Creating Muse

Muse began with Meta Superintelligence Labs’ June 2025 “Personal Superintelligence” thesis: build powerful AI that “lifts the human experience” by helping people pursue what they want, not merely automating work. Wang argues Meta is uniquely positioned for that consumer future because Instagram, WhatsApp, and Facebook already map people’s interests and relationships, while the company reaches 3.5 billion daily users. The strategic bet was therefore personal—not work, coding, or enterprise AI.

The catalytic moment came in early 2026, when Opus 4.5 and OpenClaw showed Wang and Nat Friedman what personal agents might become. Friedman’s agent monitored whether he drank enough water through security footage; Wang let his own agent conduct what felt like “3 years of therapy” using email, photos, and successive psychological probes. Those “somewhere between terrifying and euphoric” experiences led Wang to describe the super-agent as, in many ways, “the final consumer product” and led the team to build a prototype within one or two weeks.

Meta then spent seven months turning an occasionally magical prototype into a reliable consumer product—a period Senra calls roughly a decade in conventional technology time. The team enumerated more than 100 required behaviors, built evaluations for each, and refused to launch while even 20 rows remained below threshold. Its first green-across-the-board checkpoint was a specially trained Musepark 1.3, reflecting Wang’s view that the product experience is extraordinarily sensitive to model quality, communication, and knowing when to ask the user for guidance.

Muse was built by fewer than 200 people because Meta treated it as a “single work of art,” not a committee-produced feature bundle. Wang says large-company products often become a “Cronenberg amalgamation” or “smoothie” of conflicting product-manager agendas; Muse instead carried one cohesive point of view, with Friedman’s taste especially influential. Zuckerberg’s contribution was patience: he tolerated seven months of doubt and a Wall Street narrative that Meta might not pull anything off in AI.

Wang would disclose only “millions” of users, while pointing to graphs he had amplified that implied Muse was the fastest-growing consumer AI app of all time. Distribution created awareness, but growth depended on an early wow moment and an escalating “trust fall”: users hand the agent a small task, see it succeed, and gradually entrust larger problems. The clearest word-of-mouth specimen was a CEO who told the agent to recover an ID left on a Flexjet without involving him; screenshots showed it arranging the courier and navigating the recovery to his office.

Muse’s breakout marketing combined concrete user benefits, screenshot-native design, and Wang’s increasingly risky meme campaign. His feed was personally written, not generated by “a hundred Muses”; he wanted both Muse and Meta to break through a “torrential stream” of AI launches and become culturally interesting again. The lesson he rediscovered was that “the internet rewards risk” and surprise, while the Jolly mascot made every shared screenshot instantly recognizable as a Muse interaction.

For investors, Muse is also part of Zuckerberg’s post-Llama 4 reset: talent density, consumer distribution, and patient execution combined in a small, flat lab. Meta’s 49% investment in Scale let Scale continue while Wang and several colleagues joined Meta; Wang accepted after nine years because the struggling AI program offered a genuine “refounding” opportunity. He now treats some AI research as diamond mining—many failed experiments, but rare outcomes worth a million times more—while more than 200 direct reports, pod-level technical leads, and an anti-bureaucratic structure give researchers “room to cook.”