Category: Business

Mastercard is folding stablecoins into its sprawling payments empire, launching a platform that lets businesses hold, move, and spend traditional currencies and digital dollars side by side through a single connection.

Unveiled Thursday, the Mastercard Global Money Platform pairs the payments giant’s global network with the stablecoin infrastructure of BVNK, the crypto firm Mastercard acquired in August.

Mastercard struck the deal to buy BVNK for roughly $1.8 billion, and the new platform marks the first major integration of the two companies’ capabilities since that acquisition closed.

At the center of the platform is what Mastercard calls a global money account, a multi-currency account where fiat and stablecoins can sit together, with conversion between them handled through a single API.

Banks, fintechs and enterprises can embed those accounts into their own branded products, along with real-time conversion, stablecoin-linked cards and local bank payouts, or use the platform to run their own treasury and settlement. The pitch is a modular toolkit spanning sending, receiving, converting, storing, spending and earning, letting customers start with the pieces they need and bolt on more over time.

Mastercard framed the offering as plumbing for a “multi-money” world. The platform ties into Mastercard Move, the company’s money-transfer network, letting users tap stablecoin balances to send local-currency payments into recipients’ bank accounts, with coverage expected to reach more than 40 currencies across 200-plus countries in 2027. Customers will also be able to spend stablecoin balances across Mastercard’s merchant-acceptance network, with card issuing available in more than 130 countries.

Payments firm Nuvei and card-issuing platform Marqeta are among the first to integrate the platform, alongside Ontop, Beem, and Almond FinTech.

Raj Dhamodharan, Mastercard’s executive vice president for blockchain and digital assets, said the company believes “the future of commerce will be built on interoperability across multiple forms of money,” calling the launch a step toward enabling “the next generation of programmable and agentic commerce.”

That nod to agentic commerce, where AI agents transact on their own, signals where Mastercard sees the puck heading.

The company has already moved to enable AI-agent payments alongside crypto heavyweights like Coinbase and Ripple, and secured a New York BitLicense covering stablecoins and tokenized deposits. Mastercard said Global Money Platform will add support for agentic commerce and tokenized deposits down the line.

Google DeepMind went looking for proof that AI is speeding up the development of AI itself. It didn’t find any.

In the safety report for Gemini 4 Argon, its most powerful model, the lab concluded AI research isn’t moving any faster, even though AI now writes most of the code DeepMind’s engineers ship.

The finding cuts against the scenario that has spooked AI executives into calling for a slowdown. It’s called recursive self-improvement: an AI builds a smarter version of itself, which builds an even smarter one, until progress outruns anyone’s ability to test, regulate, or switch it off—what the report calls “a situation where existing guardrails are severely inadequate.”

“There is currently no significant end-to-end acceleration of ML R&D, AI-driven or otherwise,” Haydn Belfield, a Google DeepMind researcher who worked on the assessment, wrote in a thread on X. “We therefore conclude that the risk from AI-driven ML R&D is currently low.”

ML R&D is shorthand for machine learning research and development, the work of building new AI models.

DeepMind tracks the issue because “AI could speed up frontier capability gains faster than safety, governance, and societal adaptation can keep pace,” Belfield wrote. More automation also means giving AI agents broader access to company systems, more room for a misaligned model (one pursuing goals its makers never intended) to cause trouble.

To measure progress, the team built GCI v1, an internal index that blends the scores of 123 AI models across 92 benchmarks into a single number per model—think of it as an IQ score for AI. “Frontier Gemini capabilities have improved at a remarkably steady pace of ~0.1 GCI points/month,” Belfield wrote.

In the report’s chart of that index, Gemini 1.5 Pro sits near 0.5 points in late 2024 and Gemini 3.8 Flash just above 2.9 in September 2026, with nearly every release hugging a straight line. Argon is the one dot poking above it, at roughly 3.3, though its error bar (the range its true score could plausibly fall in) stretches low enough to touch the trend.

“While Gemini 4 Argon sits above [the] trend, we trace this to increased compute and organizational investment rather than reflecting AI-driven acceleration,” Belfield wrote. Google unveiled Gemini 4 Argon on September 30.

Most production code at DeepMind is now more than 75% AI-written, and the share of fully delegated pull requests (bundles of code changes submitted for review before they go live) rose from 7.5% to 11.9% between April and September. But “~88% of pull requests still require multi-turn human steering or human code authorship—and code changes remain subject to human review before submission,” Belfield wrote.

A survey of 12 senior DeepMind leaders and researchers found automation is highest in “well-specified, verifiable execution tasks and lowest in open-ended scientific ideation, novel experimental design, diagnosing subtle training instabilities, and long-horizon research taste.” In plain terms, AI is great at executing instructions and bad at deciding which ideas are worth chasing.

DeepMind also reviewed 1,389 logs of Argon working as an agent inside the company. The bigger the task, the less likely the model finished it alone and the more often a human stepped in.

The report landed a month after Anthropic CEO Dario Amodei argued that recursive self-improvement is already accelerating, a warning Sam Altman and Elon Musk endorsed and one that sent chip stocks down almost 6%. Days earlier, former Anthropic engineer Jacob Coxon had quit the industry, warning AI could “kill us all by the end of the decade.”

DeepMind’s numbers cover only its own lab, and the same report says it can’t rule out that an unrestricted Argon crosses its danger thresholds for cyberattacks and bioweapons information.

BlackRock CEO Larry Fink has a blunt warning about what could derail the U.S. economy: slamming the brakes on the data centers powering the AI boom.

Speaking to CNBC on Wednesday after BlackRock posted better-than-expected third-quarter earnings, Fink said the surge in AI infrastructure spending has become so central to economic growth that choking it off would be dangerous.

“If we have a moratorium of some sort in this country, we will be in a recession,” he said, adding that “so much of our economy is based on the build-out of this capex.”

The comments come as data centers have become a flashpoint ahead of the Nov. 3 midterm elections, with local opposition mounting over their electricity demands, water use and strain on power grids.

According to Brookings, at least 100 localities had approved moratoriums by July, and New York has paused certain permits for up to a year. Fink pushed back on the backlash, calling much of it “misinformation” and insisting that “data centers are not the problem,” pointing instead to the construction jobs and energy-infrastructure demand the buildout is generating.

The scale of the spending underscores his argument. Wall Street analysts estimate AI capital expenditures could top $1 trillion in 2027 and exceed $5 trillion cumulatively through 2030, making the sector a meaningful driver of the roughly 3% annualized GDP growth Fink wants to sustain. He has argued more private capital is needed to keep it going, and said BlackRock has spoken with chipmakers including AMD and Broadcom about the effort.

But Fink is swimming against a sharply rising tide of public opposition. An Annenberg Public Policy Center survey this summer found 61% of Americans oppose new data centers in their area, up from 49% in the spring, with majorities of Democrats, Republicans and independents all against them.

The concerns center on soaring electricity demand, water use and strain on local grids. That backlash has already produced real restrictions: New York has a moratorium on hyperscale data centers, Texas has paused approvals, and cities from Austin to Jersey City have imposed limits, with Data Center Watch counting roughly $130 billion in projects blocked or delayed in the first quarter of 2026 alone.

It has also become a live electoral issue. The fight is central to Ohio’s Senate race, where Democrat Sherrod Brown has campaigned against the buildout, and a super PAC backed by AI billionaires including Marc Andreessen and OpenAI President Greg Brockman has poured millions into ads defending data centers across battleground states. President Donald Trump has weighed in too, warning that communities resisting the facilities risk ending up “backwards and poor.”

Against that backdrop, Fink pushed back directly, suggesting the AI buildout in the United States is propping up the U.S. economy and any shift would result in a significant downturn.

The warning carries weight for crypto, given how closely Bitcoin now tracks broader risk sentiment and macro conditions.

Fink, who runs the world’s largest asset manager and oversees its dominant spot Bitcoin ETF, has become one of Wall Street’s most prominent crypto converts. He has called Bitcoin an asset for a “frightened world” and framed it as a legitimate alternative investment, while BlackRock has flagged AI agents as a potential next wave of crypto demand.