What makes an AI startup defensible?

“Yeah. But I think that's— so for us, like as we, and as we push the bleeding edge on different use cases, that's really the standard. We have to test against existing practice? Are we— the classic Hippocratic oath, like, are we causing damage? We shouldn't be causing damage. It shouldn't be worse than what is currently standard practice. And as long as doctors are taught and understand that there's like a high risk of false negatives and that they ultimately should have to do the search themselves, then yeah, it's, it's something that we can probably release into the world, but something with like heavy, heavily caveated and people understand. But yeah, I'm looking forward to a future where there's unlimited context windows that don't break the bank, and you can put all that information into the model. And then the reason it would be so much better is because that amazing powerful model that does all these magical things, like tell me about the canals in Amsterdam, they will also find— they'll be doing the search. So it's like me doing the search. I'm reading every little detail. So I'll be able to find things just as well as doctor, probably better than a doctor can. And make associations that are really critical. So I think that's where it all goes. It'll take a bit of time, but that's kind of the trajectory we're on as hardware gets better and models get smaller and cheaper to run. It'll make these amazing things come true. Also the idea of individuals having this infinite memory of everything I've done at work and trying to create self-improving AI systems. I think that becomes possible when you can put anything that an AI has ever done into its own context. It's a way to give it memory. But today that's not feasible. Like if, if every conversation I ever had with ChatGPT is in its memory, it would just break the bank. Um, so yeah, it's, it's, uh, it's going to be interesting world as it goes forward.”

“Unless it's something really specific, because the models are so general and so powerful that for almost any use case, there should be some utility out of the box. Like, you should get the feedback that, I don't know, it works well for 40-50% of the times, but there's, there's a gap, and there's some, you know, gap between all certain scenarios where maybe the way that the models behave is not, is not what you like. Like, there's a company called, I think, Springboards that's actually trying to make, like, do ad writing and creative generation from models. That's a good example. Like, they want the models to be more creative and more like great copywriters than maybe they are out of the box. But you can still get it to be good sometimes. Um, and I think that's what you want to see. Another example, trying to think for founders, like I've seen a lot of like chemistry and molecular design models where basically if you want a molecule to do something or react in a certain way, like you can type in a reaction and then the model would try to output what the right reagent would be or possible molecules or shapes of proteins, that's an example of something where, yeah, you have to build that from scratch. Like that doesn't exist. You know, there's no corpus of data in GPT that does this today. If you ask it to write out the DNA codons of like what, what to build the protein, it's not going to work. But for anything where it's like professional productivity or something that looks like Heidi for different industries, I, my general suggestion is like start with start with world-class models, as long as like state-of-the-art, the best possible models. Don't try to cost optimize in the phase when you're just trying to get product-market fit because you can always solve cost and price later within reason. Obviously you can't bankrupt yourself. You have to have enough money to run the business. But that was always our belief. Like we, when we had the free version of Heidi last year, we were giving away essentially like the absolute best models. And Yeah, it's not cheap. It was expensive, but we always had the perspective that the models would get better, the cost would go down. As we had more sessions, we could collaborate with users and find ways to either train our own models an”