Couldn’t the exact same arguments be made to argue that there would not be successful internet companies, because the fundamental tech is hard to patent, and any website is easy to duplicate?
Definitely!
(I say above that the dynamic applies to “most software,” but should have said something broader to make it clear that it also applies to any company whose product—basically—is information that it’s close to costless to reproduce/generate. The book Information Rules is really good on this.)
Sometimes the above conditions hold well enough for people to be able to keep charging for software or access to websites. For example, LinkedIn can charge employers to access its specialized search tools, etc., due to network effects.
What otherwise often ends up happening is something is offered for free, with ads—because there’s some quality difference between products, which is too small for people to be willing to pay to use the better product but large enough for people to be willing to look at sufficiently non-annoying ads to use the better product. (E.g. Google vs. the next-best search engine, for most people.) Sometimes that can still lead to a lot of revenue, other times less so.
Other times companies just stop very seriously trying to directly make money in a certain domain (e.g. online encyclopaedias). Sometimes—as you say—that leads competition to shift to some nearby and complementary domain, where it is more possible to make money.
As initial speculation: It seems decently likely to me (~60%?) that it will be hard for companies making large language/image-generation models to charge significant prices to most of their users. In that scenario, it’s presumably still possible to make money through ads or otherwise by collecting user information.
It’d be interesting, though, if that revenue wasn’t very high—then most of the competition might happen around complementary products/services. I’m not totally clear on what these would be, though.
Ah, you do say that. Serves me right for skimming!
To start, you could have a company for each domain area that an AI needs to be fine-tuned, marketed, and adapted to meet any regulatory requirements. Writing advertising copy, editing, insurance evaluations, etc.
As for the foundation models themselves, I think training models is too expensive to go back to academia as you suggest. And I think that there are some barriers to getting priced down. Firstly, when you say you need “patents or very-hard-to-learn-or-rediscover trade secrets ”, does the cost of training the model not count? It is a huge barrier. There are also difficulties in acquiring AI talent. And future patents seem likely. We’re already seeing a huge shift with AI researchers leaving big tech for startups, to try to capture more of the value of their work, and this shift could go a lot further.
related: Imagen replicating DALL-E very well, seems like good evidence that there’s healthy competition between big tech companies, which drives down profits.
One thing that might push against this are economies of scope and if data really does become the new oil and become more relevant over time.
Definitely!
(I say above that the dynamic applies to “most software,” but should have said something broader to make it clear that it also applies to any company whose product—basically—is information that it’s close to costless to reproduce/generate. The book Information Rules is really good on this.)
Sometimes the above conditions hold well enough for people to be able to keep charging for software or access to websites. For example, LinkedIn can charge employers to access its specialized search tools, etc., due to network effects.
What otherwise often ends up happening is something is offered for free, with ads—because there’s some quality difference between products, which is too small for people to be willing to pay to use the better product but large enough for people to be willing to look at sufficiently non-annoying ads to use the better product. (E.g. Google vs. the next-best search engine, for most people.) Sometimes that can still lead to a lot of revenue, other times less so.
Other times companies just stop very seriously trying to directly make money in a certain domain (e.g. online encyclopaedias). Sometimes—as you say—that leads competition to shift to some nearby and complementary domain, where it is more possible to make money.
As initial speculation: It seems decently likely to me (~60%?) that it will be hard for companies making large language/image-generation models to charge significant prices to most of their users. In that scenario, it’s presumably still possible to make money through ads or otherwise by collecting user information.
It’d be interesting, though, if that revenue wasn’t very high—then most of the competition might happen around complementary products/services. I’m not totally clear on what these would be, though.
Ah, you do say that. Serves me right for skimming!
To start, you could have a company for each domain area that an AI needs to be fine-tuned, marketed, and adapted to meet any regulatory requirements. Writing advertising copy, editing, insurance evaluations, etc.
As for the foundation models themselves, I think training models is too expensive to go back to academia as you suggest. And I think that there are some barriers to getting priced down. Firstly, when you say you need “patents or very-hard-to-learn-or-rediscover trade secrets ”, does the cost of training the model not count? It is a huge barrier. There are also difficulties in acquiring AI talent. And future patents seem likely. We’re already seeing a huge shift with AI researchers leaving big tech for startups, to try to capture more of the value of their work, and this shift could go a lot further.
Relevant: ” A reminder than OpenAI claims ownership of any image generated by DALL-E2″ - https://mobile.twitter.com/mark_riedl/status/1533776806133780481
related: Imagen replicating DALL-E very well, seems like good evidence that there’s healthy competition between big tech companies, which drives down profits.
One thing that might push against this are economies of scope and if data really does become the new oil and become more relevant over time.