When OpenClaw took the tech world by storm in early 2026, Meta's leadership saw both a threat and a blueprint. The independent AI agent tool was wildly popular with developers but confusing for everyday users. Nat Friedman, Meta's superintelligence products chief, recognized the opportunity immediately and moved to build a far more accessible version from scratch, ultimately creating Muse, which has rocketed to the top of the App Store and become a major victory for CEO Mark Zuckerberg's massive AI spending.

Friedman's response to OpenClaw was characteristically swift. He issued a two-page internal memo titled "The Claw Is The Law," praising the product, and bought hundreds of Mac Minis for Meta's superintelligence team to run the agent and study how it worked. This hands-on approach reflects Friedman's personal philosophy, which his bare-bones website spells out bluntly: "slow is fake" and "it's important to do things fast".

What Made OpenClaw So Influential That Meta Built Muse Around It?

OpenClaw represented a watershed moment for AI agents. The free desktop-based tool demonstrated that everyday users could interact with AI systems to accomplish real tasks, not just chat with them. However, it remained largely a developer's tool, mystifying to the average person. Friedman recognized that Meta's enormous built-in audience across Facebook, Instagram, and WhatsApp could unlock Muse's potential if the company could strip away the complexity.

The stakes were high. OpenAI had already hired OpenClaw's creator, Pete Steinberger, in what Meta employees saw as a competitive coup earlier in 2026. Meta was especially worried it would be upstaged by a large competitor, and the team was shocked as months went by without such an entrant.

How Did Meta Transform OpenClaw's Concept Into a Consumer Product?

Building Muse required months of painstaking work and thousands of Meta employees pulled from across the company. Friedman's team focused on teaching the agent how to actually get things done, moving beyond simple conversation to real-world tasks like booking meetings and canceling plane tickets. This process, which Silicon Valley calls "hill-climbing," involved incremental improvements to increasingly complex capabilities.

• Extensive Testing: Tens of thousands of Meta employees tested Muse for months, generating use cases and feedback that became crucial to the development effort.
• Tool Selection Training: Researchers did extensive work teaching the agent to choose the right tool for each job, enabling seamless interaction with the internet and services it had never used before.
• Behavioral Refinement: The team trained Muse to be considerate and not overshare sensitive information, such as telling people someone couldn't make a meeting because they were hungover.
• Talent Acquisition: Meta hired David Singleton and Hugo Barra, cofounders of Dreamer, an AI agent startup, along with their entire team to strengthen its agentic AI capabilities.

As recently as two weeks before Muse's launch, the chatbot struggled with speaking good English, according to someone directly familiar with the matter. The breakthrough came when it finally started scoring well on coherence tests, a relief after months of uncertainty.

"Our researchers did a ton of work teaching agents how to choose the right tool for a job," said Alexandr Wang, Meta's AI chief, at the company's Connect conference.

Alexandr Wang, AI Chief at Meta

Why Did Early Meta Employees Doubt Muse Would Succeed?

Earlier in 2026, some of Meta's top AI employees were genuinely worried about Muse's prospects. They had tested it internally and found it working poorly, feeling like just another chatbot but with a worse underlying AI model and extra steps. This skepticism made Friedman's eventual success all the more significant.

Friedman's hands-on leadership style proved critical to overcoming these doubts. Internally, the executive is described as opinionated and proud of it, sometimes chafing with other teams across Meta's sprawling operations. Meta's communications chief, Andy Stone, recently called him "Nat the Truthteller" after he derided a report about Muse's security as written by "dummies". This directness, combined with his push to move fast and ditch slow internal tools in favor of faster external ones like Vercel, created momentum that other leaders might have lost.

The effort ultimately worked. Although the arrival of the viral AI agent Instinct initially upstaged Meta, Muse took off by tapping into Meta's enormous built-in audience across its apps, including among people who had never used AI agents before. Users have praised how easy it is to set up, with one healthcare startup cofounder noting that his father saw an ad for Muse in his Facebook feed and downloaded it, promptly using it to buy a new pair of shoes.

What's Next for Meta's AI Ambitions Beyond Muse?

Meta is not resting on Muse's success. The company has hinted that much more will come as it works on a new AI model called Watermelon, which Meta says has already caught up with OpenAI's capabilities. Alexandr Wang indicated that Meta has other ideas that are "just as big" as Muse, posting on X that "The kitchen is just getting warmed up".

Wall Street is cheering on Muse, seeing it as a way for Meta to become more than an ad company. Zuckerberg told Alex Heath's Sources earlier this month that he wants people to shop and save money on Muse, which Meta plans to monetize by taking a small cut of each transaction. Mizuho Americas analyst Lloyd Walmsley wrote in a note that "We see the Meta bull case increasingly playing out".

Zuckerberg

For now, Meta is on a high. Muse's success and a more than 20% increase in Meta's stock price since its launch has become a morale boost for employees, who had been reeling from a year of mass layoffs, employee surveillance, and low morale. The company's ability to take OpenClaw's concept and transform it into a consumer juggernaut demonstrates that speed, focus, and access to massive user bases remain powerful advantages in the AI race.