Meta is preparing to introduce facial recognition capabilities to its smart glasses as early as this year, according to a report by The New York Times. The feature, internally called “Name Tag,” would allow wearers to identify individuals in their field of vision and retrieve information about them through Meta’s AI assistant.

The plan remains under internal discussion and could change. Executives have been weighing how to deploy a feature that carries what the company has described as significant safety and privacy risks.

An internal memo cited in the report shows that Meta had initially considered launching Name Tag at a conference for the visually impaired before expanding access more broadly. That limited rollout did not occur. The memo also indicated that the company believed the current political climate in the United States could provide a less adversarial backdrop for launch.

“We will launch during a dynamic political environment where many civil society groups that we would expect to attack us would have their resources focused on other concerns,” the document said.

A Bet on Wearables and AI

Meta’s renewed push into biometric identification reflects broader strategic priorities. The company has been repositioning itself around artificial intelligence and hardware ecosystems, aiming to reduce dependence on third-party platforms and create direct consumer interfaces.

Its smart glasses, developed in partnership with Ray-Ban parent EssilorLuxottica, have exceeded early sales expectations. The devices already support hands-free photo capture, livestreaming, and AI-powered voice assistance. Adding facial recognition would deepen their functionality and potentially differentiate them in a competitive wearable market that includes offerings from Apple and other hardware makers investing in spatial computing.

Meta previously considered facial recognition integration in 2021 but abandoned the idea due to technical limitations and ethical concerns. Advances in on-device AI processing, improved computer vision models, and edge computing efficiency may now make real-time identification more feasible without constant cloud dependence.

The timing also intersects with shifting regulatory dynamics. The administration of President Donald Trump has signaled closer engagement with major technology firms, potentially reducing immediate federal pushback compared with prior regulatory cycles.

Deploying facial recognition in consumer eyewear would test legal and ethical boundaries. Biometric identification technologies are subject to a patchwork of U.S. state laws, including statutes that require explicit consent for collecting or processing facial data. Internationally, regulations such as the European Union’s General Data Protection Regulation impose strict standards for biometric data handling.

Key operational questions remain unresolved:

• How the system would source identification data. Whether it would rely on publicly available images, user-uploaded databases, or opt-in contact lists.
• Where processing would occur. On-device computation would limit external data transmission, while cloud-based processing could raise additional surveillance concerns.
• How consent would be managed. Non-users in public spaces may not be aware they are being scanned, raising questions about informed consent and reasonable expectations of privacy.
• What safeguards would prevent misuse? Real-time identification could facilitate stalking, harassment, or unauthorized data harvesting if guardrails are insufficient.

Civil liberties organizations have historically opposed widespread facial recognition deployment, arguing that the technology erodes anonymity in public spaces. Law enforcement use of similar systems has already triggered litigation and municipal bans in some U.S. jurisdictions.

If Meta proceeds, the launch would mark one of the most visible attempts to normalize biometric identification in everyday consumer devices. Smart glasses, unlike fixed surveillance cameras, are mobile and discreet, potentially transforming how individuals experience public interaction.

The feature could also reshape social norms. Wearers might gain informational advantages in networking, professional settings, or social encounters. At the same time, widespread adoption could create pressure for individuals to assume they are constantly identifiable in public environments.

The decision represents a calculated risk for investors as enhanced functionality could boost device sales and reinforce Meta’s long-term augmented reality roadmap. However, regulatory backlash or reputational damage could offset commercial gains.

Credit markets are emerging as the next major arena for artificial intelligence disruption, with UBS credit strategy head Matthew Mish warning that tens of billions of dollars in leveraged loans and private credit could default over the next year.

This is because software, data services, and other AI-vulnerable companies—particularly those owned by private equity—face intensifying margin compression and revenue erosion.

In a detailed research note released Wednesday and in a subsequent CNBC interview, Mish and his UBS team laid out a baseline scenario projecting $75 billion to $120 billion in additional defaults by the end of 2026 across leveraged loan and private credit markets, which they estimate at roughly $1.5 trillion and $2 trillion in size, respectively.

The forecast assumes default rates rising by up to 2.5% for leveraged loans and up to 4% for private credit—meaningful increases from current levels that are already showing signs of stress. Mish described the acceleration of AI disruption as the primary driver behind the revised outlook.

“The market has been slow to react because they didn’t really think it was going to happen this fast,” he said. “People are having to recalibrate the whole way that they look at evaluating credit for this disruption risk, because it’s not a ’27 or ’28 issue.”

The shift in perception was catalyzed by recent model releases from Anthropic and OpenAI that demonstrated advanced reasoning, tool integration, and task automation capabilities—directly threatening routine knowledge work, data processing, and professional services that underpin many leveraged software and data firms.

Mish categorized companies into three broad groups in the AI landscape:

1. Foundational model creators — Startups like Anthropic and OpenAI (soon potentially large public companies) that develop frontier large language models and stand to capture significant value at the top of the stack.
2. Investment-grade software incumbents — Companies like Salesforce and Adobe with strong balance sheets, established customer bases, and the ability to rapidly integrate AI to defend their moats.
3. Private equity–owned software and data services firms — Highly leveraged companies carrying substantial debt, often acquired in large buyouts during the low-rate era. These firms, according to Mish, are the least likely to emerge as long-term winners in a rapid, disruptive AI transition.

“The winners of this entire transformation—if it really becomes, as we’re increasingly believing, a rapid and very disruptive or severe [change]—the winners are least likely to come from that third bucket,” Mish said.

He also outlined a more severe “tail risk” scenario in which defaults could double the baseline estimates, triggering a “credit crunch” in loan markets, broad repricing of leveraged credit, and systemic shocks. While UBS is not yet calling for this tail scenario, Mish noted the firm is “moving in that direction” as AI model capabilities advance faster than anticipated.

The warning follows a rolling series of selloffs that began with software stocks earlier this month and spread to finance, real estate, trucking, and other sectors perceived as vulnerable to AI automation. The rapid pace of disruption—accelerated by Anthropic’s Claude plug-ins and OpenAI’s tool integrations—has forced investors to reprice credit risk far sooner than the previously expected 2027–2028 timeline.

Private equity–owned software and data firms are particularly exposed. Many were acquired at peak valuations during the low-rate environment of 2020–2022, loaded with leverage, and reliant on recurring revenue from maintenance contracts, legacy system support, and routine analytics—precisely the areas most susceptible to AI automation.

As AI agents handle contract reviews, compliance checks, data extraction, and report generation, pricing power erodes, and customer churn accelerates. Mish emphasized that timing remains the key uncertainty: the pace of large corporate AI adoption, the rate of model improvement, and the ability of incumbents to adapt will determine how quickly credit risk materializes.

“We’re pricing in part of what we call a rapid, aggressive disruption scenario,” The UBS noted, suggesting that the direction is clear.

The UBS note adds to a growing chorus of warnings from credit strategists and analysts. JPMorgan and Morgan Stanley have also flagged rising default risks in private credit and leveraged loan markets, particularly among software and professional services borrowers. Moody’s and S&P Global Ratings have placed numerous private equity–backed software companies on negative watch lists in recent weeks, citing AI disruption as a key risk factor alongside elevated interest rates and slowing organic growth.

The broader leveraged loan and private credit markets—estimated at $3.5 trillion combined—are already showing stress. Secondary loan prices have declined steadily since mid-2025, and spreads have widened significantly for lower-rated issuers. Private credit funds, which stepped in to fill gaps left by retreating banks, now face their own maturity walls and refinancing challenges as portfolio companies struggle to grow in an AI-disrupted environment.

For now, investment-grade software firms with strong balance sheets and clear AI integration strategies (e.g., Salesforce, Adobe, ServiceNow) are seen as more resilient. However, the middle and lower tiers, especially private equity portfolio companies, face the highest risk of default and restructuring.

As the market recalibrates for faster AI disruption, credit investors are increasingly differentiating between AI winners and losers. Companies that can demonstrate defensible moats, rapid AI adoption, and strong balance sheets are likely to see credit spreads tighten, while those slow to adapt or burdened by leverage could face sharp repricing and distress.

Analysts expect some changes in the coming quarters. If large enterprises accelerate AI deployment and begin replacing traditional software and services contracts, default rates could rise quickly. If adoption lags or incumbents successfully integrate AI to defend their positions, the credit impact may be more contained.