Muse Wonder: Zuckerberg and Wang's $100B AI

Summary

• Meta’s AI program is now an infrastructure wager measured in $100 billion increments. Zuckerberg describes considering a “$100 billion, $200 billion” check before seeing the models, while estimating a 1-gigawatt cluster costs roughly $15 billion annually to operate and $50-$60 billion upfront. The downside is partly hedged: if Meta’s models fail but AI succeeds, the compute remains marketable; if AI broadly disappoints, Meta can still deploy it internally.
• Muse Wonder moves Meta back to the frontier with a larger model optimized for personal and professional agents, not generic benchmark leadership. Meta claims state-of-the-art results across its agent evaluations, DeepSWE and SWE-Marathon, with strong Terminal-Bench 4 performance; its differentiated features are “active memory,” long-horizon “active focus,” domain expertise and discretion. Wonder will initially sit behind paid Muse subscriptions because of serving cost, while Wonder Full targets higher-end Muse Code users.
• The strategic product thesis is that “the model is the product,” making superficially similar agents materially different underneath. Personality, proactivity, tool behavior, memory, reliability and judgment are trained into the model rather than exposed as conventional interface features. That logic also explains Muse’s lack of model and effort selectors: Wang argues ordinary consumers cannot navigate product lines whose “numbers go backwards.”
• Multimodality—not coding alone—is Meta’s proposed wedge into consumer superintelligence. Zuckerberg’s Muse watches MMA footage, constructs its own video-processing pipeline and produces coaching during 60-second round breaks; Wang’s agent, Oiler, autonomously sources obscure teas. Zuckerberg’s larger prediction is that “everyone is actually going to be writing tons of code,” but most users will never know that code was generated behind their requests.
• Agentic commerce could create demand rather than merely redirect or improve conversion. After Muse began planning baking sessions with Zuckerberg’s daughter and ordering ingredients, he went from barely using Instacart to ordering once or twice weekly; that led him to ask whether the account belongs to him or to Muse. Wang’s framing is that agents remove research and checkout friction today, then use cross-domain personal context to deliver recommendations that siloed commerce systems cannot.
• Meta believes only a small handful of companies will be able to build frontier superintelligence, and those companies will set the product agenda. Zuckerberg says Meta is “not an app company” and cannot remain an inventive technology company without owning models, infrastructure and the last-mile device experience. Its chosen lane is personal and consumer-oriented intelligence, with “invention and creation, not automation” as the highest-value destination.
• MSL’s turnaround depended on an unusually small, technical and talent-dense organization backed by the whole company. After deciding the Llama 4 trajectory was inadequate, Zuckerberg cleared much of his calendar, recruited external leaders and rebuilt the effort around roughly 100 researchers, fewer than 200 even under a broader count. The operating principle is the “absence of non-technical managers”: researchers receive unusually high compute per person, while Zuckerberg coordinates the infrastructure and company-wide support around them.
• Meta expects subscriptions, commerce, APIs, enterprise distribution and improvements to its existing business to fund the buildout. Zuckerberg wants a strong free Spark tier to maximize network effects, then paid Wonder usage and potentially a small share of the money Muse saves or earns for users; he also describes a “business in a box” for small companies and a new Meta Enterprise Platform. Even a few percentage points of acceleration in Meta’s existing hundreds-of-billions-dollar business could finance substantial AI investment: “The answer better be yes. The answer is yes.”

Deep dive

1. Muse’s best demonstrations hide complex software behind ordinary requests

• Collison opens with third-party reports suggesting Muse might be the fastest-growing agent or AI application yet, then asks for personal use cases. Wang’s favorite is Oiler, a Muse that continually finds “increasingly obscure teas from increasingly obscure vendors,” including small farms in Taiwan and Japan, and purchases a new selection roughly every week.

• Zuckerberg treats MMA coaching as both his favorite use case and a product evaluation. Cameras in his gym feed Muse footage; during each 60-second break, it extracts frames and comments on balance, hip position, underhooks and exposure to reversals. His recurring instruction is brutally practical: “You took 30 seconds to give me that feedback. I only have a 60-second break.”

• The agent appears to have built the required video and image-processing pipeline itself. Zuckerberg never sees code, yet Muse determines that it should pull stills, process them quickly and connect visual posture to MMA concepts—an example of consumers commissioning bespoke software without framing the interaction as programming.

• Zuckerberg’s broader prediction is that “everyone is actually going to be writing tons of code. They’re just not going to know that they’re writing code.” He had already experimented with this workflow using OpenClaw, then turned it into a recurring Muse evaluation because it tests perception, coding, latency and domain knowledge simultaneously.

2. Multimodality is Meta’s consumer wedge

• Collison presses on where Muse learned the difference between good and bad MMA posture: visual examples, written instruction or an emergent concept of balance. Zuckerberg’s answer is deliberately broad—Meta trained the models with substantial visual-perception data as part of its end-to-end multimodal approach.

• Wang argues that personal superintelligence must reflect how consumers actually experience the world: through conversation, sound, video and their own cameras. That put Meta at odds with an industry consensus earlier in the year that “it was just all about coding,” because coding strength alone would not produce the consumer behaviors Muse needed.

• Zuckerberg sees text chat as transitional because “most people don’t actually like reading that much.” Glasses can eventually hear what users hear, see what they see with permission, speak to them and generate visual interfaces in their field of view; he is unsure such devices will even need a conventional app store.

• Meta’s data is helpful, Zuckerberg concedes, but focus matters as much as possession. The lab intentionally weighted perception and multimodal capability because Meta’s services and planned hardware make those skills disproportionately valuable, while other labs can rationally optimize for different product destinations.

3. Agent products only look interchangeable because the model hides their design

• Collison proposes that AI products converge unusually quickly: transformers, chat interfaces and now persistent agents with computers all become obvious templates that every serious company follows. Unlike payments, whose winning specification spans developer experience, geography, payment methods and stablecoins, an AI product can seemingly be described “on the back of a postcard.”

• Zuckerberg disagrees that the space will remain narrow. A text assistant such as Meta AI may reach many users, but people primarily treat it as retrieval rather than delegation; a persistent agent that gets things done is a different product, and even giving each user a dedicated VM meaningfully changes privacy, security and confidence.

• Wang’s pushback is that traditional product specifications miss the important axes. Muse’s designers made detailed choices about behavior, personality, proactivity and tool use, but those decisions manifest as training data, methods and evals rather than visible controls. Calling every system “an agent with a VM” is like describing every early mobile product as “just an app.”

• The shared conclusion is that products are getting thinner while the trained system absorbs more product responsibility. Zuckerberg’s internal phrase is blunt: “The model is the product.” The surrounding surface still matters—especially privacy, security and connected data—but the decisive interaction design increasingly lives in post-training.

4. Consumer simplicity is a deliberate rejection of AI-industry conventions

• Muse exposes neither a model selector nor an effort selector. Zuckerberg’s rationale is simply, “I just want to make it simple,” while Wang says too many AI products are designed by highly technical teams without a “randomly sampled American” in their mental model.

• Wang calls existing model nomenclature “egregious”: numbers sometimes move backward, then give way to unrelated words or parallel naming systems. A person who has not read API specifications cannot reasonably know which option to choose, so Muse makes the routing decision and lets paid users access the more capable model.

• The same logic applies to personality and proactivity. Users experience whether an agent reliably finishes tasks, when it chooses to intervene and how it speaks—not the underlying training configuration. Wang notes that users are only beginning to articulate these differences publicly, even though they are the central product decisions.

5. Muse Wonder begins Meta’s return to frontier-scale models

• Muse Spark came from a pre-training run completed near the end of the prior year and surprised the team with how much intelligence post-training could extract from a relatively small model. Wonder is the next, significantly larger pre-train, built by repeatedly climbing through model tiers roughly four or five times larger before committing to the frontier run.

• Zuckerberg says the additional scale should appear as greater nuance, stronger memory and an ability to persist through complicated personal and professional tasks. Just as important organizationally, Meta has now completed much of the background ladder-building required after the disappointing Llama 4 trajectory; subsequent generations can focus on pushing the frontier, with shorter intervals between model classes.

• Meta is releasing Muse Wonder and a higher-end model called Muse Wonder Full—the latter name producing the intended “wonderful” dad joke. Based on Meta’s own measurements, Wang calls the series state-of-the-art for personal and professional agent tasks, while Zuckerberg places it alongside Astra and Fable at the empirical frontier.

• The model will be available through paid Muse plans, the Meta Model API, OpenRouter and Muse Code; Wonder Full goes to Muse Code subscribers. Zuckerberg still wants Muse broadly free through a large Spark token allowance and hopes eventually to deliver Wonder-level capability in Spark’s cheaper form factor to hundreds of millions of users.

6. Active memory and focus target the failures users already feel

• Wang defines active memory as remembering information disclosed over days, weeks or months and applying it to later tasks. Collison contrasts that with OpenClaw’s “buggy AF” division between working memory, long-term memory and compaction; Wang says Muse’s model is itself aware of memory sources and able to decide how to compact and retrieve them.

• Zuckerberg rejects the idea that memory is merely an external harness. Compaction and appropriate reuse are model capabilities even when supported by a broader system. Wang nevertheless preserves the hedge: internal MemoryBench evaluations show a meaningful advance, but “there’s still plenty of room to improve.”

• Active focus means completing ambiguous, high-complexity tasks over long horizons and recovering from setbacks with alternative, aligned plans. Early dogfooders described this persistence as a noticeable difference from models they had used before, particularly as Muse and Muse Code users assigned larger jobs.

• Coding is a secondary consumer use case but a major capability jump: Meta reports state-of-the-art results on DeepSWE and SWE-MARATHON, plus strong Terminal-Bench 4 performance. When Collison asks whether Stripe should therefore use Wonder Full, Wang answers cautiously—“potentially”—and asks developers to validate the internal results through experience.

7. Internal evals matter more once a lab has found signs of life

• Zuckerberg accepts Goodhart’s Law but frames saturation as the immediate problem: public benchmarks become obsolete quickly. They were useful while Meta was rebuilding and needed evidence that its science was on track; with each release, a larger portion of judgment now comes from internal evaluations tailored to Meta’s intended products.

• Wang’s research framing is “big model smell,” the elusive nuance users perceive in a genuinely large model. Meta tries to quantify it through the gap between performance on tasks inside the distribution of reinforcement learning and unfamiliar out-of-distribution tasks; Wonder shows a large improvement over Spark because its base model generalizes better.

• Personal agents also need capabilities ordinary coding benchmarks barely measure. Zuckerberg’s example is discretion: an agent should know that a user has an allergy or is pregnant, select an appropriate restaurant and disclose as little medical information as possible. A coding agent operating in shared technical context faces a different privacy problem.

• Wang argues that optimizing only for public scores effectively outsources product judgment to “some PhD student” whose benchmark happened to go viral. Public tests are reasonable when a new lab is “looking for signs of life”; mature development requires detailed internal spreadsheets where dozens of two-percent improvements aggregate into a model that feels magical.

8. Pre-training encodes product direction before reinforcement begins

• Meta’s research organization is intentionally small: Zuckerberg says fewer than 200 people and closer to 100 core researchers, while Wang describes tens of people per stage where other labs may use hundreds. The organizing bet is talent density, unusually high compute per researcher and avoiding “peanut-buttering” resources across competing programs.

• Pre-training still begins with enormous quantities of text, images, audio and video, but modality and domain exposure are deliberate. Zuckerberg stresses that a capability cannot always be bolted on with reinforcement learning afterward; a perception model needs rich visual experience from the beginning.

• For a personal agent, relevant domains include shopping, health, legal questions, finance, taxes and personal advice. Wang wants the resulting system to feel like “a team of experts in your corner”—your legal, financial and medical teams—and says Wonder leads Meta’s finance and legal evaluations partly because those corpora entered the pre-train.

• The internal codenames illustrate the scaling ladder: Spark’s pre-train was Avocado and Wonder’s was Watermelon. Wang jokes that the only larger fruit might be durian, an appropriately unpleasant destination, so Meta will need another naming convention for later tiers.

9. Scaling intelligence requires scaling data without assuming all data transfers cleanly

• Bigger models need more data to avoid overfitting. Zuckerberg says many corpora produce positive transfer and are not zero-sum, but some mixtures can create negative transfer depending on setup; deciding which tokens help, distract or interfere remains part of the science rather than a settled “more is always better” rule.

• This creates a structural scaling problem: compute can grow faster than the public internet. If labs want larger and more intelligent models while the underlying human corpus expands slowly, they must find additional data sources and improve how they construct or reuse training material.

• Wang describes pre-training as both the highest-stakes and most fundamentally scientific stage because researchers get so few samples. A large share of the lab’s compute went into Wonder, producing “all the 7 stages of grief”; researchers must extrapolate from smaller runs and reason deeply about a result they cannot cheaply repeat.

10. Post-training can make one pre-train feel like several generations

• Zuckerberg divides post-training into supervised fine-tuning and reinforcement learning. SFT supplies both prompt and desired response, initializing behaviors such as personality; Wang’s mental model is that the desired behavior may then appear only 10% of the time until RL dramatically raises its elicitation rate.

• The broad scaling rule is Wang’s “scale everything”: pre-training, RL compute, task data and eventually the agent harness. Zuckerberg notes that dimensions can be scaled separately, but coordinated scaling generally produces better results.

• Spark 1, 1.1, 1.2 and 1.3 all share a pre-train and differ through continued RL. Wang says Spark 1 to 1.3 feels “night and day” and might be mistaken for a new base model, reflecting an industry-wide improvement in extracting agentic capability from existing weights.

• RL data behaves differently from pre-training data: “less is actually more” in many environments, and recipe quality can outweigh sheer volume. Each task and reward setup applies magnified pressure, so a smaller number of high-quality environments can materially change capability, reliability and failure modes.

11. Alignment is embedded throughout training and aimed at serving the user

• Wang says safety cannot be isolated as a final corrective stage. High-compute RL can teach misalignment if its environments reward success that is possible only through unsafe behavior, so Meta curates tasks throughout the process to ensure optimization pressure points toward aligned solutions.

• Zuckerberg adds the philosophical question: “Who do you want it to be aligned to?” Meta emphasizes instruction and values alignment with the person the agent serves—high confidence it will do what was requested, without an unnecessary moralizing lecture.

• That does not mean unconditional obedience. Zuckerberg says catastrophic requests should be refused gracefully and “without pissing you off,” while ordinary requests should receive reliable execution. He presents this as a meaningfully different alignment stance from some other labs, not as a solved technical problem.

12. Frontier development is the opposite of a short feedback loop

• Wonder had been planned since the previous August, well before Spark was finished; Spark itself was conceived as a stepping stone. Wang cautions against imagining that a lab launches one model and then begins the next—several long-running generations overlap and converge only near release.

• Meta’s infrastructure commitments predate MSL. Zuckerberg says the Prometheus cluster in Ohio is scaling beyond 1 gigawatt, while Hyperion in Louisiana is intended to reach 5 gigawatts over several years; planning began around 2023 or 2024 because greenfield data-center timelines are measured in years.

• The full stack includes data centers, Meta-designed chips, low-level infrastructure, model research and product delivery. Zuckerberg proposes measuring a lab’s compute strength partly by its largest single supercomputer, because frontier training requires one coordinated system rather than an aggregate fleet that cannot perform the same run.

• Collison asks how product judgment survives a feedback cycle longer than a year. Zuckerberg answers that entrepreneurship is decision-making in an “information-poor environment”; Wang calls frontier AI “entrepreneurship on hard mode” and “the opposite of Lean Startup,” because waiting for proof guarantees falling behind.

13. Meta committed enormous capital before knowing whether Wonder would work

• At the end of the prior year, Zuckerberg and Wang were excited by research progress but had not seen the resulting models. Zuckerberg recalls confronting the decision to write a “$100 billion, $200 billion” check and asking, “Alex, are you sure this is going to work?”

• Wang could offer a “vibe report,” some plots and personal conviction, but not proof. At an executive gathering in Hawaii, CFO Susan Li repeatedly impressed on him that a poor model would be “really, really, really bad” financially; he remembers the commitment as “a fuck ton of risk.”

• Zuckerberg’s order-of-magnitude math puts operating a 1-gigawatt cluster for a year near $15 billion and its upfront CapEx around $50-$60 billion. Research adds materially to that figure because each change is tested through scaling ladders before the main run, and serving the finished model requires another major pool of compute.

• Collison summarizes the mechanism as pushing all the chips in before being dealt the cards. Zuckerberg accepts that the ante has risen radically, but argues the exposure is narrower than it appears: if Meta alone fails, valuable compute can be sold; if AI fails broadly, Meta still has internal workloads for it.

14. Product success was uncertain even after Meta decided to burn the boats

• Meta also had to build inference capacity years before knowing how much demand Muse would generate. When some executives considered being more conservative, Zuckerberg’s response was: “No, we’re doing this. We’re going to make it work”—the “burn the boats” posture Collison identifies.

• Muse then received the best possible launch outcome: immediate, unusually strong reception. Zuckerberg says the team’s median expectation was more ordinary—ship something acceptable, iterate several times toward product-market fit and only then accelerate growth—so Meta had not prepared every growth mechanism for a home run.

• Wang describes the executive team’s response as stressful but cohesive. Long-tenured Meta leaders had survived repeated daunting periods and treated the new risk as another challenge the company would navigate, despite running the profitable businesses that were effectively paying the bill.

• Zuckerberg’s larger decision rule is power-law shaped: focus on how large success can become rather than eliminating every chance of failure. Wang credits him with deeply internalizing “how big it is when you win,” while Zuckerberg insists conviction in the specific product—not merely resale value—drove the commitment.

15. Owning frontier models is necessary for Meta to remain an inventive company

• Collison links Muse to Zuckerberg’s long concern about dependence on Android and iOS and to Reality Labs’ attempt to reach users through future devices. Zuckerberg reframes the goal: platform ownership is not valuable for its own sake; the point is freedom to build the best experience end to end.

• “We’re not an app company,” Zuckerberg says. Meta’s social products depend on machine-learning recommendation systems operating at a scale he believes only Meta and ByteDance currently match, plus infrastructure that reaches “all the way to the metal.” In the next era, models become the most important layer of that technical stack.

• Zuckerberg expects a small handful of companies—not one universal winner—to possess frontier superintelligence. As with the internet generation, those with the core technology will drive the agenda across consumer products, enterprise software and hardware, while a broader ecosystem forms around them.

• Meta’s strategic opening is personal and consumer superintelligence while other labs concentrate on automating knowledge work. Zuckerberg calls automation a less charismatic future and says “the highest use of superintelligence is invention and creation”; Meta might survive without frontier models, but it could not remain the company inventing important new experiences.

16. Browser use bootstraps an economy that will eventually run agent to agent

• Meta trained strong browser use so businesses need not integrate before Muse can serve their customers. Zuckerberg treats computer use as a compatibility layer: the agent can navigate existing websites first, while willing partners later provide direct APIs that are faster, cheaper and more reliable.

• The endpoint is not perpetual web scraping. Zuckerberg expects every business eventually to have an agent, just as businesses now maintain websites, social accounts and email addresses; consumer agents will negotiate with business agents, with Meta’s implementations naturally integrating especially well with one another.

• Collison raises the adversarial case: businesses whose margins depend on consumer confusion, excessive time-on-site or overcharging may rationally block agents. Zuckerberg’s answer is categorical—“those are going to get disrupted”—though he notes that far more businesses currently request integration than attempt to block Muse.

• Wang compares agentic demand with e-commerce: stores and conventional web demand will persist, but agents should claim a growing share because they save consumers and merchants time and money. Businesses must decide whether they want access to that expanding channel; consumer benefit creates “a natural gravity towards that reality.”

17. Agents can expand commerce by becoming customers themselves

• Muse is already receiving delegated purchasing authority rather than merely recommending products. Wang highlights obvious gains: an agent can research across sites, enforce a constraint such as delivery by Wednesday and complete checkout, making keyword search and repetitive form filling look primitive.

• The longer-term advantage is holistic personalization. Muse can connect knowledge about family, friends, coworkers and prior conversations to choose a gift or purchase, whereas historical recommendation systems see only one commercial silo. Collison notes that Muse already cites context users supplied in unrelated exchanges.

• Zuckerberg’s example begins with his three-year-old daughter Aurelia, who likes baking. They discuss weekend recipes with an experimental Muse voice mode; Muse separates their voices, develops even her imaginary recipe ideas and ensures Instacart ingredients arrive by Saturday. During the Watermelon launch, it charmingly topped up one order with an actual watermelon.

• That workflow turned Zuckerberg from an occasional user of Priscilla’s Instacart account into his own subscriber ordering once or twice weekly. His economic question is whether that account is really his or Muse’s: agent accounts could expand commerce’s total addressable market, not merely raise conversion “20%,” because automated demand did not previously exist.

18. Voice becomes useful when it controls an embodied, persistent agent

• Zuckerberg says voice interfaces still mishandle real conversational dynamics: people pause, overlap, interrupt and revise themselves rather than alternating clean prompts and responses. Understanding speech is insufficient if the system cannot act, while spoken output is also slow compared with visually scanning information.

• Voice input is nevertheless exploding because it is a high-bandwidth, low-friction way to externalize thought. Wang sees researchers whispering into keyboards and students “word vomit” for 10 or 15 minutes, then ask AI to organize the stream into a coherent structure.

• Agency changes the interaction from talking to an informational novelty into something closer to talking to a person. Wang argues Muse’s little embodied character works only because memory, action-taking and personality now converge: the figure appears to be persistently working on the user’s behalf rather than generating isolated answers.

• Zuckerberg expects glasses to combine voice, cameras and visual output; even no-display or small-display glasses have proved useful before full wide-field AR. Muse Gadgets extends the model to third-party phones and devices, while a nickel-sized USB HomeLink can connect Muse to Sonos and lights—Zuckerberg has it dim his entire house as bedtime approaches.

19. Meta expects multiple revenue engines to finance free access at scale

• Zuckerberg wants Muse’s base tier affordable or free because network effects, partner interest and agent-to-agent interaction improve with scale. Paid subscriptions fund capabilities too expensive to offer universally, beginning with Wonder; his concise answer to whether AI must generate money is, “The answer better be yes. The answer is yes.”

• Commerce offers several possible models. If Muse cancels unused subscriptions, finds savings or helps users earn money, Meta might eventually capture a small portion of the created value. Wang’s recurring concept is a “business in a box” that administers operations for small-business owners and produces enough value to be profitable for both sides.

• General models create additional surplus beyond their original consumer purpose. Zuckerberg expects profitable API demand and is forming a Meta Enterprise Platform under CJ Desai, reasoning that enterprises will want an affordable frontier model if Meta builds the distribution, support and controls through which companies prefer to consume it.

• Meta’s existing business is the final funding engine: a few percentage points of faster growth across a core business worth hundreds of billions of dollars can underwrite substantial AI spending. Owning data centers also allows depreciation over years, unlike cloud rental, while still-useful GPUs may retain economic value beyond their accounting life.

20. Model diversity and MSL’s reboot are both bets against institutional inertia

• Wang advises OpenRouter to become a “wine sommelier” for models: Wonder, Fable and Astra may share leaderboard positions yet embody different priorities. Usage data and developer feedback could expose those textures better than tweet-length declarations that one model is simply good or bad.

• He offers three reasons no lab has run away with the market: frontier organizations alternate between strong and weak execution; distillation transfers substantial capability into smaller models; and many employees, companies and counterparties actively prefer a plural ecosystem. Zuckerberg similarly favors infrastructure and open-source support that create checks and balances rather than concentrating intelligence in a few hands.

• Zuckerberg says Llama 4 launched on April 4, 2025, though he qualifies the date with “I think”; he says he began calling people for advice around April 20 or 21. He became convinced that Wang was “the fucking guy,” combined external recruits including Nat and Daniel with strong internal researchers, and framed MSL as a fresh start rather than an incremental repair.

• The operating system is talent density, a flat technical culture and freedom from artificial deadlines. Wang’s nearly 200 direct reports reflect the “absence of non-technical managers,” not intensive career coaching; teams of “insecure overachievers” remain pessimistic about their projects even after repeatedly beating their forecasts. The closing doctrine is simple: “Stay in the details. Stay technical and stay at the frontier.”