing systems and reactor components began materializing at scale.

“Our CEOs are seeing that you can build these components, and they realize that maybe this is feasible,” Coblentz said. “Investors really are starting to see this.”

He said proving manufacturability became especially important because fusion systems combine extreme physical scale with unusually tight engineering tolerances.

ITER’s large size is also partly intended to manage neutron bombardment on reactor walls. Compact systems using stronger magnets may reduce reactor size but can significantly intensify neutron exposure and materials degradation.

“The neutron flux is hitting at about the limit of our materials,” he said. “If you make the reactor much smaller with more powerful magnets, the number of neutrons hitting those walls goes up exponentially.”

He added that unresolved challenges involving tritium breeding, liquid metal walls and materials resilience continue affecting multiple fusion approaches across the industry.

The engineering challenge in fusion is often underestimated compared with the plasma physics challenge. Fusion development increasingly depends on the integration of magnets, materials science, assembly systems, precision manufacturing and computational modeling rather than breakthroughs in a single scientific field.

AI-designed reactors

Artificial intelligence (AI) and digital simulation tools are also becoming increasingly important in fusion engineering as developers attempt to accelerate reactor design and testing cycles.

“Nvidia just told me that they are using AI to transform digital twins for SPARC and other projects,” Coblentz said. “They’re doing it because we open-sourced a scientific simulation code last year.”

ITER recently released an open-source scientific plasma simulation code that external organizations are now using to refine digital twin systems for fusion reactors. AI-assisted simulations could help companies improve reactor integration, systems optimization and engineering analysis without relying entirely on physical testing.

The organization also continues contributing knowledge beyond plasma experiments through systems integration work and industrial-scale engineering experience.

“ITER’s purpose is knowledge transfer,” Coblentz said. “You should send us as many companies, graduate students and technicians as possible, let them learn and take that knowledge back.”

He said collaboration remains important despite growing commercial competition across the fusion industry.

As private fusion firms push toward commercial reactors, the debate is no longer centered on whether fusion research should move faster. Instead, the industry is increasingly confronting a different question: how much large-scale scientific infrastructure is still needed before fusion can become a reliable commercial energy source.

Britain and the United States are deepening their fusion energy partnership as Type One Energy, Tokamak Energy and AECOM launched a new consortium aimed at developing the UK’s first private-sector-led fusion power plant project.

The partnership reflects a broader shift in fusion strategy on both sides of the Atlantic, as governments and companies move beyond experimental science programs toward commercially deployable energy infrastructure backed by industrial partnerships and private capital, according to a May 6 announcement.

“Fusion needs to be delivered, not just developed. This Consortium brings together the core industrial capabilities in the UK and US required to deploy real-world fusion power plant projects that are commercially viable,” said Chris Mowry, chief executive of Type One Energy.

“By aligning fusion technology, advanced manufacturing and power plant engineering, we are closing the gap between today’s energy innovation and tomorrow’s energy infrastructure.”

The new UK Infinity Fusion Consortium combines Type One Energy’s 400 MWe Infinity Two stellarator fusion power plant design, Tokamak Energy’s high-temperature superconducting (HTS) magnet capabilities and AECOM’s infrastructure engineering and construction-management expertise. The partners said the initiative is designed to create a commercially credible fusion deployment pathway aligned with the UK government’s recently announced UK Fusion Strategy.

The consortium also reflects growing UK-US cooperation in advanced technologies. During an address to the US Congress last week, King Charles III said the two countries were combining “talent and resources” in areas including fusion, quantum computing, artificial intelligence (AI) and drug discovery.

Mowry said the initiative supports British and American ambitions to become leaders in commercial fusion deployment while building an industrial ecosystem capable of supporting future projects at scale.

At a London event last month, Mowry said Type One Energy’s strategy focuses on combining deployable technologies, industrial partnerships and commercially realistic business models rather than pursuing another decades-long experimental program.

The company describes this approach as FusionDirect™, a commercialization strategy designed to shorten deployment timelines through advanced manufacturing, modern computational physics and partner-intensive execution models.

Type One Energy, established in 2019, is led by fusion scientists involved in earlier stellarator programs together with executives experienced in commercializing energy technologies. The company said its approach aims to provide a lower-risk and capital-efficient pathway toward commercial fusion deployment over the coming decade.

Tokamak Energy, founded in 2009 as a spinout from the UK Atomic Energy Authority (UKAEA), is one of the world’s leading developers of HTS and fusion technologies. A major part of its business focuses on end-to-end HTS system capabilities through its subsidiary Ridgway Machines.

Tennessee blueprint

At Fusion Fest in London on April 14, Mowry discussed how the company’s commercialization strategy evolved into a deployment partnership with the Tennessee Valley Authority (TVA), one of the largest public utilities in the United States.

The fireside chat was moderated by Alok Jha, science and technology editor at The Economist, during the Economist Impact-organized event.

Mowry said Type One Energy was established slightly more than three years ago around the idea that advances in enabling technologies, industrial capabilities and commercialization structures made it possible to take a “direct shot on goal” toward fusion deployment.

“The energy industry is interested in fusion because it brings this complement to renewables, in the sense that renewables are intermittent energy supply, and power grids for them to be stable and reliable, need an on-demand, baseload source of energy,” he said. “So you need to think about this thing from the end market perspective.”

The company’s Tennessee deployment project has become a central element of that strategy.

“We intend to remain a technology OEM [original equipment manufacturer], pure play. We’re not going to get involved in building power plants. We’re not going to get involved in operating them,” Mowry said. “Those are things that are very hard to do, incredibly capital intensive. And the good thing is, people already do those.”

Under the partnership structure, TVA would operate the future fusion plant while Type One Energy would supply the fusion technology platform.

“So we would be the technology provider, just as Rolls-Royce provides the reactor system for the SMRs [small modular reactors], but they’re not going to build or operate those power plants,” he said.

The Bull Run project in eastern Tennessee is being developed alongside Oak Ridge National Laboratory at the site of a former coal-fired power plant. Type One Energy said the Infinity One stellarator testbed will validate the operating efficiency, reliability, maintainability and affordability of its fusion pilot plant design.

The May 6 consortium announcement said the proposed UK Infinity Two deployment project would build on engineering and operational experience gained through the TVA partnership. The Tennessee deployment is targeted for commercial operation in 2034.

Type One Energy said the UK project would also leverage Britain’s existing fusion supply-chain capabilities, regulatory expertise and industrial infrastructure developed through the government-backed STEP Fusion programme.

Grid-scale fusion

A major part of the commercialization debate at Fusion Fest centered on regulation and whether fusion should continue to be treated similarly to nuclear fission.

“Licensing for fusion, at least to the degree that it’s been established in the UK and in the US, is of course very different than fission,” Mowry said. “We’re not talking about creating a new regulatory framework from whole cloth. Basically, we’re repurposing something that already exists.”

He said fusion regulation in Britain relies more heavily on existing environmental and safety structures rather than the more extensive regulatory systems used for fission reactors.

“The process has really been about educating them about what a fusion power plant is,” he said. “They understand the things that are relevant for them, which really come down to two things: how you control tritium and make sure you don’t exceed regulatory limits and radiation.”

Mowry also argued that stellarators offer operational characteristics that make them attractive for utility-scale electricity generation.

“The stellarator was demonstrated to operate in a stable and continuous manner. Once you turn it on, it just sits there,” he said. “For people who build and operate power plants for a living, that’s a pretty compelling value.”

He said future electricity grids would still require reliable baseload generation even as renewable energy capacity expands.

“Renewables are an intermittent energy supply, and power grids, for them to be stable and reliable, need an on-demand, baseload source of energy. That’s really the promise of fusion,” he said.

Mowry also stressed that fusion commercialization would require the development of an entirely new industrial workforce and maintenance ecosystem.

“Operating a fusion power plant is going to be very different than operating a research facility,” he said. “Nobody’s going to buy a power plant that runs for 10 months, and then it has a two-year shutdown period.”

The UK Infinity Fusion Consortium said its long-term plans include broader participation from British construction, finance, manufacturing and power-sector partners as the project moves toward commercialization.