Meta Platforms has agreed to pay as much as $16.68 billion and introduce significant changes to Facebook and Instagram to settle allegations by U.S. states that the company designed its platforms to encourage addictive use among children, misled consumers about their safety, and improperly collected children’s personal information.

The agreement announced Wednesday resolves claims brought by 29 states and brings an end to a federal trial that had become one of the most prominent legal tests of allegations that social media companies have contributed to harm among young users.

Meta denied wrongdoing as part of the settlement.

Under the agreement, Meta will introduce daily usage limits for children using Facebook and Instagram and restrict their access to the platforms during nighttime hours. The company will also strengthen measures designed to prevent children from accessing content subject to age restrictions.

The settlement comes as Meta faces a much broader legal campaign over the design of its platforms and their impact on children and teenagers.

The litigation has brought together state governments, local authorities, school districts and individual users who allege that Meta and other major social media companies knowingly developed features that encouraged prolonged and compulsive use, contributing to a nationwide youth mental health crisis.

Meta shares rose 2.3% in early trading following news of the agreement.

The federal trial in Oakland, California, involved claims brought by California, Colorado, Kentucky, and New Jersey alleging that Meta violated state consumer-protection laws.

It also covered claims from 29 states alleging that Meta violated the federal Children’s Online Privacy Protection Act by collecting personal information from users it knew were children without obtaining parental notification or consent. The states also alleged that Meta used children’s data to train machine-learning and generative AI systems, adding a significant technology and privacy dimension to the case.

Meta has disputed the characterization of its platforms as inherently addictive. The company has argued that it could not have misled consumers by denying that its services were addictive because “social media addiction” is not formally recognized as a psychiatric condition.

The financial exposure in the case had been potentially enormous.

Before the trial began on Aug. 18, Meta said California, Colorado, Kentucky and New Jersey were seeking as much as $1.4 trillion in penalties. The states indicated that their potential claims were more likely to total about $200 billion.

The settlement removes the immediate uncertainty associated with those claims while imposing new operational requirements on Facebook and Instagram. The agreement also resolves separate privacy lawsuits brought by California, Illinois, New Mexico and Washington, D.C., related to the Cambridge Analytica scandal.

Those jurisdictions will receive $459.3 million under the settlement.

The Cambridge Analytica cases relate to the collection of personal information from millions of Facebook users by the political consulting firm, which became one of the defining privacy controversies in the history of social media.

The broader youth-safety litigation remains a significant threat to the social media industry. Meta, Snapchat owner Snap, YouTube parent Alphabet and TikTok owner ByteDance continue to face thousands of lawsuits in federal and state courts alleging that their platforms were deliberately designed to keep children and teenagers engaged in ways that could harm their mental health.

A separate trial brought by Tennessee against Meta began last month in Nashville.

The federal cases have been consolidated before U.S. District Judge Yvonne Gonzalez Rogers in Oakland and include claims from individual users, school districts and state governments.

Meta’s legal exposure has already increased following major losses in New Mexico. Earlier this year, the company lost both phases of a landmark lawsuit brought by the state. A jury in March ordered Meta to pay $375 million after finding that the company had misled consumers about the safety of its platforms.

On Aug. 6, a judge separately found that Meta had created a public nuisance and ordered the company to pay another $567 million while implementing measures intended to improve youth safety.

The company has faced challenges in individual lawsuits as well.

In March, the first trial involving an individual’s claims against Meta and Google ended with a verdict for the plaintiff. A Los Angeles jury found the companies liable for contributing to Kaley G.M.’s depression and anxiety and ordered them to pay a combined $6 million in damages.

Meta and Google have said they will appeal those verdicts.

The latest settlement therefore represents more than a financial resolution as it could require changes to how Meta manages children’s access to its platforms, monitors usage and handles age-sensitive content and personal information.

But the agreement also reduces the immediate risk associated with a case in which potential penalties had reached extraordinary levels, though it does not remove the broader legal challenge facing the company or the wider social media industry.

The litigation is now forcing technology companies to defend not only the content users encounter on their platforms but also the underlying design choices that determine how frequently users return, how long they remain engaged, and how their data is collected and used. That issue could become consequential as platforms integrate more sophisticated recommendation systems and generative AI into products used by children and teenagers.

However, the settlement is another consequence of the growing regulatory scrutiny of social media platforms over treatment of young users. Meta has denied wrongdoing, but the scale of the agreement and the operational restrictions it accepts show the extent to which youth safety has become a major legal and business risk for the company.

While the settlement closes the federal trial involving the 29 states, Meta’s wider litigation over youth safety and privacy is far from over.

Nvidia’s earnings have become far more consequential than a routine corporate results report, with CNBC’s Jim Cramer warning that the chipmaker’s performance could influence sentiment across the stock market because of its central role in the global artificial intelligence buildout.

“Nvidia’s truly become anything but ordinary, it’s all-important, and it’s the ultimate battleground,” Cramer said Tuesday on CNBC’s “Mad Money.”

The comments came as Nvidia ended a seven-session losing streak, its longest since September 2022, just months before the launch of ChatGPT helped ignite the generative AI boom that transformed demand for Nvidia’s graphics processors and propelled the company into the ranks of the world’s most valuable businesses.

Nvidia’s shares closed Tuesday less than 10% below their record closing high of nearly $236 reached in May. The company is scheduled to report its results after the market closes Wednesday, with investors looking for evidence that the enormous spending on AI infrastructure by technology companies can continue to generate sufficient returns.

But the stakes extend well beyond Nvidia itself.

The company has become a critical supplier to the infrastructure supporting the AI industry, with its GPUs powering the training and inference of many of the most advanced models. Its fortunes are closely linked to data-center construction, networking equipment, high-bandwidth memory, advanced packaging and the capital expenditure plans of the world’s largest technology companies.

“Never before has there been a company with so many tentacles in so many segments of the economy,” Cramer said.

That interconnectedness has made Nvidia something of a barometer for the sustainability of the AI investment cycle. A strong report could reinforce the view that hyperscalers and other customers are still willing to spend heavily on computing capacity. A weaker outlook, however, could revive concerns that the extraordinary expansion in AI capital expenditure is approaching a point of diminishing returns.

Cramer remains bullish on Nvidia’s competitive position, even as competition intensifies.

OpenAI and other major AI companies are developing their own chips, while established semiconductor rivals are seeking to take market share from Nvidia. Customers are also exploring alternatives as they try to reduce their dependence on a single supplier and lower the cost of running increasingly large AI workloads.

Still, Cramer noted that Nvidia remains the industry standard.

“I say if you’re going to be part of the AI revolution, you have to go with the best,” he said.

The more difficult question for investors is whether Nvidia’s technological lead is sufficient to justify its valuation and the expectations embedded in its stock price.

Cramer said the company will be judged on more than revenue and earnings growth. Nvidia has increasingly become a proxy for the wider debate over whether the AI boom represents a durable technological transformation or an investment cycle that has become excessive.

“If the bears are right, this stock will tumble regardless of what it reports tomorrow,” Cramer said.

One area receiving increased scrutiny is Nvidia’s investment activity across the AI ecosystem. The company has invested in several businesses that are themselves customers or potential customers of AI infrastructure, prompting concerns among some investors about a circular flow of capital. The argument is that Nvidia can invest in AI companies, those companies can use the funding to expand their computing infrastructure, and some of that spending can ultimately return to Nvidia through purchases of its chips and systems.

Cramer rejected the criticism, saying that Nvidia is using its financial strength to accelerate the development of an ecosystem that ultimately expands the market for its products.

“Why shouldn’t Nvidia use its profits to help ensure that it remains on top?” he said.

That strategy also exposes the changing nature of Nvidia’s business. The company is no longer simply selling chips into an emerging market. It is now investing across the infrastructure, software, and startup ecosystem surrounding AI, seeking to bolster an ecosystem built around its computing architecture.

But several risks remain outside Nvidia’s direct control.

Data-center construction has encountered political and regulatory opposition in some markets, while shortages of high-bandwidth memory have constrained the supply chain. Nvidia also continues to face U.S. export restrictions limiting its ability to sell some advanced AI processors into China, one of the world’s largest technology markets.

Those restrictions create a challenge for Nvidia, which must simultaneously satisfy enormous demand from U.S. and other global customers while navigating Washington’s technology faceoff with China.

The company is also entering a more competitive phase of the AI chip market. AMD is expanding its accelerator business, cloud providers are developing proprietary processors, and AI companies such as OpenAI are pursuing alternatives that could eventually reduce their reliance on Nvidia.

Yet the central issue for investors is not whether Nvidia will face competition. It is whether the overall AI infrastructure market will continue expanding fast enough for Nvidia to maintain exceptional growth even as competition increases.

That makes Wednesday’s outlook potentially more important than the headline quarterly numbers.

Investors will be looking for indications of continued demand for data-center GPUs, the pace of deployment of new AI infrastructure, customer capital expenditure plans and the extent to which supply constraints are limiting sales. They will also be watching whether Nvidia can maintain its margins as customers gain more bargaining power and alternative processors become increasingly available.

The market’s reaction could therefore extend across the AI supply chain. Industry analysts say memory-chip manufacturers, semiconductor equipment companies, data-center operators and other firms benefiting from the AI infrastructure cycle could move in tandem with Nvidia.

The same applies to the broader equity market. Nvidia’s enormous market value means a significant move in its shares can materially affect major stock indexes and investor sentiment, particularly as concerns over expensive technology valuations continue to compete with optimism about AI-driven earnings growth.

Cramer’s warning captures the unusual position Nvidia now occupies in financial markets. Its earnings are no longer simply a measure of how one semiconductor company is performing. They have become a test of whether the extraordinary spending spree behind the AI revolution can continue.

“There’s no such thing as a nothing burger about the largest stock in the world,” Cramer said, explaining that every Nvidia result carries unusually high stakes for investors.

OpenAI CEO Sam Altman said he underestimated how quickly artificial intelligence would reshape businesses and displace established software products, acknowledging that companies have been far slower to change their habits than the technology’s rapid development initially suggested.

In an interview with David Senra published Sunday, Altman said he had expected the release of GPT-4 in 2023 to quickly create opportunities for new software companies and force businesses to reconsider the tools they used. Instead, customers have largely continued buying from familiar vendors and using established products.

“I think it means we’ve all been too ambitious on timelines,” Altman said. “People keep doing the same things they’re doing. They keep buying from the same company. They keep sort of wanting to use their tools in the same way.”

Altman described the slower transition as “positive in many ways,” offering a more cautious assessment of AI’s economic impact than some of the predictions that accompanied the generative-AI boom.

That matters for the technology industry because AI capabilities have advanced rapidly, but technological capability and economic adoption are not the same thing. Companies must change procurement processes, train employees, integrate new systems with existing infrastructure, and become comfortable relying on AI for business-critical tasks.

Those barriers can slow disruption even when a new technology appears capable of replacing an existing product.

OpenAI and other leading AI companies have spent years arguing that capable models could make businesses more efficient, allow smaller teams to compete with established companies, and automate substantial amounts of white-collar work.

Anthropic CEO Dario Amodei has gone further, predicting that AI could eliminate as much as half of entry-level white-collar jobs within five years.

Those expectations have already affected financial markets. Software-as-a-service companies came under heavy pressure in early 2026 as investors began questioning whether AI systems could reduce demand for traditional enterprise applications.

The selloff, dubbed the “SaaSpocalypse,” hit companies including Salesforce, Atlassian and Asana as investors considered the possibility that businesses could increasingly use AI tools to create customized software rather than purchasing standardized applications.

But Altman’s latest comments suggest that the disruption may take longer to materialize at scale. He compared the situation with the transition from physical video rental to streaming. Customers continued visiting Blockbuster even after Netflix had begun offering DVD rentals by mail, illustrating how established habits can survive even when a more convenient technology is already available.

“It was amazing to me that people still went to Blockbuster,” Altman said. “That is an example that has stuck in my head of like force of habit, and the way people do things and changing behavior is just much harder than the tech nerds realize.”

The comparison points to a central challenge for AI companies. Developing a model that can perform a task is only the first step. Convincing millions of people and thousands of companies to change how they perform that task can take considerably longer.

Enterprise customers, in particular, have reasons to move cautiously. Software is often deeply embedded in corporate workflows, data systems and compliance processes. Replacing an established application with an AI-based alternative can create operational and security risks even when the new system is technically more capable.

There is also an economic question about who captures the benefits of AI. A company may use AI to perform a task more efficiently without abandoning the software vendor that already provides its broader workflow. AI could therefore initially increase the productivity of existing products rather than immediately destroy them.

Altman acknowledged that this inertia exists even inside his own working habits.

He said he still manually works through his email inbox even though OpenAI’s Codex could automate more of the process. That example illustrates the gap between what AI can theoretically automate and what users actually choose to delegate. People may continue performing tasks themselves because they prefer existing routines, want to retain control, or simply have not developed new habits around AI.

The slower adoption cycle complicates some of the more aggressive assumptions embedded in AI valuations for investors. The technology may ultimately transform software, employment and corporate operations, but the timing of that transformation is increasingly difficult to predict.

For AI companies, the implication is equally significant. Technical progress alone may not be enough to produce rapid economic disruption. Distribution, integration, trust and changes in user behavior could determine how quickly AI moves from an impressive capability into a replacement for established products.

Altman’s comments therefore amount to a reassessment of the timeline rather than a retreat from the broader AI thesis. OpenAI still expects increasingly capable models to change how people and companies work. But the experience since GPT-4 has shown that technological disruption can move at two different speeds: AI capabilities can advance rapidly while the institutions and people expected to use them change much more slowly.