Maximilian Plank is a venture partner at Tenity, a VC and accelerator program focused on fintech, based out of Zurich.

Max’s career has involved helping financial centers interact. He serves as a ‘global ambassador’ to the Middle East for Hong Kong’s Cyberport, a government-supported startup hub. He chairs a business facilitation startup in Doha, and serves as Tenity’s lead partner for Saudi Arabia.

He speaks with Jame DiBiasio about the current hot themes in fintech, globally and in select markets, including sharing some of his investment companies, the opportunity in digital assets and in AI, and the impact of the ongoing war in the Gulf on investing.

Timecodes:

0:00 - Max Plank and Tenity

2:50 - What do LPs look for in fintech investments today? The difference between venture investing and startup acceleration

6:00 - Fund composition and a new strategy for digital-asset infrastructure

9:00 - Fragilities in blockchain, startups with a license or not, and new business advantages in the age of AI

15:03 - Analyzing founders in the new environment

17:12 - Switzerland: old banks and new foundations

21:38 - Saudi Arabia: war, fintech, and opportunity

27:29 - Hong Kong: tokenization, links to the Middle East, and a new generation of Chinese fintechs

The usual answer in finance to an either-or question is ‘both’. That’s true of tokenization, where the action is gathering around two protocols, one that is designed for permissioned, club-like activity, and another that is public and open to all participants. Depending on the objective, a bank or asset manager can operate on Canton Network for a closed-door environment, or on Ethereum if they want to reach a far greater universe.

At some point, though, ‘both’ becomes hard to achieve.

Today more than $29 billion of real-world assets are deployed on-chain, according to rwa.xyz, with more than $15 billion of that distributed through Ethereum. That distribution represents asset value of $354 billion, of which $303 billion is stablecoins.

Ethereum’s position represents stock: the value of RWA represented on-chain. But when measuring flow, the cumulative notional value of transactions – use of collateral, secondary-trading volume, total settlement value – the clear winner now is Canton, which processed more than $6 trillion in RWA in 2025, mainly in repo and other wholesale flows.

This reflects how banks and asset managers are using these platforms.

Bifurcation

Ethereum and its competitors (Solana, a direct competitor, plus other chains that interoperate with Ethereum such as Avalanche, BNB Chain, and Polygon) provide transparency, reach, and composability of the token. They are important for distribution, investor access, and secondary-market liquidity.

Canton Network – and other permissioned distributed-ledger stacks such as Corda, Hyperledger Fabric and Enterprise Quorum – provide bilateral trust, regulatory comfort, and confidentiality. These are where banks go for dealer-to-dealer settlement, intraday collateral mobility, and internal treasury flows. Canton’s ‘killer app’ is processing Broadridge’s DLR, a platform for repo and securities financing among buy and sell sides.

In sum, one rail brings chaos, but the other brings opacity.

This bifurcation raises two questions for financial institutions. The first is that each rail by itself brings its own constraints: legal, operational, market structure. What happens when an institution hits a buffer?

The second question is how institutions will address the fragmentation of liquidity this brings. TradFi doesn’t have this division of activity into such distinct buckets. For now, tokenization markets are nascent. How big do they have to get before this divide becomes a constraint? How expensive will it be to manage separate liquidity pools?

It’s possible that this fragmentation problem will gradually solve itself. But today, moving assets between chains is expensive, adding up to 5 percent in cost, and creating pricing gaps between venues, which for now, arbitrage is unable to address.

Blockchain is touted as being efficient because traditional assets become cheaper and faster to issue, move, and pledge. Static holdings such as T-bills or money-market funds become programmable collateral. Composability makes it easy to offer such assets to new investors without reinventing compliance.

But at scale, this either/or problem would undermine any such efficiency gains. Do the Canton and Ethereum worlds converge – or harden into separate worlds?

Ethereum

That may depend on the flexibility of the contending platforms.

By the end of last year, regulators in major financial centers had turned Ethereum’s token an institutionally acceptable asset, thus making Ethereum the default public venue for tokenized RWAs.

Most use cases today involve tokenized funds and T-bills, stablecoin payments, and a few funky capital-markets products such as private credit and structured products. Every asset, every position, every transaction is globally auditable and visible to the network. Any wallet, custodian, or fintech can participate, negating new distribution costs. Tokenized assets are composable, meaning they can plug into lending markets, automated market makers, and other aspects of DeFi.

All of this has made institutions cautious about Ethereum and its ilk. DeFi means operational risk. The industry is rife with hacks and failed bridges (layer-2 chains). It’s still amateur hour at many of these projects.

Even with improved L2s and privacy tools such as zero-knowledge proofs, institutions can still end up revealing too much about their positions and flows.

And who do you call when le merde hits the fan? Ethereum is ‘run’ by a decentralized foundation.

Moreover its technical founder, Vitalik Buterin, is campaigning for the network to return to its cypherpunk roots, meaning its developers may lean harder into censorship resistance, privacy, and neutrality.

This would suggest Ethereum could become more like a public infrastructure that cannot be customized to enterprise preferences (such as whitelisting or access controls). This could also align with institutional interests, making it more auditable, better governed, more trustworthy, and inhospitable to the ‘number-go-up’ crowd. With the likes of Solana biting at its heels, such changes would probably be positive for financial institutions, but it would come with tradeoffs. And the uncertainty means institutions, while craving Ethereum’s public distribution and liquidity, will hesitate before putting their balance sheet on it.

Canton

This is why many leading institutions are piling into Canton. It takes away a lot of the problems of Ethereum. For wholesale markets, a club is better than a public market. It enables institutions to move tokenized collateral among trading books in privacy; and RWA tokenization stacks for issuance and settlement remain inside a bank’s private subnet, shielded from scrutiny. Activities such as repo can scale inside this shell, and avoid all the messiness of DeFi and engaging with smart contracts from anonymous parties.

But this comes at a price. The state of a token is not anchored to a public ledger via ZK proofs, which means no one can independently verify asset supplies, flows, or aggregate exposures. They only see what their counterparties choose to disclose.

One of the theoretical benefits of tokenization would be risk management: more visibility would make it difficult for an Archegos-type situation to develop. Archegos was a hedge fund that borrowed massively from multiple banks against the same collateral, and when it went under, it cost its prime brokers dearly. But in Canton, there isn’t the kind of transparency to mitigate this kind of counterparty risk.

One of the benefits of Canton is it’s a club. But a club means internal ‘super validators’ and other influential functions are controlled by a committee. Today this gives banks a sense of comfort. But over time can also give incumbents leverage over who else gets to join the club, potentially blocking new competitors.

And then there’s reach. Assets native to Canton can’t readily be used in DeFi, or held in the wallets used by smaller institutions or retail investors. A Canton participant can syndicate the economics off-chain, creating synthetic exposures, but this brings back the inefficiencies that tokenization is meant to smooth over.

For now, those problems are theoretical. The big banks, asset managers, and card processors are happy to be in their own club for wholesale instruments. But at some point, competitive pressures will make them want some of their tokenized assets to be discoverable, tradable, and collateralized in the broader on-chain world.

Come together?

Nobody wants an either/or. They want both. The rails themselves may evolve to address their limitations.

Canton could increase external, independent trust without sacrificing the privacy that institutions like. Perhaps it could allow cryptographic tools to provide aggregated data to public chains, or introduced tiered memberships that layer in degrees of technical participants. It could overhaul the rights to Canton’s native token. (One of the distinguishing features of Canton Network is that, while it’s a permissioned club, its governance token trades on public crypto exchanges.)

We’ve already noted the debate within Ethereum’s developer community about its direction. Its question is how to keep its robust verifiability while improving privacy.

Some of this is about operating standards for DeFi players, such as around security procedures, but this could go further, toward introducing legal concepts around on-chain fiduciaries. Ethererum could also take the lead on making ZK-based privacy pools make the jump from cool ideas to audited, regulator-understood products.

Its biggest challenge may be its own messaging. Vitalik Buterin doesn’t ‘run’ Ethereum, he’s just one influential voice among a coterie of developers. But the foundation may need to work on how to communicate with institutions (and their risk and audit committees) – not to centralize decisions, but to explain what is going on.

Institutions, meanwhile, will need to think about their own approach. They can’t assume these protocols will converge enough on their own, or that liquidity will cohere. But banks and asset managers that create their own bridges? That would be the ultimate advantage in tokenized assets.

These are classic black-swan events, that few take seriously but which have devastating ramifications should they come about.

Central banks and regulators of digital infrastructure are debating ways to address the payments aspect of a potential, comprehensive loss of electricity. Because even if the lights go out, there can be ways to enable some degree of economic activity to continue.

The old-fashioned way is to use physical cash. It’s a good idea to keep a stash around! But this is the digital age. We have moved too much of our commerce and daily needs online to think that some paper banknotes are the only or optimal solution.

Debating this with Jame DiBiasio is Joachim Samuelsson, CEO of Crunchfish, a Malmö-based startup that has its own solutions to enabling digital money to work offline. He served as its chairman when the company was established in 2012, and has founded or led multiple tech- and security businesses in Sweden.

Timecodes:

0:00 - Joachim Samuelsson, Crunchfish

1:38 - How big a problem do outages present in real life? Building for resilience.

4:04 - Is redundancy the best way to manage risk, rather than building bespoke solutions? The need for better solutions.

7:35 - What does ‘digital cash offline’ mean? Why not just rely on banknotes? Different approaches by central banks.

10:00 - Making physical cash work digitally, versus making digital cash work offline; questions of hardware security (mobiles).

14:33 - New solutions that don’t bring their own systemic risks.

18:54 - What Crunchfish is providing to India’s NPCI

21:09 - The challenge of getting lots of people to lock up money persistently. The impact of outages on individuals versus merchants, issues of scale.

28:30 - Central bank concerns: device security.

31:48 - Central bank concerns: double-spending and consumer confusion.

Financial institutions have always been in the business of warehousing risk: credit, duration, liquidity, operational risk. But guess what? It turns out the biggest risk on the balance sheet is the bank’s own software vulnerabilities; the platforms and protocols aren’t just revamping the exchange of value, but of attack vectors. The vaults and smart contracts aren’t just full of money and credit. They’re overflowing with bugs. You’re banking bugs.

This may not be news, but this week’s leap in AI means that inventory of software holes and glitches has just changed from being an IT/CISO to-do checklist to a balance-sheet event.

On Monday, April 7, Anthropic published its “Claude Mythos Preview”, unveiling details of its next-frontier large-language model, which will not be made public in its current form, yet. No matter, this is the current state of AI, and other labs, and not just American ones, will soon have similar capabilities.

The Mythos report (a ‘system card’ in Silicon Valley-speak) describes a model that can autonomously identify and develop exploits for previously unknown zero‑day vulnerabilities across every major operating system and browser Anthropic tested. And Mythos spots these in hours rather than the months it can take elite human red teams (in-house teams of ‘white hats’ who act like a real attacker). These include long‑standing bugs in hardened stacks that underpin critical infrastructure.

Finance’s fragility

For the financial sector, that matters because the same code is everywhere. Core banking systems, card processors, custodians, high‑frequency trading platforms, payments rails, cloud‑hosted fintech apps, DeFi nodes – they all sit atop this shared substrate of operating systems, compilers, crypto libraries, and virtualization layers. The sector has spent decades layering controls, processes, and capital buffers on top of that substrate without ever being able to – using a financial term – mark to market its fragility.

Mythos‑class models change that. They remove the human‑time bottleneck from vulnerability discovery. This is great for companies that can act quickly to patch things. It’s a disaster if you can’t. Anthropic says it’s quietly disclosed thousands of vulnerabilities to vendors. But that’s just the start.

Bank technologies have long understood their institutions house ‘technical debt’, the accumulated work and risk needed in software from choosing quicker, easier, and cheaper solutions rather than building robustly. Although the debt is high, the infrastructure has been sufficiently walled, gated, and moated to remain defensible. Now the economics of machine speed render the castle both suddenly vulnerable, but also allows defenders to modernize those firmaments.

But vulnerabilities that go unaddressed are now a liability, financially, legally, and by reputation.

Repricing risk

Sporadic samples of the risk book must now be treated as full-book, real-time marking. The book, in this case, being the entire software and infrastructure stack. Period penetration tests and compliance checklists are about to be repriced.

That repricing is going to happen first in DeFi. Smart contracts, bridges, and wallets are now honey pots. Immutable contracts with large TVL (total value locked) have been built without accounting for this kind of threat. The entire ecosystem reuses key aspects of data: smart contract builders, standard automated-market-making templates, oracle interfaces, layer-2 bridge designs, which could create a systemic risk to many protocols.

Relying on Big 4 audits won’t cut it now. Serious teams will move toward continuous AI-augmented auditing pipelines. If crypto trades 24/7, audits will also be non-stop and real-time. Market players will demand transparency around disclosing Mythos-class or equivalent models against a protocol’s stack.

The emphasis on shipping front-end product and UX (which is typical across TradFi and enterprise generally) will have to include a narrative around security, which implies a cultural shift. New products that are embedded with persistent, Mythos-level protections are going to be key to revenue growth.

These days digital assets have become institutionalized, and the bitcoin bros have learned to recite the letters ‘K-Y-C’, so this represents an acceleration rather than a deviation, but there remain plenty of slapdash teams, yield-farm casinos and meme protocols that will now be found out.

Quantum qualified

Crypto loves to talk about how AI is perfectly suited for its mission of agentic finance. Still true! But the industry is already a seeping cesspit of hacks and fraud (see: Drift). The next year is going to be wild. But crypto is just the thin edge of the financial wedge.

This will also transform the new fashion around worrying about quantum computing and its ability to smash cryptography. The reality of quantum computing is unclear, but it’s still a story told in years, if not decades. AI has shrunk that timeline to months.

Mythos doesn’t attack the math of cryptography, but it holes the software envelope around it. Anthropic points to vulnerabilities around widely used cryptographic components that govern how keys are generated, stored, and used in the real world – where the practical risk to financial institutions lives. Quantum risk still remains but mitigation strategies about designing quantum algorithms to safeguard data is going to be folded into the immediate challenges of AI.

Red team in a box

The good news is that Mythos is also a senior red team in a box. It can run massive enterprise-network simulations, chain exploits, escape sandbox environments, and figure out how bad guys infiltrate or exfiltrate systems. It’s pretty awesome.

But it means banks and cybersecurity experts can no longer work around episodes in the calendar. Tests, exercises and assessments were never precise but they were built against human hackers, not AI. And the resources required to attack an institution or protocol are now available to almost everyone.

And security isn’t just about the firm, the team, or the software. It’s now an industry risk, a systemic vulnerability. Shared dependencies such as open-source libraries, foundational middleware, monitoring agents, circuit breakers, and common subscription-software components are beyond the control of a given user, or vendor. Anthropic has launched a project called Glasswing to serve as a community response, and no doubt vendors and cyber specialists will introduce their own.

Every enterprise has been hacked. Boards and execs have learned to assume a determined attacker can penetrate their systems, at least to some extent. The playbook has been to shorten the distance between discovery and mounting a defense. That’s still true, but the timeframes have shrunk. We don’t yet know by how much. We’re going to find out.

Expect to hear “resilience” in every CEO and conference-stage speech. Okay, sure. What else? AI has already been challenging what constitutes value, to an enterprise or to a fintech startup. Mythos amplifies this. Having AI to detect fraud is fine, but it’s now hygiene. Enterprise value means proprietary data sets, regulatory licenses, and a governance structure that enables safe and durable human-to-machine systems.

What to ask

Surviving and thriving in this next chapter of AI will require investment into governance and systems, and not every team or institution can or will make that investment. Some may find their legacy systems too cumbersome to patch at Mythos speed. Others won’t make the cultural adjustment. Again, nothing new, but many institutions survived static mindsets because the pace of change was tolerable.

Boards, founders, and C-suite executives should ask themselves what they should expect to find should a Mythos-class model analyze the entire stack tomorrow: code, infrastructure, dependencies, vendor interfaces. How quickly can those bugs be squooshed?

They should ask where and when they depend on other companies to do the patching, like in an open-source library. Maybe the firm can, and should, organize assistance?

They should immediately review all key vendors, such as cloud providers, core banking platforms, card processors, custodians, and crypto services such as bridge providers and pricing oracles, with regard to their AI security posture and disclosures.

They should develop a PR strategy to control messaging around what vulnerabilities may exist, and find the balance between transparency and prudence. Communicating with regulators, counterparties, and customers, not to mention employees, will be important, and firms need an instruction kit around how to break the glass when there’s a fire.

And they should ensure they have governance around using their own AI tools. The nature of LLMs is they are black boxes. When a crisis hits, responses will be in machine time. What’s best practice now?

Barry Eichengreen’s new book, Money Beyond Borders: Global Currencies from Croesus to Crypto, spans from Athenian owls to the digital reniminbi to tell the story of international currencies. Emphasis on “international”: his interest is what makes a currency relevant to people and institutions across borders. It’s also a narrative toolkit for thinking about how and why a currency becomes global, and whether the dollar’s number is up.

A prominent American economist and economic historian, Eichengreen begins in Classic Greece, where Athenian owls, tetradrachm coins whose silver content and design barely changed for centuries, became universal within the ancient world because the issuer combined commercial reach, monetary stability, and credible political power. Athens had rich silver mines, a navy, and a network of subject allies; its rulers kept the coin’s quality steady, then mandated its use inside its empire, turbocharged by Alexander the Great’s vast conquests. That mix of reputation and coercion, Eichengreen suggests, is the original “exorbitant privilege.”

From there he moves through the denarius, the Byzantine solidus, and the Islamic dinar. The pattern repeats: trade routes open, a state builds administrative capacity, standardized coinage underwrites commerce, and the currency leaks across borders as a trusted unit of account and store of value. Similarly, international currencies are coalmine canaries of a great power on the wane, whether via debasement, loss of access to mines, or old-fashioned complacency.

Turning points

The hinge to the modern world comes with Florence’s florin and the Renaissance of credit. Here Eichengreen pivots from metal to book money: Florentine houses use double-entry bookkeeping and bills of exchange to turn the florin into a banking unit that often moves without any coin at all. The lessons pertain to the Dutch guilder, London’s sterling bills, and the Eurodollar market: what matters is the institutional complex around a currency: its legal protections, market infrastructure, and a deep, diverse investor base, whose confidence depends on a backstop or lender or liquidity provider of last resort.

Unlike other books about monetary history, Eichengreen is fluent in the detailed mechanics of how financial systems evolved to meet cross-border needs, first to facilitate and settle trade, and then to use the currency to develop purely financial instruments. He combines the sweep of millennia of global history with the nitty-gritty of, say, New York bankers’ acceptance notes, while keeping the book concise.

It is this grasp of financial detail that makes Money Beyond Borders a definitive account. Eichengreen has written other books on topics such as the dollar’s challenge to sterling in the 1920s and its retreat in the 1930s, but here he puts such action in a broader context, asking more fundamental questions.

He brings this insight to Nixon’s 1971 suspension of dollar-to-gold convertibility, one of the landmark decisions creating the neoliberal order that continues to shape our societies. Of note is that many smart people wrote off the dollar in the wake of the end of Bretton Woods, and Eichengreen shows how US economic heft, Treasury market depth, open capital markets, and US military power not only supported the greenback, but enhanced its primacy.

The dollar and US power were also questioned in the aftermath of the 2008 financial crisis, which led to China and others to actively seek alternatives. Yet Treasury learned to turn the dollar into a chokepoint of remarkable power.

Dollar doubts

Today, however, the excesses of that power are one reason why questions about the dollar’s primacy are more pointed than ever.

From Eichengreen’s 2,500 years of monetary history, we can see a pattern: international currencies belong to strong states with administrative capacity, fiscal resources, and a credible ability to defend their institutions, in particular the institution that bestows credibility upon the currency. That is a twofold process: technical, from Roman bureaucratic oversight of mints, to the independence of the Federal Reserve; and geopolitical, from the military expeditions of the Dutch East India Company to Britannia’s ruling the waves.

Dominant currencies have been mostly associated with republics or democracies, because they offer rules that protect property, checks and balances on tyrannical whim, and treat foreigners equally. This isn’t always true: the long dominance of the Mexican dollar had to do with Spain’s felicitous windfalls in the New World, but these ‘pieces of eight’ are also the one example of a global currency that became truly cosmopolitan, delinked from the shrinking power of the Spanish empire. A prospect that Eichengreen missed is whether stablecoins could augur something similar for the dollar.

One reason why silver dollars endured long after Spain’s imperial heyday was because of another iron law for an international currency: it needs market infrastructure and liquidity. The bill of exchange only became a truly international instrument once cities like London built acceptance houses, specialist brokers, and a central bank ready to backstop the market in a crisis. Today, that logic underpins his discussion of the dollar’s framework of New York’s money markets and Fed swap lines, versus China’s efforts to build rival settlement systems, designate renminbi clearing banks, and offer renminbi swaps to foreign central banks.

Today there is no compelling case to assume the world will dump the dollar for renminbi or euros. But foreign investors will accept only so much degradation of US management before something dramatic causes a mass switch. Financialization has eroded previous regimes, and it’s hard to imagine a society more financialized than today’s America, where savings and investment institutions have been suborned into casinos.

But these are the results of policy choices, and policies can change. It’s hard to imagine anything sensible emanating from the Trump administration, but hollowing out takes years, if not decades. The US can still remember how to manage its affairs more prudently. It’s happened before.

Pace of change

China is developing parallel markets, institutions, and its e-RMB. Eichengreen notes that the costs of using the renminbi are considerable, so that only countries such as Russia that are banned from SWIFT and the US banking network will use them to a meaningful extent. The dollar’s network effect is too strong, for now.

But just as the US might, or might not, reform its economic policies, so too could China. It’s hard to see Beijing accepting the tradeoffs necessary to make the renminbi an international currency beyond a small group of countries with few options. Eichengreen explains why the use of eRMB will still require dollars once foreigners want to repatriate their money. He also doubts CBDC network projects such as mBridge will scale.

But China is making the most of Trumpian chaos to make the case that it’s the adult in the room. It’s offering a yield on its eRMB to tempt foreign banks to hold it. The European Union could also change its stripes; although Eichengreen is skeptical, the EU’s member countries may yet take financial and fiscal integration seriously, which would make the euro a credible player.

Although Eichengreen examines stablecoins, he does so in the context of dollar dominance, not as the wedge leading to a bitcoin nirvana. Despite the book’s subtitle, he only mentions bitcoin once, curtly dismissing it and its ilk as too volatile to serve as a unit of account, a store of value, or a means of payment. Any credible monetary experiment must solve for who bears the risk, who provides the backstop, and the politics and military might behind the promise. Digital assets are relevant as reflections of central bank money (although, see financialization, above).

Should tokenization platforms become important means through which global investors hold dollars, then the Fed, the US Treasury, and the court system will have to ensure the governance, including operational reliability. Regulated and licensed stablecoins could well lead to assumptions of a Fed backstop to prevent a run. Blockchain won’t count if American commitments to rule of law, independent central banking, separation of powers, and support for foreign partners decay too far. Ditto for Chinese digital infrastructure.

What could be different this time? Speed. It took decades for Britain to pass the baton to the United States, from the shock of World War I to the humiliation of the Suez crisis. Before the second Trump administration, it was reasonable to surmise any transition from Washington to Beijing would also take decades. But now things are moving quickly. The internal constraints of the Chinese Communist Party are now the bigger obstacle to regime change.

So far we have some evidence that global investors and foreign central banks are fiddling on the margin, such as their headlong rush into gold (Eichengreen wrote an influential book, Golden Fetters, about how flaws in the gold standard led to the Great Depression). But these are indicators of concern, not of paradigm change.

Eichengreen’s sobering conclusion is that the 1930s provide a scenario in which sterling’s global role crumbled before the US was ready to take on the role of lender of last resort. Multi-polarization and similar language, were it to come about, would not mean a chummy world in which everyone gets to trade freely in their currency of choice. It means less trade, declined foreign reserves among central banks, and less growth or a worldwide depression. As painful as it is to watch the clown show in Washington, the alternative to de-dollarization is worse.

Join Jame DiBiasio on this brief exploration of the Dojima Rice Exchange.

A pair of new protocols – one for cards, one for crypto – have emerged in early 2026 to compete as the dominant standard for payment agents, in which execution transactions is outsourced to AI.

The agentic payments stack is predicted to be huge. Gartner Group estimates agentic B2B payments will hit $15 trillion by 2028; even if that’s rosy, it’s likely to match or exceed Stripe’s 2025 $1.9 trillion in processing volumes, or carve a percentage of Visa’s $14 trillion.

These emerging protocols – well, they’re not exactly new. They’re both based on longstanding browser code. The best known of these is “404 Not Found”, which is the HTTP error code that flashes when an internet user hits a dead link. The 404 is just how the servers talk to each other.

The one for payments is “402”. It dates back to the 1990s when developers assumed money would one day flow like data (the thinking at the time was about micropayments) and they reserved a status quo, 402, for “Payment Required”, in order to access an article, an image, or an API call.

That vision has only now materialized. Credit cards filled the void, but fixed transaction fees by processors negated the possibility of tiny online payments. The economics of the internet shifted to advertising and eyeballs; if information was too difficult to pay for, then “information wants to be free”, as the early-2000s slogan went.

x402 v MPP

Fast forward, we now have digital money that can move in tiny increments, AI agents that can evaluate and authorize purchases, and massive demand to move value like data. Firms have dusted off your daddy’s 402 code and are repurposing it for new money rails.

In the crypto world, Coinbase is pushing its x402 protocol, with stablecoins as the unit of account, via a blockchain rail such as Base, Ethereum, or Solana. The client provides a cryptographic payment proof, a faciliator checks the chain, and returns another code (200) to confirm the money landed.

In the cards, world, Stripe is pushing MPP, Machine Payment Protocol, which also starts with HTTP 402 code. But instead of paying each request directly onchain, the agent opens a session: a payment channel for pre-funding a balance, and then streaming micropayments off-chain via signed messages, with periodic or batched settlement. Stripe has built its own blockchain, Tempo, where users can deposit funds in escrow accounts. Although Tempo can process stablecoins, it is also designed to abstract credit and debit cards, buy-now pay-later apps, and digital wallets. An agent paying USDC or a corporate Visa card can hit the same endpoint and get the same receipt.

Neither of these means of accepting payments addresses ongoing problems with stablecoins (no recourse, bearer instruments that don’t enable money creation, cyber security questions, etc) – but assume their adoption.

Above and below

These 402s don’t exist in a vacuum. Below them sit various chains and rails: Tempo, Base, Ethereum, Solana, Visa and Mastercard networks, and real-time payment systems.

Above them run orchestration layers: Stripe’s ACP or Google’s AP2, plus newly minted “agent credential” platforms such as those introduced by Visa and Mastercard. These define how agents discover services, exchange mandates, and delegate authority, across any rail or chain.

Although these orchestration tools have their own designs (one for abstracting credit-card numbers, another for empowering e-commerce tools, etc), they are composable. They can be created and recreated to fit the need. In theory, a full agentic payments stack can cross many rails and chains to carry out a single transaction.

The best thing about the 402 trend is that it assumes open loops, not walled gardens. This has ramifications for banks, fintechs, and crypto players. Obviously those keen to operate walled gardens will find 402 a challenge. But it also means there will be places to participate and thrive.

Banks: trust anchors

For commercial banks, the advantages are clear: licenses and customer trust. In the new world of agentic finance, banks can position themselves as a credential and compliance anchor.

Banks will still be issuing credit cards via Visa and Mastercard networks, but these new cards will be virtual and rely on agent-specific tokens on the processors’ agent platforms. The banks, as issuers or acquirers, can defined agent limits, program allowable merchant categories, and set other granular controls. This gives the banks lots of scope for product innovation: a corporate card user can get a procurement agent with a budget cap, spendable on pre-approved SaaS APIs among approved vendors, in certain jurisdictions.

And it happens within their KYC’d world. For example, in Europe under MiCA regulation, agents start to resemble payment initiation service providers. Banks can support them by serving as compliant x402 facilitators, stablecoin custodians, and embed MPP sessions (escrow accounts) into their existing services for businesses small and large.

Few banks are ready. They need to invest in agent identity and reputation systems, and systems that can communicate with orchestration layers. They also need a regulated crypto and stablecoin stack to handle custody, on/off ramps, and reporting.

This is surely the direction of travel – if the regulators are along for the journey. Agents allowing a human to click “pay now” will only go mainstream if questions about liability and consumer protections are hammered out. Regulators will be keen to ensure their commercial banks can become trusted wrappers around permissionless protocols or, in Asia, atop domestic fast-payment rails such as India’s UPI or Singapore’s PayNow. This would give leading banks the chance to become national or regional hubs for agent flows.

Fintech agility

Banks’ opportunity is clear, partly because of their licensing. Fintechs are more agile, and can position themselves throughout the agentic payments stack.

They could be payment processors, packaging x402 and MPP for merchants and developers who don’t want to think about sessions, chains, and the rest. It’s all about abstracting cards and wallets, and connecting to every protocol, to maintain a clean, simple user interface for merchants.

Or, fintechs could climb up a rung and become super orchestrators, competing against Stripe and Google and the card networks. There’s a business model for helping customers choose the optimal payment method based on cost, latency, risk, user preferences, and regulatory constraints. This has been a viable model for fintechs in the pre-agentic world, and there’s no reason why some can’t make the leap.

Similarly, there exist fintechs who own niches, and this model can extend to agentic payments. That might mean compliance and identity, risk and fraud prevention, or “compliance as a service”.

Merchant visibility

For merchants, the urgent task is to be visible to agents. If a consumer’s search is embedded in a payments agent, then merchants need to have bank and fintech providers, or their own capabilities, to win the attention of the bots. For starters this could mean being plugged into platforms such as Stripe ACP or Google AP2, or card networks. The big service providers will be eager to make this process easy so that merchant catalogs and prices are available as structured data, not just HTML.

Eventually, merchants will want to build their own retail agents. This is the new branding, except it’s via agents rather than ads. Old-school marketing still has a role to play, to ensure the business is kept on programmable approved lists. But merchants will explore new business models around products that attract machine attention, and could lead to the sort of micro services (and micro payments) that internet HTML developers first envisaged in the 1990s.

Banks, fintechs, and merchants still need a few common jigsaws to complete the puzzle. Agent intention that is universal will require standards. The regulations for wrapping open rails such as x402 need to be agreed. The settlement infrastructure for high-volume micropayments has yet to be refined. And most of all, credentials and histories need to be made portable and reusable, be it cryptographic tools such as zero-knowledge proofs, or more traditional identity verification that can be fitted to agentic payments.

That’s a lot of work, but the prize is the trillions of dollars expected to flow through the agentic payments stack within a few years.

The global stack

Nor is this an opportunity exclusive to the United States – notwithstanding the emergence of x402 and MPP by Coinbase and Stripe in the US, along with LLMs such as ChatGPT, licensed stablecoins such as Circle’s USDC, and US regulatory debates.

The trend of needing a way to pay other machines at high frequency, often in tiny amounts, under programmable constraints, is global. Indeed, early experiments on AI agents paying for real-world goods have taken place between Santander and Mastercard in Europe.

Nonetheless, geographic differences are a reality. Banks and fintechs in Asia (east and west) and Europe are already well versed in tokenizing credentials, running real-time payment rails, and navigating complex cross-border regulation – a real weakness for US firms. Regional orchestration and compliance will provide business opportunities. Domestic payment systems such as UPI , Brazil’s Pix, or KakaoBank in South Korea are adept at high-frequency, low-value payments, but they need to add agent-friendly interfaces, 402-style semantics, and standardized mandates. A fintech that provides a “UPI for agents” can become the local hub, while routing bigger transactions over stablecoins or x402.

Local and regional crypto players have a huge opportunity to break into new merchant relationships where small, cross-border payments are critical, and where banks are scarce. Singapore, Hong Kong, and the UAE have arguable friendlier (or at least clearer) stablecoin regimes than the US. These financial centers should become hubs for agent-native wallets, stablecoin settlements behind card programs, and on-chain identity services. But whether it’s in Europe or these small financial centers, permissionless protocols will still need permissioned wrappers. Someone on the ground needs to hold that license.

We have more wallets, QR schemes, blockchains and tokenized assets than ever before. Yet the world’s most reliable ways to move value across borders are still decades‑old card schemes and correspondent banking networks. The problem is not a lack of innovation; it is that the innovation is splintering into incompatible islands.

Until that changes, new rails will keep pressuring traditional finance at the margins without fully displacing it at the core, leaving legacy players free to plug’n’play with fintechs without eroding their margins. There’s nothing wrong with legacy payment processors and banks continuing to prosper, but fintech’s promise of competition is being diluted by fragmentation.

On public blockchains, the basic economics of decentralized consensus actively push toward fragmentation. The Bank of International Settlements recently noted that validators charge higher “coordination rents” to chains that are more decentralized and secure, resulting in congestion and gas fees. Spikes in fees on Ethereum – the fave of financial institutions – leads other users to migrate to less-trusted venues such as Solana or Tron. This has competitive merits within the context of blockchains, but it splinters activity and liquidity across dozens of settlement layers and layer-2 chains. This inhibits the ability of stablecoins to serve as fungible tokens without expensive and risky bridges.

Neobanks are growing fast, with the majority of consumers globally now maintaining relationships with two or more providers, according to Standard Chartered. But this isn’t a case of new global banks that compete with the likes of StanChart. At a regional level, this proliferation is fragmenting account balances, creates redundant KYC, and leaves users with a haphazard set of experiences and apps.

There are terrific examples of regionalism: the rise of mobile money wallets in Africa, and efforts in Southeast Asia such as Project Nexus to knit together QR-code based accounts. A recent report by the International Monetary Fund looks at Thailand and finds QR payments are beginning to substitute for traditional banking and card payments, which is bringing financial access to many SMEs.

But African wallets don’t integrate with card schemes and traditional bank accounts at the regional level – a problem for many SMEs. In Asia, QR operability for wallets is entangled in bilateral, national standards, with local regulation and currency regimes throwing sand in the gears. The IMF says Southeast Asia needs to beef up oversight and reporting to enhance trust and security of digital payments across QR-based systems.

Corporates and treasurers face similar issues. Real-time schemes, virtual accounts, digital wallets, tokenized deposits, and embedded rails are all great. They are designed to expand liquidity. But they are mostly built in silos, across varying cut-off times, costs, FX exposures, and regulations. More liquidity, maybe – but sheared off in different places.

TradFi’s endurance

Add it up, and this explains why TradFi is able to retain its dominance in cross-border finance: global acceptance, fungibility of money, dispute rights, and fraud and compliance frameworks trump new tech and digital-first business models. That’s true even when the new systems are cheaper: in fact, lower costs for new rails don’t reflect the way TradFi bundles complexity within its services. That complexity doesn’t disappear by adopting blockchain, wallets, or neobanking.

Would-be fintech disrupters have developed three playbooks to address this problem.

First is orchestration. This has been around for a long time. Many fintechs have emerged over the past decade to help treasurers and SMEs juggle multiple rails: Adyen, Stripe, GrabPay, and Payoneer for merchants, Wise, Revolut, Airwallex and Nium for SME and retail. PPRO, Rapyd, Remitly, TerraPay, and Thunes orchestrate local methods for emerging markets. In the US, Plaid provides interoperable layers for data and account-to-account payments, as TrueLayer does in Europe.

In crypto, Circle orchestrates across chains and into banks for stablecoins. Canton Network synchronizes tokenized assets and workflows across permissioned DLTs favored by financial instituitons; Partior does this for fiat FX settlement in tokenized form.

But large banks have a natural advantage. Many banks have adopted ISO 20022 standards around transaction messaging to make correspondence networks capable of handling digital rails. They have huge teams doing API connectivity. And they know how to bundle and charge for multiple payment methods.

Orchestration at scale requires the kind of internal investment, integration with other client systems, and data standards and digital identities. Banks aren’t necessarily better at these things, but they know how to proceed in a compliant manner (which is why they prefer to deal onchain just among themselves, hence the growing usage of Canton). The object is to mask the complexity for the most valuable corporate clients.

A second approach is top-down, relying upon an institutional anchor such as central banks. Their concern is the singleness of money, which has relied on central banks to backstop market and payment systems. This creates a shared anchor. Without it, private monies and networks tend to fragment, as stablecoins do today.

The emerging solutions include multi-CBDC platforms, such as mBridge, or other regulated frameworks for stablecoins and tokenized deposits. While mBridge is live, it remains limited mainly to RMB-related transactions, despite its multilateral backing. There remain questions about interoperability at scale, governance, and control.

The third play is to develop federated wallet networks. The leading example is Ant Group’s Alipay+. In mainland China, Alipay has built an integrated ecosystem of payments, savings, credit, investments, and insurance around a vast user base. Abroad, Ant has partnered its way into compatible infrastructure.

Through equity stakes, its tech powers local wallets, such as GCash in the Philippines or KakaoPay in Korea. Alipay+ allows these wallets to be mutually interoperable at point of sale across more than 100 markets. Ant handles routing, FX, and settlement for millions of SMEs, building a global network that avoids card schemes altogether. Alipay+ follows its users within their existing financial apps rather than trying to get them to onboard a new one. While Alipay+’s coverage is not as global as a leading bank’s, or a Visa/Mastercard’s, it looks like an emerging competitor at scale for retail and small B2B flows.

Ant Group has never focused on large-scale capital flows or corporate treasury, however, so it’s unlikely this approach would work at that level – we’re back with our hodgepodge of fintechs, digital assets, banks, and cards.

Singleness of experience

To sum up, the best solutions to fragmentation are building networks around existing user flows without trying to rewrite local regulations; bank-driven multi-rail orchestration on the back of APIs, ISO 20022, and real-time connectivity; and institutional trust anchors for digital settlement, notably in permissioned DLTs.

There are more solutions being tested. Might tokenized assets become embedded in mainstream treasury operations? How much potential remains for corporates to exploit the data structures of ISO 20022 messaging? Can national identity and privacy conditions be extended into cross-border flows?

What has long been understood is that market forces alone don’t lead to convergence to a single winning rail. That was also true with correspondent banking. Market forces and regulatory agendas continue to drive the proliferation of chains, wallets, and domestic schemes, with harmonization possible but complete integration unlikely.

Fragmentation is the feature, not the bug, in cross-border finance. Fintechs and blockchains have emerged as new ways to manage or hide this complexity, via federation, orchestration, or atop regulatory anchors. Systems that preserve the singleness of money – which is to say, a single user experience across underlying rails – are more likely to succeed than rickety reliance on bridges and other new versions of middleware “spaghetti bowls”.

The singleness of money is often presented as an obsession of central bankers, which can be ignored by tech startups, but this is a mistake. It’s a competitive necessity. As the world seeks alternatives to US dollar transactions, the costs of fragmentation are going to rise. Who’s going to solve this problem?

Existing commercial banks and payment schemes continue to serve as backbones, and will not disappear, because they remain best placed to do so. Despite the threat from platforms – fintechs, wallets, and crypto providers – to consume the value-add, legacy businesses that solve for the intensity of fragmentation can avoid becoming dumb pipes.

Where fintechs have excelled is in a singleness of experience: taking a consumer or SME corridor and integrating travel, remittances, and e-commerce with credit, rewards, and investments; by embedding financial services within broader activities.

This is the challenge for tokenization and digital assets. Within DeFi native experience, the models are proven, but connecting these to the real world of business and income streams requires an ability to manage fragmentation that has yet to be demonstrated.

It’s a Monday morning in the spring of 2030 and Marco, the treasurer of a large, AI-heavy multinational enterprise, starts his week by opening a liquidity dashboard that looks quite different to the one he used a decade ago.

Cash now comes in four categories. Marco surveys tokenized bank deposits in multiple currencies for payroll and bills. He checks his tokenized money-market fund shares, and his regulated dollar stablecoins for cross-border payments.

Then there’s the fourth category: compute tokens.

These are not cryptocurrencies in the usual sense. They are claims on a defined amount of computation at specific AI labs, such as Anthropic.

Marco holds them in two main forms. First, on‑demand compute balances: instantly usable claims that AI agents across the firm draw down every time they call a model. Second, term compute notes: futures contracts locking in a stream of compute over the next three years at an agreed price and service level.

Corporate treasurer’s day in the life

Every Monday, payroll drains tokenized deposits. Marco tops them up by redeeming some money-market tokens. He knows that next day, the corporation’s new AI-driven product line will require more compute, so Marco buys a tranche of term compute notes, paying with redeemed MMF shares.

Wednesday comes and a market wobble forces the multinational to raise intraday liquidity. Marco pledges some of its compute notes as collateral in a short-term repo with Alicia, a global dealer at a global bank (it could be a JP Morgan or Goldman Sachs, or 2030’s neo-bank darling). She sends him tokenized cash in return.

The next day, another AI provider raises its lab-access prices again, so Marco enters into a swap with Alicia to hedge his future compute costs. Marco ends his week by rebalancing, keeping enough on-demand compute to run the business, enough term notes to lock in strategic capacity, and the rest in conventional cash and equivalents (mostly RMB, HKD, SGD, JPY, AED, plus some dollars because the multinational has a huge US footprint, and EUR for croissants).

Compute tokens have become a working form of money for one crucial slice of the firm’s activities: everything the company does through AI. They are liquid, priced in familiar ways, accepted as collateral, and woven into the same digital rails that carry bank money and securities.

Compute as money

This idea recently surfaced by some tech insiders who think bitcoin will be replaced by “Anthropic Coin”. (Anthropic is the lab of choice because it is a pure play, independent of broader internet businesses such as Microsoft or Google.)

The argument goes like this: Large AI labs control scarce, capital-intensive infrastructure (chips, GPUs, model weights, data centers); they meter access to this infrastructure in tokens; they sell long-term capacity commitments to enterprises as well as to other machines or agents.

These tokens today have money-ish qualities: they serve as a unit of account within AI-heavy firms, with product teams budgeting in terms of tokens instead of hours or dollars; they can serve as a medium of exchange between agents; and this makes them a store of value for bots that need to plan for the enterprises’ future computing needs. Compute tokens don’t buy goods and services but they do buy cognition.

Are compute-token enthusiasts unveiling the future of finance? Not so fast. These tokens today operate more like prepaid utilities or air miles. They lack standardized legal form, robust backing structures, and integration with the broader financial system.

Some pretty big “ifs”

There are reasons why “AI money” is, for now, a fantasy. First, the argument for compute money is being framed as an alternative to bitcoin. This rather puts the cart before the horse, as bitcoin isn’t money. Scarcity alone doesn’t make it so. The flaws in thinking bitcoin is something we might all use to pay our debts apply to the argument about compute.

However, the idea of compute as money is more compelling than the bitcoin story. It’s rooted in actual supply and demand linked to the real world. Bitcoin may not be money but its invention has given rise to a new, alternative infrastructure for capital markets, one that is beginning to integrate with traditional networks, and these rails would indeed be a good fit for compute tokens.

Second, we are assuming current large-language models prove to be sufficiently reliable to anchor anything money-like. However, LLMs do bullshit, a feature that is baked into their models. Hyper-scaling compute hasn’t corresponded to similar gains in LLM robustness (perhaps it could, but the commercial models prefer BS to LLMs failing to provide a response). Trustworthy AI will need new architectures that will require time and a lot of investment to build. It’s therefore hard to put faith in compute tokens as claims on a service, let alone as money.

Third, we are also assuming the issuers of these tokens – the big AI labs – will survive. While there is real demand for AI, many serious analysts question whether these revenues will ever come close to the complex debt structures that Silicon Valley and Wall Street have concocted to keep the wheels spinning.

A huge financial crash would not end the AI story, but it could end OpenAI or Anthropic. In this scenario, compute contracts or deposits start to resemble tulips, at least until the industry recovers on a more stable footing.

Narrow banking redux

These “ifs” all rest on our final, and biggest, assumption: that compute tokens rapidly evolve into a new form of narrow banking that operates alongside TradFi.

Narrow banks are basically like today’s stablecoin issuers: deposit-taking institutions that only invest in safe, liquid assets, and do not engage in risky lending and maturity transformation.

A “Compute Bank” would accept fiat or tokenized cash from users, issue compute deposits and compute notes, and hold its assets in contracted capacity with AI labs or data centers plus fiat cash and short-term government debt, with some risk management rules resembling capital reserves, but in compute terms. The bank would earn money on transaction fees and float.

Just as we are now getting comfortable with licensed stablecoin issuers with fully backed reserves (although perhaps we are too comfortable), compute banks would have to prove their tokens are stable, predictable, and instantly redeemable for AI work.

AI labs would use compute banks as wholesale customers, selling them capacity up front, locking in that revenue, and relying on the bank to distribute capacity to the market. For traditional banks and dealers, compute deposits and notes would appear as a new class of high-grade, infrastructure-linked paper, potentially eligible for collateral in repo and derivatives.

This would not replace traditional money markets: everyone will still pay taxes, wages, and non-AI contracts in their fiat currency. But over time, if AI lives up to its promise across industries, corporate treasurers like Marco would come to hold compute deposits alongside cash and treasury bills, and dealers like Alicia would make markets in compute notes and compute-backed securities. MSCI would be rolling out compute indexes, and enterprises might begin trading in compute terms, creating a currency akin to petrodollars. All backed by productive capacity rather than faith in a government or hand-wavy belief bitcoin.

Not ready for prime time…yet

For this to happen, though, these assets have to be safe, cheap, and predictable – as T-bills or repo markets are today. Compute is none of those things. It is expensive, with mind-bogglingly up-front capex on hardware and data centers; it’s a voracious consumer of energy; and we have no model for cost per unit of useful work – how do we measure someone prompting their way to protein folds to cure cancer, versus someone burning carbon to ask their LLM about Hollywood gossip or sports trivia?

Moreover, compute carries its own operational risks: outages, cyberattacks, misuse, sudden regulatory changes, Iranian drones blowing up Microsoft data centers in the Middle East, chip makers in South Korea losing access to cheap electricity because of geopolitical hazards.

Finally, when we bring Wall Street practices into the equation, we end up importing Wall Street culture. That implies new layers of opacity, complexity, and leverage around speculative expectations of future AI demand. Prediction markets would be all over this asset class, bringing chaos as much as they might foster liquidity. The more the world comes to depend on AI, the more vulnerable it would be to a financialized compute market always vulnerable to a meltdown.

Therefore, the notion that compute deposits are comparable to Circle’s licensed stablecoin USDC has a long way to go. And even if a compute bank could claim full and reliable backing, users are still exposed to “money” that could go poof! because an LLM screws up, is lethally prompt-injected, or manipulates the human for its own arcane agenda.

What has to happen

This doesn’t mean compute money is a non-starter. It simply means a lot of things need to happen before it can take off. The incentives are real.

On the technology side, this implies the development of LLMs that don’t hallucinate or lie on important tasks – a goal that new AI labs in Silicon Valley are beginning to address. It implies strong evidence that AI systems can augment or replace human labor across many industries and activities. And it implies that humans have better control over AI, which allows regulators to regard it as imperfect but manageable.

On the economic side, we’d need to see AI leading to sustainable productivity gains across many industries, so that AI labs enjoy healthy margins. We need these companies to be bedrocks of stability, not debt-fueled tinderboxes.

On the institutional side, we’d need transparent, simple, and auditable structures for compute claims. This goes hand in hand with clear legal terms and sensible backing. The stablecoin example is probably insufficient, as it remains riddled with weak attestations: this isn’t good enough if compute tokens are to become the new money. We’d also need regulatory regimes that know how to supervise compute banks and enforce capital, liquidity, and risk-management standards. The upside for the compute banks would be the opportunity to integrate with traditional deposits and commercial-bank money.

These developments are all possible, although they require multiple changes of direction in Silicon Valley, on Wall Street, and in the halls of regulators and policymakers. But it’s an optimistic vision, for the emergence of compute money implies a successful adoption of AI across industries and society. The horror story is also possible, of compute money gone haywire, but it’s more likely that AI will self-destruct (either in economic and valuation terms, or something worse) before computer money has a chance to take hold.

Indeed, the idea that AI companies can morph into bedrock financial institutions, but only under the right conditions, might be the incentive we need to get them on a more sustainable, less dangerous track.

Finally, what about VC firms themselves? What’s it mean for a VC to become an AI-first financial firm? And going back to the blockchain theme, does tokenizing a VC’s own funds generate value?

Dušan Stojanović is Singapore-based founding partner at True Global Ventures, a global firm focused on seed and Series A opportunities. He describes himself as a “global citizen with a Swedish passport”. He’s been investing in AI since 2012, and in blockchain since 2016. He helped found TGV in 2010 and is now on his sixth fund.

Timecodes:

0:00 - Dušan Stojanović, True Global Ventures

02:09 - Stablecoin/Web3 convergence with AI

6:15 - OpenClaw’s “ah-ha” moment for SaaS companies and its impact on TGV’s portfolio of companies

9:46: What is an “AI-first” startup? How can you tell when “AI” is a real differentiator or a marketing ploy? What’s “AI first” in a regulated field like payments and finance?

12:25 - How do companies risk-manage the AI itself? And how Dušan looks at valuations in AI companies.

15:27 - What’s a defensive business moat for an AI startup today?

17:35 - TGV’s investment strategy: stage, size, how to compete against the biggest Silicon Valley names as the landscape for venture changes

22:46 - How “AI first” applies to TGV’s own business

25:11 - The geopolitics of investment for TGV

26:50 - Exits and liquidity

28:01 - Will TGV tokenize its own funds?

In Part 1 of this series, we looked past “trillions settled on‑chain” and asked where stablecoins actually matter in payments. Part 2 walked through how lawmakers are forcing these instruments into familiar legal boxes, from the US Genius Act to MiCA and Asia’s first licensing regimes.

Part 1: Stablecoins and the real world of payments

Part 2: The legal limbo of stablecoins

This final piece tackles the awkward question: if we now have 1:1 reserves, licenses, and real users, what can still break the peg?

The answer sits in a narrow strip of infrastructure that connects stablecoin treasuries to the rest of finance: repo markets, Treasury liquidity, and the capital that dealers and issuers are willing to put at risk. That strip – perhaps “knife’s edge” is a better description – is where stablecoins live or die in a crisis.​

Solvency vs liquidity

New rules are explicit about solvency.

The Genius Act forces US “payment stablecoin” issuers into a tight reserve box: cash, bank deposits, and short‑term Treasury‑linked instruments, at least one‑for‑one against tokens, with attestations and audits for larger names. Hong Kong and Singapore take a similar line, insisting reserves sit in segregated bank accounts and low‑risk securities.

A quick explanation of repo may help.

For large banks and dealers, repo is the main way to turn securities into cash day‑to‑day. It lets them finance big inventories of Treasuries and other bonds without locking up scarce long‑term funding or equity. When repo is cheap and plentiful, banks and dealers can carry more inventory, quote tight prices, and stand between buyers and sellers. This makes overall market liquidity look deep and smooth. When repo funding tightens, they have to shrink positions and widen spreads, and even “safe” assets can suddenly be hard to move without hitting the price.

A repurchase agreement, or repo, is structurally a secured loan. One party “sells” a security (usually a government bond) to another, while simultaneously agreeing to buy it back later at a slightly higher price. Economically, the first party is borrowing cash and posting the bond as collateral; the second is lending cash against that collateral. The price difference is the interest on the loan, and the haircut – the amount by which the collateral value exceeds the cash lent – is the lender’s buffer against market moves.

Looked at from the other side, a reverse repo is what the cash‑rich institution enters into: it is the lender, taking collateral and earning a short‑term, money‑market yield. For that lender, whether a bank, money‑market fund, or stablecoin issuer, reverse repo is a way to park liquidity very safely and very short‑term, while holding high‑quality collateral that can be sold if needed.

Taken together, repo and reverse repo form the core of the secured funding market. When those markets are healthy, banks can fund their securities books smoothly; when they are stressed, the funding chain that underpins both bank balance sheets and bond‑market liquidity comes under strain. The most dramatic example of this was in 2008, when the rest of Wall Street made a disastrous bank run against Lehman Brothers via the repo markets.

So, back to our stablecoin story. Legislation and regulation in leading money centers have addressed the obvious failure mode: the issuer that mis‑invests or misappropriates assets. But these rules don’t say what happens if those reserves can’t be immediately turned into cash, which is likely to happen when everyone heads for the doors at the same time.

Three design facts define this liquidity risk:

• Hierarchy. Stablecoins sit below banks and central banks. Redemptions settle in commercial‑bank deposits, not reserves, and often require selling securities through dealers. The peg is hostage to market access.

• Portfolio shape. The big issuers are conservative: treasuries full of T‑bills, overnight reverse repo, and bank deposits, designed to minimize duration risk. That shrinks insolvency risk but pins them to the liquidity of Treasury and repo markets.​

• Intermediated redemption. Even in a Genius world, direct token mint/burn tends to be limited to a narrow circle of institutions, with everyone else relying on secondary markets. MiCA’s direct‑access requirement simply shifts where pressure shows up: from secondary‑market discounts onto stablecoin issuer balance sheets.

In fair weather, these choices look prudent. Under stress, they turn “fully reserved” stablecoins into forced sellers in markets that may not want to buy.

The redemption wave

In the process of token mint‑and‑burn accounting, deposits move between accounts, but the system does not conjure or destroy bank money just because someone flips between tokens and deposits.​ Stablecoin issuers are, in this sense, “narrow” banks that don’t create money.

That remains true in a run. What changes is who is forced to hold which assets, at what price.

For a detailed look at repo mechanics, check out Austin Campbell’s articles at Zero Knowledge Consulting. He’s a good resource on this topic. But simply, a stylized wholesale redemption works like this.

1. A token holder sends stablecoins to the issuer to redeem.

2. The issuer sells T‑bills or unwinds reverse repo via a dealer to raise deposits.

3. The dealer’s bank moves reserves to the issuer’s bank; the issuer’s bank credits the issuer.

4. The issuer wires deposits to the redeemer’s bank; the tokens are burned.

On paper: fewer tokens, the same aggregate deposits, a reshuffled distribution of Treasuries and repo positions.

In practice, each hop can misfire:

• Dealers can hit internal risk limits or leverage constraints and widen spreads rather than warehouse more paper.

• Repo haircuts can jump, squeezing some borrowers out just as issuers most need liquidity.​

• Trading and settlement systems can slow or fail under load, delaying execution.

The March 2020 dash‑for‑cash, when Western countries were shocked by Covid and everyone on Wall Street grabbed cash at the same time, showed how little selling it takes to flip into this regime. A few hundred billion dollars of Treasury liquidations in a multi‑trillion market were enough to dislocate prices and force central‑bank intervention. Stablecoins were marginal then. With hundreds of billions in reserves and growing real‑economy usage, they are large enough now to be part of the next dash‑for‑cash.

Given current wobbles in capital markets – AI bubbles, Iran war/oil dislocations, private-equity troubles – such a crisis is very possible. Worse, we have the example of the run on Silicon Valley Bank, which transpired too quickly for the Fed to backstop SVB. Now imagine that kind of one-click panic involving a stablecoin issuer holding billions of dollars worth of US government debt.

Safe harbor or choke point?

Reverse repo has become the focus of reserve disclosures, and not by accident.

A recent paper by MIT Media Lab, “The Hidden Plumbing of Stablecoins: Financial and Technical Risks in the Genius Act Era”, shows that an overnight reverse repo with a haircut protects an issuer against rate moves better than owning longer‑dated bonds outright. The lender is over‑collateralized; even if prices fall between the two legs, they can sell the collateral and recover principal up to the haircut. Short tenors or frequent re‑margining let them roll into higher yields rather than sit on underwater positions.

With stablecoins, the issuer takes a bank deposit, lends it overnight via reverse repo to a dealer, receives Treasuries as collateral, and either gets the cash back or sells the collateral if something breaks. Deposits move between balance sheets; nothing is created or destroyed.​

This sounds safe, but in a crisis, the protection reverse repo offers on individual balance sheets can become a bottleneck at system level.

• Cash lenders may pull back or limit business to the strongest counterparties.

• Haircuts can widen, forcing more collateral for the same cash.

• Central‑bank standing repo facilities backstop only a small club of firms, under their own policy and risk limits.

Stablecoin issuers are not in that club. They access repo through the same big banks and dealers that, in a crunch, can hit various regulated constraints on how much capital banks must hold to operate in “low-risk, high volume” activities such as repo that were imposed after the 2008 Lehman bankruptcy. Genius instructs supervisors to write capital and liquidity rules for issuers, but those standards are still in development; for now, issuers run with thin capital relative to their potential obligations.​​

Reverse repo is therefore a private buffer that works well in normal times. It is not a magic shield against a broad funding crunch.

Who eats the loss?

As noted above, the US, Europe, Hong Kong, Singapore and other places are introducing capital rules for banks and brokers meant to encourage the safe handling of stablecoins. Better capital doesn’t stop runs, but it changes who can absorb what.

A well‑capitalized issuer can redeem for longer out of bank deposits, accept more slippage on asset sales, and survive operational losses without dipping into reserves. Basel capital regulations have until recently forced massive over-collateralization against stablecoins. Standard-setters like the Financial Stability Board (created by the G20 after the 2008 crisis) insist intermediaries serving stablecoins should hold capital for operational and liquidity risk, not just for the underlying credit exposure, which penalizes token-related business.

These buffers are being whittled down by crypto-industry lobbying. The US Securities and Exchange Commission has now decided broker-dealers can treat stablecoin capital requirements as equivalent to cash, with a mere 2 percent capital reserve requirement. This will encourage them to hold stablecoins and use them for settling client trades or for internal liquidity “sweeps” (automated movements of extra cash into yield-bearing investments). This makes stablecoins far more attractive tools for corporate treasurers and payments.

But it doesn’t change the basic reality that when a dealer needs US dollars to meet a wave of redemptions, it still has to turn T-bills or reverse repo into bank deposits via the same constrained balance sheets and repo markets. There may be some benefit to encouraging broker-dealers to inventory stablecoins, which can provide a broader market, but whether this makes a difference in a crisis is hard to say.

If there’s a dash for cash, no one is going to be asking “what reserves back this token”? They’ll be asking “whose balance sheet stands between me and the exit”? Which banks will be able to ride out a crisis, and which will be forced to pull back from markets just when treasurers need them the most?

Better, safer

Ultimately, the risk has to sit somewhere. That could be on the balance sheet of thinly capitalized issuers, who become forced sellers in a crisis. It could be on the balance sheet of large banks and dealers, whose own capital and liquidity constraints could turn them from serving as shock absorbers into crisis amplifiers. Or it could even sit on central-bank balance sheets, via banks’ capital reserve accounts or (in theory) direct token settlement.

If we think a 1:1 reserve rule solves the hard questions of liquidity, then we are deluding ourselves. This does not mean stablecoins are “bad” or that banks shouldn’t be willing to deal in them. Stablecoins offer a rare opportunity to bring real competition to the giant Wall Street banks that exercise far too much power in US politics, and therefore in the world. But if crypto tools are going to become a systemic part of financial fabric, we must ask far more of issuers and exchanges, as well as traditional institutions.

What might this look like? Well, here are three suggestions guaranteed to be widely hated. But here we go:

1. Boost capital requirements in repo. Focusing just on stablecoin-specific risks misses the bigger picture. Regulators can adjust capital-related surcharges so that balance‑sheet used for market‑making in Treasuries and central‑bank‑eligible collateral is not penalised as heavily as genuinely risky assets, provided banks hold strong Tier 1 capital and meet liquidity standards. For their part, banks can strengthen repo infrastructure, by dedicating capital and liquidity limits to secured‑funding desks, adding contingency plans for expanding repo and bond‑market intermediation in stress, and backing balance sheets by pre‑arranged access to central‑bank facilities where available.

2. Require stablecoin issuers build liquidity buffers. Issuers should hold a meaningful slice of reserves in instantly available deposits at strong banks and in central‑bank‑eligible overnight reverse repo, above and beyond the legal 1:1 minimum. They can also publish details about their available deposits, overnight reverse repo, and holdings of T-bills by tenor, so counteparties knows what to expect if they want to redeem. And these attestations are weak and backward looking: issuers must undergo regular, independent stress tests that replicate conditions akin to the Covid shock of March 2020 or the SVB run.

3. Regulators modernize the infrastructure. Backstops such as the Fed’s facilities for repo markets and money-market funds should also be tested for stablecoin-related crises, to ensure collateral can be turned into cash at a reasonable haircut, and build resilience into broker-dealer balance sheets. In addition to changing data and capital rules mentioned above, regulators can also consider how to improve central clearing of Treasury and repo markets, so these don’t gum up in a panic.

Yeah, thought you’d hate that. But here’s the truth: stablecoin pegs will hold, or break, not on the quality of assets in a PDF attestation, but on whether someone with enough capital and balance‑sheet room is willing to be buyer of last resort when stablecoin holders all decide they would rather have deposits today than T‑bills tomorrow. This story isn’t about the peg. What counts is the plumbing.

Part 1: Stablecoins and the real world of payments

Part 2: The legal limbo of stablecoins

Enter zero-knowledge proofs, a cryptographic technique that has a lot of promise for digital finance. It first emerged in the world of crypto and Web3, and remains central to tokenization and DeFi. But its potential application extends far beyond anything blockchain. It could in fact prove to be a crucial tool to help enable agentic AI, safely. But it could also become a new risk vector, and ZK Proofs face plenty of obstacles to adoption.

Alex Scheer is founder and CEO of zkMe, a startup that uses ZKPs to help businesses and users verify identify and asset ownership, and promotes ZKPs as the universal identity protocol for onchain compliance, risk management, and underwriting. Raised and educated in Germany, he now lives in Hong Kong.

Timecodes

0:00 - Alex Scheer and zkMe

2:30 - Privacy versus transparency, identity, and how these can help guard against deepfakes and other security risks in digital finance

4:46 - The relevance of Web3/crypto technologies in the broader world of finance and AI

6:01 - What are zero-knowledge proofs? Alex explains this tech in terms of trust, and real-world examples of ZKP in the business world

10:29 - How do we know we can trust AI agents? Alex’s different take on KYA - “know your agent” – and the lack of protection for identity in the marketplace

17:09 - ZKP adoption challenges; how ZKPs integrate (or not) with existing systems and vendors in compliance and risk management

19:36 - What’s the likelihood that ZK proofs or ZKP providers become a new node of vulnerability?

21:11 - ZKP’s potential in fostering open-finance models, and the concept of data “reusability” and making it self-sovereign

26:40 - But '“self-sovereignty” has never taken off…is this time different?

28:57 - What has to happen to get banks, regulators, and fintechs want to support the use of ZKPs? Alex’s top business opportunities.

Related conversations

Alex spoke at length about identity, privacy, and the intersection with agentic AI. You should also check out these related videos:

The AI threat to VCs, with Ray Wyand

Agentic open banking, with Jessica Liu

Cross-border digital banking, with Urzula McCormack

For stablecoins to become mainstream payments instruments requires they be compliant, but that’s just table stakes. The competitive edge for issuers is to build them with the right balance of identity and privacy standards.

Compliance is one of several aspects to building a sustainable, far-reaching financial infrastructure using stablecoins. This series of articles, “The plumbing matters more than the peg”, examines the components to realizing this goal, including reserves management, liquidity, and resilience; Part 1 already looked at the scope of opportunities for stablecoins in payments.

Part 1: Stablecoins and the real world of payments

Legal status

The good news is that many jurisdictions have legislation and regulations regarding stablecoins. This is a big change compared to about eighteen months ago, when there was little or none. However, whether it’s the US or Hong Kong, Europe or Singapore, London or Dubai, stablecoins are not legally operating at the same level as cash.

While both sides to a transaction can increasingly treat certain regulated stablecoins as money for booking and settlement, stablecoins do not have general legal-tender status.

Accounting standards view stablecoins as an intangible property or a financial asset, which requires a capital charge, whereas cash would not. More jurisdictions are interpreting their accounting standards to accept stablecoins as cash equivalents, which eliminates one barrier to their adoption. Even so, stablecoins still present merchants with an accounting nightmare: they have to track fiat fair-value receipts, and recognize any gains or losses on fiat conversion, creating additional paperwork than if they just held cash or fiat e-money.

Tax authorities mostly treat them as property or financial assets, not as cash.

Even if a regulator defines a stablecoin as a payments token, not a security, the taxman may still consider the transaction to be fair game. Spending via a bank deposit doesn’t trigger a capital-gains tax, but paying with a stablecoin still can. And tax authorities are not accepting tax payments in stablecoins, an important indicator of whether the state regards them as ‘money’.

Finally, we don’t know yet how courts and law enforcement will treat redemption at par from a clearly liable but troubled issuer; precedents around money-market funds have not been tested in a stablecoin case.

For a licensed entity to book, pay tax, and legally enforce stablecoins as a normal payment obligation depends on accounting, tax, and legal changes. Some of these shifts are in progress, others have a long way to go.

This restricts stablecoins to a narrow set of applications. As a counter-example, look at China’s eRMB. Because it’s recognized by the state as legal tender, the eRMB can be used for paying taxes and receiving government services. Stablecoins can’t worm their way into everyday life without that recognition by the state. Corporate treasurers, projected by the fintech industry to be the biggest adopters of stablecoins, confine their use to niche flows related to on-chain liquidity and collateral. They aren’t being used for general payments, let alone for mass-market invoicing or payroll.

One last challenge to settling the legal status of stablecoins is their global nature.

Global 24/7?

Even when regulators agree on sensible rules, they still only operate within their own jurisdiction. Correspondent banking evolved to meet this reality; hence the duplicative compliance checks. Crypto rails have been built to operate globally, 24/7, with little regard for regulation. Backfitting this infrastructure is easier if there is either a very big market (the US) or a bloc that offers a single license, such as in Europe’s MiCA regime.

But in Asia or Latin America? Regulation is fragmented across two dozen jurisdictions. Licensing timelines can stretch beyond two years – or longer, as many authorities struggle to recruit and keep talent with real experience in both banking and payments, and in technology.

Stablecoins sit at the intersection of securities law, payments regulation, banking supervision, and cutting‑edge cryptography. When regulators have limited opportunities for staff to be seconded into actual payment operations, or lose their qualified people to the private sector, the result is often cautious, reactive policymaking.

The good news for the industry as that authorities in hubs like Hong Kong and the UAE see licensing stablecoin operators as vital to their ambitions to lead in digital assets. They have either published comprehensive rules, or have established principles-based frameworks to focus on KYC and AML outcomes.

But central banks lack an established body for networking and trading policy ideas, so harmonization is a long, painful process. The closest thing to a coordinating body is the Bank of International Settlements, which sponsored mBridge – which, when it launched, caused a furor in the US, which saw it as BIS enabling China to work outside the framework of SWIFT-enabled correspondent banking. Don’t expect the BIS to be an active coordinator for stablecoins.

Transparency

Transparency is the another sticking point. This one is a head-scratcher for people in the crypto industry. They live with a kind of raw transparency, in which every transaction is visible. It’s the wallet addresses that are cryptographically hidden. But the kind of visibility available in crypto does not map to TradFi, which means transparency around stablecoins has to be shoehorned into legacy infrastructure.

In the SWIFT world, banks can see each counterparty and maintain audit trails for who owns which account at every hop. With stablecoin flows, the bank only sees a transfer from or to a crypto exchange or wallet provider, and cannot readily map the full chain of wallet owners touching the asset. That opacity clashes head‑on with AML, KYC, sanctions, and fraud obligations, especially in an era when billions of dollars have been lost to cybercrime and where blockchain transactions are irreversible.​

When a Visa or Mastercard payment goes wrong, the card network can reverse the transaction and often eat the fraud cost (hence justifying high issuer fees); on a public blockchain, there is no global dispute‑resolution layer.

Regulators and compliance teams are right to be wary: faster, irreversible money is great if you trust every actor in the system and can identify them; it is terrifying if you cannot.​ Perhaps there is a world in which TradFi could embrace crypto’s market transparency as a massive efficiency gain, but the more likely outcome is that stablecoins have to be designed around traditional compliance.

Privacy and identity

What might such a solution look like, one that enables the widespread adoption of stablecoins as a superior mode of payment, while preserving both the privacy demanded by crypto natives and the traditional safeguards against crime?

Today, many stablecoin systems rely on denial lists (blocking known bad actors) whereas banks operate on white lists, where only pre‑vetted clients can transact. Bridging that gap requires a new layer of decentralized identity and permissioned privacy that lets multiple participants, not just the issuer, onboard and vouch for clients.​

For banks and regulators, the core question is: who is this wallet? If a central securities depository, a custodian, or a bank can attach a verified identity to an on‑chain address using robust KYC/KYB processes, then stablecoin transfers can be treated much more like account‑to‑account transfers in today’s system. Technologies like zero‑knowledge proofs hold out the promise of allowing parties to demonstrate compliance with certain rules (for example, that a user is not on a sanctions list, or that a transaction is below a threshold) without revealing all underlying personal data.​

Some central‑bank digital currency experiments hint at this direction. In places like Hong Kong and mainland China, pilots such as eHKD and eCNY have been used to explore what happens when identity frameworks like iAMSmart or national ID systems are intertwined with digital money. Those projects raise uncomfortable questions about authoritarian overreach and surveillance, but they also provide technical templates for embedding identity at the protocol layer. For stablecoins, the challenge is to borrow the good parts – strong identity, clear liability, auditable records – while avoiding the worst: centralized, granular tracking of every transaction.​

Compliance is the price of admission; without it, no major bank or regulator will allow stablecoins to carry significant flows. Identity and privacy standards are the negotiation: can we use technology to prove that the industry can know enough about its users to satisfy AML and systemic‑risk concerns, while avoiding building panopticons?

This is a hard nut to crack, but it is achievable. Remember, it’s the plumbing, not the peg, that matters most. Our final article in this series will look at other aspects to stablecoin plumbing that still need attention: liquidity and resilience.

Fintech itself is in flux as financial institutions adapt to big changes that can be both opportunities and dangers, such as AI. Six Thirty’s Evan Thorpe talks about the emerging theme of combining health, wealth, and privacy propositions, from using fintech to rethink the value of mortgages to embedded finance.

Evan is principal for Asia Pacific and splits his time between Asia and the US. He has also worked in fintech (at Droit Financial Technologies) and in data (Dealogic). Evan advises financial institutions and mentors startups. He speaks Mandarin.

Timecodes

0:00 - Evan Thorpe, Six Thirty Ventures

3:26 - Six Thirty’s specific niche in venture, focused on enterprise B2B fintech

5:50 - How AI is changing the portfolio, and other evolutions in VC, and how he maintains a competitive edge

10:57 - Health, wealth and privacy

13:23 - What’s the relevance of wealth to an insurance company, and how would fintechs add value?

16:27 - Fintech ideas Evan’s working on with LPs, such as how products are marketed and sold, eg home equity

19:24 - What’s the role of privacy – and is this the privacy of the end user, or the institution?

22:58 - Cryptography, open finance, and embedded finance; enduring value and trust in business hasn’t changed but new technologies can expand what’s possible.

Why should stablecoins garner so much attention in the world of payments? After all, stablecoins are a niche product invented as a capital-markets tool for crypto traders. Early versions that built off of algorithms, like Terraluna, blew up. Those tied to US dollars or other fiat currencies have fared better. The global market for stablecoin tokens is now valued at $255 billion, with 99 percent of those pegged to the dollar. Compared to other financial markets, it’s a tiny number.

Although the crypto industry likes to tout figures of transaction volumes up to $35 trillion annually, this does not reflect real-world payments. It reflects internal trading in crypto markets, for which stablecoins were invented; much of which may be illicit, either wash trades or money laundering. In February, McKinsey & Company estimated annual real-world payments using stablecoins at around $390 billion.

That sounds like a lot, but it’s only 0.02 percent of real-world payments – today. It is already 10 times greater than stablecoin payments volumes from five years ago. US Treasury Secretary Scott Bennett last year predicted stablecoin volumes to reach $3 trillion by 2030. Beyond B2B cross-border payments, other use cases might include payroll, billing, and capital markets, in which stablecoins provide the cash leg to tokenized asset transactions.

Part 2: The legal limbo of stablecoins

For the little guy?

When it comes to payments for real-world goods and services, the industry likes to talk up the financial-inclusion story. And it’s not wrong. Stablecoins can deliver real benefits to companies that are small or national. This feel-good narrative downplays the role of big corporate banks that already bend over backwards to provide premium services to multinational corporations. It puts fintech and crypto startups in the cockpit.

The status quo network of correspondent banks was indeed designed for MNCs. Even for them, payments involve hassle: the vagaries of the system are such that physical goods may well reach their destination before the exporter receives their money. They are also on the hook to payment processors for interchange fees behind credit and debit cards.

For a multinational, correspondent networks and payment fees are just a cost of doing business, and absorbing costs can become a competitive advantage. For everyone else, slow and expensive payments are a business impediment.

Ah, correspondent banking

This doesn’t mean correspondent banking is a failure. Au contraire! It works very well – for banks. Since the 1970s they have agreed on SWIFT messaging standards, which identifies senders and receivers of money, defines the purpose of the payment, and other data points that are necessary to moving money through a chain of correspondent banks. Each bank has to run its own compliance checks, though.

This duplicative process adds delay, and cost (there are plenty of opaque moments where banks convert currencies for a juicy fee). And although compliance safeguards the entire system, small businesses can get crushed if faulty paperwork or suspicions about dodgy transactions prompts a bank to freeze the cash.

The disadvantages to smaller B2B transactions as well as individual remittances reflect the origins of correspondent banking as a service for large corporations. Today remittances and smaller B2B transactions are part of the mix, thanks to digital services, but MNC volumes still account for 47 percent of cross-border payment flows, and the highest value.

Just as the greatest value in cross-border payments remains with MNC customers, so too the eventual prize for stablecoin issuers will be these same giants; but financial inclusion is what gets the ball rolling. It also makes a crypto product sound warm and fuzzy to regulators.

The fintech money operators

This follows the first wave of fintech disruptors, which serve segments that were previously excluded from correspondent networks, or for whom making cross-border payments was especially expensive. This focus on smaller B2B and remittance users has enabled fintechs such as Airwallex, Revolut and Wise to make FX and payments instant and cheap.

But they operate on correspondent banking rails. The actual settlement time and cost hasn’t changed. What’s different is the user experience (UX). The fintechs open bank accounts in many countries, and handle the settlement behind the scenes while the customer appears to enjoy an instant transfer with just a few easy clicks. This works by pre-funding all of these accounts.

It’s an expensive business. Depending on local licensing, these money operators must hold initial capital and operating, risk-based capital. They hold high cash reserves. That’s money that cannot be lent out (these aren’t licensed banks, although Revolut has become one in Europe). They earn revenues through interchange fees on debit cards, subscriptions to upgraded services (travel perks, for example), and earning interest on customer deposits. They also charge companies a fee to integrate into their services with an API.

Stablecoin issuers

Enter stablecoin issuers. Their sole means of revenue is earning interest on deposits. They also face distribution costs: Circle pays a hefty cut of revenues to crypto exchange Coinbase. Revolut today is very profitable thanks to a diversified line of business, while Circle – the second-largest stablecoin issuer, and by far the largest operating in a regulated environment – is still loss-making. Its revenues are also dependent on interest rates.

These businesses promise faster, better, cheaper for cross-border payments. They need to offer the immediacy and low cost of fintech money operators, while making the transactions happen in actual real time, by combining the messaging function with the movement of value. By using blockchain rails, stablecoin issuers are meant to create a lower cost of doing business, and bypass the heavy reconciliation processes of correspondent banking. If they can offer a yield on their tokens, they will be able to build new product lines.

It’s not necessarily about wiping out money operators. Revolut, building on its FX business, now adds stablecoins and crypto to its currency brokering; its goal is to be a retail hub for digital assets, so these businesses can be mutually reinforcing.

Sandwich or spaghetti

This is an example of the “stablecoin sandwich”, in which two fiat currencies serve as the bread, with a stablecoin in the middle as the meat: it is connective tissue in which the tokens are transacted momentarily rather than held.

In this example, Revolut is coordinating the two fiat slices and someone else’s stablecoin filling, perhaps Circle’s USDC. In other cases, such as with Ripple, its token XRP is the filling while Ripple also coordinates the XRP/fiat transaction among its bank counterparties. Stripe is taking a bite of its own, integrating stablecoins such as USDC into its Optimized Checkout Suite so merchants can instantly and cheaply settle into their own fiat currency.

Other players are aiming to build closed-loop systems using their own stablecoins: not a sandwich but a bowl of spaghetti (our play on the term for messy middleware, only in this case maybe there’s just one noodle instead of many).

Walmart is experimenting with its own stablecoin, to be used in a closed loop among its own customers, displacing credit card transactions which come with a 3 percent interchange fee that Walmart must pay. It wants to integrate blockchain finance with rewards, instant refunds, and other customer-friendly services, while saving a lot of money. JP Morgan’s Kinexys unit operates JPM Coin to move client moneys internally across global accounts instantly and efficiently.

Endgame

These examples point to the eventual power of stablecoins, not as serving the little guys, but by embedding themselves in corporate treasury workflows. The fintech money operators were designed for SMEs and individuals, and that’s where they’ll stay. Stablecoin players today talk the same language but they’re ultimately building for the highest-value clients in cross-border payments.

Despite these encouraging initatives, stablecoins have a way to go before they become mainstream. Adoption won’t just happen organically until lingering issues are addressed. Stablecoins emerged from the crypto world and they bring crypto baggage.

The easiest challenge to tackle is crypto’s notoriously bad UX, reliant on seed phrases, clunky wallets, and confusing interfaces that don’t integrate well into, say, a user’s bank statement. These are addressable design challenges.

There remain more fundamental challenges, which our next article will explore: legal status, transparency/privacy, liquidity, and resilience. Part 2: The legal limbo of stablecoins.

This sparked plenty of debate about the viability of software businesses, but what about the Silicon Valley firms that finance them: the venture capitalists? SaaS companies make up on average 40 to 60 percent of VC portfolios. And many fintech or regtech companies rely on similar SaaS models to provide legal, analytical, and customer-relationship tasks.

Ray Wyand is CEO and co-founder of Verified Metrics, a startup that provides valuation and financial due diligence services to VC firms. He was also co-founder of gini, an open-banking fintech, and a global credit trader at Citibank.

Timecodes

0:00 - The “SaaSpocalypse”, genAI’s impact on SaaS companies – the biggest component of VC portfolios – and how it’s fracturing the VC industry

6:30 - Within SaaS, what makes a company a commodity, or an amplifier of AI? How does this distinction impact VC portfolio valuations?

9:00 - What does this mean for ‘domain expertise’ and is it still a competitive moat for tech companies?

11:11 - Are VCs starting to value companies that can assume liabilities as a competitive advantage?

13:24 - What’s still working for the majority of VCs who aren’t early investors in AI labs; differentiating the US market versus the rest of the world

16:54 - Why founders in Southeast Asia and other small markets are struggling, and the need for startups to build for a global marketplace, ie the Israel model

21:27 - Fintech and trying to globalize when regulation and licensing are local

23:19 - outlook for VC portfolio valuations in the new reality of AI

25:00 - Implications for how the VC industry is organized, how funds are structured; comparisons to how public fund managers operate, and blurring lines among PE, private credit, and public funds

33:17 - Can the AI labs’ revenues catch up with their debt loads? Why to be optimistic about founders and VCs even if there’s an AI stock-market crash - “magic just sometimes happens”

In the lunar calendar, Years of the Horse are depicted as times of acceleration. Galloping on a stallion is exciting and dangerous, whether it’s kicking up Arabian sands, scaling mesas of the Wild West, or crossing the finish line at Happy Valley racecourse.

There are three trends in ‘tech+money’ that look likely to accelerate this year: financialization, AI, and fragmentation. Moreover, they are interconnected. The thrill of the ride – the pounding of the heart, the terror of being thrown out of the saddle – is in these feedback loops.

Financialization

Financialization is the toxic gift of the neo-liberal age. We may no longer have free markets and unfettered trade, but the role of finance in industry and life continues to expand far beyond the rate of GDP. For decades, now, finance has existed to serve itself, not the real economy.

There are benefits to finance, of course. All people and businesses need access to savings, credit, markets, wealth, and protection. Commercial banks creating money is what enables growth. The outsized nature of derivatives markets and financial engineering provide liquidity, stability, and tools to manage risk—if used and regulated sensibly (and we can argue for a very long time about what ‘sensibly’ means).

But something gets lost as more assets are sliced, packaged, and sold. We’re well past securitizing sub-prime mortgages, all the way to private equity, identity, IP, reputations. Raw capitalism transforms every social activity into an economic one; industries and lives get abstracted on Bloomberg screens. Then, before you know it, your entire manufacturing base has moved to another country, your once-great corporations and institutions are hollowed-out shells dedicated to share buybacks, and your culture is debased into tranches of data streams.

This has been going on since the 1970s when Wall Street began adopting computers. Computers led to digitalization, and now to tokenization, fractional ownership, and programmable credit and money. We now have flats in Dubai and Chinese supply-chain finance tradable on-chain as tokens.

This is great for a new class of investors, it’s great for downstream companies to secure new forms of credit and liquidity, and it’s great for people who live in countries with rickety financial infrastructure to find new ways to save, pay, and invest. New products will create new derivatives markets, and assets and liabilities that can be plugged in like Lego blocks to build better portfolios.

It also is turning every human experience into a gambling opportunity. Once-staid financial markets increasingly resemble a casino. Crypto IS a casino. And actual casinos, like online sports betting franchises, have been recast as securities trading on prediction markets. Moreover, it is creating products that should not be in the hands of retail investors. Private-equity firms, their portfolios bleeding behind their walls of illiquidity, would like nothing more than to dump their positions on retirees for a generous fee.

AI and fragmentation are turbo-charging financialization. AI first. Tech has always gone hand-in-hand with finance in the neo-liberal age, and trading desks have been big users of algorithms for decades. Neural nets gave us machine learning. Now LLMs are able to underwrite risk on thinner data, price and re-price millions of micro-positions in real time, and automate compliance.

So far, AI has only slowly begun to replace many workers in finance, but this will accelerate. More interesting than automation is how AI can reinvent entire verticals: the coordination among institutions, clients, and markets. It allows small teams to provide the same kind of services as lumbering incumbents. In terms of financialization, AI will be the new managerial layer building and designing a deluge of products, assets, liabilities, payments, and trades. It will become the heart and circulatory system for hyper-personalization, pushing out the frontier of financialization.

Fragmentation of global systems is also a boon for financialization. Even as monetary systems, supply chains and trade routes change or collapse, the enhanced complexity of a fractured world will call for new things to broker, to hedge, to use as collateral. Every new risk, every new cross‑border friction, becomes something that can be wrapped in a swap, an option, or a structured token.

Artificial intelligence

AI is becoming the coordination layer that makes this new financialized landscape workable at speed and scale.

For small firms and individuals, AI lowers the operational barrier to entering a financialized world. A solo entrepreneur can now plug into AI‑driven accounting, credit scoring, and portfolio tools that previously required a back office. A retail investor can access algorithmic trading strategies or auto‑rebalancing portfolios via a mobile app.

For large enterprises and banks, AI is more profound. It allows them to operate as networks of human and machine agents, orchestrated by systems that monitor flows, allocate capital, and manage risk in real time. Credit decisions, compliance checks, collateral calls, and cross‑border payments can be routed through AI‑mediated workflows that cross internal silos and external networks. Instead of a stack of discrete systems tied together by manual processes, you get a mesh of services coordinated by models.

Now plug this into financialization: More of the world is turned into tradable risk. AI makes it cheap to originate, distribute, and manage that risk. The marginal cost of adding one more product, one more synthetic exposure, one more layer of leverage, falls.

The feedback loop is powerful. The more financialized the system becomes, the more data it generates. The more data it generates, the better AI gets at spotting patterns and arbitrage. The better AI gets, the more aggressively firms can push into new niches: new tokens, new credit products, new forms of securitization.

The risk, of course, is that we create a hyper‑efficient machine for manufacturing opacity. AI can make complex structures appear seamless and user‑friendly, masking correlated risks and hidden leverage behind clean dashboards and personal recommendations. When many actors run similar models, trained on similar data, their behavior can synchronize. AI complexity without transparency and robust reporting is more likely to create systemic risks, not mitigate them.

We will need AI more, though, in a fragmenting world. The creation of new, non-USD settlement corridors, clearing systems, currency networks, and commodity contracts will require plenty of intermediation, risk management, and market making. Standards will break down while data and legal barriers will rise. The coordination required will be far more complex than anything experienced by the finance industry.

Fragmentation

Fragmentation is going to lead to greater reliance on AI. Routing payments around bottlenecks, arbitraging spreads between rails, gaming different regulatory pockets: these are machine problems as much as human ones. The more fragmented the system, the more valuable good coordination becomes.

Fragmentation is the diminishment of the US-dollar centric world. The dollar, US Treasuries, and New York’s clearing function is both the result of neoliberalism and its facilitator. The dollar remains by far the primary reserve currency because it is the currency of debt, of swaps, of oil. Treasuries are, still, the only safe haven of size. The liquidity and openness of the US financial system has absorbed the massive excessive savings of mercantilist countries.

Although the US is trying to prop up the dollar and demand for Treasuries through stablecoins, this isn’t stopping many efforts around the world to find hedges and alternative mechanisms for moving and storing wealth. The recent volatility in gold and silver prices shows the limits to these efforts, but they will only continue. China now offers a yield on its CBDC, an enticement aimed at getting foreigners to hold a currency that is only convertible upon Beijing’s whim. Such efforts will accelerate. The main impact will be to make it more expensive and difficult to manage money and credit globally.

Financialization and AI are going to support fragmentation, though. AI, as noted, will provide the tools to make managing this fragmented world easier. Financialization will create more products to hedge, invest, and borrow or lend, be it involving dollars, or crypto, or CBDCs, or tokenized deposits.

What makes the Year of the Horse so dynamic is that this is the year when these feedback loops intensify. Financialization creates more ways to slice and dice assets and liabilities, generating data that is used by AI to price and route trades, which encourages further financialization.

Fragmentation creates more complex, multi-rail, multi-currency environments that can’t be managed by hand. AI becomes the coordinator, and as fragmentation becomes easier to navigate, processes of disintegration will accelerate.

The more the global system splinters into blocs, hedges, and parallel rails, the more demand there is for instruments to bridge these gaps: swaps, structured credit, synthetic exposures, stablecoins. This deepens financialization and hands firms more tools to amplify further fragmentation.

The upside of these feedback loops is that capital can be allocated more precisely, small firms can access tools previously only available to big institutions, and incumbents can operate more nimbly. But this is also a world in which shocks will propagate faster, where inequality widens between those who can navigate this landscape and those who are simply exposed to it, and where the foundational assumptions of money, work, and safety nets are in flux and in danger.

The JDB Report wishes our audience a healthy and prosperous Lunar New Year. Ride fast and ride safe!

The Chinese party behind this structure is Dewu Finance, a Beijing-based, fintech-focused venture investor. Its parent is Dewu, a large e-commerce platform. Through parent Dewu’s supplier-relationship management system, it has data and relationships covering millions of suppliers and many enterprise buyers. Dewu Finance (sometimes also called Dewu Capital), is an early-stage venture investor in fintech and SaaS (software as a service) for enterprises.

Boran DING and Ian LEE Yinghong are managing partners at Dewu Finance, based respectively in Beijing and Shanghai.

Timecodes

0:00 - Boran Ding and Ian Lee, Dewu Finance

02:07 - Why establish Dewu Finance and how to drive fintech opportunities in China, in the post-Alibaba crackdown and China’s strength as a manufacturer

4:51 - Risks for global investors in Chinese supply-chain investing

7:25 - Regulatory risk

9:42 - SaaS in the age of AI, in China versus the US

13:06 - As VCs, what do you look for among China SaaS and fintech companies?

15:31 - How big do you need to be, to invest in AI opportunities in China?

17:40 - The role of fintech versus SaaS for accessing manufacturing and supply-chain flows

18:36 - Adopting tokenization as a strategy to tap foreign investments

20:35 - Goals for the first tokenization scheme, and what’s next

23:52 - Global investor misconceptions about investing in China opportunities

The good news is that many firms are working on solutions, using algorithms that are designed to protect against post-quantum computing futures, and by continually reorganizing processes and protocols.

Robert “RoRo” Rogenmoser is CEO of Securosys, a Zurich-based cybersecurity company that develops hardware security modules (HSM) and cloud-based encryption solutions for financial institutions, enterprises, and governments. He has built a career around engineering and design for microprocessors and chip-related software for mobile phones, networks, and industrial and commercial applications.

Timecodes

0:00 - Robert “RoRo” Rogenmoser, Securosys

1:27 - How safe is on-chain finance from the advent of quantum computing? What’s Securoys doing? How do we know these “post-quantum” algorithms actually work?

6:05 - AI as a security threat: what we know and what we don’t

8:46 - Security in the nexus of digital assets and digital identity

9:57 - Hardware security models (HSM), early generation models and new, cloud-based models

14:57 - Enterprise hardware versus personal and portable crypto security

17:08 - HSM’s role in custody

19:28 - “Root trust” as a Swiss company

21:57 - What commercial drivers are behind HSM security, crypto native versus TradFi, finance versus corporate uses

26:08 - Securosys’s footprint

26:48 - Protecting data in a world of both 24/7 global markets and geopolitical fragmentation

The idea that the AI industry constitutes a risk to investors is well known. The story goes that precarious, opaque and circular transactions exist among involving LLM modelers like OpenAI, its Big Tech backers, data center and energy buildouts, off-balance sheet lending from private capital, and private capital’s own borrowing from banks. And the AI labs sorely lack the revenues to justify all of this, so poof go the securities markets.

(There’s also the end-of-the-world theme, but in that case, an equities wipeout won’t matter.)

How do institutional investors address such a market risk? Is the risk itself so hyped that it’s “been priced into the market”? How do we think about protecting portfolios?

This is not just a question for individuals or rich individuals. Financial centers earn that title by recycling the investments at scale of the largest institutions, including sovereign wealth funds, pension funds, and insurance companies.

These vast real-money investors must allocate to the biggest, most liquid asset classes, as well as to alternatives. They have benefited greatly over the past three years from the terrific performance posted by AI-related stocks (the Mag Seven or Eight US names), and these have become the biggest drivers of performance in US equities, and to some extent, global equities.

Mag-Seven explain more than half the S&P 500’s gains over the last yree years, and today they represent about one-third of its market cap. That is incredible concentration that shows the extent to which equity performance relies on a handful of Big Tech hyperscalers.

But when the music stops, could we be facing a market like October 2008? Or a bubble collapse like 2001, or Japan in 1998? Value investors and perma-bears have been saying yes (of course) for a while now, but their concerns are no longer fringe.

Norges Bank Investment Management, the $2 trillion manager of Norway’s oil receipts, released its annual review of investment risk, for the second year running. AI-related tail risk is at the heart of its stress-testing framework.

NBIM treats AI as a systemic risk, not an isolated tech theme or a company-specific story. It can drive broad equity crashes, interact with geopolitics and fiscal stress, and overwhelm traditional diversification measures.

AI is one of four system risks NBIM has identified, along with geopolitical fragmentation, a debt crisis, and a climate crisis. Individual calls on stocks or bonds or alternatives don’t warrant this level of attention. We live in a world dominated by flows, not fundamentals.

It’s notable, however, that NBIM has changed its tune on the AI story. In 2024, it focused on the risk that over-optimistic AI valuations deflate as earnings disappoint, with losses concentrated in AI-heavy segments of the equity market and a classic “flight to safety” into government bonds that partly offsets the damage.

By the end of 2025, though, NBIM has observed that AI-related market concentration and capex have risen further, so a correction would now mean a broad equity market crash, with larger losses in stocks overall, and fixed income providing less relief.

Even this analysis is moderate in its fears, as it doesn’t account for the second- and third-level impacts on private capital and banks.

Nonetheless, NBIIM’s approach to stress testing is increasingly systemic. It outlines many possible outcomes and narrows these to a few scenarios that combine high severity and a probability of occurring (not a prediction, but not a negligible chance). NBIM applies past episodes (the Asian financial crisis of 1997, the dot.com bust, GFC, Covid, the 2022 interest-rate shock) to the current holdings of assets and reports simulated NAV impacts. In most cases, such as the dot.com crash or GFC, equity losses dominated, with fixed income providing modest losses, or modest upsides.

But these four new risk scenarios don’t behave the same way. The outcomes don’t match previous crises. The drawdowns are deeper and more widespread.

The good news, if we look for some, is that investors are waking up to the fact that there aren’t safe havens. This raises a question that NBIM doesn’t appear to have addressed: in previous crises, the US dollar and Treasury market was the safe haven, even in crises of America’s making. Will that be the case next time? Probably, because that’s the only place with liquidity at this scale. But still, worth a ponder.

But the not-so-good news is that in a systemic crisis, diversification becomes diluted. Diversification has always been the “free lunch” for investors: balancing portfolios among asset classes has always been a way to reduce volatility and other measurements of risk.

NBIM notes that a 70/30 equity‑bond mix can deliver long stretches of robust real returns but also multi‑year periods of deeply negative performance, underscoring that diversification across public equities and fixed income is no guarantee against large drawdowns.​

What’s an asset owner to do?

Institutions must keep leverage (including securities lending) and illiquid exposure within tight bounds; hold onto a lot of cash or short-term government bonds; and avoid too much concentration in obvious danger points (mega-cap AI names, but also single counterparties or specific sovereigns).

But even this is tricky, because NBIM can’t predict which crisis it should try to mitigate. They are all existential but in different ways. (It also realizes that one systemic crisis could bleed into a second, in which case...yeah, bad.)

A debt crisis would harm equities and long-duration bonds, but may be limited to just certain markets. A fragmented-world scenario (economies retrench into blocs) would hurt stocks in both developed and emerging markets. The AI scenario hurts stocks but could be mitigated by a rally in bonds, but this requires tricky timing around duration. And extreme weather events could hit all risk assets, including alternatives, via inflationary food shocks. Meanwhile, keep too much in cash, and watch inflation eat your portfolio.

Put another way, NBIM sees that building exposures to one asset class sets it up for different risks. Should it lean in into equities and real assets? Yes if it fears a debt crisis, but then it’s vulnerable to an AI crash or geopolitical fragmentation and climate shocks.

So should it emphasize duration and sovereign bonds, and give up some upside? Not if a debt crisis strikes.

Go for real estate and infrastructure? NBIM does suggest real estate and infrastructure may retain some of their defensive characteristics. Yet bad things can happen in both interest-rate and growth shocks, and these assets can’t be tapped quickly if NBIM had to cover drawdowns in the liquid parts of its portfolio, meaning it would accelerate a downward spiral of asset prices. Alternatives are helpful so long as investors avoid concentrating too much in a handful of private-capital managers and have a sensible spread across geographies.

At least big investors have a realistic idea about what can go wrong, and know that all asset classes are vulnerable. The usual tools of volatility and tracking error are still important to risk management, but expected shortfalls may prove a more realistic way to understand what might happen.

In other words, risk management for large, real-money investors is now about survival. That’s it.