YouTube Relaxes Its Rules on Swear Words in Early Video Content

YouTube has loosened its restrictions around how bad language affects video monetization, making it easier for creators to earn money even if their clips include profanity in the opening seconds. The company has updated its Advertiser Friendly Guidelines, easing one of the more contentious policies that had caused frustration among content creators in recent years.

Reversal of Previous Tightening

This change rolls back a stricter rule introduced in 2023, which had made any video that featured strong language in its first few seconds ineligible for full advertising revenue. That earlier revision had followed an even broader update in 2022, when YouTube first tightened its rules to limit the use of violence and offensive language in monetized content. The policy especially impacted gaming creators, whose streams often include in-game violence and spontaneous speech that could contain swear words.

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After a wave of criticism, YouTube softened its approach slightly in 2023 by narrowing the restriction window to the first seven seconds of a video. But even that adjustment didn’t fully address creators’ concerns, as many videos were still receiving limited monetization, marked by the platform’s yellow icon that indicates reduced ad income.

What’s Changing Now

Under the latest update, videos that include profanity within the first seven seconds will no longer be automatically penalized. This means that creators can now retain full advertising revenue, even if strong language appears near the start of their content. While this adjustment makes the rules more flexible, it does not entirely lift all limits related to language.

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Creators should still be aware that titles and thumbnails containing bad language will continue to trigger monetization restrictions. In addition, if profanity appears too frequently within a video, even if the early seconds are allowed, monetization may still be reduced under the platform’s guidelines.

The Role of Ad Placement

The earlier policy around bad language in a video’s opening moments had mainly stemmed from concerns about how close a brand’s advertisement appeared to offensive material. Advertisers typically prefer a buffer between their message and any strong language. But changes in advertiser tools now allow brands to fine-tune where and how their ads appear, including setting limits on content sensitivity. That flexibility has given YouTube more room to relax its own rules without risking ad relationships.

By shifting the responsibility onto advertisers to control the kinds of content they want to appear next to, YouTube can now allow creators more freedom in how they speak, without necessarily hurting its advertising model.

Limitations Still Apply

Although this update gives creators more breathing room, it’s not a free pass for excessive swearing. Videos that rely heavily on profanity, or repeatedly use strong language throughout, may still see limited monetization. And inappropriate language in text elements like video titles and thumbnails remains a red flag for YouTube’s ad systems.

So while early swearing will no longer automatically lead to reduced income, creators still need to moderate how much strong language they use if they want to fully benefit from the change.

Notes: This post was edited/created using GenAI tools. Image: DIW-Aigen

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by Web Desk via Digital Information World

Meta Reshapes AI Strategy Amid Talent Surge, Billion-Dollar Bets, and Rising Caution on Openness

Mark Zuckerberg has introduced new language around Meta's AI development plans that indicates a potential shift in the company’s direction. Over the last several months, Meta has invested heavily in both infrastructure and talent. At the same time, internal communications and earnings remarks have started to reflect a more cautious approach to openness, particularly as the company begins building what it calls “personal superintelligence.”

A recently published note from Zuckerberg laid out this vision. It described a future where highly capable AI systems assist individuals in their personal goals. He did not define superintelligence in technical terms or explain how such systems would be built. Instead, the memo outlined a broad idea of personalized tools that could support users across creative, social, and productivity tasks.

This approach contrasts with other companies that are developing general-purpose models aimed at automating workflows. Zuckerberg's message suggested that Meta wants to keep the focus on individual empowerment. This vision also connects to the company’s longer-term goal of moving away from reliance on mobile devices. Meta has spent years developing smart glasses and has signaled interest in making them central to future computing experiences.

Talent Shifts and Billion-Dollar Recruiting

Meta has increased its hiring of AI experts since early this year. In one move, it brought in the founder of Scale AI through a $14.8 billion investment. That deal placed Alexandr Wang as the company’s new Chief AI Officer. The company has also recruited individuals from OpenAI and Apple, including engineers who contributed to major large language models.

Recent reporting indicated that Meta extended multiyear offers worth hundreds of millions, and in one case more than $1 billion, to staff from Thinking Machines Lab. The startup was founded by a former OpenAI executive. Meta did not confirm the financial details but acknowledged the interest in expanding the team.

Alongside those moves, Meta has committed over $72 billion to AI infrastructure. That includes compute power, model training capacity, and scaling systems. These steps suggest the company is preparing to build more advanced AI models, even as it evaluates how much of that work to make public.

Open Source Remains Unclear

For years, Meta positioned open-source AI as a safer and more inclusive approach. Company leaders argued that transparency could prevent misuse and help governments understand how models work. More recently, Zuckerberg indicated that the company may not share some of its largest models in the future.

His recent statements during Meta’s second-quarter earnings call included references to safety and practicality. According to him, some of the models now being developed are so complex that releasing them would have little benefit for outside developers. In some cases, he added, sharing might give an advantage to competing firms.

These comments followed a memo that suggested Meta would remain a leader in open-source work but would be more selective about what gets released. While this is not a reversal of past policy, it shows a growing awareness inside Meta that some advanced AI models may carry risks that make full transparency harder to justify.

Usage Gains Tied to AI Integration

Meta’s recent product performance also reflects increased use of AI to drive engagement. Time spent on Facebook rose 5 percent in the second quarter. Instagram saw a 6 percent gain. Both trends were attributed to updates in recommendation systems, which now use large language models to present more relevant content.

The company also noted that video viewership grew by 20 percent over the past year. Instagram played a large role in that growth, as Meta has focused on promoting original material and improving content ranking methods. Threads, its messaging-based app, has seen an increase in daily use following the integration of new AI tools.

All in all, Meta reported 3.4 billion family daily active people across its platforms in June. That figure included Facebook, Instagram, Messenger, and WhatsApp. It marked a 6 percent increase from the previous year and supported a 22 percent rise in revenue across those apps, reaching $47.1 billion in the quarter.

Broader Shifts in AI Positioning

Zuckerberg’s internal memo came just before Meta’s Q2 earnings report. The timing appeared aligned with the company’s efforts to frame its AI investments to investors. With delays affecting the launch of its larger Llama 4 model, internal reports suggested that Meta’s leadership had been reevaluating its approach. Some concerns were raised about the tradeoff between openness and competitive advantage.

There has also been tension around the slow progress of Meta’s generative AI roadmap. Executives inside the company have reportedly questioned whether its development pipeline can keep pace with external labs. These concerns may have shaped the more cautious stance reflected in the memo and earnings discussion.

At the same time, Meta seems to be preparing for a future in which its computing platforms are less dependent on Apple and Google. Smart glasses, which the company continues to develop, were described as key devices in future AI use. Zuckerberg pointed to this shift as an opportunity to move users toward platforms owned and controlled directly by Meta.

The company’s strategy remains focused on scaling up its AI capabilities, refining its product experience, and adjusting its messaging around transparency. While the long-term details remain unclear, the recent changes suggest that Meta is actively shaping its next phase of AI development around tighter control, personal devices, and internal platforms.

Notes: This post was edited/created using GenAI tools.

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by Irfan Ahmad via Digital Information World

Most Adults Use AI Without Realizing, But True Power Remains Untapped

A new poll has found that most adults in the United States have used artificial intelligence for online searches. Younger people report using it more frequently than older age groups, and for more types of tasks.

Online Search Remains the Main Use

Among all surveyed adults, 6 in 10 said they use AI at least sometimes to find information online. That rate rises to nearly three-quarters for people under the age of 30. Searching is the most common AI-related activity, based on the eight categories included in the poll.

This may understate its true usage, since many search engines now include AI-generated summaries automatically. People may be receiving answers produced by AI without realising it.

Work-Related Use Is Still Limited

The data also shows that AI has not become a major part of most workplaces. About 4 in 10 adults said they have used AI to assist with work tasks. A smaller share mentioned using it for email writing, creative projects, or entertainment. Fewer than one in four reported using AI for shopping.

Younger users are more likely to include AI in their work. Some use it to plan meals or generate ideas, while others rely on it to help write or code. Still, this type of usage remains less common among the general public.

Generational Differences Are Clear

Younger adults are more engaged with AI overall. Around 6 in 10 of those under 30 said they have used it to brainstorm. Only about 2 in 10 older adults said the same. Daily use for idea generation is more frequent among people in their twenties.

Older users show less interest in applying AI beyond basic information lookups. They tend to avoid using it for more personal or technical tasks.

AI Companionship Is Rare

The least common form of interaction with AI was companionship. Fewer than 2 in 10 adults overall reported using AI for that purpose. Among people under 30, the rate rises to about a quarter.

The survey results suggest that this type of usage remains outside the mainstream. Most people do not view AI as a substitute for personal interaction, although some younger users said they understand why others might explore it.

Overall Usage Remains Focused

The findings indicate that while AI tools have entered public use, they are still seen as limited-purpose systems. Most interactions involve information searches, and regular use beyond that is less frequent. Adoption has grown, but remains uneven across tasks and age groups.

The poll was conducted by the Associated Press and NORC Center for Public Affairs Research between July 10 and July 14. It included 1,437 adults drawn from a representative national sample, with a margin of error of 3.6 percentage points.

Notes: This post was edited/created using GenAI tools.

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Walmart Tops Global 500 Again as U.S., China, and Tech Titans Dominate $41.7 Trillion List

Walmart has landed in first place again, as it has for over a decade now. The U.S. retail giant sits at the head of this year’s Fortune Global 500, the annual list ranking companies by revenue. Amazon took second. Behind them came State Grid of China, followed by Saudi Aramco, and then China National Petroleum. It’s a line-up that doesn’t look unfamiliar, but the weight behind these names keeps growing. Taken together, just the ten highest-ranked companies earned more than $4.7 trillion last year. Most of that came from retail, oil, healthcare, or finance, sectors that continue to stretch across borders and markets.

UnitedHealth rises, Apple slips

Apple is still inside the top ten. But not quite where it was. It dropped one spot this year, pushed aside by UnitedHealth Group, which moved from eighth to seventh. That same reshuffle appeared in the U.S.-only Fortune 500 a month earlier, so the shift didn’t come as a surprise. Apple’s fall wasn’t dramatic, but it did reflect the fact that healthcare, as an industry, isn’t slowing. CVS Health and Berkshire Hathaway also stayed strong in the upper tier, keeping U.S. firms in control of most of the top ten.

Global revenue grows slowly, but steadily

The full list of 500 companies brought in $41.7 trillion in revenue last year. That’s about 1.8 percent higher than the year before. Profits came in just under $3 trillion, the second highest total Fortune has ever recorded for its global list. Saudi Aramco, once again, took the lead on earnings. It posted $105 billion in profit alone, the fourth straight year it has held the top position in that category.

Sectors with staying power

Finance continues to dominate in size. There are 121 financial companies in the Global 500. Energy, long a staple of the list, came next with 79. Then came motor vehicles and parts, with 35 firms. The tech sector wasn’t far behind, landing 34. Healthcare had 33. These five areas together made up most of the list and drove nearly two-thirds of total revenue. So while innovation is constant, the largest corporate machines, oil, banks, manufacturers, still hold most of the weight.

China and the U.S. still lead, close together

There were 138 American companies on this year’s list. Greater China, including Hong Kong, Macau, and Taiwan, came in just behind, with 130. Nine out of the ten most profitable companies came from these two countries. That balance hasn’t changed much lately. It’s still China and the United States shaping what this list looks like. Between them, they hold the pulse of global corporate power, even as growth slows in some industries and picks up in others.

The biggest tech names earned big, even with lower ranks

Amazon stayed at number two. Apple remained in the top ten. Alphabet landed at 13, Microsoft showed up at 22. Then came Meta at 41, Nvidia at 66, and Tesla at 106. None of them topped the revenue rankings, but their profits stood out. The group brought in $2 trillion in revenue and earned $484 billion in net income. That’s more than most countries generate in GDP. They didn’t move much in rank, but their financial output still dwarfs what most businesses achieve in years. No other group, outside energy, posted returns that high.

Record number of women CEOs

There are 33 women now leading companies on the Fortune Global 500 list. That’s about 6.6 percent of the total. It's still low, but it’s the highest count so far. Most of them are in the U.S., though China, France, the U.K., and Brazil have some as well. Some names stood out more than others, Mary Barra at GM, Jane Fraser at Citigroup, Sarah London at Centene, Sandy Xu at JD.com, but the broader pattern is slow, steady increase.

Where these companies are based

Companies on the list are spread across 243 cities and 36 countries. Beijing, Tokyo, New York, Shanghai, and London are home to more of them than anywhere else. London re-entered the top five after a few years off the leaderboard. These cities, often finance hubs or government capitals, keep drawing the biggest corporations. Their infrastructure, access, and labor markets still offer scale.

New companies join, even as others fall away

Nine companies made it onto the list for the first time. Among them were QNB Group, ICICI Bank, and Lithia Motors. They entered from different industries and countries, but their arrival signals movement in the sectors they belong to, especially banking and auto retail. Their exact rankings weren’t near the top, but entry alone is a sign of growth or change worth noting.

What the numbers don’t show

Revenue climbed. Profits held steady. But the backdrop is far less calm. Geopolitical shifts, trade disputes, and fast-moving changes in AI policy are already starting to affect how companies grow, spend, and plan. These issues haven’t upended the rankings just yet, but the next few years may look different. The Fortune Global 500 still captures the biggest players, it always has, but what happens next may depend less on size and more on how these firms navigate what's coming.

Notes: This post was edited/created using GenAI tools. Image: DIW-Aigen.

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YouTube Begins Using Age-Guessing Technology in the U.S.

YouTube has started testing a new system in the United States that tries to figure out how old users are based on how they use the platform. This technology looks at different pieces of information to estimate whether someone might be a teenager, even if they said something else when they created their account.

The new system is being introduced in stages. Only a small group of users will see it at first. YouTube plans to check how it performs before making it more widely available.

Why This Matters for Teen Users

If the system decides that someone is under 18, YouTube will automatically switch on features that are meant to offer a safer experience. This includes turning off personalized ads and reducing how often certain types of videos are shown repeatedly. It will also activate reminders for screen time and bedtime, along with other tools that support healthy use of the platform.

These settings already exist, but until now, they were only applied to users who had confirmed their age. Many teens skip that step or enter a different date when signing up. YouTube is trying to cover that gap by using patterns in user behavior instead of relying on what people enter.

How Mistakes Are Handled

Some adults may be flagged by mistake. If that happens, they’ll be asked to prove their age. They can do this by uploading a photo of a government ID, using a credit card, or submitting a live selfie. Once verified, they can access content that is only available to users over 18.

If the system believes someone is an adult based on their account history or usage habits, they won’t need to go through the verification process.

Background on YouTube’s Plans

YouTube had already mentioned its plan to use this kind of technology earlier this year. The move fits into a broader effort to add more safety features for young users. In the past, the platform launched a separate app for children and introduced supervised accounts for teens.

The new system builds on that approach and focuses on users who are signed in. Those who aren’t logged in already face limits on what they can watch, especially when it comes to age-restricted videos.

What Data Is Being Used

YouTube hasn’t listed every detail, but it said it will look at how long an account has been active and how people interact with videos. The goal is to make a reasonable guess without asking for too much personal data upfront.

This is part of a larger trend where companies are being pushed to do more to protect minors online. Lawmakers in several U.S. states are working on or have passed new rules that make age checks or parental consent a requirement. These include places like Texas, Georgia, Florida, Utah, Maryland, and Connecticut.

Some of those laws are already being challenged in court, and a few haven’t taken effect yet. But the direction is clear. Governments want more responsibility from platforms when it comes to younger users.

Other Countries Are Moving Too

In the United Kingdom, a new law passed in 2023 has also started to take effect. It requires websites to check the age of their users. Platforms that don’t follow the rules could face penalties.

YouTube’s update is one example of how companies are responding to this shift. The use of machine learning to estimate age is becoming more common, even though the full list of signals being used is usually kept private.

For now, the rollout remains small. The company said it will expand once it’s sure the system works as intended.

Notes: This post was edited/created using GenAI tools. Image: DIW-Aigen.

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From Coders to Cleaners: Which Jobs AI Is Supporting, and Which Are Out of Reach

A recent study by researchers at Microsoft examined how artificial intelligence is being used in real work conversations. By analyzing 200,000 anonymized chats between people and Microsoft Copilot, the team created a detailed picture of where generative AI fits into modern work, and where it does not.

They tracked how often users asked AI for help with work tasks, how well AI completed those tasks, and how broadly those tasks applied to each job. Using that data, they created a numerical score for over 900 jobs. A high score meant AI was frequently used for important parts of the job and performed those tasks well. A low score meant little to no overlap.

The results showed a sharp divide. Some occupations matched closely with the way people already use AI. Others showed almost no connection. This report focuses on both sides by listing the 40 most and least AI-compatible jobs based on actual user behavior.

Where AI Works Well

The top scoring roles mostly involve language, information, or communication. These are jobs that depend on gathering details, answering questions, drafting content, or presenting knowledge to others. In many cases, the AI served as a digital assistant that helped users write, explain, translate, or summarize.

At the top of the list were interpreters and translators. Their work involves transforming written or spoken language across contexts, and AI has already shown strength in performing these tasks quickly and accurately. Writers, editors, and proofreaders also scored high, as many people are already using AI tools to generate, revise, or polish documents.

Other top-ranked roles include customer service agents, sales representatives, journalists, PR specialists, and educators. These jobs often require giving people information, preparing written materials, or guiding others through a process. These are areas where AI responses are more likely to be useful and well received.

The AI was not replacing these workers. Instead, the study showed that users were using AI to assist with parts of their tasks. This distinction was key to the way the research was designed. It separated what the user was trying to achieve from what the AI actually did during the conversation.

The 40 Most AI-Compatible Occupations

Each of these roles scored high in three areas: the share of job tasks that overlapped with AI usage, how well AI completed those tasks, and how much of the occupation those tasks covered.

• Interpreters and Translators
• Historians
• Passenger Attendants
• Sales Representatives (Services)
• Writers and Authors
• Customer Service Representatives
• CNC Tool Programmers
• Telephone Operators
• Ticket Agents and Travel Clerks
• Broadcast Announcers and Radio DJs
• Brokerage Clerks
• Farm and Home Management Educators
• Telemarketers
• Concierges
• Political Scientists
• News Analysts, Reporters, and Journalists
• Mathematicians
• Technical Writers
• Proofreaders and Copy Markers
• Hosts and Hostesses
• Editors
• Business Teachers (Postsecondary)
• Public Relations Specialists
• Demonstrators and Product Promoters
• Advertising Sales Agents
• New Accounts Clerks
• Statistical Assistants
• Counter and Rental Clerks
• Data Scientists
• Personal Financial Advisors
• Archivists
• Economics Teachers (Postsecondary)
• Web Developers
• Management Analysts
• Geographers
• Models
• Market Research Analysts
• Public Safety Telecommunicators
• Switchboard Operators
• Library Science Teachers (Postsecondary)

Most of these jobs involve structured knowledge work. Some include writing technical guides, while others involve answering questions or responding to common customer issues. The overlap with AI in these jobs was not just frequent, but successful. Conversations where AI helped with these tasks often ended with a completed goal or positive user feedback.

Where AI Has No Role So Far

On the other end, the researchers found dozens of jobs where AI showed no real connection to the work being done. These occupations had an AI applicability score of zero. That meant no significant overlap between their daily tasks and what AI was used for in the dataset.

In nearly every case, these jobs required physical skills, specialized equipment, or real-world handling. Many involved cleaning, operating machinery, preparing food, or providing in-person care. Even if AI could offer instructions, the actual task still had to be done by a person, on site, using physical tools or touch.

These occupations also tended to be hands-on in a way that language models are not designed for. They required moving, lifting, installing, or interacting with the environment in ways that AI cannot simulate. Some jobs required high precision, others involved safety risks or regulatory requirements. In all cases, the study found no practical use of AI for their work.

The 40 Least AI-Compatible Occupations

These jobs showed no measurable overlap with AI use in the study. They had zero coverage, meaning none of their key work activities appeared in AI-assisted conversations with users.

• Water Treatment Plant and System Operators
• Pile Driver Operators
• Dredge Operators
• Bridge and Lock Tenders
• Foundry Mold and Coremakers
• Rail-Track Laying and Maintenance Equipment Operators
• Floor Sanders and Finishers
• Orderlies
• Motorboat Operators
• Logging Equipment Operators
• Paving, Surfacing, and Tamping Equipment Operators
• Maids and Housekeeping Cleaners
• Roustabouts, Oil and Gas
• Roofers
• Helpers, Roofers
• Tire Builders
• Surgical Assistants
• Massage Therapists
• Gas Compressor and Pumping Station Operators
• Cement Masons and Concrete Finishers
• Dishwashers
• Machine Feeders and Offbearers
• Packaging and Filling Machine Operators
• Medical Equipment Preparers
• Highway Maintenance Workers
• Helpers, Production Workers
• Prosthodontists
• Tire Repairers and Changers
• Ship Engineers
• Automotive Glass Installers and Repairers
• Oral and Maxillofacial Surgeons
• Plant and System Operators (All Other)
• Embalmers
• Helpers, Painters and Plasterers
• Hazardous Materials Removal Workers
• Nursing Assistants
• Phlebotomists
• Ophthalmic Medical Technicians
• Floor Sanders
• Bridge and Lock Tenders

These occupations span fields like healthcare, heavy industry, transportation, construction, and cleaning. Many involve specialized tools, patient care, or site-specific duties. For these jobs, AI was neither asked to help nor observed completing any relevant work activity.

A Divide Shaped by Task Type, Not Income or Industry

The researchers also examined whether salary or education level influenced AI applicability. They found only weak patterns. Some lower-wage jobs scored high, while some high-wage roles showed little AI overlap. There was a slight trend where jobs requiring a bachelor’s degree showed more applicability, but even that effect was modest.

The key factor was the type of task. If the job involved writing, explaining, organizing knowledge, or communicating, it was more likely to match how AI is currently being used. If the job involved physical motion, hands-on problem-solving, or direct care, it was unlikely to match.

Study Focused on Measured Use, Not Predictions

This study looked only at actual use. It did not attempt to forecast future changes to job markets or make claims about automation risk. It did not track how employers use AI internally, nor did it consider how jobs might evolve over time. The scores only reflect current patterns in how people used Copilot to help with tasks that align to occupations listed in federal labor data.

Still, the data offers a real-world snapshot of how AI is beginning to fit into everyday work. Some jobs already show clear patterns of use, while others remain disconnected. As AI tools grow and change, those patterns may shift. For now, the gap between roles where AI helps and those it doesn't remains wide.

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by Irfan Ahmad via Digital Information World

PayPal Launches Crypto Checkout for U.S. Merchants, Enabling Instant Dollar Settlement from 100+ Tokens

PayPal has introduced a new payment system in the United States that lets businesses accept over 100 different cryptocurrencies. The update provides merchants with a direct way to receive digital asset payments without handling wallets or volatile tokens themselves.

Under the new setup, customers can pay with assets like Bitcoin, Ethereum, USDT, and Solana. Some smaller coins, including memecoins, are also supported. Merchants receive the equivalent amount in either U.S. dollars or PayPal’s own stablecoin, PYUSD, at the moment of transaction. There is no need to wait for network confirmations or manage exchange processes.

Lower Transaction Fees for Cross-Border Payments

PayPal is offering the service at a 0.99% fee for the first year. After that, the rate increases to 1.5%. That is still lower than international card payments, which usually cost around 2% to 4%. For small businesses that serve global buyers, the savings could make a noticeable difference.

Most traditional cross-border transactions move through several financial intermediaries. That often creates delays, raises costs, and causes currency conversion losses. PayPal’s crypto tool avoids those steps by converting the crypto to dollars in real time. The funds then appear in the merchant’s PayPal account without additional processing.

Wallet Support and Settlement Options

To use the service, customers can connect wallets from platforms like MetaMask, Coinbase, Binance, Kraken, and others. The checkout accepts payments made from any of the supported wallets and tokens. On the merchant side, the system handles the conversion and settles the funds instantly.

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If a payment comes in a coin that is either not supported or thinly traded, PayPal’s system may route the transaction through decentralized exchanges. From there, the funds are converted to PYUSD or USD before being deposited. The process is automated and does not require merchants to take action.

Regulatory Limits Still Apply

While the tool is active across most of the U.S., it is currently unavailable to merchants in New York. The state’s regulators have not yet cleared the use of PYUSD for local businesses or residents. This limits the service in one of the country’s largest financial markets.

In addition, like most digital assets, PYUSD and other supported coins do not carry federal protections. They are not insured by the FDIC or the SIPC. In the event of wallet compromise, insolvency, or technical failure, funds could be lost without reimbursement.

Legislation Prompted Changes to Stablecoin Use

The feature was released after the GENIUS Act became law. This legislation restricts how stablecoins can earn interest and steers their design toward payments and trading use cases. For platforms like PayPal, that shift led to changes in how stablecoins fit into their services.

Since early 2025, PYUSD’s market capitalization has grown sharply. This suggests more users and businesses are starting to adopt it for transactions instead of only holding it. While other platforms like Stripe and Coinbase are also rolling out crypto-based tools for merchants, PayPal’s approach focuses on built-in conversion and direct access for sellers.

Market Reach and System Growth

PayPal’s network now connects with wallets used by hundreds of millions of crypto holders worldwide. By allowing them to pay with crypto while settling in fiat, the system gives U.S. merchants a way to reach overseas customers without expanding banking infrastructure.

The company said its service connects to more than $3 trillion in crypto market value. It supports over 100 tokens and plugs into major wallet platforms. The system is designed to make use of APIs and automated agents, which means the payments can be triggered by software, not only human users.

PYUSD also includes the option to hold funds within PayPal and earn yield on balances. That feature may appeal to sellers who prefer keeping earnings inside the system instead of moving them to external accounts.

Future Will Depend on Regulatory Stability

The crypto payments tool offers faster settlement and lower costs than many existing methods. Even so, its growth will likely depend on how regulators shape stablecoin policies in the months ahead. PayPal’s wider crypto plans are tied to approval in key markets and broader trust in PYUSD as a payment asset.

The company is continuing to build out the system and extend wallet integrations. If adoption continues, more platforms and merchants may treat digital currency not just as a speculative asset, but as part of day-to-day business.

Notes: This post was edited/created using GenAI tools.

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by Irfan Ahmad via Digital Information World