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Meta Chief AI Officer Alexandr Wang published an extended thank-you note after Meta Superintelligence Labs (MSL) launched its Muse AI models. He credited the researchers, engineers and collaborators who delivered Muse Wonder and the open-source Muse Spark, while warning that the lab’s work is still continuing.
Who Alexandr Wang was thanking
Wang’s message was addressed to the people who built and supported MSL, including researchers and engineers inside Meta and contributors working with the company. The Times of India reported the note on October 17, 2026, describing it as a lengthy appreciation message following the Muse releases.
He emphasized a culture in which people acted with “earnestness and urgency” rather than limiting themselves to formal job boundaries. In his wording, “Nobody asked whose job it was.” Wang also praised the researchers directly, calling them “magicians,” and said he would remember the past year for the rest of his life.
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What Muse models launched
The note followed the launch of two models identified in the report:
#1 Best Overall
- Muse Wonder: included in the Muse launch, although the report does not establish its licensing model, access method or complete technical specifications.
- Muse Spark: described by the Times of India report as an open-source model. The report does not provide a full specification table, benchmark results or detailed usage instructions.
The Times of India’s secondary report does not include a directly verifiable official Meta announcement or Wang post, so details such as model sizes, supported modalities, performance measurements and download locations should not be inferred from the announcement alone.
The one-year frontier-model challenge
Wang said MSL and its team were formed a little more than a year before the note, with an objective of producing a frontier model in roughly a year. He characterized that target as “incredibly audacious.” According to the report, many people told the team it would be impossible.
Rank #2
Wang said the group understood the odds were poor but pursued the objective anyway: “We knew it was unlikely, but still worth doing.” The timing is qualitative rather than a precise start date: the report says only that the organization began “a little more than a year ago” and aimed to deliver “within a year or so.”
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The phrase does not mean the lab had no objectives or no pressure to deliver. Wang said the team adopted a principle of avoiding arbitrary deadlines because frontier-model development involved many interdependent areas:
- pretraining
- post-training
- reinforcement learning
- multimodal systems
- infrastructure
- safety
- data
- frontier research
These workstreams can change one another’s requirements. A fixed date chosen before the technical problems are understood can encourage teams to optimize for a calendar milestone instead of a reliable model. Wang’s explanation presents “no deadlines” as a way to protect the quality and coordination of the work, not as an excuse to abandon accountability.
Why Wang called MSL a “machine that builds new models”
Wang framed the achievement as larger than the two models released that week. He wrote that the lab had built “a machine that builds new models,” meaning a repeatable organization and engineering process rather than a single successful training run.
That process, as described in the note, combines specialized research with the infrastructure and operational systems needed to train, evaluate and improve successive models. The claim is about scalability and repeatability: MSL is intended to keep advancing at the frontier instead of treating Muse as a one-off event.
Safety, security and the stated purpose
Wang said the team paid close attention to alignment and security alongside capability development. He connected that work to a broader purpose: making people’s lives better. The note therefore presents model progress and safeguards as linked responsibilities, rather than treating safety as a separate activity after a model is built.
Best Value
The report does not disclose a detailed safety framework, evaluation scores, threat model or governance process. Those specifics would require separate official documentation.
Why he says the work is not done
Wang ended by recognizing the team’s effort while making clear that the Muse launch is a milestone, not a finish line. His closing, quoted by The Times of India, was: “i’m so grateful for every one of you, and our work is not done!”
That statement fits the rest of the message. The lab still has to improve its model-building process, continue research across training and multimodal systems, and maintain its stated focus on alignment, security and human benefit as development proceeds.
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