When do you expect OpenAI to become profitable? My understanding is that it is not, and has no real prospects of being so without a truly massive jump in revenue
I think this is a hard question to answer because it's largely up to OpenAI. The situation isn't that OpenAI can't figure out how to make its models profitable. Rather, they are choosing to forgo short-term profits in favor of faster growth. So to a first approximation, they'll become profitable when they decide to — either because they don't feel there's room for a lot more growth or because the fundraising environment gets more difficult and they can't get the capital they need to fund fast growth.
This isn't an unusual situation. Amazon lost money for years in the late 1990s before turning a profit in the early 2000s. Uber lost money for a decade in the 2010s before turning a profit in the post-COVID years. I think the market for OpenAI's products is going to be massive, so I don't think there's much doubt they'll be able to pull of a similar pivot when the times comes. I don't know when that will happen, but reporting suggests OpenAI's own projections show it happening around 2030 — so that's my best guess on the timing.
Funny how this was more about markets and finances instead of actual value which AI can or will bring. I can see some value in AI autonomous vehicles, but there are still real hurdles including developmental and regulatory. I still don't understand the value of AI generated fiction/images/videos - especially not the slop which exists on Sora. I wish some articles would put more thought into current real world applications using AI for the betterment of humanity.
I agree that Sora does not seem very valuable. But I think generative AI already generates a lot of value. The two big use cases I see right now are coding agents and "deep research." I use ChatGPT for the latter on an almost daily basis and it saves me a ton of time. Programmers are getting a ton of value out of tools like Codex and Claude Code.
And I think that's just the beginning. My wife is a doctor, and her hospital is just starting to experiment with transcription tools that listen in on a patient visit and then generate the first version of the doctor's notes. The doctor still reviews and approves the final version, but tools like these should allow doctors to take more thorough notes while paying closer attention to patients and saving a lot of time. In the long run, these products should be able to save doctors several hours per week. When you multiply that by hundreds of thousands of doctors, that's easily a multibillion dollar business. And you can tell similar stories for lots of other white-collar professions — lawyers, accountants, medical researchers, scientists, teachers, etc.
Thanks for having me, Tim! I agree that the context windows of frontier models will stay around one million tokens, but for slightly different reasons - it's becoming more effective to invest in managing contexts that hit one million tokens. See Claude Code's auto-compaction tools and OpenAI's /compact API endpoint.
Yes! And obviously the causation runs in both directions. Poor long-context performance creates demand for context management tools, while the existence of context management tools makes it less urgent to improve long-context performance.
On prediction 13, per this WSJ article there's a group that is countering the pro-AI PACs. I wouldn't say it's anti-AI, but it advocates for a more cautious approach.
What a year it has been -- and there are no signs of the next year being any slowdown, either. Lets see what the year will bring, happy new year and thanks a lot for having me! :)
MCPs are not technically relevant, but rather serve as a link to technical standards and services while remaining independent of language models, i.e., as a communication standard. At least in Europe, this is to protect certain industries and access to the customer interface. Regulation protects consumers from monopolies and trusts. Standards are the main weapon.
When do you expect OpenAI to become profitable? My understanding is that it is not, and has no real prospects of being so without a truly massive jump in revenue
I think this is a hard question to answer because it's largely up to OpenAI. The situation isn't that OpenAI can't figure out how to make its models profitable. Rather, they are choosing to forgo short-term profits in favor of faster growth. So to a first approximation, they'll become profitable when they decide to — either because they don't feel there's room for a lot more growth or because the fundraising environment gets more difficult and they can't get the capital they need to fund fast growth.
This isn't an unusual situation. Amazon lost money for years in the late 1990s before turning a profit in the early 2000s. Uber lost money for a decade in the 2010s before turning a profit in the post-COVID years. I think the market for OpenAI's products is going to be massive, so I don't think there's much doubt they'll be able to pull of a similar pivot when the times comes. I don't know when that will happen, but reporting suggests OpenAI's own projections show it happening around 2030 — so that's my best guess on the timing.
Funny how this was more about markets and finances instead of actual value which AI can or will bring. I can see some value in AI autonomous vehicles, but there are still real hurdles including developmental and regulatory. I still don't understand the value of AI generated fiction/images/videos - especially not the slop which exists on Sora. I wish some articles would put more thought into current real world applications using AI for the betterment of humanity.
I agree that Sora does not seem very valuable. But I think generative AI already generates a lot of value. The two big use cases I see right now are coding agents and "deep research." I use ChatGPT for the latter on an almost daily basis and it saves me a ton of time. Programmers are getting a ton of value out of tools like Codex and Claude Code.
And I think that's just the beginning. My wife is a doctor, and her hospital is just starting to experiment with transcription tools that listen in on a patient visit and then generate the first version of the doctor's notes. The doctor still reviews and approves the final version, but tools like these should allow doctors to take more thorough notes while paying closer attention to patients and saving a lot of time. In the long run, these products should be able to save doctors several hours per week. When you multiply that by hundreds of thousands of doctors, that's easily a multibillion dollar business. And you can tell similar stories for lots of other white-collar professions — lawyers, accountants, medical researchers, scientists, teachers, etc.
Doubt on N10. Tesla will be the 1st IMO.
Thanks for having me, Tim! I agree that the context windows of frontier models will stay around one million tokens, but for slightly different reasons - it's becoming more effective to invest in managing contexts that hit one million tokens. See Claude Code's auto-compaction tools and OpenAI's /compact API endpoint.
Yes! And obviously the causation runs in both directions. Poor long-context performance creates demand for context management tools, while the existence of context management tools makes it less urgent to improve long-context performance.
On prediction 13, per this WSJ article there's a group that is countering the pro-AI PACs. I wouldn't say it's anti-AI, but it advocates for a more cautious approach.
https://www.wsj.com/tech/ai/tech-titans-amass-multimillion-dollar-war-chests-to-fight-ai-regulation-88c600e1?gaa_at=eafs&gaa_n=AWEtsqe-rxV53k5LHPYoObi3AVUa9y5I0EQUfdyWqfWJ6TBctCSlLXiHcuHAEt4idLQ%3D&gaa_ts=694c40bd&gaa_sig=bJXZcPWL5_kje5BaIh2fbRoEjhkvhrS3okC2k9yPocIvNZ-A3mwv9FCstlIgWeXXltseD2az3Px0A-fP_reLWg%3D%3D
What a year it has been -- and there are no signs of the next year being any slowdown, either. Lets see what the year will bring, happy new year and thanks a lot for having me! :)
MCPs are not technically relevant, but rather serve as a link to technical standards and services while remaining independent of language models, i.e., as a communication standard. At least in Europe, this is to protect certain industries and access to the customer interface. Regulation protects consumers from monopolies and trusts. Standards are the main weapon.