Mark Zuckerberg remains Meta’s chairman and CEO and has made “personal superintelligence” a central product and capital-allocation theme. Meta reorganized its AI effort around Meta Superintelligence Labs in 2025, invested in Scale AI, and released the Muse model family in 2026. Those are observable organizational, financial, and product actions, not merely a recruiting story.

The evidence does not show that Meta has achieved superintelligence, that one executive or recruit produced Muse, or that the company abandoned the metaverse. Meta continues to report Reality Labs while spending heavily on AI infrastructure and product development. The strategy is an expansion and reprioritization, not a clean replacement supported by the filings.

The strategy in Zuckerberg’s own words

In July 2025, Zuckerberg published a statement on “personal superintelligence”. He argued that advanced systems should help individuals pursue goals, create, connect, and use new devices. That page is direct evidence of his stated strategy. It is not evidence that the promised level of intelligence exists or that users will accept Meta’s approach.

Meta’s August 2026 “The Future Is for Everyone” statement continued that positioning and said the company expected to resume releasing some open models. This is important because Meta’s model-access posture cannot be reduced to “open” or “closed.” Access can vary by model, weight release, API, license, product surface, and safety review.

“Superintelligence” also needs an operational definition. A company can improve benchmarks, release an assistant, and connect tools without demonstrating a system that exceeds people across most valuable cognitive tasks. Until Meta publishes a measurable threshold, the term should be treated as strategic language rather than a verified technical status.

The Scale investment and lab reorganization

Meta’s 2025 Form 10-K records a $13.80 billion minority investment in Scale AI and says Meta does not have significant influence over the operations of investees accounted for under the measurement alternative. The Associated Press reported the June 2025 deal as a $14.3 billion investment for a 49 percent stake and reported that Scale founder Alexandr Wang would join Meta.

The different figures should be tied to their source and accounting context. AP documented announced deal terms; Meta’s annual report records the investment in its financial statements. Neither supports describing the full amount as compensation to Wang or calling Scale a controlled Meta subsidiary.

Recruiting Wang and organizing Meta Superintelligence Labs created a visible center of accountability. It did not transfer every Scale customer relationship or prove that Meta obtained competitors’ confidential information. Related-party governance, data separation, procurement terms, and board responsibilities remain legitimate questions, but they require evidence rather than inference from the investment alone.

Muse is delivery evidence, with limits

Meta introduced Muse Spark in April 2026 as the first model from Meta Superintelligence Labs. Meta described benchmark results and said the model would power its assistant across its applications. The announcement establishes a named release and its intended deployment. Its performance comparisons are vendor claims until reproduced under matched conditions.

In July, Meta announced that Muse Spark 1.1 could plan and act using connections such as email and calendars. A current Axios report on Muse provides named external coverage of the release and Wang’s leadership role.

This sequence matters. Meta moved from an organizational announcement to model and product releases. It still does not establish independent reliability, retention, financial return, or “superintelligence.” An agent that drafts a plan has a lower burden than one that reads private accounts and takes consequential actions.

Distribution is Meta’s structural advantage

Meta can place an assistant across WhatsApp, Instagram, Facebook, Messenger, Threads, its standalone app, and hardware. Its April 2025 Meta AI app announcement describes how the company connects assistant experiences with voice, discovery, and its social products.

That distribution can reduce acquisition cost and create frequent feedback. It can also blur context boundaries. A user may reasonably expect a private messaging thread, public social profile, work calendar, and commerce account to have different permissions. An assistant must not infer authorization merely because the same company operates the surfaces.

Product evaluation should therefore include permission scope, preview and confirmation, provenance, reversibility, deletion controls, and behavior across account boundaries. Engagement is not enough. Meta should measure accepted task completion, correction rate, harmful or unauthorized actions, and whether personalization improves outcomes without surprising data use.

Capital intensity is visible in the filings

Meta’s 2025 annual report records $69.69 billion of purchases of property and equipment. That is an audited company-wide cash-flow figure, not an AI-only number. It includes infrastructure used across Meta’s services. The filing also describes expected continued investment and the risks associated with infrastructure, regulation, competition, and product adoption.

Earlier management guidance should not be confused with realized spending. Meta’s third-quarter 2025 investor release gave a 2025 capital-expenditure outlook and warned of significant expense growth in 2026. Guidance is management’s time-bound expectation; the later annual report is the stronger source for what was actually recorded.

Meta also announced a long-term infrastructure agreement with AMD for up to six gigawatts of GPU capacity. “Up to” and a multi-year roadmap are not the same as six operating gigawatts on announcement day. Investors should track contracted supply, installation, energization, utilization, depreciation, and workload economics separately.

The Scale investment and infrastructure purchases show that Zuckerberg is willing to use Meta’s balance sheet to accelerate AI. They do not disclose model-level return on capital. The relevant outcome is whether products generate durable user value or revenue relative to compute, talent, and operating costs.

This is not evidence of a metaverse abandonment

Meta’s Form 10-K continues to report two segments: Family of Apps and Reality Labs. Reality Labs continued to generate operating losses and to fund augmented- and virtual-reality work. It is fair to say AI became a larger strategic and spending priority. It is not accurate to infer from AI hiring that the company ended its metaverse or hardware programs.

The strategies may intersect. Meta positions AI as an interface for glasses and other devices, while social products supply distribution and context. That combined thesis can fail in several ways: hardware adoption may be slow, assistant quality may be inconsistent, users may reject data use, or inference costs may exceed the value created.

For that reason, reporting should avoid a theatrical “pivot” narrative and track segment results, product adoption, device use, and AI task outcomes over time.

A scorecard for Meta’s AI program

For models, measure capability, calibration, multilingual performance, robustness, harmful-capability testing, and inference efficiency. Publish enough detail to distinguish Meta’s own evaluations from independent results.

For consumer assistants, measure successful tasks after user approval, not messages sent or generated tokens. Report tail latency, correction, abandonment, unauthorized action, and recovery from tool failures. For email, calendars, commerce, and social accounts, make permissions granular and actions auditable.

For economics, separate company-wide capital expenditure, specific investments, contracted capacity, and actual AI utilization. Track the useful life of accelerators and whether model or product improvements lower the cost of accepted outcomes.

For governance, disclose decision rights, data boundaries, incident response, external testing, and how Meta manages its dual relationship with Scale. A powerful founder-CEO can move quickly; that makes independent board oversight and transparent controls more important.

What is known and unknown

The record supports Zuckerberg’s public personal-superintelligence strategy, the MSL reorganization, the Scale minority investment, high infrastructure spending, and the release of Muse-based products. It supports a substantial AI bet with real shipped artifacts.

It does not establish private frustration, secret recruiting terms, a causal link between any one hire and a model release, or a verified superintelligence threshold. It does not show that Meta abandoned Reality Labs. Nor does public reporting yet reveal Muse’s full adoption, safety performance, unit economics, or return on infrastructure.

Meta’s advantage is the combination of capital, research, hardware work, and global distribution. Its risk is the same combination: a weak permission model or unreliable agent can propagate quickly. The strategy should be judged by measurable, safe user outcomes and financial discipline rather than the scale of the headline.

Source and correction note

This revision uses Meta’s dated strategy and product posts, its 2025 SEC filing and investor material, AP reporting, and current Axios coverage available through September 13, 2026. Company benchmarks, roadmaps, and product capabilities are labeled. The previous version relied on anonymous accounts of Zuckerberg’s frustration and recruiting, inferred private motives, and described AI as an established abandonment of the metaverse. Those claims have been removed or narrowed to the public record.