Transcript — "I Asked AI What to Do With the Next 5 Years of My Life" (Tim Ferriss / Kevin Rose, The Random Show)

Note: auto-generated YouTube captions, lightly cleaned (>> speaker-turn markers normalized to em dash, &/  entities decoded, bleeped profanity marked [expletive]). Speaker labels are not attributed to Tim vs. Kevin individually in the source captions — turns alternate but are not name-tagged.

[00:00:00] What are you doing now with the with the eye stuff? — Well, I mean, I'm more curious to hear your thoughts and predictions, frankly, because I think you're better at it, but I mean, I'm using Claude code with Ferris APIs to do tons of like inbox analysis and stuff. — Yeah, yeah, yeah. — Right? I'm doing a 20-year retrospective analysis of angel investing. — Mhm. — It's like, who made what introductions? Which companies did I not reply to that ended up being successes? Which did I turn down that ended up being really important. — really want to do that to yourself? — Well, I suspected it'd be worse than it was. I actually have not I haven't missed that many explicit opportunities. I wanted to test my own stories against data, right? Cuz I have all sorts of stories — Right. — about why I did certain things and why certain things worked out. And I have certain stories about my batting average. And I'm like, but is it true? — Right. — Really? — Is it really true? — Let's look at some hard numbers.

[00:01:01] — The sad truth, and I have a buddy that wears that the bracelet and you've seen me with the necklace around that we categorizes your AI and listens to you 24/7. — Yeah. — It's about 70-ish percent that we think we know, but is actually what we know. — Yeah. — Out of the 100% of like what we think we know, this is the truth. I said I wanted a dark chocolate bar at 7:00 p.m. — Yeah. — It's like, no, you said it at 5:00 and you said it this way. — And you said it was milk chocolate. — Yeah, exactly. So, it's always about 20% off from where you actually think your brain's at. Sure. — Which is brutal. — Well, plus, I mean, that's like last week, right? If you're talking about 15 years ago. — I mean, if you listen to any genesis story of any startup, you're like, — Oh my god. — Wait a minute now. Like, this is like a startup comic working on material, but he's been working on this one 5-minute bit so long that now he believes that's actually truth. — Yeah. — That I mean, the sanitizing and the editing of these startup genesis stories is hilarious, and there's no reason to think that I would be or you would be

[00:02:00] exempt from it. — Right. — Right, when you're telling your own story, even if you're just telling it to yourself. — So, what's the number one thing that you've learned by applying AI to your life in this fashion? Like, what's the thing where you walked away and said "Damn, that was insightful, and I'm going to change my behavior, or I learned something new about myself that I wouldn't have if if I had not used AI." — Well, I think from a holistic health perspective, by holistic I mean having enough data related to medications, supplements, predispositions, side effects, what happened to me 2 weeks ago, the LLMs have been incredibly helpful. I mean, the picture that they get and the speed with which they can deliver an answer that I can interrogate is just incredible. — What did you learn? Like, what was the thing that you — I mean, honestly, it's mostly avoiding disaster, right? It's like, are any of these things contraindicated with one another? — Could A, B, or C explain D? — And you have to keep in mind, these things can still hallucinate, but

[00:03:00] you can I don't want to say eliminate that, but minimize it by just fact-checking across LLMs. There's that. I would say that there's a lot of insight on, hopefully, that that can translate to future decision-making related to investing, which investing for me is not just amassing more chips. What's fun about investing to me is it's a way to scorecard your thinking and decision-making. It's just a very objective way to decide if something was the right or the wrong decision. And you can fine-slice that, and there are ways that you could maybe question that, but if you're asking yourself, "Was I thinking well last month?" That's not a very helpful question. Where do you go from there? If you're logging maybe every decision you make every day and then trying to cross-reference outcomes with blah blah blah. Like, yeah, but you're never going to do that. — Mhm. — But when you're making relatively frequent investments, you can do that.

[00:04:01] You can also run counterfactuals, right? What if I did the opposite? What if I had not sold that? What if I had kept that? What if I had done this? What if I had done that? — Is that worth your time though? At the end of the day, you could put everything in the S&P 500 and just go to bed. — Well, there's that. I would say it's worth it to me because I find it interesting. I actually enjoy the intellectual exercise of it. But otherwise, I would say with in terms of like how AI has has impacted me, I would say that the honest answer is not that much because most [expletive] isn't worth doing in the first place. People are finding very, very clever ways to expedite automating workflows of all different types and doing something well does not make it important or worth doing in the first place. So, there's a lot I think the level of [expletive] that is being done just at a very fast, efficient rate is skyrocketing. Yeah. But simultaneously, there are definitely cases where

[00:05:01] I look back at say this analysis of 20 years of stuff. To do that manually would be impossible. — Sure. — All right, it would take me a year full-time with multiple people to do that. And with a Claude code Gmail API and leaving my computer running for a handful of hours a few times, it's like, what you get back is [expletive] credible. — Yeah. — Like, it's unbelievable. And I haven't even scratched the surface. — Yeah. — I will say also another way that AI has maybe affected my life in a net negative way, and I'm not We have another mutual friend who maybe we shouldn't name who feels very similarly is We'll leave that out, but yeah. — [laughter] — Jesus Christ. So, if you train AIs on your writing, they're really good. — Yeah. — And I think I feel This is a stretch of a comparison, obviously, cuz I'm not an adept like a world-class Go player,

[00:06:02] but when AlphaGo defeated one of the top Korean players, — Mhm. — he was kind of like, "I'm done. Like, I don't find joy in this anymore." — Yeah. — If we're playing against machines. — Yeah. — And when I see these AIs very beautifully, I'm not going to lie. And they're We're like in the top of the first inning, right? This stuff is going to get so much better. Spit out stuff that is so much better. — Yeah. — I mean, I can still write, but what they can do in 30 seconds is what would take me 30 hours. And I'm just like, "Fuck." It really drains the motivation for me to put in those 30 hours. — Yeah. — Why wouldn't it? Of course it would, right? — Yeah, but in some sense, you can consider it a really good co-pilot because for it to come up with novel ideas that would engage an audience, that's still the holy grail where it's not quite there yet, right? Like, it's going to make you sound It's going to button up your copy. And it might expand upon it in ways that

[00:07:01] you wouldn't. But it's not going to come up with the original thesis for the whole thing, right? — Yeah, it's going to have trouble with the original thesis, but even there, I think it does a pretty good job. — Really? It's getting better. I haven't tried with writing stuff. — do a data dump and you're like, "Create." — Oh, oh, you sent me that link. — Yeah, if you just do a data dump and you're like, "Create." — Where you sent me that link, you were like, "What should Tim do in the next 5 years?" — Oh, yeah, that was good. — really interesting. Tell people what you did because like they might find this They could apply this to their own life. — Sure. So, you could do this in whichever model you're using, whether it's, you know, Claude or ChatGPT or whatever. If it knows you, — you can tie in your inbox, too. — Yeah, you can tie in your inbox. In my case, I didn't do that, but if it has enough history on you, you can just ask, "What do you think I should do in the next 5 years? What might be some rewarding paths of exploration?" I think I put something like that. "What are three to five ideas that you think could be rewarding career

[00:08:01] exploration for me in the next X period of time?" — So, for people listening, if they've used AI for, let's call it, 3 to 6 months, and you've probably given it several hundred things to think about, yeah, it will span across those conversations, as long as you turn this on. It's I think it's on by default now, but it used to be an opt-in thing, where you have to say, "Allow the AI to look cross-conversation." So, it has a holistic understanding of who you are. — And the answers were [expletive] outstanding. — Yeah. — I mean, really, really good. — Yeah. — And I sent it to a few friends, sent to you, I sent it to a few of my closest friends, and they're like, "That's pretty [expletive] good." — Yeah. — I mean, — It was really cool. Some of the ideas I was like, "Damn, you should do that, dude." — Yeah. — It had this one business idea for you to do, and it wasn't a book. — Yeah. — And I was like, "Dude, I texted you back. I was like, that's awesome. Like, go go build that." — Yeah. There were business ideas, there were certainly kind of non-revenue, but philosophically aligned ideas. It was shocking to me. So, that's actually a very good example of something that

[00:09:02] has deeply informed what I'm mulling over. As I imagine the future, I was like, "Man, that actually is a really good." Because, keeping in mind, I'm asking questions about things of interest, things I like, things I don't like. I am asking questions about different scientific interests related to Sci Sci Foundation, my nonprofit foundation. I'm asking questions about investing. I'm asking questions about writing. I'm asking questions about relationships. I'm asking questions about organizing trips for friends. I'm asking so many different questions. I — on the board of your nonprofit? I think you're like secretary or something. — Yeah, something like that. [laughter] — I've never heard anything about that. — Yeah, I don't know. Maybe you were honorably discharged. I don't know. — I did. [laughter] I never got any paperwork around it. I've never heard anything in like 3 years. I'm like, okay. — Yeah, well, I said it was going to be a light lift. It's a light lift. — It's a light lift. — [laughter] [snorts] — That's a very good example, right? I mean, that's that may be the best example because if that even 10% informs

[00:10:01] like a major next chapter. — Yeah. — 100%. — Like that's a big deal for me, certainly. And it makes me think a little bit about podcast listeners, especially. Readers also, but to a greater extent podcast listeners who come up to me and most most listeners I run into are really great. And not I mean, there are always a couple of weirdos, but most are fantastic and they'll say something often like, "I'm so sorry, you don't know me at all and I feel like I know you." And what I say a lot of the time is actually, "If you listen to my podcasts every week or even every month, you do know me pretty well." — Yeah. — And then you think about a machine that never forgets. — Yeah. — It's going to know you pretty damn well. — of course. — And it's spooky in a way. — Yeah. — But I started getting more out of the LLMs when I started asking questions and I

[00:11:00] have to be I think a little careful with outsourcing this and absolving yourself of responsibility to think about these things, but when you ask it open-ended personal questions in the way that you would ask a close friend, — Mhm. — what do you think are three to five creative ways I might explore things professionally in the next 5 years? — Yeah. — As opposed to something that you think is more suitable for a robot. — Yeah, yeah. — You get some really interesting responses. — That's a great use case.