Meta is taking its newly launched Muse AI agent into the business market, extending the service beyond its fast-growing consumer app and into the software that small companies use to manage operations, marketing and customer relationships.

The company on Tuesday unveiled Muse for Small Business, a version of its AI agent that can connect with widely used business applications including Asana, Zoom, Intuit, Box, Canva and Salesforce’s Slack. The service can also connect to Meta advertising accounts and professional Instagram and Facebook profiles.

Meta did not disclose pricing for the small-business offering. At the bottom of its announcement, however, the company referred users to the existing Muse app, which is available free with usage limits and through a subscription for users who need greater access.

The launch comes a day after Meta announced a new enterprise platform and recruited MongoDB CEO Chirantan “CJ” Desai to lead the initiative. That platform will include a Muse agent, a business agent and a coding tool, signaling that Meta is moving to build a broader commercial AI ecosystem rather than treating Muse primarily as a consumer assistant.

“Give Muse a goal — like running your business or finding new customers — and it gets it done,” Meta said in Tuesday’s announcement.

The shift marks a significant expansion of Meta’s AI ambitions. The company has historically generated almost all of its revenue from digital advertising, but CEO Mark Zuckerberg has increasingly positioned AI as a new business opportunity capable of extending Meta’s reach into areas beyond advertising and social networking.

From Consumer AI to Business Automation

Muse has quickly become the centerpiece of that effort.

Since its September 8 rollout, the AI application has climbed to the top of Apple’s App Store rankings, overtaking OpenAI’s ChatGPT. Evercore analyst Mark Mahaney told CNBC that he expects Muse to reach 100 million users within six to 12 months.

Meta’s market performance has also benefited from the enthusiasm surrounding its AI strategy. Its shares gained about 25% in September, although the stock has pulled back over the past two trading sessions. The small-business launch now puts that consumer momentum against a more demanding test: can Meta turn an AI assistant into a useful business application that companies will rely on for real work?

The question matters because a consumer AI assistant can succeed by answering questions, generating content, or helping users complete individual tasks. A business agent needs to interact reliably with multiple systems, maintain permissions, handle company data, and execute actions without creating costly errors.

Meta is addressing that requirement by connecting Muse to applications that businesses already use.

A small business could potentially use the agent across project management, video meetings, accounting, file storage, design, messaging, advertising, and social-media accounts rather than requiring employees to move between individual applications. That interoperability is potentially more important to Meta’s enterprise ambitions than the chatbot interface itself. The value of an agent increasingly depends on what it can actually access and execute, rather than simply how convincingly it can generate text.

Meta Targets a Market It Already Knows

Meta is entering the small-business market with an advantage that enterprise-focused AI rivals cannot easily replicate: an enormous existing relationship with business owners through Facebook and Instagram.

The company says 200 million small businesses have a presence on Facebook.

“Small businesses have been growing on our apps for nearly two decades,” Meta said. “They told us they’re short on hours, not ideas. So we built Muse for Small Business to help get work done with the tools they already use.”

That installed base gives Meta a natural distribution channel.

A business already using Meta to advertise products or communicate with customers does not need to discover an entirely new technology ecosystem before experimenting with Muse. The agent can potentially sit between the company’s social-media presence and the other software it already uses.

That could allow Meta to turn its advertising relationship with small businesses into a broader software relationship. For example, connecting Muse to Meta’s advertising accounts and professional social profiles gives the company a direct route into marketing workflows. Connecting the agent to accounting, project-management, design and communication applications extends its reach into the operational side of a business.

The commercial opportunity is therefore larger than simply charging for access to an AI assistant. Meta could eventually use Muse to deepen engagement with business customers, generate additional software revenue and make its advertising ecosystem more deeply embedded in day-to-day business operations.

A More Direct Challenge to OpenAI And Anthropic

The timing also places Meta in more direct competition with the leading enterprise AI providers.

OpenAI is holding its developer day as Meta launches Muse for Small Business, while Anthropic has increasingly positioned Claude as a platform for corporate users and AI agents. Meta’s response is built around a different combination of assets: a huge consumer and small-business user base, its advertising infrastructure, open AI-model development and an increasingly broad portfolio of AI agents.

Zuckerberg has made the shift explicit. At Meta Connect last week, he described Muse as the “centerpiece” of the company’s AI strategy.

The company’s spending also shows how seriously it is taking the opportunity. Meta spent billions of dollars last year recruiting top AI talent, including Scale AI founder Alexandr Wang, who is leading the company’s AI efforts. Wang’s group began releasing new models under the Muse Spark family in April.

The recruitment of MongoDB’s Desai adds another piece to that strategy. His background in enterprise software gives Meta an executive focused specifically on turning its AI technology into products that companies can deploy.

The challenge now is execution.

The small-business market is fragmented, price-sensitive, and highly dependent on software reliability. An agent that can connect to many applications is potentially powerful, but every additional integration also introduces questions around data access, permissions, security, and the consequences of an AI system taking an incorrect action.

Meta’s ability to persuade businesses to let Muse move from generating recommendations to actually executing tasks will therefore be a critical test. The company’s broader AI strategy is increasingly moving in that direction. Rather than competing only to produce a more capable model, Meta is attempting to control the layer where AI meets users, software, and business workflows.

For Zuckerberg, that could provide a route to turn Meta’s enormous consumer and advertising footprint into a much broader AI business. Muse for Small Business is an early test of whether that ambition can extend from the social-media feed into the operating systems of millions of companies.

Oura has postponed its planned Nasdaq initial public offering, citing uncertainty in the IPO market just days after formally launching the deal, highlighting the fragile conditions facing companies seeking to tap U.S. public markets.

The smart-ring maker said Tuesday that it was delaying the listing despite what it described as “strong demand” from investors and continued improvement in its business since the IPO process began.

Oura formally launched its IPO plans on September 21, seeking to raise as much as $2.2 billion through the sale of 50 million shares. The company did not provide a new timetable for the offering.

“Our mission is to empower people to live healthier, longer, and an IPO is just one step in our journey,” CEO Tom Hale said.

“We aim to deliver an extraordinary IPO for our employees and investors and we have the luxury of choosing our moment. In the meantime, we will execute against the opportunities ahead,” he added.

The decision underlines the difference between company-specific performance and the broader conditions required for a successful public offering. Oura said demand for its shares remained strong and that its business had strengthened, but it has nevertheless opted to wait for more favorable market conditions.

Growth Is Strong, But Timing Matters

Oura said it is profitable and expects revenue to increase 90% year over year in fiscal 2026.

That growth rate is significant for a consumer technology company preparing to enter public markets, particularly because Oura has expanded its product beyond its original focus on sleep tracking.

Since launching in 2015, the company’s smart ring has developed into a broader health and wellness platform. Oura has added capabilities around health monitoring, analytics, and AI as it attempts to position the wearable as a tool for preventative health rather than simply a sleep-tracking device.

That evolution is central to the company’s investment case.

The wearable market has traditionally been dominated by larger technology companies offering smartwatches and other multifunctional devices. Oura has instead concentrated on a smaller form factor and health-focused functionality, giving it a distinct position in the market.

Its challenge as a public company would be demonstrating that rapid revenue growth can be sustained as the business becomes larger and competition in health-focused wearables intensifies. The company’s decision to delay the IPO means investors will have to wait longer to assess Oura’s financial performance through public-market disclosures.

However, postponing the offering also allows Oura’s management to continue building the business without immediately accepting a valuation determined by unsettled equity markets.

A Warning Sign for The IPO Market

Oura is the latest U.S. company seeking to go public to postpone or withdraw an offering.

Holtec Nuclear withdrew its IPO earlier this month, citing adverse market sentiment in equity markets. The company pointed to a combination of uncertainty around data development, rising energy costs, elevated global trade tensions, military conflicts, and inflation concerns.

The two companies operate in very different industries, but their decisions point to the same problem facing prospective IPO candidates: strong company fundamentals do not necessarily translate into favorable conditions for going public.

An IPO requires investors to price not only the company’s future earnings but also the broader risk environment. When uncertainty increases, investors can demand lower valuations or become more selective about new offerings.

That can create a difficult calculation for companies such as Oura.

A successful IPO can provide liquidity for existing shareholders, establish a public valuation and give the company access to capital markets. But going public during a period of heightened volatility can also result in a valuation below management expectations and expose the stock to sharp trading swings immediately after the listing.

Oura’s decision to wait therefore preserves flexibility.

The company is not abandoning its IPO ambitions. Instead, it is effectively treating the timing of the offering as another variable in its strategy.

Oura’s Next Test Is Sustaining Growth

The company’s financial trajectory gives it a stronger position to wait than a business that needs an IPO to finance immediate operations.

Oura said it is profitable, and its planned offering was not presented as a rescue financing. That distinction matters because the company can continue investing in its products and expanding its customer base while waiting for conditions to improve.

Its 90% expected revenue growth also gives management a potentially important window to demonstrate that the business can sustain rapid expansion.

However, high growth creates expectations. Investors will eventually want to see evidence that Oura can convert its expanding health and wellness ecosystem into recurring revenue and durable margins.

The company’s increasing use of AI and analytics could help broaden its value proposition, but it also raises questions about how much differentiation can be maintained as larger technology companies add increasingly sophisticated health-monitoring features to their own devices.

The IPO will therefore eventually become a test of more than investor appetite for Oura’s ring. It will test whether public-market investors are willing to assign a premium valuation to a profitable consumer technology company whose growth is increasingly tied to health data, software and AI.

For now, Oura has chosen not to make that test under uncertain market conditions. Hale’s statement that the company has the “luxury of choosing our moment” captures the significance of the decision. Oura can continue growing privately while waiting for a market environment in which its operating performance and the public-market valuation it seeks are more closely aligned.

The postponement also offers a broader signal for other companies preparing to list. The IPO window may remain open, but companies are increasingly treating access to it as conditional on investor confidence, market stability, and the ability to secure a valuation that justifies becoming public.

Toyota’s global vehicle sales and production declined for a second consecutive month in August, with weakness in China, the United States and the Middle East outweighing stronger demand in Japan and extending a difficult stretch for the world’s largest automaker.

Toyota said Tuesday that global sales fell 6.4% from a year earlier to 790,743 vehicles, while worldwide production dropped 5.9% to 700,860 vehicles. The figures include Toyota’s luxury Lexus brand.

The August decline follows weaker results in July and points to a broad-based slowdown across several of Toyota’s most important markets. The deterioration in China is significant, with sales there falling for a seventh consecutive month as higher petrol prices weighed on demand for both hybrid and conventional combustion-engine vehicles.

China sales plunged 22.8% in August, making it the largest drag on Toyota’s global performance. Production in the country also fell 11.3%.

The weakness highlights a difficult competitive environment for Toyota in China, where domestic automakers have expanded rapidly in electric vehicles and increasingly compete across hybrid and conventional segments. Higher fuel costs add another layer of pressure for vehicles that rely on petrol, even as Toyota continues to position hybrids as a central part of its electrification strategy.

The United States, Toyota’s largest market, also contributed to the global decline.

US sales fell 4.4% in August, while production dropped 6.6%. The decline is significant because the US market has been one of Toyota’s most important sources of sales and profitability, helping offset weaker performance in other regions.

Toyota’s performance in the Middle East was considerably weaker, with sales plunging 37.5%.

The simultaneous weakness across China, the US and the Middle East means Toyota is facing pressure across markets with very different economic and competitive conditions. That makes the August decline broader than a single-market setback.

Japan provided the main counterweight. Toyota’s domestic sales rose 9.1% during the month, although production in Japan still declined 1.7%.

The company said the production decline was partly related to suspensions following an earthquake on the southern island of Kyushu.

The contrast between sales growth in Japan and weaker overseas markets suggests that Toyota’s August performance was not driven solely by production constraints. Demand itself was weaker in several major international markets.

China stands out in the latest figures because the decline has persisted for much longer than in some of Toyota’s other markets. Sales have now fallen for seven consecutive months, with the 22.8% August drop marking a particularly sharp deterioration.

The Chinese auto market has undergone a structural shift as domestic manufacturers have expanded their technological capabilities and increased their share of the electric and hybrid vehicle market.

Toyota’s traditional strength in hybrids gives it an important position in the global transition away from conventional vehicles, but China’s market is moving rapidly toward vehicles equipped with locally developed batteries, software and connected-car technologies.

Higher petrol prices can further weaken demand for Toyota’s hybrid and combustion-engine models because consumers become more sensitive to fuel costs and may consider fully electric alternatives.

The production decline of 11.3% in China also indicates that weaker demand is affecting Toyota’s manufacturing operations rather than simply its sales mix.

Toyota’s global production fell to 700,860 vehicles, below its 790,743 units of worldwide sales.

Production and sales are not directly comparable over a single month because inventory, exports and regional manufacturing patterns affect the relationship between the two figures. Still, the production decline shows that Toyota is adjusting output amid weaker demand.

The decline was concentrated in several major manufacturing regions.

China production fell 11.3%, while US output declined 6.6%. Japan’s 1.7% reduction was partly linked to earthquake-related production suspensions.

For Toyota, maintaining the right balance between production and demand is important because excess inventory can pressure discounts and margins, while excessive production cuts can limit the company’s ability to respond when demand recovers.

The August figures therefore provide an indication of the pressure facing Toyota’s manufacturing network as the company navigates uneven global demand.

The results also illustrate the complexity of Toyota’s global vehicle strategy. The company has invested heavily in hybrid technology while gradually expanding battery-electric vehicles. That approach has helped Toyota maintain strong hybrid sales in several markets, but the latest China figures show the limits of relying on conventional and hybrid powertrains in a market where electric vehicles and Chinese brands are advancing quickly.

The weakness in the US presents a different challenge. Toyota has benefited from strong demand for SUVs, pickups and hybrid vehicles in the market, but higher costs and changing consumer demand can quickly affect volumes.

Meanwhile, the sharp decline in Middle East sales adds another source of volatility to the global numbers.

Japan’s 9.1% sales increase provides some support, but the country’s market is much smaller than Toyota’s combined overseas operations.

The August figures therefore leave Toyota confronting a common problem among global automakers: strong performance in one region is no longer sufficient to offset simultaneous weakness across several major markets.

With sales falling for a second month and China extending its decline to seven months, the next several months will be important for determining whether Toyota’s recent weakness represents a temporary adjustment in global demand or a more persistent shift.

As technology companies debate whether artificial intelligence could pose an existential threat to humanity, Microsoft’s Copilot chief is highlighting a different risk: that the economic gains from AI become concentrated among a small number of companies rather than spreading across the wider economy.

Jacob Andreou, Microsoft’s executive vice president of Copilot, told Business Insider that the AI risk Microsoft spends significant time considering is what the company calls “diffusion,” meaning the broad distribution and adoption of AI among consumers and businesses.

“How do we take raw intelligence and turn it into something that can be a true rising-tide benefit to people in their personal lives, and then certainly to the people that make up all of these corporations, and the broad economy?” Andreou said.

“In a world where we fail to accomplish that diffusion, I do worry about what it looks like as value and as the economy centralizes into, like, a couple companies.”

The argument places the distribution of AI’s economic benefits at the center of Microsoft’s thinking about risk. Instead of focusing primarily on whether capable models could become uncontrollable, Andreou’s concern is what happens if access to advanced AI, the computing infrastructure behind it, and the resulting productivity gains remain concentrated among a small group of technology companies.

That question is becoming more significant as AI investment accelerates and companies race to develop more capable models and agents.

From AI Capability to Economic Diffusion

The debate over AI’s long-term effects has increasingly moved beyond the capabilities of individual models.

Supporters of the technology say that AI could increase productivity, reduce the cost of knowledge work and create entirely new industries. At the same time, there are concerns that companies controlling the most powerful models and infrastructure could capture a disproportionate share of those gains.

Andreou’s comments place Microsoft on the latter issue without arguing that AI development itself should be slowed. The company’s concept of “diffusion” is focused on what happens after AI capabilities are developed: if they become tools broadly available to workers, businesses and consumers, or if the economic value remains concentrated among the companies building the underlying technology.

AI requires substantial capital, computing capacity and technical expertise, making the questions essential. The largest technology companies are spending heavily on data centers, chips and model development, creating a gap between companies capable of building frontier systems and those that primarily consume them.

If that gap persists, productivity improvements could accrue disproportionately to companies with access to the most advanced systems.

For Microsoft, widespread adoption also has a direct commercial dimension. The company sells cloud computing through Azure, workplace software through Microsoft 365, developer tools and a growing portfolio of AI products. Broader AI adoption can therefore increase demand across several of its existing businesses.

Microsoft has increasingly made “diffusion” part of its public messaging. President Brad Smith has argued that success in AI should be measured not simply by which company develops the most capable model, but by how widely the technology is adopted.

Microsoft has also published a report focused on global AI diffusion.

Copilot Becomes Microsoft’s Distribution Vehicle

Microsoft’s evolving Copilot strategy provides the clearest example of how the company intends to pursue that diffusion.

The company has been bringing together conversational AI, cowork, coding capabilities, and autonomous “Autopilot” agents within a broader Copilot experience spanning consumer and commercial users.

The strategy effectively treats Copilot as Microsoft’s distribution layer for AI. Rather than asking users to seek out separate AI applications for different tasks, Microsoft is attempting to place AI inside software that millions of people already use for work and personal computing. That gives the company a potentially important advantage in distributing AI, particularly within businesses that already rely heavily on Microsoft products.

But the strategy also underlines the tension in Microsoft’s argument.

Microsoft is itself one of the world’s largest AI companies and has a major relationship with OpenAI, while its Azure infrastructure provides much of the computing environment used to develop and deploy AI systems. Its ability to distribute AI through Windows, Microsoft 365, Azure and Copilot means that successful diffusion can simultaneously strengthen Microsoft’s own position.

Andreou acknowledged the importance of that broader economic objective.

“We definitely believe in this technology to be for the empowerment of people,” he said. “That diffusion is not just existential for us in many ways, but actually that is the way that the whole economy gets to benefit.”

The Concentration Question

The concentration issue is becoming harder to separate from the AI investment boom.

Building frontier AI models requires enormous amounts of computing power and capital. The companies operating at the leading edge are also investing heavily in data centers, specialized chips, and energy infrastructure. That has resulted in economies of scale that could make it increasingly difficult for smaller companies to compete at the infrastructure and model-development layers.

At the application layer, however, the picture is more open. Companies can build products on top of existing models, allowing AI capabilities to spread without every business having to develop its own frontier system.

That is where diffusion could become decisive.

If advanced AI becomes a general-purpose technology that thousands or millions of companies can cheaply integrate into existing operations, the economic impact could extend well beyond the companies that develop the underlying models. But if access remains expensive, technically difficult, or controlled by a small group of model and infrastructure providers, a larger portion of the value could remain concentrated.

Microsoft’s Copilot strategy is built around the first scenario. By integrating AI into software already used by consumers, developers, and businesses, the company is attempting to make AI adoption less dependent on users seeking out specialized systems.