Following our discussions about Muse last week and the release of our report on Monday, we received a lot of questions. 1. Can OpenAI and Gemini build a personal agent as good as Muse? We’ve spoken with quite a few researchers and teams building personal agents. Our main takeaway is that success depends less on the gap between models today and more on how seriously a company commits to the product: how many people it puts behind it, how much it spends, and how quickly it learns from users. In some ways, this feels like the early debate around Manus. The model matters, but so does all the work around it. You need a large team to understand what people actually want, improve individual use cases, and get the agent working reliably with other apps. One difference already stands out to us: Muse feels more like a consumer product, while the alternatives feel more like productivity tools. That distinction matters. Helping people with everyday needs is a different challenge from helping them get more work done. It’s also expensive. Beyond the product and integration work, there’s the compute bill. Based on what we’ve learned, running the service currently costs roughly $15–20 per DAU per month. There haven’t been many breakout consumer products in the US lately. But when Zuckerberg sees an opportunity like this, he tends to go all in. We think Meta has a better shot here than it did in VR or in the race to build the best AI models. Understanding users, refining use cases, and putting substantial resources behind a product are things Meta does well. 2. How do we think about Muse’s ROI? The biggest opportunity, in our view, is better purchase-intent data: a clearer understanding of what users are considering buying. In our best-case scenario, those additional signals could lift revenue by 25%. Using our estimate of roughly $50 in US revenue per DAU, that would mean another $12.50. Costs also have room to come down. We think better efficiency could eventually bring Muse’s running cost to around $10 per DAU per month. If Meta can get there, the advertising upside, together with other potential revenue streams, could more than cover the cost—even without a subscription fee. 3.Could Muse take ad share from Google? We think it could over time, but we don’t expect a meaningful impact over the next year. Muse gives Meta more purchase-intent data—a clearer picture of what users are actively considering buying. Over time, those signals could help Meta close the gap with Google in categories like travel, auto, and finance, and win some of the ad spending that currently goes to Google. But that will take time. Muse needs to build a regular user base, and Meta needs to turn those signals into better results for advertisers. We see this as a longer-term opportunity, not a near-term threat to Google’s ad business. 4. Can Tencent and Alibaba build a personal agent that is just as valuable? We don’t see the same incremental revenue opportunity in China, at least based on Muse’s current use cases. Many of those use cases address problems that are less common in China. Customer service is already efficient, for example, so people have less need for an agent that spends time making calls on their behalf. Tencent can also identify purchase intent much earlier through its existing apps and services. A personal agent may therefore add less information than it would for Meta. That doesn’t mean Tencent or Alibaba can’t build a useful product. But we wouldn’t assume the same revenue upside we see for Muse.