Vertical expertise and execution remain startup moats

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 32:38

   So you want any agent to be able to like kind of fit into the team. I'm just curious, like if you apply that to a new startup, let's say you're like, I don't know, starting a company tomorrow and you want to use computer use to like solve a workflow within the enterprise, how would you think about it? And would you be kind of, what would be the things that jump to mind in terms of like, oh, I want to know when it fails well, I want it to be able to communicate with people, or I want it to be like, I don't know, a black box, just like, maybe if you have to talk to founders right now, what advice would you give them as they try to use your models and capabilities to just solve problems for enterprises?

2. 33:16

   In my mind, it depends on it depends so much on the modality of, you know, are you trying to are you a small team and you want sort of individual on the team to be able to use some piece of software that that your that your team is using, but in a more efficient way? or are you maybe building something that itself wants to interact with existing software in which case maybe you have some you know machine in the cloud somewhere that's doing computer use in the background i think that will really affect the modality of how you want the human agent collaboration to be right um and um yeah you know in the former case i think a lot of our products are serving this use case um you know where you sit down at chat gbt and uh you know there's a lot of great, you know, collaboration you can have there. And in the other case, you know, if you're

3. 34:10

   building something with the API, you're doing your own computer use implementation, then, yeah, then it really is interesting to figure out when do you want the human to be in the loop? Is the agent telling you when something's wrong? Is it telling you when something went right? You know, is something summarizing that so that you don't get bombarded with notifications saying, hey, this happened, this happened, this happened, which actually you're going to ignore.

4. 34:33

   Or, I don't know, what do you think, James? Yeah, I mean, you know, both Ari and I were startup founders before we joined OpenAI. And so, you know, I'm still very bullish on startups. You know, I think the question, right, kind of comes down to like, what, is there still a mo, right? Is there something that startups can still tackle while labs are doing things? I mean, in general, I just think there's like two things.

5. 34:53

   Like one is like, I think there's so many things to build, right? And like, even though we're quite big now, thousands of people, we are still feeling very stretched thin internally. And like, you know, it's like the teams are not very big for all the problems. And so if you have a problem that you really deeply understand, you have industry expertise, you understand how regulatory works, the go-to-market motion, how to service those customers.

6. 35:15

   It is still, I think, a very green field out there. And the tools that you have through our APIs, like Ari mentioned with computer use, I think will still let you accelerate. And speed, really, I think at the end of the day, speed and pain is kind of the moat that exists today. Anyway, I think- The other thing I think about a lot is like there are, in my opinion, still a lot of like vertical industries where the diffusion of AI is like much slower.

7. 35:40

   And so, you know, I was talking to a company the other day who uses computer use to help restaurant groups to, you know, take pictures of like their back storage, you know, and then using that, the vision looks at kind of like what is going to be missing based on a projection for tomorrow's traffic. And then it takes that to then use computer use to go to like a Shopee or some kind of like procurement software to then order your groceries for the next day.

8. 36:08

   And then based on that, it will go into your ERP to adjust like the timetable for your like employees who are, who you might need more people to help. Because in the last year, at this time of the day, there was a lot more traffic coming in from like a holiday or something like that. And so like, you know, those restaurant groups are probably not like tech companies that are super, you know, familiar with all the tools that are available.

9. 36:34

   And so I think like, you know, living in San Francisco, we see a lot of thinking, wow, like the labs can do everything, the codecs and like, you know, why doesn't it just install codecs and just have it do stuff? But like for the average American and the average small, medium business owner, the average enterprise, even understanding what the tools are capable is one. Connecting tools in a way that makes sense is two. Deploying it is three. Servicing is four. And so there's still a lot, in my opinion, where if there are complex workflows that require a lot of disparate software, computer use is a great way to join them and complete the entire intent or flow.

10. 37:05

    And then building it in a format and UI that feels good to hold for those customers feels still like a really, really big opportunity that, you know, I think is still open.