A TechFlowPost feature traces the history of offshore dollars from the birth of the eurodollar market to the rise of stablecoins and self-custody wallets, arguing that the core question has never gone away: who actually owes you $1? The article says the dollar has moved through several institutional containers over the past seven decades, from New York bank ledgers and London bank balance sheets to fintech databases and the reserve structures behind stablecoin issuers. At the same time, the relationship between users and their accounts has also shifted, moving from full institutional custody toward direct user control over onchain assets. The piece links three historic fault lines in the offshore dollar system to three forms of power: settlement, last-resort liquidity, and pricing. It points to the 1974 Herstatt failure, the 2008 global dollar shortage, and the eventual shutdown of the U.S. dollar LIBOR panel in June 2023. It then places Revolut, Wise, stablecoins, and Bitget Wallet along the same continuum. In that framing, stablecoins did not invent a new dollar. They separated redemption from transfer, keeping reserves in traditional finance while moving transfer onto public blockchains. Self-custody wallets, meanwhile, did not replace the issuer’s redemption promise, but changed who controls the movement of assets. The article argues that this is where the latest shift in dollar infrastructure becomes most visible.

Foresight has published a long-form essay by Bitget Wallet researcher Lacie Zhang tracing a 70-year line from the birth of the eurodollar market to today’s stablecoins and self-custody wallets. The piece argues that stablecoins are not a clean break from the past. Instead, they represent a new stage in the offshore expansion of the U.S. dollar, one that changes how dollar claims move rather than eliminating the traditional financial system behind redemption. Zhang revisits how Soviet and Eastern European entities moved dollars into banks in Paris and London to avoid the risk of account freezes in the United States, how post-Suez British policy helped turn those deposits into a lending market, and how U.S. regulation and later petrodollar flows helped that market grow from millions to trillions of dollars. The essay then uses the 1974 Herstatt collapse, the 2008 dollar funding squeeze, and the fall of LIBOR to show that offshore banks gained the ability to expand dollar credit but never captured final control over clearing, emergency liquidity, or pricing. The article closes by contrasting fintech apps, stablecoins, and self-custody wallets. In Zhang’s framing, stablecoins move dollar balances onto public blockchains while leaving redemption anchored to U.S. Treasuries, bank deposits, and legal claims. Self-custody wallets change something else: who controls the movement of assets.

Galaxy Digital research vice president Lucas Tcheyan argues that an “on-chain inference capital market” is beginning to emerge as AI inference, GPU supply, payment rails, tokenization tools and financing infrastructure converge into a more integrated system. In the piece, republished by WuBlockchain and translated by TechFlow, he frames inference as a fast-growing economic layer that is moving beyond centralized APIs controlled by companies such as OpenAI and Anthropic. The report breaks the market into several connected parts. On the off-chain side, GPU index providers including Ornn and Silicon Data are trying to standardize compute pricing, while ICE and CME have announced plans for GPU futures. On-chain, the stack includes decentralized inference providers, model developers, router layers, agent payment standards, tokenized access markets and credit protocols that finance GPU hardware. Tcheyan focuses on four examples. Venice turns future inference access into transferable claims through its VVV and DIEM token structure. Pearl and Ambient try to tie network security to real inference work through “useful proof of work,” though both still face open questions around real demand and token value capture. USD.AI takes a different route by using stablecoin deposits to fund GPU-backed loans for smaller compute operators. Galaxy’s conclusion is that the sector remains early: financing has found the clearest product-market fit so far, while the broader tokenized inference economy still needs to prove durable demand, execution and pricing power.

Galaxy Digital has laid out a broad framework for what it calls the “inference capital markets,” arguing that AI inference is moving from a purely technical service into an asset class that can be priced, hedged, financed and traded. In a research report written by Galaxy Digital Vice President of Research Lucas Tcheyan and circulated in Chinese by TechFlow, the firm links several parallel developments into one market structure: the rise of GPU price indexes, planned GPU futures from Intercontinental Exchange and CME Group, tokenized claims on future AI inference, useful proof-of-work networks that subsidize inference production, and stablecoin-funded lending against GPU hardware. The report’s central claim is that inference has now overtaken training as the main driver of global GPU demand, while autonomous agents are emerging as a new class of machine-native buyers that can pay for model output programmatically. Galaxy argues that the market is still early and fragmented. It sees progress on the off-chain side, where Ornn, Silicon Data and Compute Desk are building reference pricing for compute, and where Kalshi, ICE and CME are already moving toward tradable GPU-linked products. On-chain, the report highlights Venice’s VVV and DIEM system for tokenized inference access, Pearl and Ambient’s different attempts to turn inference production into useful proof-of-work, and USD.AI’s stablecoin-based credit model for financing AI hardware. Even so, the report says the sector has not yet solved its hardest questions: whether real demand for verifiable, censorship-resistant inference will grow beyond a niche, how token value can be tied to actual product usage instead of emissions and speculation, and whether legal enforcement and collateral recovery in GPU-backed lending can hold up in a true stress cycle.

Finance expert Patrick Bet-David on the PBD Podcast identified XRP as a potential SWIFT replacement, citing its ability to capture a share of the $5 trillion daily transaction volume, while SWIFT plans a blockchain-based ledger system in response.

CESR (Composite Ether Staking Rate) is emerging as Ethereum's institutional staking benchmark, tracking validator yields. FalconX executed the first fixed-floating swap, and Rho Labs launched derivatives, paving the way for a $500 trillion rates market in crypto.

The Composite Ether Staking Rate (CESR) is becoming a key institutional reference rate for Ethereum staking yields, with derivatives like swaps and futures already launching, aiming to replicate LIBOR's role in traditional finance.

CESR is becoming a key benchmark for Ethereum staking yields, already supporting swaps, futures and institutional risk models. Its rise could help build a crypto forward rate curve and expand yield-based products.