Stablecoins Seen Powering Future AI-Driven Machine Payments

TheNewsCrypto2026-03-23 tarihinde yayınlandı2026-03-23 tarihinde güncellendi

Özet

Bernstein analysts highlight the growing potential for stablecoins, particularly USDC through the collaboration of Circle and Coinbase, to power future AI-driven machine payments. These transactions are fully automated, programmatic, and executed by software or autonomous devices without human intervention. Stablecoins are deemed ideal for this use case due to their programmability, speed, micro-payment capability, and global accessibility. They enable real-time decision-making, instant settlements, and eliminate the need for traditional banking infrastructure like SWIFT or FX conversion. Companies such as Coinbase, Circle, and Stripe are already developing infrastructure to support these agentic payments, with early protocols showing initial transaction volumes. This represents a significant future growth driver for stablecoins.

Bernstein analysts noted Circle and Coinbase as prominent vehicles for stablecoin exposure, highlighting the USDC collaboration between the two companies and the emerging role of stablecoins in agentic machine payments as a potential upside driver.

The analysts headed by Gautam Chhugani wrote in a note to clients on March 23 that “we see agentic machine payments as an upside optionality for stablecoins. And this is not a ‘here and now’ material influence on stablecoin demand but some potential role of stablecoins in the agentic machine economy.”

The analysts marked out machine payments as transactions started, authorised and settled completely by software or autonomous devices instead of humans. Dissimilar to automated bill payments or repeating subscriptions, these payments are naturally programmatic, permitting real-time decision-making, price negotiation, and settlement without human interference.

Bernstein mentioned stablecoins are mainly suited to this environment, as they are programmable, quick, micro-payment-friendly, and accessible all over the globe. Payment logic like escrow, conditional release, or revenue cutting can be rooted directly in stablecoins, permitting agents to transact without calling a bank or waiting for confirmations.

The Contribution To The Future

According to the note, transfer settlements can be done in seconds, permitting AI agents to pay for compute or data in real time. To make it financially efficient, high-throughput blockchains and state channels can be used to perform microtransactions at scale.

Also, stablecoins are borderless, eliminating the need for SWIFT, correspondent banking or FX conversion, the analysts mentioned. A lot of companies have started making infrastructure to operationalise these capabilities.

Coinbase is making the x402 agent payments protocol, which sets payments into the HTTP layer of the internet, while Circle rolled out nano-payment infrastructure for agents. Meanwhile, Stripe, via its blockchain investment in Bridge and Privy, rolled out the Machine Payments Protocol on the Tempo blockchain.

Bernstein mentioned that the traction on machine payments protocols has already been restricted, highlighting that Stripe’s MPP registered $5,000 in volume in its first week of launch. Coinbase’s x402 protocol has generated around $25 million in volume in the past month.

Highlighted Crypto News Today:

SIREN Meme Coin, Based on BNB Chain, Marks a Significant Surge

TagsAICoinbaseStablecoin

İlgili Sorular

QWhat are the two companies highlighted by Bernstein analysts as prominent vehicles for stablecoin exposure?

ACircle and Coinbase.

QAccording to the analysts, what is the emerging role of stablecoins that is seen as a potential upside driver?

ATheir emerging role in agentic machine payments.

QWhat are the four key attributes that make stablecoins particularly suited for the agentic machine economy?

AThey are programmable, quick, micro-payment-friendly, and accessible globally.

QWhich two specific machine payment protocols are mentioned in the article and which companies developed them?

ACoinbase developed the x402 agent payments protocol, and Stripe rolled out the Machine Payments Protocol (MPP).

QWhat volume did Coinbase's x402 protocol generate in the past month, according to the Bernstein note?

AAround $25 million in volume.

İlgili Okumalar

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.

marsbit26 dk önce

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

marsbit26 dk 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.

marsbit30 dk önce

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

marsbit30 dk 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.

marsbit31 dk önce

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

marsbit31 dk önce

İşlemler

Spot
活动图片