2 Ex-AI CEOs Debate the Future of AI w/ Emad Mostaque & Nat Friedman | EP #98
The conversation covers the rapid growth and adoption of AI, the debate between open and proprietary AI models, and the lack of full understanding of how AI models function internally. Peter Diamandis discusses the future of AI in business, including its potential as a thought partner, the creation of AI-native companies, and the development of autonomous AI agents and voice AI models. The podcast discusses the impact of AI in health, including tools like Viome, and its potential to transform the economy and society. It also emphasizes the importance of high-quality data and transparency in AI models, especially in health and education. The conversation focuses on the potential of AI in transforming biology, accelerating scientific discoveries, enhancing education and science, and providing a vast workforce. Nat Friedman discusses the upcoming 'Gemini moment' for AI in biology and the be
“I think one of the most important things is uh the accuracy and then these long context windows so explain what the long context window is again so you're absorbing information right now and it's quite high definition because you can see everything here and other stuff but you're still writing it down and the reason organizations get big is because text is a lossy trans transmiss format we lose so much context the final PDF loses all of that stuff now with the new Google models Nat has some amazing companies in this space as well you can upload hundreds of thousands of words thousands of documents and the AI can interpret them all at once there doesn't need to be trained on it so you can upload all of your ideas and say build a business based on this and it will do that or you can upload like a whole bunch of movies and then tell it to write a script that incorporates all of that and it'll do that in reference time that again is something super human but we all have these massive like repositories of all these ideas we've had being able to dump that now and then the AI without having to be trained spit back answers ideas and things like that I think is a really huge step along with that composition step that kind I've discussed before I think yeah I totally agree with that I think there will be a couple things coming probably pretty soon um it's hard to predict exact timelines on these things uh sometimes things happen faster than you expect effect and sometimes a little slower but one of the clearly amazing clearly possible now U products to build is a voice too model that's indistinguishable from talking to a human maybe for a conversation of up to a couple minutes where what happens after a couple of minutes well maybe you can kind of just tell somehow that it's not quite human after a couple of minutes uh I'm I'm setting a milestone that I think is achievable this year maybe when if you can do 2 minutes you can do 12 minutes I don't know um but uh yeah you know you would it's it's actually about all the Technologies there it all just has to be integrated and so you need the sort of the ability to recognize speech is there the ability to interpret it with a language model and generate responses is there and then the ability to turn that text into incredibly realistic voices there and kind of putting that all together into a package that has very low latency that's talking the way we talk where you can kind of interrupt me and maybe there's an avatar that's giving you this human like I mean Aristotle was very impressive but I knew that that was not a real person right and so um I think we could yeah he was a real person yeah that's right and then I think the other thing that's a very big deal is this idea of autonomy and agents um there's been a lot of talk of it with AI over the last year today these things are not agents they're tools they're call and response you go to chat gbt you type something you hit enter you watch the response kind of scream back and I think what people don't necessarily understand is that when these language models are responding to you that it's almost like a rap battle they have a fixed amount of time to generate each word and so they can't sort of sit there and Ponder for a minute you know what they're going to say they have to talk to a metronome and um so that's why when you see the words that they're writing out that's not like they've thought about it a lot and then you see the words it's actually the thinking is happening during the output would you say that's how a human does it too I often a lot of times sometimes I'm really pissed off at what came out my mouth cuz I didn't think about it in advance yeah yeah I think so I mean sometimes you have a conversation with a smart friend and you just forming the words to respond you have a better idea and so I think we we definitely do that too but um the agent the autonomy is about kind of increasing the unit of work you can trust the AI to do without making a mistake so right now you can ask it one question get a response you interpret it you figure out the next thing but what if it could go do 10 or 100 steps you're talking about an AI business you know do you trust it to come up with the title of the blog post that you're going to post or do you do you trust it to actually come up with the whole idea of the marketing campaign right come up with a strategy for how to execute it all of the content it's going to generate the partners it will reach out to and negotiate advertising or or whatever it's going to do have those conversations that'll multi-step successfully all the way to like measuring its results without supervision and I think that agency thing we're starting to see it in programming there was a very impressive demo a week or two ago of a company called cognition that um I think it was maybe the it was arguably the first really impressive demo of working agents in Ai and and they did it with gp4 so not with a new brand new model they did it by being very clever about the way they squeeze and distill the intelligence out of gp4 by repeatedly calling it and and analyzing and evaluating its results and choosing the best ones and”
Your call:
openUnknown: By 2026, the focus will shift from AI models to applications and use cases.
“next year going forward no one give a down about the models it's all about what you can do with the models bringing them together because the models have satisficed they've got good enough fast enough and cheap enough they will get even better and there's probably two orders of magnitude Improvement still in the speed and reliability of the model”
Your call:
openUnknown: AI investment will grow to a significant fraction of global GDP within the next decade.
“I actually think if AI keeps working which it seems like it will um you should expect the future to look more like that Railway or solar situation where there's you measuring the investment in intelligence because it's so valuable intelligence AI is intelligence is power power is valuable it's power over nature it's power over others and so you'll probably measure the amount of investment in it in points of global GDP”
Your call:
openUnknown: By 2033, digital super intelligence is likely to be a reality.
“How concerned are you about digital super intelligence I'm defining this for a purpose of conversation as AI a billionfold more advanced than the human being um how do you think about that what's your position in that”
Your call:
openUnknown: AI will play a significant role in health, particularly in microbiome analysis.
“the pace of change you know is going to be very high and um Global GDP growth has been kind of in the 2% range and we have a society and civilization that's able to adapt to that amount of change per year”
Your call:
openUnknown: AI models will become more specialized and transparent, especially in health and education.