Can Meta Make Personal Superintelligence Available to Everyone?

Key Takeaways

• Meta links personal superintelligence to agents, glasses, affordable access, and distributed control.
• Meta AI already includes planning and action-taking features, but superintelligence remains a future objective.
• Compute, energy, privacy, safety, employment, and access will determine whether the vision can scale.

Meta’s Personal Superintelligence Proposal Starts With Distribution

On August 10, 2026, Mark Zuckerberg published The Future Is for Everyone, presenting Meta’s personal superintelligence strategy as an effort to distribute advanced artificial intelligence broadly rather than concentrate it within a small group of corporations, governments, or artificial intelligence laboratories. The statement reaches far beyond a more capable chatbot. Zuckerberg describes personal agents that could assist with learning, work, health, finances, relationships, scientific research, creative projects, and business formation. Meta says it intends to offer free versions accessible to billions of people and affordable access to additional computing capacity.

Distribution is the organizing principle behind the proposal. Meta argues that advanced artificial intelligence should increase individual agency rather than transfer more decision-making authority to centralized institutions. Zuckerberg’s argument is essentially a balance-of-power theory: a society containing many people and businesses equipped with capable agents would create competing centers of intelligence rather than allowing one provider, government, or autonomous system to dominate access to advanced capabilities.

That position gives Meta a distinctive strategic identity. The company isn’t presenting personal superintelligence solely as a future version of Meta AI. It is connecting models, consumer devices, data centers, energy procurement, custom processors, privacy architecture, developer tools, small-business services, scientific research, education, and public policy into a single technology strategy. The breadth of the proposal also creates a demanding standard for success. Giving billions of people access to a capable assistant is difficult. Giving them an affordable agent that understands personal circumstances, protects sensitive information, acts reliably on their behalf, and remains accessible through multiple devices is considerably harder.

Meta already possesses a distribution advantage few artificial intelligence laboratories can match. According to Meta’s second-quarter 2026 results, its Family of Apps averaged 3.60 billion daily active people in June 2026. Meta AI can potentially reach users through WhatsApp, Instagram, Facebook, Messenger, Threads, the Meta AI application, the web, and wearable devices without requiring Meta to construct an entirely new consumer network.

That installed base could allow Meta to distribute a capable personal agent faster than a technically comparable system offered through a smaller consumer platform. Reach isn’t identical to control. A service can reach billions of people yet remain technologically centralized because model training, inference infrastructure, identity systems, software updates, safety policies, and access rules remain under the provider’s authority.

Meta’s August statement attempts to address that tension through privacy, open models, affordable access, competing agents, and distributed computing availability. Whether those mechanisms can produce the balance of power Zuckerberg describes will depend on implementation rather than scale alone.

What Meta Means by Personal Superintelligence

Meta defines personal superintelligence primarily through intended capabilities rather than through a fixed numerical intelligence threshold. The August 10 statement anticipates systems that may exceed human capability in some forms of creation, invention, research, and problem solving, but its practical description focuses on a persistent agent working for an individual. Such an agent could gather information, teach skills, manage tasks, develop ideas, support scientific inquiry, and assist with everyday decisions.

Meta’s existing models shouldn’t be confused with the completed capability described in that statement. On April 8, 2026, Meta introduced Muse Spark, the initial large language model in its Muse series from Meta Superintelligence Labs. Meta described Muse Spark as supporting reasoning and multimodal tasks and positioned it as progress toward personal superintelligence rather than declaring that the goal had already been achieved.

The development path became clearer in July. Meta released Muse Spark 1.1 on July 9, describing it as a multimodal reasoning model designed for agentic tasks, tool use, computer interaction, coding, and multimodal understanding. On July 24, Meta announced that Meta AI could use Muse Spark 1.1 to make plans, connect with email and calendar applications, produce presentations, conduct research, generate recurring briefings, and perform some tasks on a user’s behalf.

Those capabilities move Meta AI closer to the persistent personal-agent model described in Zuckerberg’s August statement. The distinction between answering and acting is economically significant. An assistant that provides information competes with search engines, chatbots, and reference tools. An agent that plans activities, maintains context, interacts with applications, monitors changes, and performs authorized actions begins competing with software applications, personal assistants, business services, and parts of conventional office work.

Persistent agents create requirements that isolated question-answering systems can sometimes avoid. An agent expected to understand someone’s schedule, goals, projects, preferences, finances, health information, or relationships requires persistent context. If it can perform tasks rather than simply recommend actions, identity verification, authorization limits, reversibility, error recovery, auditability, and security become more important.

Meta’s vision also assumes that intelligence can become inexpensive enough to distribute at enormous scale. That makes personal superintelligence an infrastructure and economic proposition as much as a model-development proposition. Training capable models is only part of the cost. Serving billions of interactions requires processors, memory, networking, storage, electrical generation, cooling, transmission infrastructure, data centers, software, security systems, and continuous maintenance.

The AI value chain illustrates how semiconductor production, computing systems, data-center capacity, networking, energy, models, software, and applications contribute to the cost and availability of artificial intelligence. A consumer may encounter AI through a simple conversational interface, but a large industrial system operates behind every response.

Meta’s August statement explicitly acknowledges the scarcity of computation. It says free versions should be accessible to billions of people and proposes a market mechanism for users who want to purchase more compute. That proposal recognizes a physical constraint sometimes obscured by discussions of digital abundance: useful artificial intelligence still depends on finite processors, electrical capacity, networking, and data-center infrastructure.

AI Glasses Turn the Strategy Into a Consumer Computing Bet

Artificial intelligence glasses make Meta’s personal-agent proposal more concrete because they allow software to accompany a person through ordinary activities without requiring continuous interaction with a phone or computer. Cameras can provide visual context, microphones provide audio input, speakers return information, and display-equipped products can place information within the user’s field of view.

The Meta Ray-Ban Display, introduced on September 17, 2025, combines cameras, microphones, speakers, an in-lens display, artificial intelligence, and the Meta Neural Band. The wrist-worn band uses electromyography, or EMG, to interpret muscle activity as control inputs. Meta says the interface can translate subtle muscle signals into actions such as clicking and scrolling.

Accessibility demonstrates why this interface may have value beyond convenience. In May 2026, Meta described how its AI wearables support accessibility through hands-free communication, visual assistance, real-time captions, connections to Be My Eyes, voice controls, and developer applications. Meta has also been researching how EMG-based interfaces could help people with spinal cord injuries and other mobility disabilities interact with digital systems.

Meta expanded the product strategy again on June 23, 2026, with additional Meta Glasses developed with EssilorLuxottica. Meta described them as its first AI glasses to launch with Meta AI powered by Muse Spark from day one in the United States and Canada, with expansion into additional markets. The line launched with 26 styles and compatibility with prescription lenses.

Wearable artificial intelligence increases the privacy and social-design burden. A personal agent using cameras and microphones can interpret more context precisely because the device can encounter information about nearby people, locations, conversations, documents, and surroundings.

Meta’s July 2026 explanation of AI-glasses privacy controls says its glasses use a white capture light to indicate when photos or videos are being captured for the user’s gallery. Meta states that covering or disabling the capture indicator prevents normal camera capture. Photos and videos recorded for the user’s gallery remain privately stored on the glasses until the user chooses to import them to a phone.

Those controls address conventional recording, but future context-aware agents create broader questions. A system can process environmental information without saving a permanent video file. People will need understandable distinctions among sensing, temporary processing, model inference, recording, retention, and sharing. Trust in wearable agents may depend on making those boundaries observable.

The larger commercial bet is that glasses could become an important interface for personal artificial intelligence. Smartphones established touchscreens as the dominant interface for mobile computing. Meta is betting that voice, vision, displays, and subtle muscle signals can support a more ambient computing model.

Success won’t result from stronger models alone. People must be willing to wear the devices, charge them, trust their sensors, accept their appearance, and find their functions useful enough to replace interactions already handled efficiently by smartphones.

Enormous Compute Spending Sits Behind the Promise

Meta’s personal-superintelligence ambitions are being accompanied by unusually large capital commitments. During the quarter ended June 30, 2026, Meta recorded $31.08 billion in capital expenditures, including principal payments on finance leases. Revenue for the quarter was $60.80 billion, and total costs and expenses reached $42.03 billion. Meta narrowed its expected 2026 capital expenditures to a range of $130 billion to $145 billion.

Hardware is one component of that spending. Meta is developing its own Meta Training and Inference Accelerator processors and has expanded semiconductor partnerships with other suppliers. In April 2026, Meta announced a Broadcom partnership covering multiple generations of custom artificial intelligence silicon, advanced packaging, and networking. Meta said the initial commitment exceeds 1 gigawatt of accelerator deployment and forms part of a longer multi-gigawatt roadmap.

The Meta Compute initiative combines custom processors with externally sourced accelerators and a growing network of AI-optimized data centers. Meta says hundreds of thousands of its MTIA chips are already being used for inference workloads across content and advertising systems.

Energy is inseparable from that strategy. The International Energy Agency’s updated 2026 analysis, Key Questions on Energy and AI, estimates that worldwide data-center electricity consumption reached about 485 terawatt-hours in 2025 and projects approximately 950 terawatt-hours by 2030. The IEA says artificial intelligence is a primary source of additional demand and reports that data-center electricity consumption increased about 17% during 2025.

The 950-terawatt-hour projection supersedes the approximately 945-terawatt-hour 2030 estimate contained in the IEA’s earlier Energy and AI analysis. The difference is modest, but the newer assessment incorporates another year of observed growth and revised assumptions about computing demand.

Meta’s August statement says communities hosting its data centers should benefit through employment, public investment, energy arrangements, and environmental commitments. Meta also reiterates its goal of becoming water positive by 2030. These are corporate commitments whose performance will need to be evaluated against actual projects, resource consumption, local environmental conditions, and independently observable outcomes.

Artificial intelligence’s energy demand is also creating a direct connection between Meta and emerging space technology. On April 27, 2026, Meta announced a partnership with Overview Energy under which it reserved up to 1 gigawatt of proposed space-based solar energy capacity. Overview Energy plans to collect solar energy in geosynchronous orbit and transmit it to terrestrial solar facilities as near-infrared light.

The underlying system isn’t operational in August 2026. Meta states that Overview Energy is targeting an orbital demonstration in 2028 and that commercial delivery to the U.S. grid could begin as early as 2030 if development and demonstration milestones succeed.

New Space Economy’s examination of Meta and space-based solar power places the agreement within the larger effort to find energy sources capable of serving expanding AI infrastructure.

The cost of personal superintelligence will consequently depend on more than model efficiency. Semiconductor supply, electrical generation, grid interconnection, networking capacity, cooling, water availability, financing, construction, and land can all affect the cost of an AI interaction. Meta’s promise of free or inexpensive access becomes easier to sustain if inference costs decline faster than demand rises.

Employment Claims Face a More Complicated Economic Record

Meta’s August statement takes an optimistic position on employment. Zuckerberg predicts that personal superintelligence can increase individual capabilities, accelerate entrepreneurship, create new businesses, and generate new occupations even as automation changes existing jobs. He argues that smaller companies may eventually operate at much greater scale with assistance from personal agents. These statements remain forecasts rather than measured economic outcomes.

Research on employment exposure supports a more cautious interpretation of optimistic and pessimistic predictions. The International Labour Organization’s 2025 global occupational analysis estimated that one in four workers worldwide were employed in occupations with some exposure to generative artificial intelligence. It placed 3.3% of global employment in its highest exposure category.

Exposure doesn’t mean that an occupation disappears. Many jobs contain combinations of automatable tasks and responsibilities that still require human judgment, physical work, accountability, interpersonal interaction, or specialized situational knowledge.

Meta itself illustrates why the employment relationship is difficult to reduce to a simple automation-versus-jobs argument. Meta’s June 30, 2026 headcount was 75,472, down 1% from a year earlier. That reported figure still included approximately 8,000 employees affected by a May 2026 headcount reduction, most of whom Meta expected to leave the reported total by the end of the September quarter.

At the same time, the company is spending heavily on processors, data centers, energy systems, networks, construction, and research. Those expenditures support employment in sectors that don’t necessarily appear inside Meta’s own employee count.

The economic test for personal superintelligence is consequently the distribution of productivity gains rather than productivity alone. If one employee equipped with an advanced agent can perform work that previously required several people, an existing company may produce more with a smaller workforce. If the same technology reduces the cost of establishing and operating businesses, new firms may create additional demand for labor. Both processes can occur simultaneously and at different speeds.

Small businesses could become an important test case. A professional practice, media company, software developer, engineering consultancy, retailer, or specialized manufacturer may gain access to capabilities previously purchased from larger organizations. Artificial intelligence can assist with research, drafting, coding, scheduling, translation, customer support, design, and administration.

The economics of AI services already depend partly on the cost of compute and the ability to convert model capabilities into services customers value. Meta’s economics differ from those of a pure subscription provider because advertising revenue can subsidize consumer services distributed through its existing applications. That structure gives Meta considerable freedom to offer AI functionality without charging every consumer directly.

Whether AI-driven entrepreneurship outweighs job displacement can’t be determined from model benchmarks. Employment, wages, company formation, business survival, capital availability, productivity, education, and occupational transitions provides better evidence.

Privacy, Safety, and Human Control Define the Trust Problem

Meta’s vision requires personal agents to understand far more about individuals than conventional search engines or isolated chatbots. The August statement anticipates agents that know a person’s preferences, circumstances, goals, and sensitive information. Zuckerberg says Meta intends to build a fully private mode in which even Meta cannot see or grant access to information entrusted to an agent.

Parts of that privacy architecture are already appearing in products. On May 13, 2026, Meta announced Incognito Chat with Meta AI for WhatsApp and the Meta AI application. Meta says Incognito Chat uses its Private Processing infrastructure so conversations are handled inside a protected environment that Meta itself cannot read. Meta also says the conversations aren’t saved and disappear by default.

The underlying Private Processing architecture uses confidential computing and trusted execution environments designed to prevent Meta, WhatsApp, or outside parties from accessing protected requests during processing. Meta has described auditability, non-targetability, stateless processing, and verifiable transparency as design objectives.

That architecture builds on a longer privacy lineage at WhatsApp. The February 2026 WhatsApp Encryption Overview describes its end-to-end encryption model for private communications. A future personal agent presents a more complicated problem because it may require authorized access to calendars, documents, communication history, purchases, locations, applications, and other personal context.

Incognito Chat shouldn’t be treated as equivalent to the comprehensive private personal-agent architecture described in Zuckerberg’s August vision. A fully featured personal agent may require long-term memory, cross-application context, permission management, and the ability to perform actions. Each feature changes the privacy problem.

A private agent may consequently require some combination of local processing, confidential cloud processing, encrypted storage, strict separation of sensitive context, permission controls, identity verification, and auditable deletion. More capable agents may also require connections to external services. Every connection adds usefulness and another authorization boundary.

Meta’s safety argument rejects the assumption that reducing advanced-AI risk necessarily requires concentrating systems within a few institutions. Zuckerberg proposes a balance-of-power model in which individuals, businesses, governments, and competing AI laboratories possess enough capability to constrain one another.

The August statement also proposes giving governments early access to intermediate frontier-model training checkpoints and technical assistance so public institutions can prepare defensive capabilities before more powerful models receive broad distribution. Whether governments and laboratories could implement that approach effectively across jurisdictions remains uncertain.

The proposal contains an unresolved tension. Wider distribution can increase the number of people able to use advanced systems productively. More capable systems can also increase the abilities available to people seeking to misuse them. Meta argues that defenders can possess greater resources and that distributed defensive capabilities can strengthen security. That is a policy theory rather than evidence that defensive capabilities will always advance faster than harmful uses.

Human control becomes harder if artificial intelligence systems eventually become capable of contributing substantially to their own improvement. Meta’s August statement explicitly discusses recursive self-improvement as a hypothetical future condition and argues that most global intelligence and computing capacity should remain directed toward human goals. Zuckerberg also favors multiple frontier laboratories and many competing agents rather than a singular autonomous system.

Trust will depend on observable properties rather than philosophical statements. People need to know what an agent remembers, what it can access, when it can act, whether its actions can be reversed, what information reaches the provider, and how failures are corrected. Personal superintelligence becomes meaningfully personal only when the individual has practical control over those boundaries.

Universal Access Depends on More Than Free Software

Meta’s commitment to free or affordable access addresses one barrier to widespread AI use, but price is only one part of digital participation. A person also needs an appropriate device, reliable connectivity, electricity, language support, digital skills, and legal access to the relevant services.

Geographic differences may become more important as artificial intelligence becomes more computationally intensive. Countries with substantial data-center capacity, inexpensive and reliable electricity, semiconductor access, high-capacity networks, capital, and skilled technical workers can host more of the infrastructure supporting advanced models. Other countries may rely heavily on services physically and legally controlled elsewhere.

The debate over sovereign AI reflects this issue. Governments increasingly consider control over computing infrastructure, data, models, technical skills, and operating rules to be components of national technological autonomy. Sovereign AI doesn’t necessarily mean that every country must create a frontier model. It can also mean retaining enough domestic or allied capacity to avoid complete dependency on a single external provider.

Meta’s distribution model offers one counterweight because a service embedded into applications used by billions of people can cross national infrastructure gaps more easily than a model requiring each jurisdiction to build its own AI laboratory. The same advantage creates a governance concern. If personal artificial intelligence becomes an important interface for communication, education, commerce, information discovery, and software use, whoever controls that interface gains substantial economic influence.

Language provides another test of Meta’s “for everyone” framing. Artificial intelligence performance can differ across languages, dialects, cultures, professions, and local legal systems. Broad numerical availability doesn’t guarantee equivalent quality. Model training, evaluation methods, safety systems, and product development must work across many linguistic and cultural contexts if access is intended to be meaningful.

Hardware creates another potential divide. Basic Meta AI functions can run through existing phones and computers. More context-aware functions may work better through glasses, wearables, displays, sensors, or future interfaces. Advanced hardware can improve accessibility and convenience, but it also introduces an additional purchase cost.

A practical definition of broad access is consequently more demanding than global availability. Income, geography, language, disability, and institutional affiliation should not determine whether someone can access a useful baseline of advanced intelligence. Meta’s proposed mechanism for purchasing additional compute implicitly acknowledges that users won’t receive unlimited capacity. The quality of the free baseline will become an important measure of whether the company’s distribution philosophy is being realized.

The Space Economy Is Already Touching Meta’s AI Infrastructure

The connection between Meta’s personal-superintelligence strategy and the space economy is presently indirect, but it is becoming more substantial. Energy provides the clearest example. Meta’s Overview Energy agreement reserves up to 1 gigawatt of proposed space-solar capacity for future data-center power.

The underlying technology still requires an orbital demonstration and commercial deployment, so it shouldn’t be treated as an operational source of electricity as of August 10, 2026. Meta says Overview Energy’s demonstration is planned for 2028 and that commercial supply could begin as early as 2030 if the system performs as intended.

Another connection involves computation itself. Satellites increasingly perform artificial intelligence processing near sensors instead of transmitting every piece of raw data to Earth. On March 16, 2026, NVIDIA announced its space-computing platform, including the planned Space-1 Vera Rubin Module for higher-performance orbital artificial intelligence processing and existing Jetson Orin and IGX Thor platforms for edge computing.

New Space Economy’s coverage of NVIDIA space computing examines how accelerated processors are moving closer to satellite sensors, communications systems, spacecraft autonomy, and ground-based geospatial processing.

More ambitious companies are pursuing orbital data centers. New Space Economy’s review of orbital compute infrastructure describes an emerging market involving compute satellites, data storage, optical communications, edge processing, and dedicated orbital infrastructure.

These concepts remain less mature than terrestrial hyperscale computing. Launch cost, radiation, thermal management, communications bandwidth, maintenance, hardware replacement, orbital debris, spacecraft reliability, and regulation impose constraints that terrestrial data centers don’t face.

The link with Meta’s August proposal is economic rather than architectural. If personal agents eventually drive very large increases in global computing demand, the technology industry has incentives to search for new energy sources, processor architectures, cooling techniques, network systems, and locations for specialized computation. Terrestrial data centers retain substantial advantages, but extreme demand can make unconventional infrastructure worth investigating.

Satellites also support the connectivity needed to extend AI services into locations where terrestrial broadband is weak or unavailable. Personal agents can’t function as universally accessible services without connectivity. Satellite broadband, direct-to-device communication, fiber networks, mobile networks, edge computing, and regional data centers can complement one another.

Personal superintelligence may appear to be a software concept, but implementation at planetary scale depends on semiconductor fabrication, electricity, networks, buildings, devices, transmission systems, and potentially space infrastructure. Companies supplying those layers can capture economic value even if they never develop a frontier artificial intelligence model.

What Would Make Meta Personal Superintelligence Credible?

Meta’s August 10 vision can be evaluated through observable milestones rather than predictions about the arrival date of superintelligence. Model capability is one measure, but it isn’t sufficient. A useful personal agent needs reliable memory, permission management, multimodal understanding, application integration, privacy protection, acceptable operating cost, and enough accuracy that people are comfortable delegating consequential tasks.

Meta has already begun assembling pieces of that architecture. Muse Spark provides multimodal capabilities. Muse Spark 1.1 strengthens tool use and agentic operation. Meta AI supplies consumer distribution. AI glasses add visual and audio context. Neural interfaces provide another input mechanism. Custom processors and data centers supply computing capacity.

These components are real products, models, or active infrastructure programs as of August 10, 2026, but they do not yet amount to the personal superintelligence described in Zuckerberg’s statement.

Affordability provides another measurable test. Meta says free versions should reach billions of people. That commitment can eventually be evaluated by comparing the capabilities available without payment with premium services requiring additional compute. If free services become substantially less capable than systems available to wealthy individuals and large institutions, broad distribution may exist without producing the balance of power described in Meta’s vision.

Privacy provides an equally concrete benchmark. Meta’s proposed private-agent model can be evaluated through technical architecture, confidential processing, encryption, data-retention policies, independent security research, user controls, and separation of sensitive information from unrelated commercial systems.

Economic outcomes will take longer to judge. Meta predicts increased entrepreneurship and continued job creation, but gains may be distributed unevenly among occupations, industries, income groups, and countries. The ILO’s employment-exposure research indicates that generative AI already affects tasks associated with a substantial share of global employment, yet exposure can mean augmentation, restructuring, or automation.

Infrastructure provides more immediate evidence. Meta’s projected $130 billion to $145 billion in 2026 capital expenditures indicates that its artificial intelligence strategy is already being translated into physical investment. Semiconductor manufacturers, construction companies, utilities, energy developers, communities, network operators, financial institutions, and governments are being drawn into the buildout.

The hardest milestone is human control. Meta’s argument assumes that distributing intelligent agents can produce checks and balances analogous to competitive markets and pluralistic institutions. That is a political and social theory as much as a technical architecture.

Competing agents could distribute power. Network effects, capital requirements, semiconductor scarcity, data-center scale, and model-training costs could also concentrate power among a relatively small group of organizations capable of operating the strongest systems. The outcome will depend on competition, interoperability, model availability, compute pricing, privacy, regulation, and the ability of individuals to transfer their information and preferences among providers.

Summary

Meta’s August 10, 2026 The Future Is for Everyone statement places personal superintelligence at the center of a strategy much larger than the development of increasingly capable artificial intelligence models. Zuckerberg’s proposal connects intelligence with individual control, consumer devices, economic participation, privacy, distributed power, infrastructure construction, scientific research, national policy, and human oversight.

Parts of that future already exist in recognizable form. Muse Spark powers Meta AI. Muse Spark 1.1 adds stronger planning, tool-use, and action-taking capabilities. Artificial intelligence functions are moving into glasses and wearable interfaces. Meta is investing tens of billions of dollars each quarter in physical infrastructure and expects 2026 capital expenditures of $130 billion to $145 billion. Its applications give the company access to billions of daily users.

Superintelligence remains the future-facing part of the proposition. Meta’s products demonstrate multimodal reasoning, agent-like software behavior, wearable interfaces, large-scale distribution, privacy-oriented AI processing, and massive compute investment, but the company’s own language continues to describe personal superintelligence as something it is building toward.

The distinction matters because claims concerning employment, scientific discovery, personalized education, highly autonomous agents, and broad economic abundance depend on technical capabilities and economics that haven’t yet been demonstrated at the proposed scale.

The most consequential part of Meta’s position may be its assertion that access itself should serve as a mechanism for distributing power. That places the company in a debate extending beyond model benchmarks. If advanced artificial intelligence becomes important economic infrastructure, decisions about who owns compute, who can afford it, what devices mediate access, what information agents can see, and whether people can choose among competing systems will influence the distribution of its benefits.

Meta has the capital, infrastructure, consumer reach, hardware programs, and artificial intelligence development capacity to test that theory on an exceptional scale. Success won’t be determined solely by whether Meta creates a model more capable than existing systems. It will depend on whether large numbers of people can use increasingly powerful agents without surrendering disproportionate control over their information, choices, economic opportunities, and access to computation.

That is the difference between building increasingly capable artificial intelligence and fulfilling the much larger promise expressed by Meta on August 10, 2026: making the future available to everyone.