Hello everybody. Good morning, good afternoon, good evening, wherever you are. Welcome, welcome. We are basically waiting for Tesla and Meta's earnings. Let me turn on my news channel to actually see whether or not we are getting the reports coming in as we speak.
Good morning, everybody. Good morning. Well, the portfolio somewhat recovered today. Uh, let's see. We're getting earnings.
Okay, I think something is up with my feet today, or at least the internet is a bit wonky on my end. Uh, let's see.
I mean, one of the headlines I see is that people are still looking for clarity around the Tesla SpaceX merger. And honestly, I think if you didn't get much out of Elon yesterday, I don't think you're getting much out of him today.
Uh, let's see. So, do we have Tesla's numbers yet? 22nd, 22nd.
Yeah, I think we do, right? Is this Tesla's numbers? And let me take a look. Meta's numbers. The call, I believe, is 1:30 Pacific time. So, that's like 4:30 Singapore time.
So, I'm going to just share my screen if you can see.
Okay. Uh, well, let's take a look. Um, well, it seems like, yeah, so I do have the numbers up on screen.
What are the expectations of Tesla? Uh, third quarter adjusted EPS 48 cents estimated 44 cents, so it was a beat. Revenue, or revenue came in slightly lower than forecasted. Uh, gross margins 19.6%, right up from the 17.8% that was expected. So, how is the stock doing after hours? Let's take a look.
Uh, Tesla, Tesla, Tesla, well, basically flat. I think up half a percent after hours. Let's actually dive into the report itself. Um.
Do we have anything around Meta's earnings? Do we have Meta's earnings?
We have waiting on Meta's numbers. I don't think we have it yet. But let's take a look at Tesla's numbers over here. Give me, pull up my Tesla expectations.
Okay, Tesla is moving three. It's up 1 and 1/2%. I think people like what they're seeing, the wider than expected margins. I think that is definitely good. Although revenue did come in slightly cooler than expected.
Okay, Meta running. Meta's basically up 4%.
I'm not getting any Meta's numbers over here.
Okay, let's see. All right.
Meta is running. I mean, sort of, it's up 4%. Tesla is up 1.7%. So I think both companies did better than expected. Let's actually dive into the numbers, okay? Because I don't have the expectations off the top of my head. Let me see. Uh, Q3.
Okay, so Tesla's revenue expectations was 28.1 billion dollars, is it?
That point, this, I think expectations were around.
Uh, okay, so they basically came in at expectations around revenue because I'm seeing some places say 27.8 billion, some places say 28.1, they are 28.04, basically right in between. Adjusted EPS expected 44 cents, and they actually came in at 48 cents, am I right? EPS 48 cents, okay?
And basically get EPS to. Um, automotive revenues we were expecting 19.9 billion, energy expecting around 8 billion. So, if we take a look, automotive revenues 20.5 billion, so automotive revenues was a beat. And energy.
Yep. Okay. Um, on a year-over-year basis, yeah.
It's basically their services revenue that is carrying the bulk of the growth. You can see in terms of their overall revenue, this was basically flat on a year-over-year basis. So, not a fantastic quarter growth-wise. Why, I guess the stock price is slightly muted. Uh, total gross profit, gross margins 19.6%. So, slight increase in gross profits because of slightly better margins, but better compared to last quarter.
Q4 and Q1 of this year margins were actually slightly better around 20%. So, we are just recovering. Uh, operating expenses and come from operations adjusted EBITDA.
Right. So, I think what we are seeing with Tesla is that the top line is not growing as fast as expected. I mean, I guess it came in along in line with expectations, but I wouldn't say that this is fantastic growth. Basically, no growth on a year-over-year basis and at the kind of multiples that, I mean, the Tesla move is being faded right now. I think I see that they're only up 0.7%.
Okay, let's take a look. Uh, Model 3, Model Y production. The models deliveries. Total deliveries of which, uh, okay. Uh, cumulative delivery all times, okay, 10.2 billion. Active FSD subscriptions 1.74 million up 64%. How much are they charging for FSD? Do we know?
Okay. Global vehicle inventory days of supply. So they have around 11 days of supply. That's fine.
I think that's healthy, you know, because what we don't want to see is an inventory build-up. I think this was a concern leading coming into 2026. You can see the days of inventory was like 10, 15, 27. So, we are coming back down, and I think it shows that the supply and demand is still in balance. Storage deployed, yep, 13.7. Okay.
Supercharger stations, all right. Uh, record quarterly deliveries in several markets, including Canada, Australia, Japan, Taiwan. Strong demand we're seeing across. Record production at Giga factory Berlin.
I mean, what you see, the thing is when I read this, the numbers don't actually back this up. So, I'm also wondering where did they lose their mar- in which areas did they lose some market share in? And I'm also thinking that it's likely that they are being heavily competed against with some of the Chinese automobile companies.
Some of the Chinese companies are definitely very competitive. So, I wouldn't be surprised if that's why they're losing their market share. There was a period of time where I felt like, 'Okay, maybe Tesla was the, I would say, outstanding EV in Singapore.' But I think over time, what I have seen is actually more and more Chinese vehicles are actually competing with them.
Let me take a look.
So, later on we'll probably be listening to Meta's earnings. And I mean, the reason why I'm listening to Meta's earnings is because for Tesla we have already heard Elon Musk speak yesterday, and I think in terms of the broader AI narrative he would have already said what he wanted to say yesterday. And in terms of the data center build-outs and things like that, SpaceX is a lot more relevant compared to Tesla. Of course, people are looking for nuggets around acquisitions. I don't think we really get that today.
But I think with Meta, I think it's a different perspective around the data centers. I think it's a different perspective around the adoption and how they have managed to catch up with some of the big boys, I would say, because I think before Meta Muse was launched, I don't think that many people saw Meta as really a big competitor in the AI space.
Okay, let's take a look. I'm trying to listen to the news articles to see what they actually are coming up with. We also have IBM Q3. So.
IBM beat. Well, IBM is doing pretty well, coming at $7.8 billion versus $7.6 billion. Let's take a look at IBM investor relations.
Do we have the numbers? No. Okay, no, we do not have the numbers.
Okay, so, uh, let's see whether... Okay, I got Meta's numbers over here.
I think for Meta, up 3%. I mean, I guess the markets are pretty happy. Let's take a look at the numbers.
Meta's revenue came in at 64 billion dollars. This is up 26% on a year-over-year basis. Uh, income from operations 12.9 billion dollars, okay. Uh, operating margin of 20%. Uh, provision for income tax, okay. So, net income 9.8 billion dollars, yep. Let me fill up my numbers as we go through this.
So over here, if you take a look, the family of apps daily active users for Meta, do we have the numbers over here? I'm pretty sure we do. Uh, let's see. 3.64 billion people. So if we come back here, 3, 3640, okay.
So this is still around a 2.8% increase on a year-over-year basis. Uh, in terms of their revenue, we're at $64.6 billion, $64.601. So, we drag this across. This is what, 26% growth, okay? Uh, more or less in that ballpark, and then we're looking at net income as well as free cash flow.
So, I think this is really the quarter where they turn free cash flow negative, but apparently the market still seem to be pretty happy, so let's see. For Meta's earnings, uh, net income comes in at $9.8 billion, $9.860. So, $9.860, this is basically a 15% margin, okay?
Like with some of these companies, like I don't have the expectations at the back of my head. So, EPS came in at $3.83. Uh, let's see.
Right, right, right. Okay, so this is really the effects of the payments around their lawsuit, right? So, in August 2026, we announced an agreement with 52 attorney generals to resolve litigation related to teen mental health and social media, which provides for payments of up to approximately $18.59 billion. So, third quarter 2026 cost and expenses includes a $9.9 billion charge related to this agreement.
Excluding this charge, income from operations would have increased... Oh, wow, okay. So, basically, their income from operations would have been $22.8 billion. So, if you take $22.8 billion, you divide it by... I mean, then in that case the operating margins is pretty similar, I would say, divided by 64,601.
Yeah, yeah, around 35% operating margin. I think that is more or less in line with what we were expecting. Diluted EPS would be around $7.40 rather than 385, right? Because that would have increased by another $3.57. So effective tax rate would have decreased by 7 percentage points to 17%. That's fine. So, okay. Wow.
I'll see how we can adjust for this in the numbers later on. But I will keep this here and then, uh, let me just put a note page. $9.9 billion in legal settlement. I'll come back on this later on as we probably listen to the earnings call. So,
ad impressions increased 10%, average price per ad increased 14%, revenue, yes, cost expenses, cash flow, cash flow from operations was 32.7. Free cash flow was -2.2 billion dollars. Wow, okay. So, this is not surprising. I think last quarter we already know that Meta was turning free cash flow negative. Let's take a look. So, we are basically two. Yeah.
So if you take a look at the numbers over here, you can actually see free cash flow was pretty strong and then after that, you know, as the data center build up continues now we are turning free cash flow negative. This is why people are starting to get a little bit worried and of course you have to remember that Meta still have that Blue Owl Capital deal that is in the sense of off the balance sheet debt that they are technically liable for as well. So that is something that also adds to this narrative, but I think today the street doesn't really care, right? Meta is up 3% on the day.
And this is what I was saying on stream the other day as well. Yes, we are starting to rely more on the debt markets, but we are not yet at the level compared to... and it's a lot more than where we were previously, but it's not yet at the level where we were during the dot-com bubble. So this is something that we just have to look out for.
And look, they are funding most of it still through their free cash flow, right? Now cash flow from operations came in pretty strong. Cash flow from operations was actually $32 billion. So technically, if they wanted to, they can just choose to stop building up, choose to stop spending on CapEx and pay off all their debt. And that will actually clear that debt in no time.
So net income has come down a little bit. Basically with this, I think with the tax rate at $7.40 of EPS, how many shares does Meta have? Let's take a look.
Let's see if I can find the actual number. So what will be their net income without the... Do we have the... Yeah, okay. So, let's see. Meta over here. Um.
I think this is a strong quarter for Meta. I think what is going to be interesting is to listen to management commentary. Uh. Okay, so.
Let's take a look here. Expected fourth quarter revenue to be in the range of 73 to 76 billion dollars. Uh, okay. I think that's a bit higher than expected, right? Well, am I... Let me just double-check this. I believe I have this somewhere.
Okay, I think I know. Okay, so the initial consensus around Meta's Q4 revenue was around 73 billion dollars. So, when they reported 73 to 76 billion dollars, I believe that's why the streets were happy with that number because the midpoint of their guidance basically beats the streets' expectations, okay? Um.
And they were expecting... Oh, so the general expectations around their EPS numbers is between $3.56 if you're accounting for the legal settlement, $6.77 if you are adding it back in. So, what mattered to all of us is that if you add back in the legal settlement, EPS would have come in at $7.40. So, it was a beat across the board and they sort of raised guidance, right? Because the expectations was $73 billion, expecting 73 to 76, midpoint above that. So, that is likely why we are seeing the kind of results that we are seeing.
So, I think from this result it actually shows you that the AI trade is still humming along pretty well. I mean, companies are increasing their profit margins. And for me, like I've said all along, right? That the best way to play the AI trade is through some of these hyperscalers.
While I personally am not going to be waiting for Anthropic's and OpenAI's IPOs, largely because at the moment I see them primarily as a subscription-based business. And I have still yet to see how they can extract the value out of their AI software. Yes, okay, basically, you know, you can use Claude to do many things within the enterprise, but ultimately I am still just paying some sort of a subscription based on token usage.
And it's basically that curve where, you know, does token usage get to that critical mass point where even if token cost eventually starts to come down, revenues actually hold up for them. Whereas, in terms of Meta, in terms of Google and companies like this, they are much less reliant on purely just a subscription-based business to their frontier models in order for them to generate value for shareholders, generate value for the companies out there.
Because for the companies that they actually work with. Because like for example, with Meta, some of the advertisers that they're working with, yes, you know, Meta Muse is fantastic, but at the same time, Meta's AI is also what is powering their ads platform. So, as they can see more efficient advertising, then of course, generally you're going to see the ad impressions actually rise and the average cost per advertisements actually rise.
So, that's why you're seeing over here, average price paid per advertisement increased 14% on a year-over-year basis, right? Of course, this is slower than the overall revenue growth. So, I'll break down the actual segments of the revenue later on, but you are seeing that look, there is a way for them to monetize AI.
Then the argument will become is whether or not this 14% is justified, right? So, I think we are seeing most of the after-hour moves actually hold. We have Meta up 4%, Tesla up 1.5%. We're a couple of minutes away from the earnings call. I'll just be paying attention to whether or not we get the call starting soon because yesterday I got... Well, basically, I got my first revenue sharing like claim thingy because of the music from SpaceX's earnings. Okay.
Let me see. Okay, we are still in the music phase. So, when they start speaking, I will jump in. Okay. Fantastic.
At this time, I would like to welcome everyone to Meta's third quarter 2026 earnings conference call. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question and answer session. If you would like to ask a question, please press star one on your telephone keypad. To withdraw your question, please press star one. We ask that you limit yourself to one question, and this call will be recorded. Thank you very much. Rob Baveja, Meta's director of investor relations, you may begin.
Thank you. Good afternoon, and welcome to Meta Platforms third quarter 2026 earnings conference call. Joining me today to discuss our results are Mark Zuckerberg, CEO, and Susan Li, CFO. Our remarks today will include forward-looking statements, which are based on assumptions as of today. Actual results may differ materially as a result of various factors, including those set forth in today's earnings press release, and in our quarterly report on Form 10-Q with the SEC. We undertake no obligation to update any forward-looking statement.
During this call, we will present both GAAP and certain non-GAAP financial measures. A reconciliation of GAAP to non-GAAP measures is included in today's earnings press release. The earnings press release and an accompanying investor presentation are available on our website at investor.fb.com. And now, I'd like to turn the call over to Mark.
All right. Hey, everyone. Thanks for joining today. This has been a busy and exciting few months. Our family of apps is now used by 3.64 billion people each day, and we are seeing all-time highs in engagement on both Facebook and Instagram driven by strong growth in video. Threads has now overtaken X in monthly actives and continues to grow quickly. WhatsApp is hitting all-time highs in messaging and is becoming an increasingly important platform for agents as well.
In the last few months, we have had several major milestones towards our goal of delivering personal superintelligence to everyone. We had one of our most successful launches ever with the Muse personal agent. We shipped Muse Wonder as our first frontier model, and we shipped several new glasses and gadgets with Muse integration, including coming soon to our new audio glasses.
We've been discussing our vision for personal superintelligence for more than a year now. We want to help every person have an extremely capable personal agent that understands your goals and can help you achieve them and improve your life in whatever ways you want, whether that's improving your relationships, helping you save time, save money, build a business, create new things, or anything else that you can dream of. Muse is the product form of this vision.
So now, we have the foundational technology in place to deliver this. There is still a lot of work ahead, but our initial position is very strong. In just a month and a half, more than 10 million people in the US and Canada have set up Muse personal agents. Growth continues to be strong, and we have been number one in the App Store almost every day.
Engagement and retention are also off to a strong start. About 25% of people who have set up Muse use it on any given day. For our earliest adopters, the longer that they use Muse, the better the retention seems to get. So, to put this in perspective, this is more than twice the level that Threads started at, or where we believe that other AI apps started at, as well.
The most common use case for Muse is shopping, with more than a quarter of people who are using Muse engaging in this with an average purchase size of over $200. Plaid told us that they hadn't seen activity like they're seeing with their connector on any other service before. And in the first week, we had to work with Stripe to raise credit limits, since the volumes were above what any of us expected.
Beyond shopping, the most common connectors are for personal productivity and connecting Muse to your other services like email and calendar. And this shows the importance of the Muse secure VM that we've built to provide state-of-the-art privacy and security, and we are looking forward to following up soon with a Muse confidential VM, which will make it so even Meta cannot see your content.
We've also seen that Muse Wonder improves quality, engagement, and retention further, which suggests that as we continue to improve intelligence, Muse will perform better and better over time. Now that Muse has clear product market fit, we are entering a new phase and a more operational phase that is focused on scaling it.
The goals for this next phase play to many of Meta's strengths. We need to do three things: scale the product, grow revenue, and optimize compute costs. We expect to grow Muse to serve billions of people. This is both our mission and our strategy.
It's our mission in that we believe in putting the power of personal superintelligence in everyone's hands. We believe that that is the path to ensuring a positive future for everyone with individual empowerment as the source of prosperity and balance of power as the foundation for safety. On a values level, we believe that delivering this capability widely is the best thing that we can be doing.
This is also our strategy because we think that billions of Muses in a network at scale will be able to get more done for people and businesses by working together. So, we'd like to create a large-scale Muse network as quickly as possible.
From a business perspective, the average revenue per American user for our existing family of apps users is several hundreds of dollars annually, and we expect that Muse will reach an even higher level over time due to the amount of commerce that we already see taking place. This will take some time, but we're starting to work on this now.
Our business model will have a few components. For most people, the product should be free, and we will build commerce tools so we get paid a fee based on the transactions that we help facilitate. We expect this will be paid for by businesses. For people who want the most advanced models or to use extra tokens, they subscribe directly and pay.
For business owners who want additional features to use Muse to grow their businesses or for companies that want people to use Muse for work, they will be able to subscribe directly and pay as well. And for those who want Muse to help improve the personalization and quality of their content and ads across Meta, they will be able to connect to those experiences as well. Just to clarify, the average revenue that I was talking about before is the family of apps revenue for our existing Muse users.
On the compute side, we are building out the capacity to serve more than a billion people over the next few years. In the near term, Muse is very compute intensive to run, but we expect to improve its cost efficiency by more than 10x in the next year, which will enable it to be profitable once the revenue lines are at scale.
If compute efficiency continues improving quickly for another few years, which we think is highly likely, then Muse will be highly profitable over multiple years. In the near term, the strength of our core business enables us to scale the Muse network before then, and we believe that we are the best positioned company to pursue this strategy.
One other note on compute economics is that since training is a large fixed cost, now that we have leading models, we want to serve as much high-margin inference demand as possible. That is why we are building the Meta enterprise platform led by CJ Desai.
Muse Wonder and even more advanced models that we're training can serve additional high-margin services beyond the Muse network, including our API, Muse Code, and other enterprise services. A fundamental feature of our business is that we have many good uses for this compute.
It is difficult to forecast the exact growth rates for the Muse network, for our enterprise platform, and for how Muse Wonder and beyond will accelerate our core business. But through building up multiple ways to deploy these advanced models, we increase the chances that one or more of them significantly outperforms our expectations.
Beyond serving other businesses, we have not yet found the limit of how effectively we can apply compute to accelerating our own core business. We have implemented an eval with Muse Wonder to improve how effectively it can autonomously improve our recommendations and ad systems. And as our upcoming models get even more advanced, I expect that we're going to see an increasing rate of improvement in these systems.
All right, let's talk about the models now. Muse Wonder is state-of-the-art across many dimensions that matter for personal superintelligence. This validates the importance of training models ourselves to deliver the best experience.
In addition to Muse, we have also seen that engagement per person in Meta AI has more than doubled since April when we started rolling out our new models. While Muse Wonder has been our most important release, I've been very pleased with our pace of execution here.
In the last few months, we've shipped Muse Spark 1.2 and 1.3, Muse Code, two open weights models with Muse Glimmer and Muse Spark Open, and also Muse Voice.
Going forward with the field advancing quickly, we plan to focus on both pushing the frontier of intelligence and distilling those models into much smaller and more efficient product models that will be optimized for personal agents. My view is that we will be able to build superintelligence through scaling, so we are methodically working our way up the ladder there.
We are already working on larger models after Muse Wonder, and they're going to be very impressive and capable. We're also working on new smaller models that are coming out soon that will deliver Muse Wonder-level intelligence more efficiently.
Moving on, our work on personal superintelligence is now also the focus of Reality Labs. Muse is coming to Meta glasses starting next month, and that will be an important step towards this. People already love many features of the glasses already: music, calling, capturing moments, hearing assistance, and more.
But over time, we expect Muse and having your personal superintelligence with you throughout the day will be the most important part of the experience. Muse is also central to the business model for the glasses.
Again, our mission and strategy are to serve everyone here. We believe in putting the power of this technology in everyone's hands. So we have priced our devices not to make a large profit on the hardware, but to build a large software and superintelligence business on top of them over time.
Glasses sales continue to grow rapidly, and now with Muse, we expect that paid subscriptions will increase and that this business will begin improving meaningfully from here.
I also shared at Connect that we are changing our approach to virtual reality to only ship new devices when we have major breakthroughs like we had with the Meta VR glasses this year, which of course are the first VR experience ever in a pair of glasses.
I continue to believe that the path to delivering holographic experience of bringing together the physical and digital worlds in everyday glasses will come through VR first, but we will pace this aspect of our investment more to focus on delivering personal superintelligence first.
So, with these factors combined, we expect that the P&L of Reality Labs to improve from here with operating loss coming down to around $15 billion next year and trending towards profitability from there.
Before we conclude, I want to spend a moment on the sheer velocity that we are seeing. An important part of our approach has always been to move fast so that we can learn quickly and serve people better at scale. But recently things have accelerated. In the last year, we shipped edits and it is already past 50 million actives.
New apps like Forum and Seller were built by small teams and are scaling quickly to serve power users. Muse went from an idea on a whiteboard to a successful product in just about 8 months. And we're about to ship Muse Charm, which will have gone from concept to a hardware device in people's pockets in less than a year.
I am proud of what we are building. People have all kinds of different opinions about the future, but at Meta, we believe that the future is for everyone and that putting power in people's hands is how we get there. I think that this matters.
There needs to be a company out there that represents these ideas. But this isn't just about ideas, it's also about execution. So, I am grateful for everyone who is working so hard to deliver all of this to billions of people around the world. And as always, thank you to everyone who's on this journey with us. And now, here's Susan.
Thanks, Mark, and good afternoon, everyone. Let's begin with our segment results. All comparisons are on a year-over-year basis unless otherwise noted. Q3 total family of apps revenue was 64.2 billion dollars, up 26%. Q3 family of apps ad revenue was 63.1 billion dollars, up 26% on both a reported and constant currency basis.
In Q3, the total number of ad impressions served across our services increased 10%. Impression growth was healthy across all regions, driven by growth in engagement and users, as well as ad load optimizations. The global average price per ad increased 14% year-over-year, driven by ad performance gains and improvements in macro conditions relative to Q3 of last year. This was partially offset by strong impression growth, particularly from lower monetizing surfaces and regions.
Family of apps other revenue was 1.1 billion dollars and grew 59%, driven primarily by WhatsApp paid messaging and subscriptions revenue. Within our Reality Labs segment, Q3 revenue was 443 million dollars, down 6% due to lower Quest headset sales, which were partially offset by continued strong growth in AI glasses revenue.
Moving now to our consolidated results. Q3 total revenue was 64.6 billion dollars, up 26% on both a reported and constant currency basis. Q3 total expenses were 51.6 billion dollars, up 68% compared to last year, and included a 9.9 billion dollar charge related to the August 2026 multi-state attorneys general agreement.
Year-over-year growth was primarily driven by legal related costs, infrastructure costs, and third-party AI token costs. The growth in infrastructure costs was driven by higher third-party cloud spend, depreciation, and data center operating costs. We ended Q3 with over 66,000 employees, down 12% from Q2 following the May 2026 head count reduction.
Third quarter GAAP operating income was 13 billion dollars, representing a 37% decline year-over-year and a 20% operating margin. Excluding the Q3 legal charges related to the aforementioned legal settlement, our third quarter operating income would have increased 11% year-over-year.
Our tax rate for the quarter was 24% above our outlook range, primarily driven by the non-deductible portion of legal expenses. Net income was 9.9 billion dollars or $3.83 per share.
Capital expenditures, including principal payments on finance leases, were 34.9 billion dollars, driven by investments in servers, data centers, and network infrastructure. Free cash flow was negative 2.1 billion dollars. We ended the quarter with 82.4 billion dollars in cash and marketable securities and 83.7 billion dollars in debt.
Turning now to the business performance. There are two primary factors that drive our revenue performance: our ability to deliver engaging experiences for our community, and our effectiveness at monetizing that engagement over time. On the first, we continue to see significant gains from our content recommendation initiatives.
On Instagram, global time spent per user reached an all-time high and grew double digits year-over-year in Q3 with strength across both Reels and feed. On Facebook, Reels time spent reached all-time highs both globally and in the US with both growing double digits year-over-year. Threads also continued to scale with time spent per user growing over 20% year-over-year.
Engagement gains across our services continue to be driven by improvements we're making to the relevance, personalization, and timeliness of the content we recommend. Investments into our recommendations engine across retrieval and ranking have continued to yield positive results on Instagram and Facebook.
For instance, on Instagram Reels, our relevance launches in Q3 increased Reels time spent by over 4%. Driven in part by our upgraded model architecture that ranks sequences of Reels at a time instead of evaluating each Reel independently, as well as improvements we made to add richer watch time signals.
On Facebook feed and Reels, we completed the global launch of a new generative retrieval architecture which, inspired by LLMs, labels pieces of content with semantic representations and generates potential recommendations helping our systems surface relevant new posts before they've accumulated significant engagement.
This new architecture is now rolled out to roughly 1/4 of unconnected feed views and half of Reels views and increased Reels watch time by 1%.
Our recommendations work is also helping us deliver fresher and more personalized content. Nearly 45% of recommended Instagram Reels are now less than 1 day old, up more than 11 percentage points from Q2. On Facebook Reels, we doubled the amount of history our ranking model can consider, driving a 1% increase in Reels watch time in the US.
LLMs are also helping us understand content more deeply. We continue to process public Instagram posts each day through LLM-based content understanding systems. These signals have improved the engagement with Instagram search results by 1% through the end of this quarter.
This quarter we also began moving our content understanding models onto our first-party Muse model family. In offline evaluations, these models are outperforming our prior production models and are reducing per-unit content processing costs by more than 80% across Instagram, Threads, Facebook, and ads.
Beyond improving quality, owning more of this stack gives us greater flexibility to develop models around the signals that matter the most for personalization.
Turning to the second driver of our revenue performance, increasing monetization efficiency. We see significant opportunity to continue improving performance for the businesses who use our services. Within our ad systems, we are continuing to drive performance gains as we deploy more predictive models that understand users more deeply and deliver ad experiences better aligned with their unique preferences while also making our infrastructure more efficient.
Last quarter we introduced Meta Generative Recommender, which uses LLMs to reason across ad content and user preferences to predict the best ad for each person. In Q3, we launched this approach to generative retrieval on Facebook feed, driving a nearly 2% increase in conversions across targeted segments.
In addition, we expanded our GEM model for ads ranking to additional services surfaces, driving a 1.7% increase in conversions for offsite conversion campaigns and a 9.6% increase in app ad conversions. And we continue to scale our retrieval systems efficiently. In Q3, improvements to Andromeda, such as scaling model complexity and throughput, increased conversions by 1.4% on Facebook.
We're also leveraging AI to make it easier for businesses to manage campaigns, develop creative, and act on performance opportunities. Meta AI business assistant is becoming a more proactive and capable partner for advertisers. We estimate that advertisers who adopted its recommendations drove almost 5% more conversions at the same cost per conversion and increased their advertising investment by 9% compared with similar non-adopters.
We are rolling out additional capabilities, including creative insights, stronger business context, and more agentic actions. These features will allow marketers to plan, create, and optimize campaigns, as well as schedule tasks and alerts through a conversational experience with approvals and previews that keep them in control.
I'll share one example of how Meta AI business assistant is empowering our advertisers. Simply Organic Maple, a family-run maple syrup producer in Vermont, used Meta AI business assistant to improve its campaign messaging. After the business assistant recommended testing longer, more descriptive advertising copy, the business saw a 57% increase in purchases and a 36% reduction in cost per purchase. Its strongest revised ad delivered a 3x return on ad spend, a 52% improvement from the original.
Adoption of our generative AI ad creative tools also continues to scale. In Q3, nearly 10 million advertisers used at least one of these tools. Advertiser adoption of our video generation features grew 25% quarter over quarter, driven in part by ready-made videos that reduce generation time to just a few seconds from a single image upload.
We're also expanding the opportunity at the intersection of creators and advertising. Partnership ads, which allow advertisers to run ads with creators, brands, and other businesses, continue to scale rapidly in Q3. Our partnership ads product grew over 97% year-over-year and surpassed $15 billion in annual revenue run rate.
In Q3, we also completed the global rollout of Creator Marketing Hub to all advertisers, bringing creator discovery, content organization, and partnership ad activation into one place with more than 10 million creators now discoverable. We are integrating Meta AI business assistant into the hub so advertisers can conversationally search for creators, identify content to use in partnership ads, and get guidance across their creator marketing workflows.
Outside of ads, we are also building out additional ways to monetize engagement across our ecosystem. In September, we announced the global expansion of Meta One, our unified subscription service for consumers, creators, and businesses. Meta One is rolling out gradually with more than 50 features across Instagram, Facebook, and WhatsApp, including Meta Business Agent and Meta AI, and has surpassed 15 million subscriptions and trials to date. The core experience across our apps and Meta AI will remain free, while subscriptions offer more specialized capabilities and expanded AI usage.
And beyond monetizing engagement across our existing services, we are building new experiences and revenue streams around consumer agents. Mark highlighted the impressive early momentum with Muse. Users have found many reasons to use Muse, with the top three categories being shopping, personal productivity, and creativity. We're encouraged by the breadth of use cases, with shopping in particular being used by over 25% of Muse weekly actives.
While our near-term priority is to grow adoption and expand Muse reach, we see multiple paths to monetization beginning with subscriptions. We also see a significant opportunity to extend Muse to small businesses. Based on a recent user survey, we estimate that at least 500 million self-identified small businesses use Meta's family of apps to reach customers and generate revenue.
Muse could help these businesses acquire customers and operate more efficiently while helping people complete transactions. Over time, consumer and business agents could connect and transact directly, creating opportunities around paid usage, commerce, and business services.
Alongside growth, we are focused on building sustainable unit economics. Muse's computing intensity means that expanding its user base will require significant investment, and we expect total serving costs to rise. At the same time, we expect to improve compute cost efficiency by more than tenfold over the next year, driven by better utilization of off-peak capacity, more efficient models, and inference optimization. As revenue scales, these efficiency gains give us confidence in our ability to build a highly profitable business over time.
Moving now to our financial outlook. We expect fourth quarter 2026 total revenue to be in the range of 73 to 76 billion dollars. Our guidance assumes foreign currency is an approximately 1% headwind to year-over-year total revenue growth based on current exchange rates.
Turning to the expense and CapEx outlooks. We are raising our expense outlook to incorporate the 9.9 billion dollar charge related to legal proceedings recognized in Q3. We now expect full year 2026 total expenses to be in the range of 175 to 179 billion dollars.
Excluding the Q3 legal charge, we continue to expect 2026 operating income to be above 2025 operating income. At a segment level, we now expect Reality Labs losses to decline compared to 2025 to a range of 18 to 19 billion dollars.
We anticipate 2026 capital expenditures, including principal payments on finance leases, to be in the range of 135 to 145 billion dollars, narrowed from our prior outlook of 130 to 145 billion dollars. Absent any changes to our tax landscape, we expect our fourth quarter 2026 tax rate to be between 15 and 17%.
Additionally, we continue to monitor active legal and regulatory matters that could significantly impact our business and financial results. For example, we continue to see scrutiny on youth-related issues in several markets, and have a number of youth-related trials scheduled in the coming months in the United States.
Looking beyond 2026, we are still developing our budget and capacity plans for next year, but we expect to continue investing aggressively to support both our core business and emerging AI initiatives. Our current capacity and what is scheduled to come online gives us the flexibility to allocate resources across model training, core business growth, and the development and support of new products.
Our AI investments are already delivering substantial incremental advertising revenue through stronger engagement, better recommendations, and more efficient monetization. Building on this progress, our MSL investments are creating the technology and infrastructure needed to unlock significant new revenue opportunities and strengthen capabilities across the business.
While these investments will take longer to generate returns than those in our core business, the breadth of monetization opportunities and internal efficiencies gives us confidence in their long-term potential.
Looking ahead, although we are not providing a full year 2027 revenue outlook, the benefits our AI investments are delivering to the core business, combined with emerging opportunities from model advances and product launches, position us to sustain strong overall revenue growth throughout 2027.
Separately, we remain focused on improving Reality Labs profitability. As Mark mentioned, we expect losses to narrow next year to around $15 billion as we reallocate investment towards other priority areas, primarily AI.
Finally, today we announce plans to create a new class of non-voting capital stock, which will be distributed to all existing shareholders through a stock dividend. For each existing share held, stockholders will receive one share of the new non-voting stock, effectively resulting in a two-for-one stock split. We expect trading to begin in late January 2027.
In closing, our business momentum continued in Q3 with strong execution across our core ads and engagement initiatives, and we're also excited by our progress towards bringing personal super intelligence to everyone. With that, Kristelle, let's open up the call for questions.
Thank you. We will now open the lines for a question and answer session. To ask a question, please press star one on your touch-tone phone. To withdraw your question, you may press star one again. Please limit yourselves to one question. Please pick up your handset before asking your question to ensure clarity. If you are streaming today's call, please mute your computer speakers. And your first question comes from the line of Brian Nowak with Morgan Stanley. Please go ahead.
Thanks for taking my questions. I've got two on the news, one on enterprise. Maybe I'll first congrats on the success of news, but as you analyze the user behavior in the product, can you talk with us a little bit about any of the constraints to the slope of forward user growth that you're most focused on addressing, whether it's capabilities, model efficiencies, capacity, etc. Just so we understand what are the limiting factors to forward user growth that you want to address.
And the second one is on enterprise. Mark, can you just sort of walk us through your vision for the Meta enterprise product offering and the key value drivers you hope to provide for the enterprises adopting the service.
Yeah, I think I can talk about both of those, and then Susan can jump in with anything else. All right. So, right now for news, we are, and one of the things I pointed out is, you know, we have more than 10 million people in the US and Canada who have set it up, but I think one of the biggest constraints so far is that because this is a compute-intensive service to run, we have not launched it globally yet. That's partially because of wanting to optimize it for each place, and there's some work around connectors for that, but it's partially that we want to make sure that we do this efficiently.
So I mean, there's pros and cons there. The pro, of course, is that that just makes it even more impressive that you have 10 million people using it and signing up to use it in the first 6 weeks because those are all kind of Americans and Canadians in a small number of markets. So, if we actually did roll it out more broadly, then you would expect that number to be significantly larger than what we're seeing today. But then on the flip side, we actually have to go do the work to bring the efficiency in line to make that make sense.
And so that's a big part of it that we need to do. The compute efficiency work is going to be important on this, also because, you know, we want to make sure that, you know, we've built enough compute to be able to serve what we think is going to be a billion plus people if we can achieve certain compute efficiency targets, but we obviously have a lot of work to do there as well. And that'll be one of the limiting factors over the next year.
We want to build out the business around it. So, I mean, I talked about that and I think doing that in tandem with bringing down the compute or increasing the compute efficiency is going to be important for getting this to work from a business perspective, which I'm optimistic about, but I think that's one of the things that we need to do. And then there's just continuing to increase the value in the product, which I think we will largely do by continuing to develop new models.
That's why I mentioned that, you know, we launched Muse first on Muse Spark, and that's what is currently the default across the free tier. And we rolled out Muse Wonder, and people can access that in the paid tier today. And we know that it performs a lot better, so we're working on getting a more efficient version of Muse Wonder that we'll be able to roll out more broadly, but you can imagine just as we iterate forward and, you know, two, three models into the future when you have significantly higher intelligence and capability to solve many more problems for people and the ability to deliver that at a much more affordable price point, I think that's going to just unlock a lot more value to people. So, we're very excited about that.
Transitioning to talking about the enterprise piece, because I think that these two pieces are, there is some connection here. In that, you know, the main enterprise offering in the near term is going to be the model API and Muse Code, and we have business agents that we've been scaling for a while, and each of those three things is growing quite quickly. So, we're excited about that.
Over time, we would like to make it so that we have a whole kind of business network of Muse agents as well. And there's the business agent, which we've talked about on previous earnings calls for businesses to help out with sales and customer support. But what we see is a lot of people are actually using Muse today to help run, especially small businesses.
And we think that there's both an opportunity to help create an agent that people use to really enable them to automate and do a lot of the parts of spinning up a small business that are just kind of difficult and time-consuming, but aren't the core thing that a person is trying to do. So we think that's a big deal. And then making a version of Muse that people can bring into different corporate environments and work there, we think it's going to be a big deal.
And then, beyond that, building what I refer to as the Muse network, which is making it so that Muse is not just a kind of single-player game where you have your Muse, but making it so that your Muses can work with your friends and your families. And if you want to buy something, it's not just navigating to that business's website, it's talking to their agent. I think that getting that all to work well together is going to unlock tremendous value at scale.
So I'm very excited about all of that. I think the enterprise use is going to be a big part of it, but it's also going to kind of have this very valuable boost in the utility for consumers, too. So, I'm very excited about both of those.
I would add to Mark's comments on enterprise. AI is creating huge amounts of disruption in enterprise today. And, you know, we don't, from what we can see, we really don't believe that traditional enterprise companies have this locked down. And in fact, you know, some of the leading companies in this space today didn't exist a few years ago.
Those companies made large investments in model development, and we've done the same, and we have increasingly competitive models to bring to bear for the benefit of all of our users, including small businesses and including enterprises. And already today we have 500 million businesses using our products. So, we really believe that AI is going to reshape how businesses operate. We expect that businesses of all sizes are going to use AI agents across software development and productivity, sales, marketing, consumer customer service, etc.
We have differentiated assets to serve this market, bringing together models, infrastructure, global distribution, and long-standing relationships with businesses. And it's our hope that this will be able to be a new diversified recurring revenue stream for us that will be able to support multiple business models, including subscriptions and usage-based API and messaging and other revenue lines. So, we expect that this will be an opportunity that will develop over multiple years, and we intend to scale our investment behind demonstrated customer demand and product market fit in this area.
Your next question comes from the line of Eric Sheridan with Goldman Sachs. Please go ahead.
Thanks so much for taking the questions. Maybe just the first one building on Brian's question. When you think about the friction points around the agent commerce, how do you guys as a company think about solving for friction that's rising where there are aggregators or suppliers that don't want agents to interact with their platforms or their services, and how you think about lowering that friction or creating the right balance between the existing services that consumers access today relative to how this might all be changed or altered by agentic behavior over the medium to long term.
And the second one would be with the announcement of the new class of shares tonight with earnings, any updated thoughts broadly about how you think about sources of capital and optimizing your capital structure to make sure you invest the proper amounts to capitalize on the opportunities that AI are presenting in the next couple of years. Thanks so much.
I can answer the first one. So, okay. So, the question, sorry, what was the first question there?
Friction points around agentic commerce.
Got it. All right. Yeah, so right now what we're seeing is that overwhelmingly businesses are very excited about enabling this because they recognize that it's going to be a new opportunity for them not only to increase the sales and the quality of the experience for existing customers, but also potentially to bring in new customers.
So now across the whole business ecosystem, different people will kind of adopt this at different paces. And how our strategy evolves on that to develop the ecosystem will depend on how that goes, but overall, I would say we've been very positively surprised by how quickly I think a huge amount of businesses have wanted to, you know, not only figure out how to make it so that Muse can use their services more effectively, but have reached out to us to integrate around it.
So, I view that as a very good sign. And, you know, obviously, not everyone wants to be an early adopter. That's fine. We'll work with folks on the terms that makes sense for them, but I do think that over time this is going to be quite valuable for everyone to make this work well, and I think we're on a good trajectory on that so far.
Yeah, I would add to that. And I will also answer your second question and just say, you know, I think agents are going to be an important part of, you know, how reality evolves. And I think everyone is going to have to adapt to what that means, the new age of agentic commerce.
We obviously had a lot of businesses who are very excited to partner with us. There are also businesses that are understandably focused on protecting customer relationships. And we expect the market, you know, I think ultimately to evolve away from agents attempting to navigate every digital experience like a human. And I think over the long run, this will move towards more permissioned, structured interactions.
And that will include authenticated APIs, approved agent partnerships, or direct communications between a consumer's personal agent and a business's own agent. And we're helping establish the standards needed to support that model. Earlier this month, we previewed the personal agent protocol, an open standard across Muse and Meta business agents that we are developing with industry partners like Sierra, Walmart, Stripe, Shopify, etc.
The protocol's intended to define how a consumer's personal agent can interact securely and reliably with the business's agent and preserve appropriate controls for both the business and customer. So, again, you know, I think adoption will likely be uneven and will take time. And in the near term, we expect many experiences will remain AI-assisted rather than fully autonomous.
But, you know, as trusted integrations develop and agents demonstrate that they can generate incremental value and demand and operate safely, we expect that more and more businesses will be excited to participate.
Your second question was about the announcement of the new class of shares. So, I should say, you know, we created this new class of shares with the purpose of creating a publicly traded non-voting stock usable for, you know, capital raising, employee compensation, acquisitions, other general corporate purposes while preserving the voting power of existing class A and B shareholders.
We believe the share class will maintain our founder-led structure. It will expand our funding tools and give us flexibility to invest behind the unprecedented momentum we're seeing across every part of our business driven by AI. We don't have any specific financing plans to announce at this time, but we thought it was prudent to establish the appropriate capital structure framework to ensure that we have access to a diverse pool of funding sources.
Your next question comes from the line of Doug Anmuth with JP Morgan. Please go ahead.
Great. Thanks for taking the questions. I have two. First, just wanted to follow up on your last comment, Susan, just around as Muse evolves and more businesses adopt agents, just wanted to get your sense of how things kind of change and shift as you go to more permission-based and agent-to-agent transactions and like what does that really unlock over time?
And then secondly just on kind of expenses and CapEx thinking about 27. I know you're not really there yet, but if I go back to some previous comments you've talked about maximizing compute in 26 and 27 and then having more flexibility in 28. A year ago you talked about kind of notably larger dollar increase in 26 than 25 and then you've also used kind of growing operating income on an annual basis as well. So just curious if there's any of those that you can kind of tie or to help us think about 27. Thanks.
Thanks, Doug. So on your first question about how the agentic ecosystem evolves, you know, I wish I could sort of predict that more perfectly. You know, and I think it will take some time and again I think it will be a little uneven, but I definitely think that, you know, when I use Muse today, it's clear that there are a lot of parts of the experience that would be even better if Muse was able to interact more seamlessly with business agents on the other side.
For example, when, you know, if Muse has to call a business on my behalf, I think it would be easier for the business if that call was handled by business agents that were representing it. And I think also one of the things that is really distinctive about Muse and one of the things that makes the product experience so great is the degree of proactivity that it exhibits on my behalf, right?
I can give it a task and it's going to go execute against that task for me even as it encounters some obstacles or, you know, can't quite find the right thing and needs to chart a new path to helping me look for the thing I want. And so I think that sort of dimension of proactivity exhibited by either consumer agents or business agents, I think are going to enable more transactions to happen or more activity to happen broadly than happens today.
Where, you know, when more actions are taken primarily, you know, when like the latent demand expresses itself versus, you know, things where I can have Muse searching for things for me on an ongoing basis, giving me regular updates, you know, on things I'm looking for, sending me 10 new books a month. And agents should be doing similar things for businesses and I think that creates sort of opportunities again for a new more proactive sort of framework for what agent-to-agent interactions look like over time.
On your second question, which is about 2027, you know, we are quite early in developing our budget and capacity plans for next year. So, you know, we are, I would say, at the point where we are both evaluating a lot of investment proposals, you know, that have attractive return profiles over different time horizons and we want to figure out how we can fund as many of those as possible within, you know, the construct of our overall budget and financial goals.
But it's very dynamic and it's still pretty early and so we don't have anything more specific or quantitative to share until we get to the Q4 call, which is typically when we share guidance for the next year.
Your next question comes from the line of Justin Post with Bank of America. Please go ahead.
Great, thanks. One for Mark, one for Susan. Mark, you've got this powerful new model on your hands and it seems like there's a lot you could do internally besides externally. But specifically to the ad business, you re-architected your systems around AI. How much room is there for improvement as you deploy that new model? How are you thinking about that? And then for Susan, you mentioned strong revenue growth next year. Just wondering if that's advertising or does that incorporate some incremental AI revenues hitting next year? Thank you.
I'm happy to take both of those. Great. On your first question, you know, I think our ads business has been benefiting for now many years from our ongoing investments in, you know, both kind of what we would have called traditional AI machine learning models and then of course from the sort of more recent generation of models.
I would say that there are three themes there that I think are worth outlining. The first is just, you know, the kind of traditional road map of performance improvements that we have in the pipeline that is what I would describe more as business as usual machine learning work. That continues to be a robust and healthy pipeline and as we are looking into 2027 again, we're not providing an outlook, but I would say that our investments in product improvements across both engagement and ads initiatives give us, you know, confidence that we have meaningful runway to grow our core business throughout 2027.
That also gets to your second question, but I should say basically I think the ranking and recommendations pipeline is healthy. The second theme I would say is that, you know, we are excited about some of the new research bets that we have as we move into sort of a more LLM-oriented AI infrastructure to help us more deeply reason about people's preferences, what they're interested in both across organic content and recommendations, and we expect that we will be able to unlock benefits to our ranking and recommendations engine from that research, too.
And then the third thing is that we expect that generative AI tools, which have already been very valuable for advertisers, will continue to grow in terms of the features that we make available to advertisers primarily through the Advantage Plus suite, but features that make it easier for advertisers to quickly and seamlessly generate whether it is, you know, creative or take an image and generate video or generate sound.
All I think in service of making the ads increasingly personalized for the person that they're being served to. So I would say we feel like there is, you know, meaningful runway across all three of those things. Your second question was about revenue growth next year and so as I mentioned I think we're both, you know, feeling good about the budget investments that we expect we will make in both core ads and engagement initiatives next year.
We're also, you know, building several new AI businesses and we expect that those can begin contributing incremental revenue in 2027 but more importantly can scale meaningfully over time and those will include monetizing, you know, our AI products through subscriptions, transactions, advertising, developing enterprise offerings as we talked about in one of the earlier questions.
So, I would say while many of these opportunities remain early and our near-term focus is really on building high-quality products and achieving product market fit and scaled product market fit, I think our track record of reaching, you know, billions of people and building businesses at scale makes us feel good that AI is going to become a meaningful source of revenue diversification over time.
Our next question comes from the line of Mark Shmulik with Bernstein. Please go ahead.
Yes, thanks for taking my questions. Mark, coming out of Connect, you know, Connect felt like we were ushering in the next era of the company, you know, and the business reorienting around kind of Muse at the center of everything. You know, is that a fair assessment of kind of how things are going internally? And, you know, and if so, please talk a little bit more about how some of the business mandates and outcomes may have changed.
And then, Susan, you know, it's interesting to kind of see the launch cadence where enterprise announcements kind of preceded some of the small business ones. And, you know, I appreciate your comments on, you know, some of what's going on there around the SMB agent opportunities. But, can you just share more details on kind of the work required to serve and scale the offerings to SMBs? Thank you.
Yeah, I mean, I can talk briefly about the first one. I mean, look, it was more than a year ago that I think I wrote the letter about personal superintelligence as the North Star for the work that we're doing here. And it basically fits with this line of work that we've done for more than 20 years at the company that is focused on empowering individuals, giving people a voice, giving people the tools to connect in the ways that they want.
And when you extend that to the work around AI now, we think that the most important thing that we can be building for this next generation of technology is basically putting the power of this technology in everyone's hands, which is the personal superintelligence vision. So, we've been working on this for a while.
And this is when we did the reboot around MSL and building the lab, a lot of it was this idea that we need to build models specifically to be good as personal agents, that we'd be able to build the best experience by owning and building and designing ourselves the end-to-end technology. So, that includes the models and then the agents. And this has kind of been in the works for a while.
I mean, the vision around the glasses has also been this. I mean, we've wanted to build the next generation of hardware platforms so that we can deliver the best end-to-end experiences to people. And I mean, that's starting to come together as well. And I think we'll even more over the coming years. So, I mean, these are long-term bets. I feel like this is what we're here to do is make long-term technology bets and design the best experiences end-to-end that we can for billions of people around the world.
And I think as long as we continue to do that, then, you know, over time the business takes care of itself as long as, you know, we continue executing well, which I'm really proud of the team for how we're doing on that.
Thanks, Mark. Your second question was around Muse for small business. So, we launched that, which you can think of as an expansion for Muse, in late September. And, you know, rather than a separate product, it's kind of a set of new skills and connectors inside the existing Muse app aimed at helping SMB owners run operations, you know, find new customers.
It connects to the business's, you know, for example, Instagram account analytics or Facebook pages or Meta ad accounts in a few clicks along with dozens of third-party connectors that businesses already run on, like Shopify, QuickBooks, Canva, etc. It already understands what businesses sell, its brand voice, what customers are asking for, and Muse can take actions, including analyze ad and content performance, draft campaigns, customer responses, flag issues.
You know, and we are actively listening to our SMB users to understand what their top use cases are. You know, we are seeing them use it for ads optimization, for direct message organizing, use it to expand their sales footprint, to keep their website current based on, you know, things like inventory and new products, to deal with employee load balancing.
So, we're pretty closely following how businesses use Muse to make sure that we are being thoughtful about new features that we release in Muse to make it maximally useful and productive for SMBs. I'd also say, you know, we have unique touchpoints to again, you know, I mentioned in my opening remarks, 500 million self-identified small businesses, and, you know, we think that many of the investments that we make in consumer products are going to carry over to help power those businesses also.
We have multiple excellent channels to surface those opportunities to small businesses globally. So, you know, this is a place where we feel like we have an existing extremely strong platform, and we're very excited about it, and we also, you know, believe the transition to AI has broadly unlocked a new opportunity for us as well.
Your next question comes from the line of Ross Sandler with Barclays. Please go ahead.
Great. Mark, you touched on this earlier, but could you elaborate a little bit on maybe some high-level metrics around the per user cost structure of Muse today, and then how you kind of see that cost curve coming down as you go forward. And what are some of the things you're doing to bring that cost curve down? Thank you.
I'm happy to answer that and Mark, you should add anything that you feel like I'm missing. You know, we don't have sort of specific numbers to share around the cost structure of Muse today, but of course it is, as Mark said, pretty compute expensive. And making sure that we are focused on compute efficiency is going to be one of the key vectors towards being able to both scale Muse and grow it into the business that we want.
I think there are a couple dimensions of achieving that efficiency. I think some of the more nearer term pieces, you know, include things like leveraging, you know, off-peak or spare computing capacity, you know, to run some sets of jobs, using smaller models. There's a lot of work to be done across different inference efficiency initiatives like all in service effectively of GPU utilization and other product efficiency work we can do also.
And, you know, I think over the longer term, you know, I think this is a place where we expect that we're going to be able to over time as LLMs scale get more tasks, you know, done with fewer tokens and also rely on AI models themselves to help us improve the efficiency of the software that we use to run the models. So, I think it's a place where we both feel like there is a lot more work on our efficiency road map that is going to help us, you know, bend the curve here and we also expect that the sort of arc of how the AI-driven technological advances are going to apply here will also help us achieve larger savings over time.
Your next question comes from the line of Mark Mahaney with Evercore ISI. Please go ahead.
Okay, thanks. I have two questions. You know, there's a bit of a playbook or there has been at Meta over the years when you surface new surfaces and, you know, you build up the years of growth engagement then you layer in...
monetization. Do you think it's sort of the similar playbook here, or does it have to be a little bit faster with news just given that the costs associated with it are materially greater arguably than launching those surfaces in the past? And then just a clarification on that commerce data point that you provided about 25% of users in the $200 or greater ASP. Are you including in commerce travel purchases, or is that just a retail data point? Thank you.
My guess, I think that we are going to work on monetization somewhat sooner on this than our normal playbook for a couple of reasons. One is just the size of the numbers that we're talking about in terms of both the costs and the opportunity, I think, make it important to make progress on that in order to be able to scale it. The other is just that such a large part of what people are doing is commerce. And to your question, that does include travel too. I think we might consider travel a different case, but travel is actually one of the other top use cases for news. It's basically people planning that out and planning different kinds of things that they would get, and they might get through our family of apps. So, I think that this is going to be an important thing for us to work on to deliver the consumer value that people want. And in doing so, I think we will be able to build a model where we get a percent of the value that we are helping to create for people. So, I mean, that's going to be very important to kind of get the math on all of this to work out so that way we can continue the compute build out that we want to build in order to get this to reach everyone at scale. And in addition to everything that Susan said in response to the last answer, I would also just add that like we just see this trend that, you know, year over year, I just expect that we will be able to deliver the same intelligence that we have now in Muse Wonder or frankly any of the current frontier models at a fraction of the cost in a smaller model a year from today. So, to the extent that that represents some kind of baseline level that people, you know, seem to, you know, that kind of is sufficient for product market fit for Muse, I think that we will just continue through kind of continued model development make progress on that efficiency. The thing that is, you know, of course, less clear is that I don't think in a year from now most people are going to want to use the current model level of intelligence because that's also going to make progress. So, I think we're going to need to work on all of these things, both the revenue pieces of it and scaling the different aspects of that, as well as progressing on the model development and all of the efficiency in order to be able to, you know, both fund and support the compute build out and reach billions of people, which, again, we want to get to that as soon as possible. Part of the reason why we're not, you know, just charging everyone up front to use this is we want to establish this Muse network, and we think that it's going to be more valuable for people at scale when there are a lot of people and businesses using Muse so that way they can collaborate and work together. I think that that's really a very important state to get to and one that we're quite excited about. And again, I don't think that there are many companies in the world that are well situated to be able to do that in the way that we are. I think we are very well situated to do that, and we plan to push on that quite aggressively.
And that concludes our question and answer session. Mr. Baveja, I would like to turn the call back over to you.
Thank you for joining us today, and we look forward to speaking with you again soon.
And ladies and gentlemen, this does conclude today's conference call. Thank you for your participation, and you may now disconnect.
I think that we are actually just in time for Tesla's call, and I think it's quite interesting because what I actually saw from Tesla is that we are actually seeing them put up a positive free cash flow despite the amount that they're spending on compute. So, let's take a look at what Tesla has to say. Let me just make sure the music is not playing.
and uncertainties, including those mentioned in our most recent filings with the SEC. During the question and answer portion of today's call, please limit yourself to one question and one follow-up. Please use the raise hand button to join the question queue. Before we jump into Q&A, Elon has some opening remarks. Elon?
Thank you. So, the third quarter was very strong, and I'd like to give a big congratulations to the whole Tesla team for working hard to make it happen. Demand is growing rapidly for our vehicles. It's exceeding our production by a significant margin. And obviously we're going to be ramping, and we are ramping production meaningfully. The biggest driver for demand is full self-driving. And today there's really no competitor for FSD in any car that you can buy. And to be clear, we're all producing and delivering as many cars as we can, and we're pushing very hard for FSD approval in additional markets. We're now approved in many countries in Europe, and we're hoping for an EU-wide approval. It's hard to predict, but maybe, you know, within a few months, but I would be surprised if it takes longer than Q1. But obviously that's not a time frame we control, but we do anticipate being fully approved throughout Europe and in many other countries. We also began production of the Tesla Semi truck in Q3 and did the launch the new Roadster demo, which was I think one of the most exciting events. I mean, arguably the coolest demo of any product ever. And that's a testament to amazing work by the Tesla and SpaceX teams. So, we've also been laying the groundwork for a lot more manufacturing capacity. We're, I think, aiming for 5 million vehicles a year by approximately 2030. And that's, I think, we've got a good shot of achieving that goal. And yeah, so, and energy demand is very strong. We're actually almost sold out of all Megapacks for 2027, and we're ramping production as quickly as possible to meet demand. We also expect gross margins on that to increase meaningfully. So, in a nutshell, after many years of sort of grind and things being fairly flat, I think we're now headed for a lot of fun and significant growth.
Is it just me? Because I'm wondering why is this not a video call like yesterday? Seeing a robot taxi, miles driven or fully autonomous miles driven scale, and it'll be hard to overstate this point. It really is an extremely fast week-over-week growth in autonomous miles. And I don't think there's any company in the world that can match our scale with autonomous vehicles as we continue to ramp. The cybercab fleet in Austin, for example, has grown by a factor of 10 since launch about a month ago. And the cumulative mileage is really, when I look at the weekly chart, it's wow. I mean, it looks like a rocket going to orbit basically. It's growing very fast. And if I recommend, if you want to try it out, you know, come to Austin and you'll see cybercabs in use at scale in Austin, as I think a lot of people saw with the Austin City Limits concert recently. So, this is really going to a very good place. And regarding Optimus, we started what I'd call early production on our high-volume line in Fremont. Now, I want to elaborate on this point because I've said this before that the growth of Optimus, it's an S-curve, but the initial part of the S-curve has a shallow slope and is somewhat bumpy because it is such a radically new product. So, the way to think about it is there's like 10,000 things that have to go right for production of the robot, and it'll move as fast as the slowest, least lucky of the 10,000. But we do have the vast majority of a million unit per line production line landed in Fremont right now. But I would expect that it takes us probably at least a year, maybe a little longer, to get to that annualized rate, to get to 20,000 units a week. So, that's just fundamentally because there's still parts of the design that are changing. Like a lot of people ask me about Optimus, that's why I think I've got to kind of sort of belabor this point, because if people don't sort of understand new technology that well or volume production of complex new hardware that well, it sort of seems like, well, can't you just make a whole bunch of these things immediately and send them out? And it's like, no, you've got to basically build up an entire gigantic global supply chain and have domestically work. And there's still some things we need to tweak about the design because you need to deliver a robot that is safe and reliable and has long duration, so it can't wear out after a month. And there's still quite a lot of work to do with regard to the AI in the robot for it to be truly useful for open-ended tasks. But like I said, I would expect that the AI, the electromechanical design and the reliability and duration, usage duration, like the usage life of the robot, I do expect these issues to be solved around the end of next year. So that's the time to expect when we're delivering Optimus to companies outside of Tesla. And I remain convinced that Optimus will be the biggest product on Earth, and humanoid robots in general will be the biggest product on Earth. So Optimus is a very big deal, but I don't want to have our expectations that we still have a lot to get through, and it's about a year or so that'll take us to get through all that stuff. But as to the best of my knowledge, there is no one that is even close to Tesla with respect to a general purpose humanoid robot. Regarding the Terafab, the Austin-based research fab is progressing quickly. We'll begin commissioning machinery in the first half of next year, and hopefully things go well, we'll make our first test chip just to see if we can make any chip at all around May or June. So that's pretty exciting. And then our aspiration is to be making some kind of useful chip at volume around Q3 of 2028. And as I said publicly on X, the Terafab I think will be the most valuable building in the world, and will probably have an economic value that will place it in the top 20 of countries. So it's an epic project, and this will be an incredibly important asset, if not necessary asset, in order to scale, especially Optimus, as we can address bottlenecks in semiconductors, whether it's memory, packaging, logic, or whatever the case may be. With regard to a potential merger with SpaceX, I think I said everything that should be said yesterday. Stepped on a sinkhole minefield there. And yeah, that's it. In conclusion, we're super excited about our autonomy and robotics roadmap. There's a lot of incredibly awesome stuff coming, and I'd like to once again thank the Tesla team for their strong execution and all of our supporters for joining us on this journey.
Great. Thanks very much, Elon. And Vaibhav has opening remarks as well. Vaibhav.
Thanks, Travis. We continued our progress on a lot of fronts in Q3. Elon already covered a lot of them. Additionally, which I'd like to add is we also started deliveries of Megapacks with our in-house LFP batteries, and for the first time, two consecutive quarters of vehicle deliveries of over 480K. On the automotive business, we exited the quarter with our highest backlog in 4 years, with deliveries constrained primarily by production. We're therefore focused on increasing production at all our factories to meet this growing demand. While we are actively working to increase production, it does take some time to get every aspect dialed in, from supply chain to manufacturing to delivery logistics. The Model Y continued to outperform in the quarter and set delivery records in several key markets. Based on the preliminary data which we gathered, we believe that it was the number one selling BEV in Europe and the number one selling passenger car in South Korea. We also set new records in key markets including Australia, Japan and Taiwan. As Elon said, FSD continues to be the key driver for demand. In Q3, over 75% of deliveries in North America included a sign-up for paid FSD subscription at the time of delivery, which is approximately 20% higher than Q2. Overall, the FSD attached rate continued to improve, reaching over 1.7 million paid customers globally, of which for the first time 50% is based on subscriptions. Given the strength of FSD in the US, we are taking extra steps to sell certain cars in Europe with some benefits to try and get more fleet out there before broader FSD approval is obtained. On automotive margins, excluding credits, increased sequentially from 16.3% to 18.1%. Improvement in margins is driven by a higher mix of deliveries in APAC. While commodity price increases and interest rate changes continue to add to our cost and create headwinds, we have adjusted the vehicle financing programs to partially offset such costs. On the energy business, we had our second best quarter of deployments of 13.7 gigawatt hour of energy storage. Energy gross margins improved from 20.4% to 33.1%. The sequential margin improvement is primarily attributed to tariff refunds recognized in the quarter and warranty true-downs resulting from alignment with a vendor on certain cell related costs to be reimbursed by them. As Elon said, the business is witnessing extreme demand from data centers, and we are doing our best to increase production, and accordingly we'll see improvements in ASP and further improvements in margins as we move forward. Services and other margins improved sequentially from 14.1% to 15.9%, an all-time high. This was the result of strength across several of the businesses in services and other, including supercharging and insurance. The robotaxi business, which is part of services and others, continued to grow with the launch of cybercab rides in Austin. Accumulative unsupervised miles 8X in the quarter, and we had weekly double-digit growth throughout the quarter. We're taking all the learnings from our Austin expansion to help us prepare for broader cybercab launch in other markets. Our priority is progressing the density and expansion of the network through vehicles that provide the best experience, like the cybercab. We'll also continue to augment the fleet with other variants over time to address the terrestrial transportation needs of the total population. I'd like to stress that what we are trying to do is literally take over every part of terrestrial transportation. Not just getting people, and we're starting with getting people from one place to the other, but we want to basically own this transportation of any kind on Earth. We're in a huge investment cycle to set us up for the future. Therefore, as previously guided, operating expenses continue to increase sequentially. Most of the increase came from significant research and development related activities, including SI development costs such as depreciation from additional compute, chip development, and pre-production ramp costs for new products like the Optimus, Megapack 3, and Semi truck. This trend of growing OpEx would continue till we commercialize these new products. Net income was impacted from a mark-to-market reduction of our SpaceX holding of 142 million, which was offset by a Bitcoin mark-to-market gain of approximately 290 million. We continued our efforts to manage cash efficiently and responsibly. We ended the quarter with positive free cash flow of over 1.5 billion, and an overall cash and investment balance of over 45 billion. We're revising our CapEx guidance to be approximately 20 billion for the current year as a result of managing our capital deployment efficiently, and plan to fund it primarily from our cash flows from operations. Additionally, during the quarter, we secured capacity to borrow up to 30 billion to support the expected growth in CapEx. Our confidence in the cash generation capacity of our business and the investments we're making drove our belief to secure shorter-term financing. We don't plan to make any draws under any of the new facilities in this calendar year. The money that we received from the tariff refunds is being reinvested to expand production capacity in the US. I'd like to stress on this that we like to do a lot of hardcore real grunt work to get production facilities up and running. And so, our CapEx for the next few years would continue to expand as we add more robotaxi fleet, we expand our production capacity for Optimus, install manufacturing capacity for solar, purchase additional SI computer infrastructure, and build a semiconductor fab. This strategy will position the company for the next era of growth and disrupt many existing markets while creating new ones. This is by far the most exciting time to be at Tesla. The foundation being laid around autonomy, robotics, and energy is setting us up for an extremely bright future. I would like to end by thanking the Tesla team, our customers, investors, and suppliers for the confidence in us on this journey.
Fantastic. Thank you, Vaibhav. Now we're going to go to questions from say.com. The first question is, 'How does the cybercab fleet scale past Austin, and what's the regulatory path outside Texas given NHTSA's open safety review?' Ashok, would you want to take that?
Yeah, I'll start here. First of all, we launched cybercab this quarter, which is very exciting. We achieved that by mostly learning from data from the other vehicle platforms, for example, Model Y, Model 3, etc. All of the data helped make the cybercab without requiring a ridiculous amount of vehicles to produce the data. So, we are seeing very good scaling in terms of cybercab's AI capabilities. We launched in Austin, but that's only because, you know, we had some cabs, and we want to swarm Austin with cybercabs everywhere. But there's no fundamental reason that we are limited to Austin. In fact, next month we're launching in Dallas, and then the same software will scale to lots of other cities along with the Model Y robotaxis.
Great. Thanks very much. The next question is, 'What are your thoughts on crowdsourcing training data for Optimus versus your internal Optimus Academy approach? Is data currently the primary bottleneck?' I think you covered this quite a bit, but any other color there?
Mostly talked about this already, but crowdsourcing is not the issue for Optimus. There's a lot of things we have to get right about the AI architecture that we're still working on. We're making good progress, but that needs to be sorted out. And really, in order for Optimus to get the training data we need, we actually need to make a lot of Optimus. So we need to make, I think, on the order of 10 or 20,000 bots and then have them do self-play. And we're building up an Optimus Academy where Optimus can practice doing a very wide range of tasks, and then we can use that data in sort of a flywheel to make it better and better at these various tasks. A big difference with Optimus versus the cars is we don't have this vast fleet of cars from which we can gather training data. So with Optimus, we have to bootstrap it by making the robots and having them do self-play, which is really how a human learns to navigate the world. You know, you can see this when you have a toddler and they grow up and they initially are sort of wobbly and they don't know how to do things, and eventually they learn to wipe their own ass, which is a big milestone. Anyone who's had kids knows. And then they learn to read and do all sorts of things, and they'll be able to do these things. And they learned this by playing. And we actually need Optimus to play at scale in order to have it be really good at tasks in the real world. And Ashok, do you want to add to that?
Yeah, like I mentioned on the car, we actually have the data from the car's embodiment itself, but for Optimus, there's no external data for humans to get into and make Optimus into a puppet or something like that. Like we have to learn, Optimus has to learn by watching other people do tasks initially and then start practicing the task yourself. For example, you can imagine if you're watching a soccer game, just because you're watching it doesn't mean you'll be good at soccer yourself. You got to start practicing and get good at the game and learn from the bot's own experience. And we are setting up this huge reinforcement learning cycle with initial base training done from humans to bootstrap and then put it on Optimus and do a lot of self-play both in simulation and in the real world to learn on the embodiment of Optimus itself. I think we're pretty close to having very good results in this big loop.
Yeah, I think we may be ready to release an Optimus video in a month or so. I want to make sure that when we release this video, that it's not like a canned demo that, you know, like when you see these humanoid robot demonstrations, some of them look very impressive, but they're not really using any kind of advanced AI or SI. I mean, they're like pre-programmed deterministic tasks. And if you actually try to use that humanoid robot in the real world, you would have a lot of trouble. It wouldn't work, basically. So, I want to release a video where it's Optimus doing things that are not pre-programmed. And I think that's maybe a month or two for that, to have a demo that you can believe what you see type of thing. And then there's another milestone that I think is important we want to get to, which is to integrate the Tesla AI 5 chip into Optimus, which if things go according to plan, will be in approximately April of next year. And that's a giant improvement in capability with an order of magnitude more operations per second. Like it's an order of magnitude more compute, AI 5 versus AI 4. And that will help Optimus a lot because the degrees of freedom that a humanoid robot has to deal with are far greater than what a car has to deal with. So, it is a bit of a struggle to squeeze everything needed to operate with all the degrees of freedom of Optimus. And the wide range of tasks, you know, can you think like a humanoid robot is going to do like a thousand times more things than a car would do? That's some very big number, basically. So, I think the additional compute of AI 5 will make a big difference, and that comes in hopefully in volume around April of next year. And that's why I think all the pieces, like the refinements in electromechanical dexterity, making sure Optimus can operate for long periods of time without wearing out, getting the AI software right, and the AI 5 chip integrated, all of these things kind of come together. That's why I think about a year from now is when it's something that you could ship to customers, and it would also be reaching scale in production. So, that's probably the right timing to think about. And in terms of the initial customers for Optimus, I think it probably would be industrial applications at first, things that are like dangerous repetitive tasks, things that people really don't want to do, and that you want done basically 24/7. So, like around-the-clock stuff. That's going to be the highest value for Optimus in the beginning. As we scale production, of course, it would be something that people can buy for their home. But the initial high-value use will be heavy industry, dangerous tasks, or things that may cause repetitive stress injuries, and that you want done 24/7. So, that's the way to think about it.
Great. Thank you, guys. The next question is very similar. It's about Optimus scaling, so we can... Yeah, that sounds about right, basically. And the next question is, 'When can we anticipate FSD unsupervised in customer cars?' Ashok, do you want to kick that off?
Yeah, like I mentioned earlier, the software for Cybertrucks and Model Y is the same, and pretty soon we'll be able to release the same software as part of the V15 release to customers. So, the only additional requirement is to make sure that, for example, the customer Model Y can be part of the geofences and the fleet management that Tesla has for it to be fully autonomous. We suspect somewhere around Q2 next year is when we can turn on some of the first customer vehicles to be part of the robotaxi fleet.
Great. Thank you, Ashok. The next question is, 'When will the Cybertruck be available for customer purchase/ownership?' Lars, did you want to kick this one off?
Sure, thanks Travis. I think it builds a little bit on what Ashok just said. But in short, we're trying to get Cybertrucks to customers by the end of this year, 2026. Obviously, we're producing them in our line in Texas, but all of those are currently going to our own internal robotaxi fleet. And as Tesla grows, we'll mainly deliver fully designed autonomous vehicles to our customers. And Cybertruck will be the first. And so as we start small this year and we learn about all the things Ashok mentioned about delivery and ownership and fleet management of autonomous vehicles in customer hands, we'll continue to scale that. Elon, I don't know if there's anything you want to add to that.
You know, I mean, Cybertruck is primarily going to be something that is offered as like transport as a service. Whereas our other cars would be more, you know, the cars that you'd want to own like the 3 and the Y and whatnot. And the robotaxis are mostly, but, you know, the vast majority of the Cybertruck stuff will be essentially Tesla-owned and transport as a service.
Great. Thanks, guys. The next two questions are quite similar around unit economic costs. The first one is on per paid robotaxi mile. And the second one is cost per Optimus unit. And we already covered what the first paying customer looks like. So, Vaibhav, did you want to get into the unit economics question here?
Yeah, so on the robotaxi side, you know, the main geographies where we are operating, we're already seeing utilization improving, and you know, week by week as Elon was talking about, we look at the miles and how many paid rides are happening. So, are we at our goal of 30 cents today? No, but do we see a path to get to that? Yes, we do. And it's all about scaling operations. And what you'll see over the next few months is that we'll... Today, Cybercab, which is the primary robotaxi product, is available only in Austin. But we do plan to bring it out in some other cities before the end of this year. And you know, as we add more Cybercabs, you know, we're also learning from the operation experience of managing a fleet, pickup and drop-off, you know, cleaning, all those things. So, all those costs are being dialed in. And that's why, like I said before, we definitely see a path to get to our 30-cent target.
And then, did you want to cover the Optimus cost?
Yeah, I mean, I think Optimus cost internally, we are tracking pretty well in terms of how the design has matured. But it would be a little too early to say that we have got there because, like Elon was saying, we're still making a lot of design iterations. The good thing is, you know, when you look at it from a cost perspective, we're trending closer to where we would say our Cybercab cost would end up being.
Yeah. Great. Thank you very much, Vaibhav. The next question is, when do you... We've kind of already answered this. When do you anticipate Optimus will be generating revenue? So, we covered that in the prior question. The next one is mapping for FSD continues to be a problem. The three biggest issues are recognizing proper speed limits, parking, and typical routes. I understand you use various third-party maps, but why not use your own maps? Authorize trusted and verified Tesla owners to provide the input. Ashok, did you want to kick off here?
Yep. Yeah, it's totally right that we shouldn't be depending on external people's maps. So, we are building our own maps, but not like the same way as Waze maps. We're building an entirely new architecture with essentially AI-based mapping and navigation. It's going to be super awesome. It's going to be integrated with voice. You can talk to it, ask for different routes. It's going to learn your personalized preferences, plus also the fleet preferences. The collective intelligence of all the cars are going to flow into making the maps and navigation be super cool. We'll start, you'll see the start and end of trips already use this technology pretty soon as part of the V15 release. And over the next several months it's just going to take over all of the navigation functions, parking, destination handling, and just we'll know you better than you know yourself in some regard for the driving task. And I handle it beautifully.
Great. Thank you, Ashok. And then our last question is, what is the current production rate for your existing factories? Do you anticipate building new ones? Lars, do you want to kick that off?
Yeah, sure. I think Vaibhav kind of touched a little bit on this in his opening remarks. And I'll say our factories are operating at record rates this quarter. And of course, we can only the rate of at least likely process or component. And Vaibhav mentioned that we are actively managing all the aspects of production. You know, as we've spoken in our past calls, we've made strategic investments globally to increase rates of our current factories over the last year plus. And you know, this quarter we are starting to see that, and we expect that to continue in coming quarters as those investments come to fruition. I mean, beyond that, we've also are working to ramp Cybertruck obviously in Giga Texas, and we applied for proper permits to expand the factories in Berlin. And as also Elon mentioned, we just started a new semi factory in Reno. So, in short, we are building new factories, yes. And we're aiming to hit that 5 million goal by 2030, as Elon said.
Great. Thank you very much. That's it for the say.com questions. Now we will be going to analyst questions. As a reminder, as Elon said, we will not be addressing any questions today on a potential merger with SpaceX. With that, the first question comes from Tom from RBC. Tom, please unmute yourself when you're ready.
Yeah, thanks for taking the question. It's about FSD. So, the attach rate, I think according to my math, is...
Like 30 to 40% in North America on a paid basis. It's becoming a flywheel, as you said, for more car sales. And the attach rate exceeds other automakers' subscription-based autonomy by a lot. I guess, in a weird way, could you guys be a victim of your own success in seeing other car makers noting the success and trying to aggressively come up with similar L2 plus type products, maybe partnering with Nvidia or Qualcomm? And if that happens, could that be deflationary for FSD pricing?
I think people take this thing simply that, you know, you can if the software can be solved, it's there. It's not. Because, I mean, I'll let Ashok get into it, but basically, it's a very hard problem. And it's a super hard problem.
Yeah, it's easy to get to the first 90% and make like a cool-looking demo, but when you get in the long tail of this problem, it's so long that people don't realize anyone with their sane mind should not attempt this problem. It's like so difficult and so long tail. And especially to get to a safety point where you can drive safely for millions of miles with no injuries and so on, that's going to be extremely hard. As we release unsupervised FSD to the product customer fleet, it's just going to be — I don't think anyone can like match it in several years.
And one other thing which I'll say, if it wasn't clear that, you know, especially in our showrooms here in the US, customers are now coming in and saying, 'Can we get into an FSD drive?' They're not asking for 3Y or Cybertruck. They're saying, 'Can we get an FSD drive?' And that has become the key driver because what it does is it actually opens up a lot more flexibility for different parts of the population wherein they had issues driving conveniently before. Now they're getting their freedom back by being able to use FSD. And a lot of folks are just like — I mean, it's a game-changer. I mean, if you haven't tried it, I keep saying it every time, if you do it, you won't be able to go back to driving any other car.
You know, I get it. My follow-up is similar to that question on China. I mean, there it seems like autonomy is like table stakes. Is it the same kind of answer? That's question one — that, you know, they're not going to be able — those car makers aren't going to be able to develop similar products you guys have had on FSD, or do you see there that being definitely a different pricing dynamic?
