James Wo, the founder and chief executive of crypto investment firm DFG, says bitcoin remains the dominant institutional asset in crypto — and ether is unlikely to reach the same status anytime soon.

Speaking to CoinDesk at the Proof of Talk conference in Paris, Wo rejected Bitmine Immersion Technologies Chairman Tom Lee’s big prediction that ether would hit $250,000, arguing that Ethereum lacks the same consensus and institutional recognition that have formed around bitcoin.

“I totally disagree with him,” Wo said.

“Bitcoin has a very strong consensus. If you talk to everyone who is an early backer… they believe in bitcoin. Now, beyond the early backing of bitcoin, all the people in crypto, and also traditional finance people, are trying to recognize bitcoin as a safe haven or asset class. I don’t think Ethereum is there yet.”

Ether was trading around $1,775 as of time of writing, while bitcoin was near $63,000.

Wo argued that ether’s fundamental valuation remains heavily dependent on the localized application layer running directly on top of the network to capture fee value. With modern Layer-2 networks now diverting transactional volume and capturing fee utility independently, Wo explains that the network’s value accrual has been structurally different.

“The value of ether has been more diversified or decentralized,” Wo noted.

“The Ethereum token as a whole is not going to capture a lot of value. Onchain activity is not as big as people expected… I don’t think Ethereum will even hit an all-time high. I think bitcoin will perform well, but not Ethereum,” he claimed.

Not everyone agrees that Ethereum’s value accrual problem is permanent, however.

In February, Ethereum co-founder Vitalik Buterin reignited debate within the community after suggesting that Layer-2 networks, which have long been seen as the primary scaling solution, may “no longer make sense” as Ethereum becomes faster and cheaper. The discussion reflects broader questions about whether future upgrades could allow more economic activity to accrue directly to the Ethereum base layer.

‘What is bitcoin?’

Wo’s view, however, reflects the perspective of an investor who has spent more than a decade deploying capital across digital assets, that started with bitcoin.

After studying mathematics at university, Wo began watching classmates trade bitcoin during the 2014 bear market. He later entered the sector with $20 million in initial capital from his mother, who, at the time, managed an established enterprise and private equity firm in China.

“At the beginning, I don’t think she trusted me,” Wo recalled. “What is bitcoin? She has no idea.” But she gave him the money regardless and said, “Okay, so I’m going to support you anyway.”

He deployed that initial capital into bitcoin during the market lows of late 2014 and 2015. As the 2016 bull market developed, he diversified DFG’s balance sheet into alternative layer-1 protocols, becoming an early venture participant in ecosystems including Solana, Polkadot and Near.

He also directed early-stage corporate investments into consumer applications and Web3 infrastructure, including an early $10 million allocation into Circle’s USDC stablecoin project in January 2018.

Those investments helped transform DFG from a bitcoin-focused investment vehicle into one of crypto’s larger venture investors. Today, the firm manages more than 100 portfolio entities with over $1 billion in total assets under management.

Bitcoin’s new all-time high

While Wo remains cautious on ether, his multi-year outlook for bitcoin is constructive. He frames the asset as a superior liquid investment compared with regional real estate and traditional equity markets.

“I firmly believe this is going to outperform the Chinese stock market and also the U.S. stock market,” Wo stated. “Bitcoin in any aspect you can think of from the investment angle—liquidity is the best in the world.”

Wo expects bitcoin could undergo a near-term correction before reaching new highs later in the cycle.

“If it goes down 50% as a correction… the bottom should be around $60,000 to $62,000,” Wo calculated, adding that only an extreme geopolitical black swan event would push the asset lower.

Looking further out, he expects bitcoin to reach new records in the coming years.

“At the peak, we have somehow like $125,000… I believe we will see an all-time high in 2027 or 2028.”

Travala, the Singapore-based crypto travel platform, unveiled what it describes as the first agent-enabled AI travel protocol on Base, enabling artificial intelligence agents to search, reserve, and pay for hotels using USDC. The Travala Travel MCP (Multi-Channel Protocol) is live via Claude Desktop and opens to external developers who want to integrate their own travel agents with Travala’s hotel inventory.

The company says the system links Travala’s hotel inventory to AI agents through the Model Context Protocol, an open standard for connecting AI apps to external tools. Payments flow over Coinbase’s x402 protocol on Base, delivering gasless USDC transactions, near-instant settlement, and projected transaction costs around $0.01 per booking. Final payment authorization, however, still requires manual approval from the traveler, so the workflow remains semi-autonomous rather than fully automated.

Key takeaways

• The world’s first agentic AI travel protocol on Base enables AI-driven hotel bookings using USDC via the x402 framework, with gasless transactions and sub-cent costs per booking.
• Ultimate payment approval stays in the traveler’s hands, making the system more a guided automation than a full-autonomy checkout.
• Travala’s protocol provides access to more than 2.2 million hotel listings, including properties from Marriott, Hilton and IHG, and supports integration with third-party travel agents.
• Beyond hotels, Travala plans to extend the protocol to other travel products such as flights, with its AVA loyalty token poised to enable future MCP use cases.

How the AI-enabled travel protocol works and why it matters

At the core, Travala’s Travel MCP connects its hotel inventory to AI agents using the Model Context Protocol, a framework designed to let AI applications interact with real-world tools and data. This setup is paired with ERC-7715 session keys, allowing an AI agent to request a payment while preserving final signing authority within the traveler’s own wallet. In practice, a user can initiate a search and booking flow with an AI agent, and the agent will trigger a payment request that the traveler can approve, all within a single, continuous chat thread that preserves context across searches, bookings, and cancellations.

Travala emphasizes that the payment layer operates on Base via Coinbase’s x402 protocol, which enables gasless USDC transactions and faster settlement without the typical on-chain fee burden. The company estimates booking costs around $0.01 in transaction fees, a tiny fraction by crypto standards, though the exact figures can vary with network conditions and off-chain processing. In a nod to real-world practicality, the protocol does not eliminate human oversight—travelers must authorize every payment, ensuring consumer protection and control remain central to the experience.

Branding the move as a step toward a more autonomous travel economy, Travala’s leadership frames it as a shift away from the traditional checkout button. Chief Executive Juan Otero described the launch as “the death of the checkout button” and framed it as the opening salvo in a broader transition toward AI-driven bookings—while still keeping the traveler in the loop for final decisions.

The protocol’s architecture is designed to maintain continuity across the entire trip lifecycle. From a user’s initial search to a potential cancellation, the AI agent can operate within a single conversation, pulling in live inventory, rates, and terms without forcing the user to repeatedly re-enter information or re-approve steps in separate flows. The design aims to streamline user experiences and reduce friction in AI-assisted travel planning.

As part of the rollout, Travala is offering developers a 10% Coinbase Wrapped BTC (cbBTC) rebate on completed stays booked through its AI agents, providing a tangible incentive for integrating with the protocol and building out new use cases for AI-driven payments in travel.

A broader context: a wave of AI-agent payment infrastructure

The Travala announcement sits within a growing wave of crypto infrastructure aimed at enabling AI agents to transact. Cointelegraph reports that x402-linked wallets on Base have surpassed 100 million transactions, underscoring the momentum behind agentic payment rails on Layer 2. The ecosystem has seen a string of product launches from notable names, including Fireblocks, MoonPay, Exodus and Oobit, all pursuing AI-centric stablecoin payment capabilities to power autonomous or semi-autonomous workflows for users and businesses alike.

Travala also notes that its protocol taps into a broad pool of hotel inventory, with listings drawn from major brands via aggregator partners. In total, the platform’s catalog covers more than 2.2 million hotels, including properties from Marriott, Hilton and IHG. Beyond hotels, the company intends to extend the MCP to other travel components, notably flights, while the AVA loyalty token could underpin future MCP use cases as adoption grows.

From a competitive standpoint, Travala’s move shifts the frame from traditional crypto-enabled checkouts to AI-agent-enabled booking infrastructure. The company previously positioned itself as part of a competitive ecosystem that includes Sleap.io and Alternative Airlines, but the current announcement emphasizes infrastructure for AI agents, with a focus on seamless, agent-mediated payments rather than purely crypto-only checkout experiences.

What to watch next for AI travel and payments

Travala’s foray into agentic travel payments raises several questions for stakeholders: How will final-approval controls evolve as AI agents learn to anticipate user needs? Will more airlines and hotel chains join the Model Context Protocol ecosystem, and how will loyalty programs like AVA adapt to machine-to-machine or agent-mediated bookings? Regulators may also scrutinize semi-autonomous payment flows to ensure consumer protections keep pace with technology.

For developers and investors, the key signals will be how quickly the MCP gains traction across the travel vertical, how easily third-party agents can integrate, and whether cost savings from gasless Base transactions translate into measurable efficiency gains or improved customer experiences. Observers will also be watching how the wider crypto payments landscape adapts to growing AI usage, including the balance between automation and human oversight in financial transactions.

As the technology matures, travel participants—consumers, developers, and hospitality partners—will need to assess how far AI-driven, semi-autonomous bookings can improve convenience without sacrificing control or safety. The coming months should clarify how broadly the Model Context Protocol-based integrations will expand, which travel segments will follow hotels into automation, and what policy guardrails will shape the adoption of agent-based payments on Layer 2 networks.

TLDR:

• Crypto liquidation heatmap recorded $1.28 billion in losses as market leverage rapidly unwound.
• Long positions suffered nearly $996 million in liquidations, far exceeding short-side losses.
• Bitcoin and Ethereum accounted for over $830 million of total liquidations during the selloff.
• More than 264,000 traders were liquidated as cascading margin calls accelerated declines.

Crypto liquidation heatmap data revealed one of the largest leverage flushes seen in recent weeks, with more than $1.28 billion erased from crypto derivatives markets in a single day.

The event exposed excessive bullish positioning as traders faced a rapid wave of forced liquidations across major digital assets.

Long Traders Bore the Brunt of the Market Unwind

The crypto liquidation heatmap showed a clear imbalance between bullish and bearish positions. Of the $1.28 billion liquidated during the period, nearly $996 million came from long positions, while short liquidations totaled about $289 million.

The figures suggest traders entered the session with strong expectations of further upside. However, once prices started weakening, leveraged positions quickly became vulnerable.

As margin levels deteriorated, exchanges automatically closed positions to limit losses, accelerating selling pressure throughout the market.

Data from liquidation trackers showed how quickly conditions worsened. What started as modest liquidations during the early hours evolved into a broad market deleveraging event.

The process created a chain reaction where each forced sale contributed to further downside pressure, triggering additional liquidations.

More than 264,000 traders were reportedly liquidated during the move. The scale of participation indicates that both retail and larger market participants were caught in the downturn. One of the largest reported liquidations involved a BTCUSD position valued at approximately $9.02 million.

Bitcoin and Ethereum Dominate Liquidation Activity

Bitcoin and Ethereum accounted for the majority of losses displayed on the crypto liquidation heatmap. Bitcoin registered approximately $476.53 million in liquidations, while Ethereum followed with around $354.02 million.

Source: CoinGlass

Combined, the two largest cryptocurrencies represented more than $830 million of the total liquidations. Such concentration reflects the amount of leveraged capital typically deployed in major digital assets, particularly during periods of strong market optimism.

Separate heatmap snapshots also placed Ethereum at the top of liquidation rankings in certain intervals. This trend is often observed when traders seek higher returns through ETH exposure during bullish phases. As sentiment shifted, those positions faced heavier pressure.

The timeline of liquidations further demonstrated the speed of the move. Losses climbed from roughly $7.82 million in the first hour to $40.76 million within four hours. By the 12-hour mark, liquidations had surpassed $336 million before ultimately reaching $1.28 billion.

While the selloff caused substantial losses, the event also removed a significant amount of leverage from the market.

The crypto liquidation heatmap captured a rapid transition from aggressive risk-taking to defensive positioning, illustrating how quickly sentiment can change within the digital asset sector.

A Bitcoin address that had held 35.55 bitcoin worth $2.54 million untouched since March 2011 moved its coins earlier this wee,, becoming one of the first publicly visible responses from a named defendant in a New York state lawsuit that claims legal title over 39,069 dormant bitcoin wallets.

The wallet, 1LwWtSs7tMCwcRczQd5kVMv3xpWw6w4Sxe, sent 15 BTC to a new address and held the remaining 20.55 BTC as change in transaction b90755b at 16:46 UTC on June 2, recorded in Bitcoin block 952,104, per mempool.space data.

The original coins were received on March 27, 2011, when bitcoin traded at less than a dollar.

The lawsuit, filed March 11, 2026 at the New York County Supreme Court under index number 153119/2026 and amended on May 1, names a pseudonymous plaintiff identified only as Noah Doe along with two Wyoming LLCs holding assigned interests, ABC Company and XYZ Company.

The plaintiffs seek legal ownership of roughly 3.8 million bitcoin valued at approximately $285 billion under New York Personal Property Law Article 7-B, the state’s lost-property statute, with Noah Doe positioned as a “finder” under abandoned-property doctrine.

The court authorized on-chain service of the defendants through OP_RETURN messages, a Bitcoin transaction field that lets users embed short text or URLs permanently on the blockchain.

Noah Doe’s blockchain consultant, Salomon Brothers Strategic Advisors, broadcast 98 batches of dust transactions across Bitcoin blocks 950,446 to 950,576 in June and July 2025, each carrying 546 satoshis and a link to the abandonment notice. The 1LwWt wallet was served on July 31, 2025, with a 90-day window to respond.

Galaxy Research’s Alex Thorn flagged the move on X Tuesday morning, identifying the wallet as the firm’s tracked Noah Doe defendant #38215. “Apparently, they were not, in fact, abandoned,” Thorn wrote.

The move came nearly seven months after the 90-day response window expired and roughly three months after the lawsuit was formally filed. Per Galaxy’s analysis, hundreds of wallets moved coins during the original notice campaign and were excluded from the final defendant list.

The 1LwWt move, occurring after the lawsuit was already underway with the wallet named as a defendant, is among the first publicly visible responses from inside the active case.

Meanwhile, a separate 15-year-dormant wallet, 1CDSyXAQxro4FPUoqAQb81642ruqDsUiNp, moved 20 BTC ($1.48 million) to a SegWit address approximately 13 hours before the 1LwWt move, per Arkham Intelligence data. The 1CDSy wallet received its original coins around the same 2011 window but does not appear to have been targeted by the Noah Doe notice campaign or named in the lawsuit.

The movements come during a sharp bitcoin slide that has taken BTC to near $70,000 for the first time in weeks, with Strategy’s first publicized bitcoin sale, a record 10-session spot ETF outflow streak, and stalled U.S.-Iran ceasefire talks all weighing on the market.

Satoshi-era coins were acquired before bitcoin had a meaningful dollar price, meaning any sale at current levels would mark a near-infinite gain on cost basis.

Chicago-based trading giant DRW has spent decades profiting from mismatches between different asset classes, and now it’s building a dedicated prediction market desk targeting platforms such as Polymarket and Kalshi.

The move is one of the clearest signs yet that sophisticated “quantitative trading” firms — traders that use complex math and analysis to set up strategies — are increasingly viewing prediction markets as a legitimate trading venue rather than a niche betting product.

The firm that has been a dominant force in derivatives, fixed income and crypto markets since 1992, recently posted a job listing requiring candidates to monitor prices in real time across both platforms simultaneously, identify gaps where one is mispricing an outcome relative to the other and react quickly to profit before the pricing converges. The strategies listed in these posts — including microstructure arbitrage, cross-platform arbitrage, and news-driven momentum trading at sub-second speeds — are techniques honed in crypto derivatives markets and now being applied to sports and political events.

DRW is not alone. Wintermute, the algorithmic market maker that processes billions in daily crypto volume, is hiring algorithmic traders with experience in prediction markets. IMC, another proprietary trading firm, is also looking for quantitative traders comfortable operating across binary event contracts. Meanwhile, traditional crypto exchanges like OKX and Crypto.com have also recently posted job listings.

The hiring wave suggests institutional trading firms increasingly believe prediction markets have matured into a serious asset class and are ripe for profit.

Exploiting the mismatch

So what’s driving the sudden push? The catalyst is the volume being traded on these platforms.

Polymarket alone processed between $22 billion and $40 billion across political, economic and sports markets in 2025, up from virtually nothing three years ago and a growing share of that is concentrated in sports.

As of last week, Polymarket’s market on the UEFA Champions League Winner has processed $256 million, the 2026 NBA Champion market has done $399 million, and the 2026 NHL Stanley Cup market sits at $79 million after wild swings that saw Carolina Hurricanes rise from sub-10% implied probability to around 50% as they emerged from the Eastern Conference.

Combined, those three markets alone represent over $730 million in volume on sports outcomes, approaching the annual trading volume of some mid-sized European sports betting exchanges.

But the real reason traditional firms are pushing into this industry may not be to predict outcomes better than everyone else, market observers say.

“I don’t expect the institutional capital is contributing meaningfully to the accuracy of these markets, especially in the case of sports,” said Harry Crane, a statistics professor at Rutgers University who studies prediction market calibration.

“The accuracy of the markets is driven by specialized sports betting groups, which are much sharper at pricing sports outcomes.

Instead, Crane argues, firms such as DRW are likely applying trading techniques developed in traditional financial markets to exploit pricing mismatch.

“To the extent they are profitable, the institutions are likely applying techniques on short-term market dynamics and other technical aspects of trading that capitalize on short-term market fluctuations without insight into the event outcome.”

Simply put, DRW is not trying to predict who wins the Champions League. It is trying to profit from the way prices move before that question is answered.

A recent example appeared in the market for Britain’s next prime minister.

On the morning of May 14, Andy Burnham’s odds of becoming the next U.K. leader in the betting of “Next UK Prime Minister” on Polymarket surged from 24 cents to 43 cents as political speculation intensified around a potential Labour leadership challenge. But Betfair, the London-based betting exchange with over a billion pounds in annual volume, had already identified the move, pricing Burnham at the equivalent of 50 cents while Polymarket still showed 24 cents.

It took Polymarket hours to catch up.

For casual bettors, the gap was an interesting anomaly, but to a sophisticated quant trader, it was a textbook cross-market inefficiency waiting to be exploited.

In theory, a trader could have bought $10,000 of Burnham contracts on Polymarket at 24 cents after noticing the mismatch, before locking in $7,900 worth of profit in a matter of hours by selling when it caught up to Betfair, which would have made a profit without the event even needing to take place.

It’s a technique that has been used for decades by traditional trading firms: finding a mispriced asset across exchanges and either simultaneously buying/selling, as in arbitrage, or buying the underpriced asset and waiting for it to catch up.

Prediction markets, however, introduce an additional challenge. Betfair settles in sterling while Polymarket settles in crypto, requiring infrastructure capable of moving capital across currencies, exchanges and settlement systems.

That kind of complexity plays directly into the strengths of large trading firms, such as DRW

What’s driving them?

Beyond outright arbitrage, traders point to two structural features that make prediction markets attractive today.

The first is information lag. Traditional betting exchanges often react more quickly than decentralized prediction platforms, creating windows where prices have not yet fully adjusted.

The second is liquidity fragmentation. Champions League, NBA and Stanley Cup markets can trade simultaneously across Polymarket, Kalshi and traditional sportsbooks, meaning no single venue necessarily reflects the full market consensus.

For traders focused on forecasting outcomes rather than market structure, the toolkit looks increasingly familiar to quantitative finance.

Soccer traders often rely on “Dixon-Coles Poisson” models. The toolkit, developed in a 1997 academic paper, estimates team attack and defense strength and generates probability distributions for potential scorelines. This is something similar to how a weather forecaster assigns precise probabilities to every possible outcome rather than making a single prediction.

Meanwhile, Basketball traders frequently use “Bayesian Hierarchical” models that update assessments of team strength as new information arrives.

The goal for both models is to identify discrepancies between a model’s estimated probability and the probability implied by market prices.

A trader whose model values Arsenal’s Champions League chances at 47% while contracts trade at 43 cents may buy and profit if the market eventually converges toward that estimate.

The concept is known as closing line value, or CLV.

Crane explains why the CLV matters: “It incorporates all known pre-game information, such as injuries and lineup changes, and the sharpest players tend to wait until closer to game time to place bets because that is when the limits tend to be highest.”

Competition is here

Still, Crane remains skeptical that institutional firms will dominate sports prediction markets simply because they have arrived with larger balance sheets.

“Right now, the sharpest players in the sports betting markets are not the institutions,” he said. “The sharpest players have been in these markets for decades, and the prevailing market prices are likely driven by the same groups and the same information sources since long before prediction markets existed.”

Despite the skepticism, the talent migration is already underway.

Crypto market makers are studying sports analytics and expected-goals models, while traditional sports betting specialists are increasingly being recruited by crypto firms seeking expertise that took years to develop.

And it’s not just theoretical.

HyperLiquid, the onchain perpetuals exchange that processed over $10 billion in daily volume at its peak, is already preparing to launch prediction markets ahead of the 2026 World Cup, featuring 64 games over six weeks and generating thousands of correlated binary outcomes.

The infrastructure is being built, and the desks are now being staffed, with models working on potential outcomes.

The main question is whether institutions can outperform veteran sports bettors by finding their edge and applying sophisticated trading models used in traditional finance. But on latency, market structure and cross-platform inefficiencies, the competition has already begun.

Read more: Hyperliquid is emerging as a challenger to traditional exchanges and prediction markets, says FalconX