Banks Move Toward 24/7 On-Chain Finance as Franklin Templeton and SWIFT Outline Blockchain Future

TheNewsCrypto2026-02-11 tarihinde yayınlandı2026-02-11 tarihinde güncellendi

Özet

Top executives at Consensus Hong Kong 2026 outlined a future where banking operates 24/7 using blockchain technology. Franklin Templeton is focusing on tokenizing money market funds to enable round-the-clock trading and reduce administrative costs. SWIFT is developing a system to convert traditional bank balances into digital tokens to expedite settlements and eliminate cutoff times. While tokenized finance remains small compared to traditional markets, the industry is building early infrastructure. Key barriers like regulation and private key security need addressing for broader adoption. The future is expected to be hybrid, blending decentralized services with traditional intermediaries, as major financial institutions push for secure, continuous blockchain-based systems.

Speaking at the Consensus Hong Kong 2026, top executives from the traditional finance and crypto firms said that the future of banking will run 24/7 with assets issued directly on blockchain. Their message was clear that the financial system could soon be working continuously without shutting down.

Franklin Templeton said that it will be focusing on the money market funds in the blockchain infrastructure. By putting funds on the blockchain, they can allow the investors to buy or sell anytime, which also reduces the paperwork and admin costs. An executive explained that taking the existing financial products and making them cheaper and easier using blockchain.

From the Swift side, it is working on ways banks can turn normal account balances into the digital token. Their goal is to expedite the settlements and have no cutoff times. Executives say that payments in Swift are quick, but they want instant availability at any time.

Traditional Finance Moves Toward 24/7 On-Chain

Despite the rapid growth in tokenized finance, it is relatively small when compared to the traditional markets. While there are a billion dollars in the stablecoin and a billion in the tokenized securities, this is very much minor compared with the trillions managed across the global banking system. So executives called it an early infrastructure.

Two barriers, such as regulations and security, are repeatedly coming up in the discussions. Banks and institutions need clear rules about accounting and compliance. Crypto requires managing the private keys. For institutions, managing the private keys and ensuring access control should meet the enterprise standards for broader adoption.

Speakers believe that the future will be hybrid, with some of the services becoming decentralized and some remaining intermediate. The tone of the conversation shows a clear sign that big finance wants blockchain to run 24/7 with more security.

Highlighted Crypto News:

‌Ethereum Slips Toward $1,900 as Selling Pressure Intensifies

İlgili Sorular

QWhat is the main vision for the future of banking as outlined by executives at Consensus Hong Kong 2026?

AThe main vision is that the future of banking will run 24/7 with assets issued directly on blockchain, allowing the financial system to work continuously without shutting down.

QWhat specific financial product is Franklin Templeton focusing on in the blockchain infrastructure?

AFranklin Templeton is focusing on money market funds in the blockchain infrastructure.

QWhat is SWIFT working on to improve the banking system?

ASWIFT is working on ways for banks to turn normal account balances into digital tokens to expedite settlements and eliminate cutoff times, aiming for instant availability of payments at any time.

QWhat are the two main barriers mentioned that are hindering broader adoption of on-chain finance?

AThe two main barriers are regulations and security, including the need for clear rules on accounting and compliance, and the challenge of managing private keys to meet enterprise standards.

QHow does the current size of tokenized finance compare to the traditional global market?

ATokenized finance, with about a billion dollars in stablecoins and a billion in tokenized securities, is relatively very small compared to the trillions of dollars managed across the global banking system.

İlgili Okumalar

How Likely is it for TradeXYZ to Go Solo from Hyperliquid?

The article analyzes the possibility of TradeXYZ, a dominant project on Hyperliquid's HIP-3 real-world asset (RWA) perpetual contracts market, splitting off to build its own independent trading platform. With TradeXYZ commanding over 90% of HIP-3 volume and over 35% of Hyperliquid's total open interest, its leverage and influence are significant. The primary motive for a split is seen as capturing the underlying protocol fees, of which TradeXYZ currently receives only half. However, several factors strongly discourage such a move. First, replicating Hyperliquid's high-performance infrastructure would be challenging and could degrade TradeXYZ's product quality. Second, Hyperliquid serves as a crucial distribution channel, with most users accessing TradeXYZ's liquidity through its frontend. Third, the close, trusting relationship between the founders of both projects makes a hostile "betrayal" unlikely. The conclusion is that a split would likely result in a lose-lose scenario. Hyperliquid would lose its main growth narrative and see its valuation impacted, while TradeXYZ would face immense technical hurdles, lose its primary user channel, and damage its reputation. Instead of a full separation, the more probable path is for TradeXYZ to negotiate better terms while deepening integration, focusing on enhancing its own brand and user ownership.

marsbit18 dk önce

How Likely is it for TradeXYZ to Go Solo from Hyperliquid?

marsbit18 dk önce

"Teletubbies" Robot Cleaning Service, $30/Hour, Pure·Manual·Intelligence

Anthropomorphic "Teletubby" robot offers cleaning services in San Francisco at $30/hour, but it's entirely remote-controlled. The robot, created by startup Tau Robotics, can perform household tasks like washing hands, mopping floors, and taking out trash. While the initial demo videos appear impressive and are notably shown at normal speed (unlike many sped-up robot demos), the company reveals the actions are performed via human teleoperation, not autonomous AI. Tau Robotics, founded in 2024, argues this "cheat" is a strategic way to bridge the current capability gap, ensure task completion, and collect real-world home data to eventually train autonomous systems. Their service features three robot models: Chelsea for kitchens/bathrooms, Elon for regular tidying with memory, and Tony for deep cleaning. Priced at $30 per hour, it's cheaper than average human cleaners in the US. The article discusses the broader challenge of deploying humanoid robots in homes, comparing Tau's approach to others like China's Ziliang and the US's 1X Neo, which also use teleoperation. A key reason for choosing a humanoid form is to make remote control more intuitive for human operators. The piece also notes the potential "emotional value" of having a humanoid servant. The service is currently invite-only in San Francisco.

marsbit33 dk önce

"Teletubbies" Robot Cleaning Service, $30/Hour, Pure·Manual·Intelligence

marsbit33 dk önce

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbit1 saat önce

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbit1 saat önce

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbit1 saat önce

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbit1 saat önce

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbit1 saat önce

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbit1 saat önce

İşlemler

Spot