Alexandr Wang Chief AI officer of Meta
Meta AI chief Alexandr Wang has shared a lengthy post on social media platform X (formerly known as Twitter) thanking the researchers and engineers behind the company’s latest AI model launches, saying he is ‘so grateful for every one’ of them stressing that the team’s mission is far from complete.
In a post following the launch of Meta’s Muse AI models, including Muse Wonder and the open-source Muse Spark, Wang described the releases as a major milestone for the organisation, which was formed a little over a year ago with the ambitious goal of building a frontier AI model withinPost by Alexandr Wang roughly a year. According to Wang, many observers believed the objective would be impossible, especially given the position of Meta's earlier Llama 4 models.
However, he said researchers and engineers from both inside and outside Meta joined the effort and worked with urgency to achieve the target.
Alexandr Wang talks about ‘audacious’ goal
Wang said MSL and its TBD team were set up a little more than a year ago with the aim of producing a frontier model within about a year. Given where Llama 4 landed, he called that goal “incredibly audacious.” “Many people told us it would be impossible,” he wrote. “We knew it was unlikely, but still worth doing.”
He said researchers and engineers from inside and outside Meta joined the effort. “Every single person came in, picked up a shovel, and got to work with earnestness and urgency,” he wrote. “Nobody asked whose job it was.”
‘No deadlines’
Wang said the team deliberately set a principle of no deadlines when TBD started, even as it faced work across pretraining, post-training, reinforcement learning, multimodal systems, infrastructure, safety, data and frontier research.
“There was no use setting arbitrary timelines,” he wrote. The aim, he said, was to get to work and build the foundations of a frontier research effort in record time. “Run after run, this team did exactly that.
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‘A machine that builds new models’
Wang said the lab has built a repeatable system for advancing at the frontier. “More than just the models we released this week, we have built a machine that builds new models,” he wrote. He acknowledged that it can sound like a cop-out to say labs can keep scaling by “turning the crank.”
But, he said, “now I have seen it first-hand.” He added that he is proud of how quickly the team keeps improving the models.He described the team as “the most brilliant people I have ever had the opportunity to work with,” including people “who will chase a weird loss spike past midnight just because they need to know why.” Of the past year, he said: “I will remember this past year for the rest of my life.”
Read Alexandr Wang’s complete post here
After an exciting week of launches (muse wonder [full] & muse spark open), i want to write a note of appreciation to the amazing research teams at meta superintelligence labs on reaching such a special milestone ♥️we started MSL and TBD a little more than a year ago and set a goal of producing a frontier model within a year or so. given where llama4 landed, that was an incredibly audacious goal, and we knew it wouldn't be easy. many people told us it would be impossible. we knew it was unlikely, but still worth doing. a remarkable group of researchers and engineers joined the effort from inside and outside meta. every single person came in, picked up a shovel, and got to work with earnestness and urgency. nobody asked whose job it was.we assembled a team of the most brilliant people i have ever had the opportunity to work with (and remember, i was an HRT intern 🙃). i am surrounded by ambitious, visionary, technically excellent, hard-working, pragmatic, thoughtful scientists, engineers, and product folks—people who will chase a weird loss spike past midnight just because they need to know why. these models are the product of their passion, hard work, and brilliance.somewhat paradoxically, when we started TBD, we set a principle of no deadlines. we knew we had an astronomical amount of work ahead of us—across pretraining, post-training, RL, multimodal, infrastructure, safety, data, AND frontier research. there was no use setting arbitrary timelines—we just had to get to work, be thoughtful, and build the foundations of a frontier research effort in record time. and run after run, this team did exactly that.our researchers are magicians. i can only describe building these models with this team as a wondrous journey of scientific discovery. i am constantly amazed by what we can accomplish with scaling. i will remember this past year for the rest of my life—the late nights, the broken runs, the evals that came back better than any of us dared to hope. watching this team create wonder after wonder has been a true and deep gift.more than just the models we released this week, we have built a machine that builds new models. it often sounds like a cop-out to say labs can just turn the crank to keep scaling, but now i have seen it first-hand. sure, it's a lot of work and a careful process, but we have built a scalable system to keep advancing at the frontier, and i am really proud of how fast this team keeps making these models better.lastly, i get to work with researchers who care deeply about how their work benefits humanity. it's easy to forget when everything is changing so fast, but the whole damn point is to make people's lives better. time and time again, i have seen our researchers, engineers, and product folks build our technology with care so it can truly be good for the world—whether that's building the best models for muse, thoughtfully open-sourcing strong models, or making sure the alignment and security of our systems are rock solid. this is a team of people who never forget there's a real person on the other side of every prompt.thank you to everyone at MSL for being part of this crazy journey over the past year. i'm so grateful for every one of you, and our work is not done!