Creating Muse: The Fastest-Growing AI Product Since ChatGPT | Alexandr Wang, Meta
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.”
1. Muse began as a thesis about what humans should do with superintelligence
Wang traces Muse to the June 2025 “Personal Superintelligence” memo developed with Zuckerberg, Nat Friedman, and others at Meta Superintelligence Labs. The mission was broader than an assistant or chatbot: build powerful AI that “actually makes all of our lives better” and “lifts the human experience broadly speaking.”
The underlying division was between things people want to do and things they have to do. Wang expects advanced AI to absorb much of the latter, leaving humans more time for relationships, interests, hobbies, and ambitions—the terrain Meta’s existing products already occupy.
That philosophical fit also creates a business advantage. Meta has 3.5 billion people using its apps daily, a heritage in connecting friends and family, and consumer distribution at enormous scale; Wang therefore saw personal agents and consumer AI as the company’s natural arena.
2. OpenClaw supplied the terrifying, euphoric proof of concept
Early in 2026, Opus 4.5 pushed agents to the foreground while OpenClaw offered a rough glimpse of personal agency. Friedman became its earliest champion inside MSL, having an experience Wang describes as “somewhere between terrifying and euphoric.”
Friedman trusted the agent deeply enough to let it monitor security footage after he said he wanted to drink more water; it checked whether he complied and congratulated him. For Wang, Friedman’s conviction carried weight because he had been early to Stripe, open-source developer tools, and GitHub Copilot: “I think there really is something here.”
Wang’s initiation was a Friedman-authored psychological-probing prompt. With access to his email, photos, and other personal information, the agent ran successive rounds that felt like “3 years of therapy” compressed into one moment. Wang says he was “very vulnerable through that process.”
Both men wrote February memos after immersing themselves in agents during every spare moment. Wang framed the super-agent as, in many ways, “the final consumer product”; Friedman’s memorable titles were “The Claw Is the Law” and “Trust Is a Must,” which helped define the product’s core principles.
3. A two-week prototype became a seven-month reliability campaign
Meta assembled the first prototype in roughly one or two weeks and showed it to the board. Several durable elements were already present: Jollybot, an early Muse Charm, and many of the eventual interaction ideas, even though the full set of concepts and tabs had not yet arrived.
Zuckerberg was experiencing the same euphoria and terror. He used an agent around his home, with his children, and during MMA training, asking it to watch footage and suggest improvements; staff meetings began featuring stories about the strangest or most useful agent experiences.
The hard work was not demonstrating magic but making it dependable. Wang says OpenClaw’s phenomenon peaked and faded because users encountered flashes of brilliance amid frequent breakage; Meta spent seven months moving from a prototype that “sometimes worked and was magical but most of the time didn’t work” to something suitable for billions.
4. Meta chose personal agents while competitors crowded into coding
Wang recalls that Opus and Claude were “way ahead in coding” at the start of the year, creating what looked like an Anthropic takeover. Industry consensus increasingly treated coding agents as the only meaningful AI form factor, capability, or competitive battleground.
Meta resisted that gravitational pull because its own agent experiences had created conviction elsewhere. Rather than chase the same developer use case, it aimed its model roadmap at personal superintelligence and the specific behaviors a personal agent would require.
Senra compresses the distinction sharply: “It’s not work. It’s not coding. It’s personal.” Wang agrees; Meta’s consumer heritage and distribution gave it a plausible right to win that would have been weaker in enterprise software or developer tools.
5. Musepark 1.3 crossed a launch bar defined behavior by behavior
The model team maintained spreadsheets covering more than 100 specific capabilities and behaviors. It built evaluations for each, reviewed deficiencies repeatedly, and trained toward them one by one—a “very meticulous process” of sanding down the model’s rough edges.
Wang connects this to Meta’s older product discipline. The company once optimized conversion, referrals, and every step of onboarding; with Muse, that instinct shifted toward the underlying model—task completion, communication, reliability, and knowing when to return to the user for guidance.
Every spreadsheet row had a launch-blocking threshold and a higher definition of “good.” Checkpoints that were green on 80 rows but red on 20 remained unacceptable; the first model checkpoint to clear the whole sheet was a specially trained Musepark 1.3.
Numerical gates were paired with experiential judgment. The team ran repeated user tests, model-version A/B tests, and first-time onboarding sessions because consumers had to understand that Muse was “not a chatbot”—it could act, but that unfamiliar capability needed careful explanation.
6. A small team protected Muse from large-company product entropy
Fewer than 200 people worked across Muse’s model and product layers. Wang concedes that sounds substantial, but within a company Meta’s size it reflected organizational restraint around what executives expected to be one of the year’s most important launches.
The governing idea was that Muse constituted a “single work of art.” Wang feared the large-organization failure mode: a “Frankenstein,” “Cronenberg amalgamation,” or “smoothie” in which each product manager jams a separate agenda into one incoherent surface.
Friedman’s taste supplied much of the unifying point of view. Senra likens that authorship to the description of Apple as “Steve Jobs with 10,000 lives”—a product transmitting one person’s sensibility rather than exposing the org chart behind it.
Zuckerberg’s role was patience and restraint. “Seven months in AI time” felt like a decade while Wall Street questioned whether Meta could pull anything off in AI, yet he continued publicly alluding to personal agents and trusted the team to wait for quality.
7. Reliability determined whether magic compounded or churned
Personal agents sat “on a knife’s edge”: reliable magic could create one of the greatest consumer products ever, while nondeterministic failures would make users conclude the entire category was trash. Wang says OpenClaw’s exciting moments were not enough to sustain the phenomenon once users encountered its limitations.
Muse’s product experience was highly sensitive to the underlying model. It needed not just to complete a task, but to communicate about it appropriately, recognize uncertainty, request input when necessary, and do all of that consistently enough for users to risk a larger assignment next time.
This explains the unglamorous difference Wang draws between invention and commercialization. He credits Peter Steinberger and OpenClaw’s contributors as visionaries; Meta’s contribution was “really grinding out and sanding out all of the details” across model and product until the combined experience held together.
8. The launch broke through by making Meta culturally surprising again
Wang would disclose only that Muse had “millions” of users. When Senra notes that could mean hundreds of millions, Wang still declines specificity, pointing instead to graphs he retweeted that would imply Muse was the fastest-growing consumer AI app of all time.
Meta’s distribution could ensure awareness, but Wang believed awareness alone could not cut through the “torrential stream” of models, products, startups, and features. The team explicitly discussed how Muse could become culturally interesting—and how the campaign might make Meta “cool again.”
Wang says he personally wrote every post in the supposedly unhinged feed. Memes interrupted meetings and dinners like bugs demanding to be solved; after casual posts unexpectedly performed well, he remembered that “the internet rewards risk” and surprise, then leaned further into that register.
Friedman joked that the team expected to learn about Muse users but was instead “learning a lot about Alex’s mind.” Wang believes the campaign genuinely mattered; Senra’s own inbound evidence was that people discussing the memes had also downloaded and begun using the app.
9. Product marketing worked when the user benefit fit in a screenshot
The recurring question was why someone should care. Wang’s answer is that most people do not care about Meta, OpenAI, Anthropic, or research for its own sake; they care about what a product can do for them.
The product therefore promoted real use cases rather than institutional branding. Wang initially retweeted dozens of interesting interactions, while Jolly—the small mascot—became a flexible cultural object that could carry a briefcase, inhabit memes, and communicate a lighter personality.
Wang says Meta ran a few ads, but he was struck by how little benefit-led content appeared in Anthropic’s and OpenAI’s campaigns. Modern consumer products, he argues, must “work on the screenshot.” Jolly’s presence made shared conversations immediately identifiable as Muse, turning screenshots in group chats and social feeds into recognizable distribution alongside the few ads they ran.
The strongest example came from Senra’s friend, who left his ID on a Flexjet and told the agent he wanted no involvement in recovering it. The agent worked through the courier, office security, and retrieval; three or four screenshots conveyed the product more effectively than an abstract campaign.
10. Retention follows an escalating trust fall
Wang describes adoption as a sequence: give the agent a small task, watch it succeed, then offer something slightly larger. Each completed step expands the user’s belief about what is possible until the relationship becomes “this trust fall” with an AI agent.
Senra notes that college students sometimes make a full trust fall immediately: they record voice notes, brain-dump into AI, and trust it to organize their thoughts into plans and actions. That shows where broader consumer behavior might head once reliability is assumed.
The practical objective is therefore not generic engagement but an early wow moment followed by uninterrupted successful escalation. If the agent works “every one of those times,” curiosity becomes trust, and trust expands both task size and product stickiness.
11. Wang frames personal agents as a second mind that restores human agency
Wang mainly uses the product for recurring workflows and as a “second brain,” since his job still requires presence in meetings. His larger framing asks what happens “if everyone had a second mind beyond their own”—an AI that supports a person’s wants, dreams, and unfinished intentions.
Senra suggests that the product could give people “the adulthood they dreamed of in childhood,” describing children who imagine becoming astronauts, saving the planet, or changing the world before work and obligations erode those ambitions. Wang responds that adults are “zombies,” having lost agency and ambition because “the life has been beaten out of them.”
Wang’s promise is an “escalator of agency”: accomplish one ambition, dream bigger, and repeat. He sees iconic entrepreneurs such as Elon and Mark living this cycle already; personal AI could make some version available to everyone rather than only unusually fanatical founders.
Senra supplies the widest speculative framing: history has been shaped by the few fanatics who made their wants real, but humanity has never seen “every single person’s agency fully switched on.” He imagines billions gaining abundant intelligence and producing a strange, diverse, artistic world—his analogy is “Rick and Morty Interdimensional Cable” manifested in reality. Wang responds with a line from the show: “The universe eats smart people.”
12. Muse emerged from Zuckerberg’s rapid refounding of Meta’s AI lab
Wang’s earlier relationship with Zuckerberg consisted of occasional conversations after an introduction around 2021 or 2022; he thinks their first meeting may have taken a year to schedule. After Llama 4 disappointed Meta internally and AI’s strategic importance intensified, Zuckerberg called to ask what the company should do.
The entire path from that call to the announced Scale transaction took five or six weeks. Scale’s enterprise and government business did not resemble Meta’s iconic product acquisitions, so Wang initially doubted the industrial logic; the real objective became clearer when the discussion turned to talent.
The resulting structure left Meta owning 49% of Scale while Scale continued independently and Wang plus several colleagues joined Meta. Wang calls it a “win-win-win”: shareholders benefited, Scale retained a future, and Meta gained people who could help rebuild its AI effort.
Leaving remained emotionally difficult after nine years—roughly a third of Wang’s life—and he had genuinely treated Scale as his life’s work. What changed the decision was the chance to refound a program that looked like “damaged goods,” establish new principles, and build much of the lab from scratch.
13. The new lab is organized for diamond mining, not bureaucracy
Wang’s founding organizational thesis was a small, flat, highly technical team with extreme talent density. Meta could offer a startup-like chance to shape the institution without startup bottlenecks in compute, infrastructure, or distribution, all oriented around the clear north star of personal superintelligence.
Recruiting was not simply about offering an obviously superior cash package. Wang says the stock-price appreciation at OpenAI and Anthropic meant that staying could already be very lucrative, making Meta’s offer net neutral in compensation for many people. The attraction was the chance to shape a new lab and do meaningful work with Meta’s resources.
His own role changed from author to coach. At Scale, believing a founder should do every job gave the company his point of view but made him a bottleneck; at Meta, he instead recruits exceptional people, identifies unusual talent, sets direction, and creates conditions for “the greatest expression of their talent.”
The operating analogy is diamond mining versus skyscraper construction. Senra contrasts frontier research with the operational optimization of data businesses; Wang says that is the difference between what he felt he was doing at Scale and what he is doing at Meta. In some AI areas, most ideas fail but one result “can be literally a million times more valuable than everything else.”
Wang illustrates the power-law idea with his 19th birthday: 10 or 12 Harvard and MIT students, including Jeff Yan of Hyperliquid, Scott Wu of Cognition, Jesse Zhang of Decagon, and Vicky Ye of Anthropic. He jokes that HRT, where he was interning, should simply invest in its interns.
More than 200 people effectively report to Wang, with pod-level technical leads resolving technical choices. He admits he is not the ideal conventional manager for that many people; direct reporting is an anti-bureaucratic statement that proven researchers “don’t need managers, they need a great environment” and “room to cook.”
Full transcript
Tell me the history of Muse.
1. Rebuilding Meta's AI lab from scratch
The first thing to start with is probably what our vision was when we started Meta Superintelligence Labs. When we started it, Mark published this memo that Nat Friedman, myself, and a bunch of others worked on closely with him called “Personal Superintelligence.” The whole idea was: How do we make AI—how do we build powerful AI—that actually makes all of our lives better? How do we build something that actually lifts the human experience, broadly speaking?
In that memo, we spoke to a bunch of those ideas, and we alluded to a lot of what Muse ultimately became, but we were gesturing at it very early. This was in June 2025, before agents had happened in any meaningful way. In the summer of 2025, AI was in a totally different spot. But that was what we wanted to build toward. That was the mission of MSL, and that was really where we wanted to take everything.
Then we got to work building models because the end-to-end process of developing any of these frontier models takes many months. You have to build your entire stack, produce the data sets, pre-train the model, do post-training on the model, and build a whole stack for that. It’s a very lengthy, end-to-end process.
2. Discovering the potential of personal AI agents
This whole production line continued into the start of this year. Opus 4.5 obviously caused many people, including us, to see what the future of agents could be—or was—and really brought the potential of agents to the foreground. OpenClaw was happening at the start of the year.
Nat Friedman, whom I work closely with, was, I think, the first person within MSL to work with OpenClaw and try OpenClaw. He had an experience I would describe as somewhere between terrifying and euphoric. I think he really went all in and trusted it entirely. He’s told some of these stories—he had a Stripe Sessions interview where he talked about some of this—but he told his OpenClaw that he wanted to drink more water. His OpenClaw would watch him on security footage to make sure that he was drinking water and tell him, “Good job.”
He had many crazy stories like this, and it also makes you realize, “There’s something quite magical about what’s happening here.” I remember one of the things that Nat told me, and this is one of the things I remember the most. He said, “I think there really is something here.”
3. Leaving Scale AI for Meta
Early-adopter Nat Friedman is a pretty good indicator because he was one of the first customers of Stripe back in the day. He was also really early to a lot of open-source developer tools, helped create GitHub Copilot, and did many other things. That was a clear signal: There’s clearly something pretty magical about personal agents, generally speaking.
4. Three years of therapy in one AI session
When you actually trust the personal agent and really bring it into your life, it can do so many things. After hearing all of these crazy stories, I started using it very deeply. For many weeks, I think both of us spent every spare moment talking to our OpenClaws and really immersing ourselves in what personal agents could be.
What was your initial reaction: euphoria or terror?
Nat had built this prompt to do basically a full psychoanalysis of yourself. There were multiple waves of deep psychological probing. He had written this prompt for you to use with an OpenClaw, and I think that was one of the very first things I did with my own OpenClaw. It was maybe the equivalent of 3 years of therapy compressed into one moment.
I’m curious: When you get your first shot at OpenClaw, why is this one of the first things you do?
I downloaded it and installed it, and then Nat said, “Oh, you should do this.” I was next to him, and he said, “Okay, I’ll just do the thing you’re telling me to do.” Then I had this crazy experience.
Say more about the crazy experience, though.
It was very intense. It was like 3 years of therapy because it was successive rounds of deep psychological probing. It had other information about me, including access to my email and a bunch of my photos. It could look up other information and pull it all together. I was very vulnerable through that process.
This was in February. I think both of us had come to the joint conclusion that there was clearly something really special happening here. We both wrote memos at that time and sent them up through the Meta board. For whatever reason, it was board season.
I wrote a memo about how I felt that, in many ways, this was the final consumer product—that the super agent was, as far as consumer products went, a clear endpoint in many ways. Nat actually wrote 2 memos, which had very memorable taglines: “The Claw Is the Law” and “Trust Is a Must.” Those ultimately defined a lot of the core of how we built these products.
5. Building Muse in 7 months
We came to the conclusion that there was something very important here. This was in February. Then we built our teams, built a prototype within a week or 2, and demoed it to the board. It was a very fast cycle, and many of the decisions that made their way into the final Muse product were present in the first prototype.
The Jollybot was there. An early version of the Muse Charm was in that first meeting. Not all of the ideas and not all of the tabs were there, but a lot of them were present.
From there, obviously, that was February, and we launched the product 7 months later. The really hard process was taking this prototype—which sometimes worked and was magical, but most of the time didn’t work, constantly broke—and turning it into something highly reliable. We wanted it to be a beautifully crafted consumer product that we felt confident offering as a contribution to the consumer world. That was a very lengthy and quite painful process.
Before we get there, I want to go back to the idea that you created the prototype in 1 to 2 weeks and showed it to the board. What was Mark’s role in all this?
He was experiencing the euphoria and terror of the product at the same time as all of us. I think he’s talked about this in various interviews, but he used it for a bunch of things around the home, used it with his kids, and used it in his MMA training. He had it watch videos of himself doing MMA training, and it would give him tips on how to improve.
There were also staff meetings—and this became a thing for much of the Meta team—where we would have a staff meeting and ask, “What were the crazy experiences you had with your agent?”
Again, this was back in February. It’s interesting to think back to this whole cycle because the OpenClaw phenomenon peaked and then disappeared. It didn’t persist. I think a lot of people had this experience: You’d have these magic moments, but for the most part, it didn’t work that well. There were all these problems, and then it just died down.
Part of building the product was figuring out how to make this an actual, reliable consumer experience that you feel comfortable putting in front of, over time, billions of people—and feel confident that most of those people will have an amazing experience.
Why were you able to do that and the developers of OpenClaw were not?
First of all, we respect Peter Steinberger and everyone who contributed to OpenClaw immensely. I think Peter is definitely a visionary. Whatever happened in his beautiful brain that created OpenClaw is magical.
The unglamorous part of what made Muse so magical was really grinding out and sanding down all of the details to make the combined model-and-product experience amazing. We did many iterations on the model itself. We aimed the overall model roadmap toward building personal superintelligence, broadly speaking, but certainly personal agents as—
Say more about that and how it differentiates you from your other competitors.
This is useful to go back to the moment because if you go back to the start of the year, in February, Opus and Claude were way ahead in coding—way ahead of anyone else—and they were exploding. It was the beginning of the Anthropic takeover of the whole industry, and it was a very scary moment, generally speaking. It was like, “Wow, they’re so far ahead on coding.”
Obviously, they had Claude Code, and they had this clear lead.
I think the conventional wisdom in the industry was that coding agents were the only form factor that mattered, or maybe the only technology or capability that mattered for advanced AI. There was this incredible force in the industry to crowd that use case and compete with Anthropic on coding agents because of our experiences and our conviction, which I think formed around this idea of personal agents.
Taking a step back, I think the reason why we did personal superintelligence in the first place was that it was also what we believed Meta was uniquely positioned to do. We can talk more about that, but I think that was a big part of it.
Well, let's talk about that now.
Okay, we'll do the weave. I think one of the first things I told Mark, even before the whole Scale and Meta deal happened and everything, was that I thought Meta was actually a really special company if we had AGI or ASI, or whatever you want to call it.
Part of the promise of AI is that all of us have a mix of things that we want to do and things that we have to do. The promise of AGI is that we're going to spend almost none of our time doing things that we have to do, because the AIs will just start doing a lot of that for us, and we can spend all of our time on things that we want to do.
For me, Meta's products represent the things that we all want to do. My use of Instagram, WhatsApp, or Facebook—these are products that represent what I want to do. I follow my interests and hobbies. I follow people that I like, my friends, and the people I care about. The product span of Meta encapsulates the realm of what we want to spend our time doing.
I think on a long arc, Meta is an incredible winner through all this. That's one of the first things that I believed about Meta and superintelligence. Ultimately, a lot of those ideas made their way into this concept of personal superintelligence.
You can look at it a few ways. One is the heritage of the company. The heritage of Meta is connecting people with their friends and family and helping them discover their interests. These are things that are very human.
Then you can take the business lens. Meta has distribution to 3.5 billion people using the apps every day, and the natural area where Meta was going to be successful is consumer AI.
It's not work. It's not coding. It's personal.
Yeah, exactly. Personal agents, personal AI, and consumer AI, broadly speaking, were always the zones where we felt that Meta was going to be able to do something very special.
That brings us to early 2026. Now you have the prototype, you have the buy-in, everybody knows Mark's on board, and now you're doing the 7 months of difficult work refining this product.
Yes. There was kind of a specter around personal agents. They came and went, and we always felt that it was because the experience wasn't perfect or polished yet, and all that kind of stuff. But we knew that was something we had to think about as we were developing this product.
We focused our model development roadmap on personal agents. The top focus, or one of the top focuses, was building personal agents. We had these long spreadsheets of hundreds of different—more than 100—specific behaviors and capabilities that we knew the model needed to have to build the ideal personal agent.
It was very meticulous. Over time, we built evals for all of those. We figured out how to train the model to be really good at every single one of those things. We had reviews where we looked at that spreadsheet, figured out the things we were bad at, and made sure that we were improving on them.
We did this very meticulous process to slowly keep improving and sanding out all the edges of the model so that it could power the product.
Is that really the culture of Meta, too? This constant sanding of the products?
Yeah. A lot of the culture of Meta is that once you know what you're measuring, and you have very talented people who can optimize for those things, you can sand out all the details and make those things amazing.
In the pre-AI consumer product era, a lot of this was about how to optimize your conversion, how to optimize referral rates, and how to optimize every part of the onboarding flow. How do you make all that stuff perfect?
In the AI era, a lot of that, especially from Muse, has been in the form of sanding out every detail of the model to make it perfect for the product.
6. Mark Zuckerberg's obsession with improving products
Did you see this profile on Zuck in Colossus written by Jeremy Stern?
I saw that. He's writing the best profiles on entrepreneurs and investors in the world right now. I think he's literally the best in the world at what he does. In that profile, Zuck was talking about the advantage that he has relative to Elon, Sam Altman, and others. He was saying, "Listen, my skill set is very unsexy, but I'm really good at building teams and then improving a product slowly over a long period of time."
Relative to Elon, Sam Altman, and others, he's not going to lose control of his company, and he doesn't need to raise any more money, so he can just do this for an excessively long period of time. That section of the profile is popping into my mind when you're talking about this 7-month stretch of trying to refine and build Muse.
I think that's exactly right. It took immense patience. 7 months might not sound like that long, but AI is fierce.
7 months in AI time is maybe like a decade in non-AI time. I think it was that way for all of us, but I really give Mark a lot of credit for this. It took a lot of patience, restraint, and trust in the process.
You can go back and look at what Mark said in earnings calls for this entire period, from February through September. He alluded to the fact that we were building personal agents, and we weren't really trying to hide it. We were building personal agents and talking about it.
But I think there was a lot of doubt in the company for that entire period. One of the dominant Wall Street narratives was, "Oh my gosh, Meta is just burning all this money. Are they even going to pull anything off in AI?"
It's more than that. It's that Zuck cannot win AI. That's been said over and over again for a long period of time.
Going back to Jeremy Stern's profile, he starts talking off the record to his competitors, including one former researcher who used to work with Zuck and is now at another lab. The researcher says, "I don't want to have to compete with them because Mark's the [__]." I think the line is, "Mark is the [__] Terminator."
He just will not stop.
And then he talks about one of his superpowers: He's never happy with the state of the product, so he always wants to constantly improve. But he's not an asshole. He is definitely a dictator, but he's not rude or the kind of person who will destroy the chemistry of a team. He actually talks about, in the piece, how excessively important team cohesion is to him.
I think it's all right. One of the magical things about Muse was that we took quite a bit of time to sweat all the details before ultimately coming out with a product, because I think that personal agents was one of those product areas that rested on a knife's edge. If it can reliably deliver these magical experiences, it has the potential to be one of the greatest consumer products ever.
But you're dealing with models, something nondeterministic, and agents. They can break; they can be unreliable. If people have these unreliable experiences, they think it's trash. It was one of those things where getting the product to a point of quality and value such that it resonated with so many of the people who downloaded and used it was really important.
7. How Meta knew Muse was ready to launch
I want to talk more about this process. I'm curious: How many resources did you have? How big was the team? How many people were working on it? Do you guys have any data on, okay, I'm using Muse—9 great experiences, 2 inconsistent or shitty experiences—and then I tap out? Do you have any information on this?
You can see this from a lot of the other agent products at the start of the year. Almost every one of those turned out badly, and it's because they were unreliable.
We did a lot of user testing of Muse. We did many rounds of new people trying to download it and use it from scratch, just seeing what that experience was like.
A huge part of the premise of Muse, and I think what's really landed, is that it is a different thing from what most people have experienced with consumer AI. It is not a chatbot. It is an agent, and it can do agent things for you.
I think to a developer this is old news because they've had to download agents and have been using them for a while. But for most people, even that graduation process needs to be explained, and people need to be onboarded in a very thoughtful way.
For sure, we've seen that the product experience is very sensitive to model quality. We've done tests with different versions of the model and continuously do those A/B tests. The product experience is very sensitive to how reliably the models can undergo these tasks, how reliably they communicate with the user about them, and how reliably they check back with the user if there are things that require guidance.
But how did you know it was ready to be released?
Part of it was pretty numerical. We had set—you go back to that spreadsheet of 100-plus things—for each of those rows what threshold was launch-blocking and above what threshold was good.
We had many checkpoints that were green on 80 of the rows but red on 20 of the rows. We knew that wasn't good enough. We had our first model checkpoint that was green across the board, which was a specially trained version of Musepark 1.3.
So there was the very numerical part of it, and then there was just the experiential part. We tried the model, and we could tell that it was a lot better than anything we had iterated with before.
So, going back to that 7-month period you were talking about, how many people were working on the product between the model work and the product work?
Under 200 people total, which I guess sounds like a lot, but in big-company land—
It's actually a lot smaller.
It's not very many. That was kind of cool, looking back on this and thinking about it. We knew this was going to be the most important product. The whole executive team—everyone knew that this was probably going to be one of the most important products that Meta would ship this year.
But we had the restraint, organizationally, to make sure that it was a really small, focused team working on it and that it didn't balloon into huge swaths of the company working on it, because we knew it was a single work of art. It required a strong point of view and strong focus to pull that through into one beautiful experience.
8. Why great products need a single point of view
Say more about this. Why did you just call it a single piece of art and say that you need a single perspective or point of view?
I think one criticism of products that come from larger organizations—and I think this is even now starting to plague a lot of the AI labs—is that they become kind of this Frankenstein, almost like a Cronenbergian amalgamation of a bunch of different people's points of view, visions, and beliefs.
It's sort of PM hell, in some sense, where every PM has a thing that they're trying to get into the product and jam into the product, and then all that blends together into this smoothie. People feel it as users and consumers. You can feel when a product feels like lots of different people worked on it and there were clearly different people who were goaled differently, so they just jammed it all together.
Muse had to be the exact opposite. There was one clear point of view. There was one cohesive experience that we were trying to deliver to the world.
I love that you said that, and it just happens to be pure coincidence. I don't know when this is going to come out, but we're recording this on the 15th anniversary of Steve Jobs' death.
When you think of somebody like a pro at consumer products who had a single point of view, you obviously think of Steve first. I've read every single book on Steve and the history of Apple, and I've done 15 or 20 episodes on him for my other podcast, Founders.
One of my favorite lines to describe Steve's impact says that Apple is just Steve Jobs with 10,000 lives. The products are to his taste, his perspective, and his point of view. What he wanted to see in the world is exactly what the end user got.
In Muse's case, Nat Friedman deserves a lot of credit. His taste and sensibilities ultimately dictated and shaped what that product became.
Do you talk about how many people are using Muse? Are you guys talking about this publicly or not?
We've said millions, and I've retweeted other people who have shown graphs where we would imply that Muse is the fastest-growing consumer AI app of all time.
So you won't say a number, though?
We've said millions.
Yeah. Well, millions could be hundreds of millions. That's also millions. So you're just not adding the beginning to that.
9. Making Meta cool again with memes
I want to go into—you've been kind of going, and I mean this in a loving way, unhinged on X. You talk about your—what is this? Is this a marketing strategy? What is going on? I feel like there's a distinct difference in your public tweeting since Muse launched. What are you doing?
There are a few pieces of what's happening here. The past few weeks have been some of the most fun I've had building—certainly of my career.
This has got to be more fun than Scale AI when you were building Scale. It has to be. We'll talk about that later, but it has to be.
I think there are a few things happening. One is that it felt important that we figure out a way to make Manus break through.
Going back to this, it's a pretty torrential stream of updates. For most people, they're constantly being bombarded with new AI products, startups, releases, models, and features. Here's another model, here's another product, here's another feature. It's just this torrential stream.
10. Why Meta bet on personal AI
We actually had large meetings where we talked about this as a team. We needed to break through. Obviously, at Meta, we have lots of distribution, so we can make sure everyone knows about it, but that's not enough to make it break through in an interesting, cultural, or fascinating way for the world to pay attention.
I separately happened to have met and become friends with memers—people who literally spend all day thinking about memes on the internet and understanding them. I have people who are obsessed with Manus, and they're also obsessed with your X feed. They're convinced that you have 100 Muses coming up with memes to be used.
It turns out you just have a collection of schizophrenic friends who are terminally online.
The X feed, just for what it's worth, is literally every one of those tweets I write. There were quite a few periods when it was really hard for me to pay attention in meetings because I would just think of a meme and be like, "Oh God, I have to get this out."
They were moments of inspiration. I was trying to have dinner with one of my coworkers, and during dinner I was like, "Wait, I figured it out." Then I posted a different meme. It was almost—I almost felt like I was an engineer solving bugs again, because you could tell when something was going to click.
One thing we wanted was to break through. Another thing that I thought was important was, how do we make Meta cool again? For whatever reason, despite having these incredible pieces of cultural software, like Instagram and Facebook, historically Meta was not cool for a while.
I wanted to make Meta cool and interesting and kind of funny. The unhingedness happened naturally over time. When Manus first came out, I was mostly focused on promoting all the interesting use cases that I saw. I was retweeting tens of interesting use cases that I saw of people using Manus.
And then, at some point, because I was tweeting so much, there were a few tweets where I didn’t think that much about it and just said, “Fuck it, post.” Some of those tweets did really well. Have you seen this? “Time spent thinking about it and then banger.” Have you seen that? Great.
I just didn’t spend that much time on some of these. I tweeted them, didn’t even check my phone for a few hours, and then I was like, “Oh, wow. That one went really well.” I remembered something about the internet: It rewards risk, surprise, and things that people don’t expect.
Then I just went off. A lot of my coworkers were somewhat surprised at something that Nat said during the week when I was posting all the memes. He said this in a bunch of meetings: “I thought we were going to learn a lot about Muse users and how people were using Muse, but actually, we’re just learning a lot about Alex’s mind.” It was a lot of fun. I felt like I was drawing on all of my internet knowledge from years and years and years to pull into those moments.
I think it genuinely made an impact on growth, which is kind of hilarious.
I definitely think it did. I can’t stop hearing about it. All the people I’m hearing about it from also downloaded the app and are now using it.
Go back to what you were saying, though. We have distribution, but that doesn’t mean that we can make something that people are going to come back to and that’s actually sticky. How did you do this with Muse?
I think the question was: How do you make this breakthrough as a product? There were a few parts to that, but one is, how do you get someone to a wow moment with the product as early as possible?
This product naturally lends itself to that because it’s AI. It can do a lot. If someone has never used an AI agent, there’s a lot it can do that will surprise you. I think that was a big part of it.
Another part was: How do you harness all of those stories of people having these wow moments and use them to help make the product sing and make the product fly?
How are you using the stories, though? You guys aren’t running ads for Muse, are you?
We ran a few ads. Honestly, at first, it was just the fact that I would retweet so many of them. Anytime I saw someone do something interesting with Muse, I would quote-tweet it.
I couldn’t understand why both Anthropic’s and OpenAI’s ads were so bad. Anthropic was doing this huge outdoor campaign, and it was all about them. Same thing with ChatGPT. They’d have the icon, which I don’t even think people recognize as a logo, and it would just say “ChatGPT” and show 2 people sitting at a desk. I’m like, “What the fuck does this mean?”
You have all these use cases. All you have to do for your ad is show the benefit of your product. That’s it.
You have millions of them. Run those ads. It’s so elementary that it’s interesting that people make this mistake over and over again.
Yeah, 100%. I think there are a few insights. First, most people in the world don’t give a fuck about OpenAI, the research, these other companies, or even us and our research. They care about what it can do for them. Everybody’s self-interested.
We had this gift of the mascot, which was the little jolly guy. I remember the tweet where I realized, “Oh, wow. This is mileage.” I was driving into work, and on the way I generated a version of the guy holding a briefcase. I tweeted, “MFW I go to work on Monday.” I remember thinking, “This is one of my best tweets in a while.”
Then I realized you can put him into a lot of situations. One thing I realized as we were going through that process was: How do you create surprising moments that are very different from any other marketing you’ve seen for a product? I don’t really remember the last time someone leaned into racy memes to market a product. I don’t know if it’s been done before, but I realized, “Oh, wow. This is something we can do with this little guy.”
11. Building trust with AI agents
That’s interesting. It drives intrigue for the average person.
Okay, but that might raise awareness and might even get them to download the app. How do you get them to stay engaged once the app is on their phone?
There’s a cycle that people who really love the app—and we even had this back in February—go through. You try to give it a little thing, and it does it. You’re like, “Oh, wow. It did that.” Then you try to give it a little bit more, and it does it, and you’re like, “Oh, wow. It can do that. It could do that first thing, and it could do the second thing.”
Then you give it a slightly bigger problem. It’s almost this trust fall that you have with an AI agent. At first, you don’t really trust it, but you’re intrigued, so you give it a little nibble. Then you give it something a little bigger, and something a little bigger, and something a little bigger.
Certainly, when I talk to college students, some of the ways people use AI are a full trust fall. They’re recording voice notes and brain-dumping to the AI, sending them, and then trusting the AI to organize all the thoughts, give them clear plans, and tell them clear things to do.
For successful Muse users, it really is about getting them onto this trust-fall process of using it for slightly bigger and bigger things, and then having it work every one of those times.
I wonder how much of this is actually driven by word of mouth. Obviously, talking to you, I was going to download the app no matter what. But what really piqued my interest was that a friend of mine was sending me screenshots of how he was using Muse.
When you download the app, it doesn’t really tell you too much about what you can do. It’s kind of open-ended, like an open text box. But a friend of mine who’s running a $100 billion company left his ID on a Flexjet, and he told me, “I don’t want to be involved in this process at all. You have to figure out how to get the ID. Figure out where it is, figure out where I am, figure out all the steps along the way, and arrange the courier.”
Manus went there, got through security at his office, somehow got upstairs to his office, and retrieved it. It was like, “Holy fuck, this is the best marketing.” It's just 3 or 4 screenshots of Muse going back and forth, with him saying, “I don't want to be involved. You have to do everything.” And it's figuring it out along the way.
You know, this is actually one of the things about modern product marketing: it's very screenshot-driven. I think for a modern consumer product, it has to work in a screenshot. The screenshot has to fly, so to speak, either through group chats, word of mouth, or online.
12. The future of human ambition
I think that's been a big part of it. This wasn't intentional or engineered, but I think the fact that we had the little guy in the screenshot was a big deal. Having Jolly, or whoever your Muse is, in the screenshot immediately communicates, “This is a Manus screenshot.” It's just the best marketing you could possibly have.
So, are you on your Muse all day long? What is your own personal usage?
I mostly set up a lot of workflows using it and use it in my meetings a lot of the time. In practice, in my job, quote-unquote, I'm supposed to pay attention, be present, and interact with people. I set up a bunch of workflows and treated it as a second brain.
You said that in the post you wrote, and it was really short. I think the last time I saw it, it was at 5.5 million views. I love how you describe Muse: “What if everyone had a second mind beyond their own?”
Philosophically speaking, I think this is one of the most interesting questions about what it means to be human when you have powerful AI, and what that relationship looks like. Different people have different takes on what exactly that looks like. The fear case, obviously, is that AI is above us and we're just doing what it tells us to do. There are other worlds where that relationship is different. Before you go on, what is your own personal view?
I really believe in Muse. I believe in the personal agent as a long-term form factor. It is human nature to have wants, desires, and dreams, and I think AI and personal agents like Muse will be the mechanism and the bridge that enables us to accomplish those things and continue accomplishing them.
Humans will dream bigger and bigger and bigger and bigger. Kids will dream of having their own star systems or whatever, and Manus will help them do that. There's this thing that Bezos wrote in one of his shareholder letters—I think—which is that the beautiful thing about consumers is that they're always beautifully unhappy, or something like that, or beautifully unsatisfied with the options they have.
That is one modality of our relationship with AI. I think there are other modalities. AI will start doing more and more of the scientific discovery process. AIs will start inserting themselves more and more into various parts of the economy. But I do believe in this form factor long-term, of everyone having an AI that supports them and what they want.
So you see AI as removing all the kludge of life—the stuff that we don't want to do. In the piece you said something like, “The world is full of gatekeepers and obstacles, and Muse can get around this for you without spending any more of your time, so you can focus on the stuff you want to do.”
I think Manus can give everyone the adulthood they dreamed of in childhood. When people are kids, they have big ideas and big dreams. When they imagine their life and play it out, they imagine a life where they can be an astronaut, save the planet, change the world, or whatever it might be. They think big.
Then, for a variety of reasons, by the time people finish school and enter the workforce, and by the time they've been in a job for a while, all that hope, ambition, and ability to dream has been sucked out of them.
No, it's been beaten out of them. The life has been beaten out of them. As adults, I don't think we appreciate the degree to which we're all zombies. We lost our agency, and we lost our ambition. I think ambition is a good word for it.
One of the promises of AI, broadly speaking, and of products like Muse, is to keep that going from when you're a kid. You're a kid, you have big dreams, and you use AI to help make those things happen. Then you dream bigger, make those things happen, dream bigger, and make those things happen. You experience this escalator of agency—an increase in agency throughout your life—versus having lots of agency and then having it crushed.
You mentioned earlier this experience that you and Nat Friedman went through, where you had this disturbing psychological audit coming in waves from this AI. What if we had that, but it was the opposite of what you're saying? What if it actually gave you more self-confidence and more understanding that the world is malleable, and that if you push on it hard enough—if you go after it with enough energy and drive—you can actually change the world around you?
Yeah. I think this is something that the most impressive entrepreneurs exhibit. They dream big, accomplish that, then dream bigger. Maybe they work on that for a decade, and if they accomplish that, they dream bigger. Elon is obviously a great example of this. I think Mark is a great example of this.
A lot of iconic entrepreneurs have this as their lived experience, and I think there's a version of that that should be true for every person. What's tragic is that, for most people, you have bigger dreams and big ideas, then you enter the corporate workforce and become a zombie. Maybe at some point you want to go tackle your dreams, but it's really hard because you have all these commitments. Maybe you have a family, maybe you have whatever it is. You almost become trapped in that sort of zombiehood.
There's one more thing on Muse before I want to get to Scale AI and the partnership you did with Meta and how you made that decision. In this essay, or this short post, that you wrote, I love what you said: “The world until now has been shaped by the small number of fanatics who have somehow found a way to make their wants real, but we've never seen humanity with every single person's agency fully switched on.”
I think the promise of this world, where every single person has an increase in agency throughout their lifetime and has the ability to accomplish their wants and dreams, looks crazy in a very good way. I think it will be very interesting, artistic, and cool. You could go into different pockets of the world and it would be very diverse. It would be kind of insane to think about what that looks like, where literally billions of people have, all of a sudden, because of abundant intelligence, the resources to make incredible things happen.
I think that's part of the promise. In my head, it's almost like the Rick and Morty Interdimensional Cable, but somehow manifested into reality for humans. It could just be really awesome.
I keep hearing this line from that show where it's like, “The universe eats smart people.”
13. The call from Mark Zuckerberg that changed everything
Okay, so I want to go back. What I'm personally curious about is that you founded Scale AI, you're running Scale AI, and then one day Mark Zuckerberg reaches out and says, “Hey.” I assume he says, “Hey, I want to talk. Can you tell me about this?”
Yeah, I think the exact message was something like, “Hey, do you have time for a call?”
Did you have a relationship with Mark previously? Did you spend any time with him? What was the background there?
I had a friend, Alex Schultz, whom I've known for many years, since Scale was maybe 1 year old. I met him in San Francisco. He's currently the chief data officer at Meta, but he was a longtime Meta executive and lieutenant, and he introduced me to Mark—I want to say in about 2021 or 2022—to talk about AI.
I think at that time Mark was getting a lot more interested in AI, and Scale was doing a lot of stuff in AI. My memory is that it took 1 year to schedule that first meeting. From the introduction to the meeting being scheduled, I think it was a full year.
Why?
Mark obviously has an insane calendar and a bajillion different things to deal with. He's also very good at prioritization. He'll spend a lot of time on the things that are really important—in some ways, too much time on the things that are really important.
The only way he's able to do that and be a good dad, be a good partner, and do all this other stuff is to ruthlessly prioritize. I had spoken to him a few times after that and had gotten his advice as a founder. Getting advice from Mark Zuckerberg is obviously a really big deal.
I remember at that time, he would write these really long responses. I would ask him a question on WhatsApp, and he would write a really long response. I was really confused. I thought, “How does he have time to write these really long responses?” But now I realize he's just really fast and rigorous at writing a lot of WhatsApp messages.
He'll write a very rigorous, long response very quickly. It's a very impressive skill of his.
It gives you a sneak peek into what's going on in his mind.
Yeah. There's a reason. Meta was an important customer of Scale. Prior to this instance, maybe we spoke once every 6 months or so. Maybe that was the cadence of our interactions.
Then he called, and I think it was actually not obvious at first what the endpoint was going to be, because the first conversation was just—this was after Llama 4—and it was kind of just asking, “What do you think we should be doing?”
Why is it an important point that this conversation is happening after Llama 4?
Llama 4 was not on the trajectory that Meta wanted as a company. I think, internally speaking, it was definitely a disappointment, and it was also at a time when AI was becoming increasingly important. It was clear that AI was going to be really critical to the future of Meta.
The first few conversations were on the phone, and he was asking for advice: “What do you think we should be doing? What do you think we should be focused on?”
This is what I've heard about him privately: He has this insane—it's not a board of directors, like a Meta board, although there are some people on the board that he also does this with—but he's got this group of world-class entrepreneurs around him. I've spoken to some of them, and you don't understand—he asks for advice constantly. He's hitting us up: “Here's what's going on in my life or at work. What would you do?”
He'll do that over and over and over again. I think this is quite an impressive trait because it takes humility, obviously, to continue doing that even after all the incredible things that he's been able to accomplish.
The whole timeline from when he first reached out to when we announced the deal was 5 or 6 weeks. It was pretty quick.
Another thing that world-class entrepreneurs have in common: We had Jonathan Ross, the founder of Groq, on this podcast. The first time we ever spoke publicly about the $20 billion deal he did with NVIDIA was on the show. He's like, “From the time of the first call from Jensen to the money being in my bank account was 3 weeks.”
Yeah, that's amazing. I had lots of ideas about what I'd be doing if I were him and how I'd be thinking about Meta's AI strategy.
One of the first things I said was what I said earlier, which is, “Hey, I actually think that because Meta is, heritage-wise, focused on the things that people want to do, it's one of the most well-placed companies for this incredible shift to AI.”
I had lots and lots of ideas that I sent his way, and that was this conversation happening in parallel with, “Oh, yeah, maybe we should potentially consider if there's a way to work together.”
So when he says that, what do you think? This was not predictable to you.
Not predictable. No. The whole thing was a really crazy sequence at the time. I was just like, “Oh, that's nice, but there's no way that—”
Well, you know what that means when they say that, right? They want to buy you. It's always the same thing, but they say, “Oh, we have to find a way to work together.”
Yeah. I don't think I'd had that kind of coaching at the time. One thing that I explicitly felt at the time was, “Yeah, it probably doesn't make that much sense, even.” I think I personally was like, “Yeah, maybe he's teasing it or throwing it out there, but does it even make that much sense?”
Why wouldn't you think it makes sense?
Obviously, a deal ended up happening, so what do I know? But—
No, no, but back then—not now that we know what happened—I'm very curious about your thinking as you were experiencing this. The fact that you said, “Oh, yeah, this doesn't make sense.” Why wouldn't it make sense?
Taking a step back, the iconic Meta acquisitions have been Instagram and WhatsApp, and these were very clear, product-logic-driven decisions. Scale is a deep enterprise- and government-sales business, an entirely different kind of business from Meta.
The logic that I thought would have made sense at the time was if Meta wanted to get into those things. Then maybe that's the industrial logic that would have made sense, but this wasn't that, because I think Mark was clearly predominantly interested in getting the Llama program and his overall AI program on—
He wanted talent.
Yeah, he wanted talent. It was shrouded and unclear the whole time, in a lot of ways.
When did it become clear?
After the money hit the account—when we started talking numbers, I was like, “Oh, wow. Okay. All right.” That's when things became more clear.
At first, you're like, “Oh, this will make sense.” Then you guys keep talking. How fast did he convince you this was a good path for you?
Part of it was just, “Wow, this is a really fascinating deal construct.” We ultimately landed on this deal where Meta invested and owns 49% of Scale, Scale continues, and I and a few people join Meta.
What I was kind of incredulous about at the time was that it's such a weird kind of deal, but it's one that I think genuinely was this very interesting win-win-win, so to speak. There's a win for the shareholders of Scale because all the shareholders of Scale got a great deal and benefited a lot. Scale continues, and I truly believe the best days of Scale are ahead of it.
My question to you—or what I'm personally interested in is this: He can explain this to you, and you come around to that perspective in one phone call? Is this just, “Wait, Mark, I've got to think. This is so fucking crazy. I've got to think about this for a few days”? Explain this process as much as you can.
It was many weeks of thinking, “This is insane. Is this even real? Does this make any sense? If it does make sense, how do I feel about it?”
The predominant emotion was more like, “This is kind of insane.” Then, as I thought more about it, I realized, “Oh, I actually have to make a decision about whether or not I do this.” That was it, in and of itself.
There were a lot of conversations with the people at Scale and with our investors. There were a lot of interesting conversations on that side. It's obviously really hard to let go of or give up your baby—
Because you worked on Scale for how long? How many years?
9 years, from founding until the—
And you're still young. You're not even 30 yet. This is a third of your life.
There was something that Paul Graham said for a long time, which is, “If you think of your company as your life's work, you will operate differently.” I genuinely thought of Scale as my life's work for the whole time that I was working on it.
That was a tough emotional process. What ultimately got me was a combination of, “Wow, this is a win-win-win. It's a good deal for all parties,” and also that I saw the potential of what could happen at Meta.
This was a point at which I think Meta on AI certainly looked like damaged goods in many ways. It was maybe not the most appealing place to work on AI, but I think that triggered my entrepreneur side, where I was like—
You can essentially rebuild it. It's like a refounding of the lab.
Yeah. I think this is one of the things that ended up being fascinating. Because it was clear that there was so much work to do, it really cleared the way for me to build a lot of stuff up from scratch, set up the right principles, and build the right culture.
It created the conditions for us to build something amazing.
Is that how you and Mark discuss it? Is it like, “Hey, we have to hit a reset here. Obviously, if we keep on this path, we're essentially going to throw out what we have and redo it with a completely different level of talent”?
14. Diamond mining vs. building skyscrapers
Are these the conversations that are occurring between you and him? It didn't seem like he wanted a minor adjustment. You understand what I'm saying? He's just like, “Oh, this is not working at all. It's not going to work. Then let's rip it down to the foundation and rebuild.”
Correct. It was an evolving conversation. One of the things that I believe really strongly is that Elon has demonstrated this at various times in his career: You can have a small, incredibly cracked, highly technical team that's very flat, and if you do that, you can accomplish a lot very, very quickly.
That was something that I had a lot of conviction in, and I believed in AI. That is the right approach, especially if you have to do what we had to do, which is move very, very quickly.
Early on, that was one of the things I talked to Mark a lot about, and I think he was excited about it as well. That ended up becoming one of the tent poles of our overall strategy: a small, very flat, highly technical team with very high talent density. How quickly can you move if you have those ingredients?
From an outside perspective, it just seems like Mark empties the clip. That's the way I think about this, right? Have you ever read this book called The Mind of Napoleon?
No.
Okay. So, The Mind of Napoleon was published in 1957. I found it because I saw an interview—I think Tyler Cowen interviewed Sam Altman—and I think this was in 2018. He asked him, “What’s the most important book that you read this year?” and he said, “The Mind of Napoleon.”
It’s very hard to find, so I bought the book. It’s 300 pages of just Napoleon’s own words, organized by topic. In it, he talks over and over again about how hesitation is fatal. But he says that before you engage in a course of action, there’s a lot of deliberation: he’ll study it from every angle and make sure he’s making the right decision.
I feel like there’s an echo in the way Mark operates. He does a lot of deliberation and makes sure he’s on the right path, but once he makes that decision, he goes all in. He just empties the clip. That’s the way I felt about what was occurring at this point, where he comes and gets you and then you guys start rebuilding this entire organization.
I think he’s really internalized that failure doesn’t matter. What matters is when you win and how big you win. I think this overall concept enables him to operate truly without fear in a lot of circumstances, because he’s very comfortable taking lots of risk.
He’s got that great line where it’s like, “In a world that’s changing all the time, the biggest risk is not taking any.”
Yeah, exactly. I think he’s internalized this quite deeply. There are certainly versions of the world where Meta looks a lot more like Google, in some sense—it’s more slow-moving and bureaucratic and just looks very different as a company. But because he’s internalized some of these lessons so deeply, he keeps the thing dynamic, moving, and alive.
15. From founder to coach
Talk me through the difference between how you were as the founder running Scale and how you’re approaching your work now, refounding this lab and working with Mark. What are the different ways that you approach your work in those 2 environments at Scale?
One of the things I really internalized—and I don’t even remember where this advice came from—was this idea that if you want to be a good founder, you have to be able to do every job. I had a very strong point of view about how every little thing at the company should happen and should work. That resulted in a company that was very much a product of my effort and my point of view. It expressed itself in all sorts of ways throughout the company, but it also made me the bottleneck in very big ways.
There are pros and cons to that for a company. I definitely think that if you were to start a company and it’s your first time starting one, you should take that approach, because it’s much more likely to be successful than if you don’t.
Scale was your first company?
Scale is my first company. But at Meta, it was a very different assignment, because we had to accomplish so much in so little time. I didn’t have the luxury of being able to “do every job” within the lab. I certainly am not an AI researcher. I’m also not a product designer. I’m not all these things that are totally pivotal to the organization being successful.
At Meta, I very much adopted this philosophy around creating an environment where we could hire incredible people. Job number 1 was to hire brilliant and incredible people who were at the top of their field across every discipline.
Why do you think you’re able to do that at Meta? Is it just because you have more resources? Why can you do that?
I think one of the things that was charismatic and attractive about the founding moment of Meta Superintelligence Labs was that there was an opportunity to build a lab from scratch with a really, really small team. We had a strong point of view about where we were going: personal superintelligence. I think that resonated with people.
Everyone who works on AI wants to help people. They want it to ultimately mean something to their mom, their grandma, or their grandpa. They want it to be something meaningful to every single person in the world.
Because we were doing this with Meta’s resources, there was an opportunity to not be bottlenecked by compute, infrastructure, or distribution. We weren’t going to be bottlenecked by the things that startups often are. But it was an opportunity to really build something that you could put your stamp on, have a lot of influence over, and shape. I think that was quite exciting for a lot of people.
And so that’s why he chooses to spend what looks like an insane amount of money getting a handful of top talent, because he knows that those top people will recruit other top talent in turn. But you need that as a starting point. Correct?
Yeah. I think one thing that’s maybe somewhat underappreciated about that specific part is that the stock prices of OpenAI and Anthropic ran up a lot. If you looked at how much these people were making by staying at OpenAI or Anthropic, it was also a lot of money. So, for a lot of the talent, it ended up being kind of net neutral in many ways, comp-wise.
Obviously, I think it’s the same thing: oftentimes, a lot of us don’t think about how much the early employees or early researchers at these labs that have had immense stock-value appreciation are making.
We started this fully on the belief of having and hiring the most brilliant people. That’s 1 pillar. Personal superintelligence, that’s 2. How do we create the environment where people do their best work at Meta and in MSL?
I’m doing much more of what you might call traditional management, so to speak. These are the things I’m thinking a lot about: how do I create the environment where all these brilliant people, who are exceptional and very, very special, can all operate and achieve the greatest expression of their talent?
I’m thinking more like a coach in some ways. At Scale, it was very different. I was the person whose point of view had to be expressed in everything that we did.
Say more about that. You feel like a coach?
I think my job is to help set the north star and the directionality of where we want to go—which is, again, personal superintelligence, winning in consumer AI, and building AI that means something to every person—and then spotting special talent on the team and giving people the environment and resources to express that talent maximally.
I think one thing that big companies often mess up is that there are exceptional people at most big companies, but they’re not empowered to do their best work. That’s why they often leave to go to smaller companies, startups, or wherever else it might be.
You mentioned Jeff Bezos’s shareholder letters earlier. That’s something he would repeat as well, in all the books I read about him and in his shareholder letters: if you have great people who can’t build, they’re going to leave.
Yeah, exactly. AI is such a special thing. This term “research” now feels like it almost doesn’t mean anything, but by definition, humanity doesn’t know the limits of what these models are capable of or what you can do with them. They really are these unknown artifacts that we don’t understand super well.
It really is science and research. We’re exploring the limits of what you can do with this technology, what can be built, and it’s a process that requires lots of trial and error. It requires brilliant people to have incredible insight. You can’t think about this process as anything other than a very intellectually intense process. People need space, time, and resources to do their best work.
I think about this in many dimensions. Nat Friedman is absolutely brilliant, and as I mentioned, so much of MSL—if there were a single person whose single point of view came through on MSL, it’s Nat. It’s important for him to be in a position where he can fully express all of that.
Then we have some brilliant researchers who have very, very exciting ideas. How do we create an environment where they’re able to explore those ideas? Most of them won’t work out. Some of them will work out incredibly well. The cases where they do work out justify all the investment.
There are 2 kinds of problems, so to speak, in that analogy. It’s like diamond mining or building skyscrapers, and it depends on what the distribution of the payoff is for the things that you’re working on. There are some areas where the distribution is a super power law: most of the things you do will be kind of useless, but some of the things you do can be literally a million times more valuable than everything else.
VC is a great diamond-mining kind of industry, to give you a sense. It’s all about being able to identify the special ideas and see those through.
16. The future billionaires at Alexandr's 19th birthday party
And that’s easy to do. Just look at your 19th birthday party.
Yeah. I mean, that’s unbelievable.
For people listening who don’t know what the hell I just referenced, can you talk, real quick, about who was at your 19th birthday party and why that would have been a good bet if they had just invested—everybody around the table?
This is a really crazy thing to think about.
My 19th birthday party. I was a freshman at MIT. It was in January, and I was doing a winter internship at a trading firm called HRT. There were 10 or 12 of us, something like that, almost all of whom were students at either Harvard or MIT. Most of us already knew each other from various math, science, or computer science competitions.
Some of the other people there were Jeff Yan, who went on to start Hyperliquid, which is very successful; Scott Wu, who went on to start Cognition, which is very successful; Jesse Zhang, who went on to start Decagon, also quite successfully; and Vicky Ye, who is, I think, a brilliant researcher who works at Anthropic. Everyone was incredibly talented. I actually talked to the founder of HRT recently, and HRT itself, by the way, is now crushing it. Unbelievable.
What does HRT do?
HRT is a proprietary trading firm. They trade their own money. Q2 of um 6 had 11 billion of revenue and like I think 8 billion of profit. That’s the record number. Fewer than 1,000 people work at HRT. It’s unbelievable.
I recently met up with the founder of HRT, and they were talking about, “Oh yeah, we’re starting to do some private investments.” I was like, “Just invest in the interns. All the interns get seed checks.” That’s great.
I grew up doing math, science, and computer science competitions and Olympiads, and got to know all sorts of people from all around America doing this kind of stuff. It’s quite surreal to see the people I did competitions with become so successful.
It’s going to be fascinating to see what all of you do over the next few decades.
Before I interrupted you, did you have more to say about diamond mining and skyscraper stuff? Basically, is it a power-law payoff, where some things just pay for literally a million times the investment? Or is it more like building skyscrapers, which I would describe data annotation or data businesses as being more like?
Amazon Prime deliveries are another example, where the payoff is pretty linear and you have to develop a process where you’re really, really amazing at doing it. If you’re building skyscrapers, then it’s a very operational business, and it’s all about squeezing every ounce of efficiency out of that process. You look at it and figure out, “I can make it 2% more efficient this way, 1% more efficient that way, and half a percent more efficient that way,” and you just try to squeeze every ounce of optimization out of it.
Then there’s diamond mining, where you’re kind of like, “Hey, everyone, just go forth, try all your crazy ideas, and we’ll nurture the ideas that seem promising.”
That’s what you feel you’re doing at Meta right now?
That’s what I feel I’m doing at Meta. And building skyscraper stuff, generally speaking, is what I felt I was doing at Scale. That’s this big paradigm difference.
17. Why Alexandr has more than 200 direct reports
Going back to the coaching analogy and the difference in how you’re essentially managing now at Meta compared to the way you managed at Scale, why do you have so many direct reports? You have, what, 100 direct reports? How many direct reports?
Yeah, I think more than 200.
How and why?
Going back to it, one of the founding theses of MSL and what we want to do in rebuilding this lab was: How do you have really high talent density? How do you make it the best place for a lot of these brilliant people to do their best work? How do you build an organization that’s technically focused, where the technical people are doing their best work and have the opportunity to really do their life’s work?
One of the design principles around this was making it very flat. Effectively, all the researchers we hired into this group called TBD report directly to me. It’s more a statement of the fact that this is anti-bureaucracy. TBD is literally anti-bureaucracy. The point is, we’re hiring brilliant people, and you’ve all demonstrated clearly that you can do brilliant work. You’ve done it before in your careers, so I don’t need to micromanage you to do brilliant work. You’re going to be able to do that on your own.
We need technical leadership, so some of you are going to be responsible for setting clear technical direction for your groups. We have this pod structure, where we have various pods and various technical leads of the pods who tie-break on the technical decisions. There’s a technical leadership structure in place, but from a quote-unquote people-management perspective, or from a bureaucracy perspective, we’re just anti-bureaucracy.
The other part of this is that we do make a lot of group decisions. We want to debate because it’s a very talent-dense group of brilliant people. We want to have those conversations where we’re all discussing what we think the right path is. Everyone in the lab has done brilliant things. We want to hear all these opinions and arrive at things that we all believe are the best path forward.
It’s certainly an unconventional way to run the team, and I would be lying if I said I was the best manager to more than 200 people. But part of the point is that they don’t need managers; they need a great environment. They’re all brilliant and extremely capable. They just need the room to cook.
Yeah. So maybe not a coach. You’re kind of the steward of a great environment so they can do great work.
Yeah. Exactly.
Alex, this was awesome, man. Thanks for taking the time.
Yeah.