M.G. Siegler • #meta • Aug 6, 2026

If You're Not Paying for the Tokens...

Meta has a new *business* model to drive down AI usage costs...

The most interesting element in Meta's newly announced 'Muse Code' product isn't the product itself, it's the model. Not the AI model, which is an updated variant of their 'Muse Spark' (non-flagship), but the business model. Per The Wall Street Journal:

Meta’s Muse Code agent has two price tiers: one that’s the same as its general Muse Spark model and another that is less than one-10th the cost. To access the less-expensive tier, which costs 20 cents per million output tokens, users must agree to provide feedback to help improve the agent. Tokens are the basic unit of artificial-intelligence computing.

Yes, you read that correctly. If you opt-in to having your data used to improve the models, you get more than a 10x discount on using those models. Actually, depending on the token type, quite a bit more! If my math is right, it's a 75x discount on cached input tokens. That is... sort of wild.

And also sort of brilliant. As everyone knows, Meta is coming from behind in AI after having to restart their efforts. And the fruits of such labor have been pretty good to date, but also still not frontier-level. And there's some concern that they won't be able to get to frontier level because that line keeps moving, pushed by the incumbents, Anthropic and OpenAI. I mean, not only has xAI struggled to catch up even after billions spent, even Google is struggling to keep up.

Part of the issue is obviously that the leaders simply have so much more usage that they've reached a kind of virtuous cycle, not unlike Google did back in the day with Search. Microsoft and Yahoo poured billions into trying to compete, but they simply could never catch up (let alone make Google "dance").

What's one way to spur usage and try to break customers away from the leaders? Well, a better product can work. But beyond not being so simple, that often takes time, enough time that if it starts working, the incumbents are likely to copy you. So what's a better way? Price.

Mark Zuckerberg has made no secret of the fact that he plans to undercut those competitors to get back into the race. And given the pressure both Anthropic and OpenAI are under to show improvements in their economics as they angle to go public means that a full-on price war is going to be a problem for them. Meta, as an already public and profitable – well, at least before all that AI spend came along – company can afford to undercut, quite literally. And so their margins are Meta's opportunity.

But this is actually even more interesting than that relatively simple and straightforward playbook. Currently, many businesses are having the AI cost come-to-Jesus moment. This includes both small businesses and even Big Tech. Many are learning the hard way just how fast AI costs can spiral out of control.

As such, we're seeing a pivot in the messaging around AI for businesses from maximizing usage to cost controls. Microsoft is leading the charge here, but Meta is right there too. Sure, this is easier to do when you don't have an actual frontier model to sell, but that doesn't mean it's not a good angle. And while Microsoft is focused on being a router between any and all models (well, aside from maybe Google's) so customers can make up their own mind on costs, Meta is here with a new model – again, a new business model.

This also taps into yet one more element being talked up right now against the 'Big AI' incumbents: customer data. Palantir's Alex Karp is leading this charge, but Microsoft's Satya Nadella is right there with him. The argument is essentially: you'd be crazy to give your data over to the Big AI players. You're giving them free rein to use that data to train their models and you're paying them for the privilege!

Never mind that this is fairly overblown given that there are some data protections in place around training and data security and what not, but the high-level point remains. And it does lead to the flip-side question, the one Meta is now trying to answer: if you were paid, would you be open to letting one of the AI model makers use your data?

By "paid" I of course mean, given that massive discount on token usage. Still, it's an interesting trade off. It's one that many big businesses can't make for security reasons. But individuals and perhaps small businesses can probably live with such a choice. At least, that's what Meta is trying to find out.

And actually this also plays into yet another trend at the moment: the push to keep the Chinese "open" models in play in the US market. Why? Well aside from the whole open weight debate, they're simply so much cheaper to use at the moment. Granted, there are already signs this may be shifting. But probably not so far so as to be close to what the incumbents are charging for their frontier models. Per WSJ:

Claude Code and Codex come bundled in pricing plans that cost roughly $20 a month, with more expensive plans for increased usage. Exceeding the usage cap switches the user to pay-as-you-go rates. Rates per million output tokens range from $12 for GPT-5.6 Terra and $30 for Sol, with Claude Sonnet 5 at $10 and Opus 5 at $25.

Meta’s new coding agent is priced roughly on par with models from China, such as DeepSeek, that cost as little as 18 cents per million output tokens. OpenAI has also slashed prices on older models, such as GPT-5.6 Luna, which dropped from $6 to $1.20 per million tokens.

In other words, without the data opt-in Meta's Muse Spark price, at $1.25/million (input) and $4.25/million (output), is priced fairly in line with the American competition (again, for the non-"flagship" models), winning in some cases, losing in others. But where things get really interesting is with that data opt-in. Because now we're talking about $0.10/million (input) and $0.20/million (output). Yes, 10 and 20 cents. Again, that's roughly inline with DeepSeek (which is apparently in the process of raising their prices).

Granted, this is all to use Meta's new Muse Code product (and API), but there's no reason such prices – and the business model – couldn't translate to their broader Meta AI suite as well. The company is in the process of trying to figure out how best to monetize that element of the business. And if it does, will it pressure OpenAI and Anthropic to offer the same kind of deal?

That will be a painful pill to swallow as they're currently getting such data for "free" (but yes, there are ways to turn such rights off or restrict them). And yes, OpenAI does grant higher token limits if you remain opted-in to letting your data help train their models. But if Meta's token discount trade-off idea takes off...

You can't help but be reminded of the famous line around advertising-based business models, "If you're not paying for the product, you are the product." Here, the equivalent is sort of, "if you're not paying for the tokens, you are the tokens" – meaning, the trade-off to get those tokens for "free" (or insanely cheap) is that you're giving up your inputs to help train those models.

Of course, with many (but certainly not all) advertising-based models, "free" really is free. With AI, at least to date, free is free up until a certain point and/or capability, at which point you have to pay. If you squint, you can see a path forward here, where the data trade-off perhaps keeps and expands free usage, but also makes the paid tiers (or pay-as-you-go token usage) cheaper.

The issue is that whereas with advertising-based businesses, the advertisers are paying the companies, with AI data sharing, there is no actual money coming in from that alone. You can certainly argue there's still value in that training data, but just how much and if it will always be constant is a question.

And, of course, that alone wouldn't be enough to help AI companies actually pay for all of this. Again, there's no actual money coming in from those data rights, simply (potentially) less going out. So the model would have to be some sort of hybrid of data-supported free tier, data-supported paid tier, non-data-supported higher paid tier and perhaps even advertising to augment all of them.

I've been skeptical about advertising working well alongside at least our current AI products. And certainly that it could ever work as well as it does with Google Search and/or Facebook/Instagram. But what if it simply needs to augment that data "payment" and/or that actual payment to keep the whole system working in a sustainable manner?

You can see a path to such business models supporting the training and usage of such AI models. There would be trade-offs, for sure, but to truly scale AI, I'm not sure it's the worst idea. Let's see where it gets Meta.

👇

Heading back on the road, but plenty of writing from the past couple of weeks to keep you busy... 🍻