I understand the pay to use model but so many companies are creating their own models and allowing limited free usage now. Is it just bragging rights and other intangible benefits like more data to work with in the form of prompts, etc?
Partially it's because getting people using them now will keep people using them later when there's more options for monetisation, because people don't typically switch services. And partially it's because companies can and do charge for more/better access. And I'm sure a few other factors I'm missing
It's how google started. Free search.... then, when they became a household name, came the ads.
Many people are using ChatGPT for getting answers nowadays. So we know what's coming...
Plus, the influx of user data the users happily supply to them. And it's not just search keywords anymore. It's full conversations. They can build super detailed user profiles out of this data, plus training material for their later models.
I'm waiting to see "product placement" responses from ChatGPT. Like it would answer "Here is what you need to set up your Raspberry Pi board for home automation. While you're reading the explanation, I suggest you enjoy a nice cold Budweiser."
I can't wait for ChatGPT to start flogging Nord VPN
If it's entirely free, you're the product.
Google and other monolithic tech companies benefit from training data, and trying to implement that in other parts of their tech stack. They can also influence the training data and outputs, in a way that benefits them more.
The pure AI companies just benefit from giving the absolute best product they can, and you generally pay for that. Or they're running the VC model, so once they have millions of users, they'll start to twist the knobs and turn up the heat to squeeze money out.
To be fair, there's a paid version, and it can get very expensive. That would make it a freemium model.
They are willing to lose money now to gain users because they figure that they'll be able to monetize those users in the future.
That monetization might take the form of selling users' data, selling advertising, or (this is already happening) getting the customers to pay to access premium versions of the A.I.
The consumer market is the largest market in existence, and reaching consumers is hard. So if you have a product, you want to reach consumers who are interested in your product.
People tend to research products before ordering, and search has historically been the tool for that. This means that Google — who is the leader in search — is in a great position to insert ads for searches that are relevant for a given set of search terms. Sellers of products are willing to pay a lot for this.
LLMs are the new search. It's really that simple.
To put that into perspective, Google first hit 90% search engine market share back in 2002. Google's search related ad revenue was $102.9 billion dollars in 2024, and it was up nearly 16% from the previous year. There is a lot of money at stake for whoever wins the LLM race, because it is predicted that a lot of search traffic is going to redirect through LLM tools. Google still has a strong position, but they have never faced a threat as great as this.
Data, data, data. The more you use Google products (or any other company's products), the more info they gather on you, and that information has value. Everything from targeted ads to selling to data brokers to just having a better understanding of how to manipulate consumers to drive business.
There's also the need to have lots of interactions to consistently train and retrain the models. For general purpose AIs, the more users, the better.
Finally, there's getting people in the habit of using one particular product/model now so if it is ever directly monetized, there will be at least some users that will be willing to pay to continue their use.
Google photos and drive. Suddenly now i need to pay or I won’t be able to send emails.
Two ways, one short term and one long term.
In the short term, every time you interact with an LLM you are generating data that will be used in the training of the next model. So instead of having to pay for that training data or go looking for it, they use what is already going in there (there is a lot more to training data than just this though).
Next is the long term. It is easier to look at where these companies came from and how they became wealthy in the first place.
Google (Alphabet) is a good example. Google came out as a relatively early search engine for the web. Their selling point was it was free and no ads, which made it really fast (noticeable back then) and free. It quickly became the preferred search engine around. People used it so much their brand name became synonymous with searching the web 'I'll go google that.'
People saw how useful this was and how much of an affect it was having and how Google had become integral to the growing internet ecosystem... so when they asked for money they gave it. They weren't sure how they would make money, but they were starting with building a massive user base and then figuring out how to monitize later.
They did it, initially with things like ads and really the development of SEO optimization. The ad business grew and grew and that is what initially built their wealth.
So tl;dr, build the user base first and then figure out how to sell them... er... capitalize on that.
AI models are doing the same thing. They are in the prove their worth phase of existence. A lot people are seeing all the potential and willing to throw money at them right now. Like in the 90s and early 2000's this is a bubble of sorts that will burst at some point with the ones who remain being set to reap large amounts of money.
That wasn't Google's selling point. It was that their search produced better results.
a) the more people use these models, the more they can refine them and the better they get
b) these companies are already experts about spying on people and taking their data - what you say to the AI is even more data for them to collect about you - what you are interested in, etc. They can turn around and sell this.
c) I'm not sure we're quite there yet but eventually these AI's will tell the "truth" that their owners want them to, influencing people (us) in all sorts of ways we probably haven't even thought about yet. The more people who use a given AI, the more influence the owner of that AI has.
OpenAI needs investor cash to stay afloat, if no one is using it, maybe it's not the revolutionary technology it's been priced at, and investors pull out. So they need users and they were even losing money on their $200 a month subscription.
Google is in a tough spot. Their whole thing is free search to deliver ads. AI and LLM can actually get people off of search engines in favor of AI. So they need to have their own AI solution to not lose search users and lose ad clicks in the process. So google is trying to keep people off of other information providers and so if AI doesn't actually turn profitable, then these AI companies will disappear and google will go back to only offering free search
If no one wants to use it when it's free, why do you expect them to pay for it later?
The economic model of the 2010s was that you need to subsidize your product for years until you build a critical mass of users and starve out all the competition, then you can recoup your investment by squeezing your very-locked-in and massive user base (Uber is the most familiar and obvious example, though Google is easily the most lucrative). All the major AI companies are trying to repeat this.
Ironically, Meta is trying to play spoiler a bit by doing an open weights model and trying to encourage other companies to get in on the action and compete with Google/OpenAI/etc. Unclear how well this is working, but it’s an unusually shrewd bit of 4D chess on Zuckerberg’s part.
It’s also not totally clear if this model of product growth and lock in is fiscally sustainable in a world of higher interest rates. We’ll have to see how things play out. But essentially this is the bet.
FB's plan is, IMO, to enable the development of good models to run its social-media empire.
A model is good only as its inputs, and here FB has a firepower the competitors can only dream of
Probably to do with Mark Zuckerburg having made a big multi-billion dollar annually bet on his "metaverse" and it failing badly. But still wanting an AI competitor. And having to dump its own proprietary software in favour of open source kits and Android.