The Besties Take Napa | All-In Special

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00:00:45.840 | Everybody give it up for the dictator himself,

00:00:48.600 | Chamal Palihapitiya.

00:00:50.640 | The rain man, yeah, definitely David Sachs.

00:00:53.360 | And your sultan of science, David Friedberg.

00:00:57.640 | I love how it's freezing and there's one heat lamp.

00:01:00.440 | One heat lamp.

00:01:01.040 | We're all just going to chat.

00:01:03.160 | Save $6.

00:01:04.600 | I mean, these are literally like $80 for the night.

00:01:07.240 | Yeah, they cost about $50 a night to rent,

00:01:09.560 | so I just thought $250 was enough.

00:01:11.680 | Yeah, it'd be fun.

00:01:13.080 | No, no, I mean, this is a super expensive place,

00:01:16.800 | especially since this is what happens.

00:01:18.560 | This is my life.

00:01:19.240 | Oh, my god.

00:01:19.740 | I come in and they're like, Jacob.

00:01:23.520 | I'm like, yeah.

00:01:24.240 | It's like, Chamal's been here since 430.

00:01:25.680 | I'm like, yeah, that's fine.

00:01:26.400 | That's great.

00:01:26.960 | You know, he got here early.

00:01:28.200 | And they're like, he's been ordering wine on your tab.

00:01:30.840 | I'm like, oh, no.

00:01:32.920 | So the event just flipped from being in the black

00:01:34.720 | to being in the red.

00:01:36.080 | A lot of people had questions for us,

00:01:37.640 | so we thought we would do like a little Q&A.

00:01:39.720 | But I thought I would start with maybe asking the besties,

00:01:44.920 | what has it been like, I think, having this podcast get

00:01:48.280 | so big, and the scrutiny it's under, but the excitement,

00:01:52.400 | and just how it's affected your life,

00:01:54.520 | and maybe how you operate in the world?

00:01:57.540 | I think I may have told the story on the pod,

00:01:59.320 | but I'll tell it again because I do like the story.

00:02:02.800 | So I think I was visiting Chamath in Milan.

00:02:08.200 | I think it was either last summer or two years ago.

00:02:12.160 | And we were literally just walking down the street

00:02:15.680 | in Milan, and somebody comes up to Chamath and stops him.

00:02:21.040 | And it's like it's a tourist from Australia

00:02:23.520 | who's a fan of the pod.

00:02:25.600 | And he wants to take selfies.

00:02:27.560 | I mean, he definitely--

00:02:28.880 | Chamath was definitely his favorite bestie.

00:02:31.040 | And it was like this excited conversation.

00:02:33.480 | And then that all happened, and then he moved on.

00:02:36.920 | And Chamath turned to me and said--

00:02:38.960 | oh, by the way, just some context here--

00:02:40.720 | is that these two were in like a big fight.

00:02:44.480 | And there was some question about whether the pod would--

00:02:47.000 | whether the pod would survive.

00:02:49.320 | And I think we were just talking about that

00:02:51.360 | as this guy came up to us.

00:02:53.320 | In any event, so he comes up, takes a selfie, and then

00:02:56.840 | leaves.

00:02:57.680 | And Chamath turns to me and said,

00:02:59.200 | these two idiots better figure things out

00:03:01.000 | because I like being famous.

00:03:02.280 | [LAUGHTER]

00:03:05.280 | I'd love to hear you guys talk more

00:03:08.040 | about the current state of Gen AI and particular software

00:03:12.100 | companies.

00:03:12.600 | I know, Chamath, you got 80/90 going.

00:03:14.440 | I'd love to hear about the lower cost of actually engineering

00:03:19.680 | software and building companies versus actually Gen AI doing--

00:03:24.280 | replacing human judgment, replacing judgment,

00:03:26.160 | self-driving the software itself.

00:03:28.400 | And those two dimensions feel like they're not

00:03:31.120 | exclusive to each other, but they are two different axes.

00:03:34.200 | I'd love to hear current thoughts on the return

00:03:37.240 | potential from an investment standpoint.

00:03:39.000 | I think that the entire software stack is going to get rebuilt.

00:03:44.680 | And I think it's going to be a bunch of 10- and 20-

00:03:47.120 | and 30-person companies that do it.

00:03:50.040 | And I think the core artifact of a new company today

00:03:55.080 | are two decisions.

00:03:56.680 | The first decision is that you must

00:03:59.200 | demand that the people that join you

00:04:02.280 | use every available tool at their disposal.

00:04:05.120 | And you measure them against the counterfactual,

00:04:07.880 | where that counterfactual is what your productivity was

00:04:10.840 | at a traditional company.

00:04:12.560 | And you need to be 100% to 200% more productive.

00:04:16.720 | So if an engineer is writing x amount of features

00:04:20.320 | or x amount of code, they need to be at a 2x or 3x.

00:04:23.440 | And if you do that in an existing company,

00:04:26.020 | all I've seen is organ rejection.

00:04:28.640 | So that's the first decision.

00:04:30.040 | So the people that join you, you must

00:04:33.040 | expect that they do that, which means that you also

00:04:35.600 | have to deprive them of any help along the way--

00:04:39.600 | no administrative help, no HR help, no finance help.

00:04:43.400 | All of that stuff needs to be a workflow or a bot

00:04:47.800 | or some form of AI logic.

00:04:49.160 | So that's one critical decision.

00:04:53.080 | And I think the second is that the document that

00:04:57.040 | matters more than ever is that PRD that defines

00:05:00.960 | the v0 MVP of the product.

00:05:03.280 | Because I don't think you should be writing much code.

00:05:06.460 | I think what you really need to be doing

00:05:08.120 | is defining in excruciating detail what the feature

00:05:11.720 | set is, what the parameters are, what

00:05:13.320 | the guardrails of how features should behave in certain

00:05:16.680 | boundary conditions, fed into an LLM that then spits out

00:05:19.680 | the code that then compiles, and then you

00:05:21.600 | have a working product.

00:05:23.560 | And that is a 10%, 20%, 30% effort

00:05:26.520 | that can probably topple giants.

00:05:28.720 | It'll take a couple of years for us to get to that place.

00:05:31.720 | But in that world, there is no room for $500 million

00:05:35.240 | into a SaaS company.

00:05:36.520 | It doesn't make any sense.

00:05:38.320 | And the reason is the 10- and 20-person company

00:05:40.260 | can price that thing at just a fraction of what

00:05:43.400 | the incumbent charges.

00:05:44.640 | Because the incumbent has an OPEX load that's

00:05:47.280 | not correlated to features.

00:05:49.780 | It's correlated to the fact that you have 50,000 people

00:05:53.000 | across 50 offices all over the world

00:05:56.000 | with all this ridiculous infrastructure that all

00:05:59.680 | of a sudden becomes obsolete.

00:06:01.480 | So I think there's two models.

00:06:03.200 | One is more of the lightweight approach.

00:06:05.160 | So like, Sachs and JCal both seed a lot of stuff,

00:06:08.460 | get it off the ground, spin it up, and then raise money.

00:06:11.040 | He just did with Glue, which is like,

00:06:12.580 | I'm going to build something, get it to a place

00:06:14.600 | where the rest of us can kind of pile on.

00:06:17.040 | But the other version is much more distributed.

00:06:19.480 | And I'll say a dirty word here, but I

00:06:22.360 | think it matters, it's like this web 3 model does actually

00:06:25.560 | apply here, where there are some really interesting early

00:06:30.040 | adventures in crowdsourcing technical completion.

00:06:34.140 | I would encourage you to look at a project

00:06:35.880 | that I'm starting to focus a little bit on called BitTensor.

00:06:39.960 | And I was like, what is this thing?

00:06:41.560 | And it's basically a thing where you go and solve

00:06:44.960 | technical problems and produce features that people require--

00:06:48.040 | that need, and you get paid in this underlying currency.

00:06:51.780 | That is actually a distributed form of venture

00:06:54.040 | that actually makes sense in a world--

00:06:55.720 | Like a bounty?

00:06:56.600 | Essentially, where like, you know,

00:06:58.680 | 15 companies that need a replacement to Salesforce just

00:07:01.360 | say, here's the bounty, and here's the feature set,

00:07:04.480 | and it's a crowdsourced or a community-sourced PRD,

00:07:09.120 | and then some LLM spits out the code.

00:07:10.640 | Like, that is this crazy new world that we're going through.

00:07:13.800 | So it's all about speed of execution, I think.

00:07:15.960 | Sachs, do you have any thoughts on it

00:07:17.580 | and what you're seeing in the field right now?

00:07:20.120 | Yeah, I mean, I think in having conversations

00:07:23.280 | with enterprises and businesses, what they basically want

00:07:27.840 | is to be able to throw all of their data

00:07:29.480 | somehow into a LLM and be able to ask it questions.

00:07:33.800 | So they've seen ChatGPT, and they basically

00:07:36.680 | want to be able to do that, but with their own enterprise data.

00:07:40.040 | And it sounds easy, but it's actually

00:07:41.960 | like pretty hard to make that happen.

00:07:43.520 | One problem is that the LLMs don't

00:07:47.200 | have that big what's called a context window, which

00:07:49.480 | is kind of like, it's almost like active memory

00:07:52.200 | or something like that.

00:07:53.640 | And we're in the early days of-- it's

00:07:55.960 | like in the early days of computers where you had

00:07:58.760 | to worry about your RAM.

00:08:00.440 | It was like 8 megabytes, 16, then 32, then 64.

00:08:04.080 | Remember that?

00:08:05.200 | We had floppies and the 3 and 1/2 inch disks or whatever.

00:08:08.480 | And eventually, just people stopped talking about RAM

00:08:10.680 | because it went away as a constraint.

00:08:12.640 | But we're still in the days of, you

00:08:14.320 | have to be selective about what you feed into the model

00:08:17.520 | because it just can only absorb so much information.

00:08:21.120 | So if you give it all of your enterprise data,

00:08:23.040 | how does it even know where to look?

00:08:24.520 | And so you've got to figure out how to optimize it so

00:08:26.680 | that you can give the AI model the right chunks of data

00:08:31.880 | to give you the right answers.

00:08:33.600 | And that's actually-- it's a complicated problem right now.

00:08:38.120 | And we're dealing with it at Glue.

00:08:40.640 | We just invested in a company, a startup

00:08:42.600 | called Raggy, which is basically RAG as a service.

00:08:45.240 | It does retrieval augmented generation as a service.

00:08:48.120 | It basically helps solve this problem.

00:08:49.880 | So I think that's the stage that we're at,

00:08:51.920 | is everyone knows where they want to get to.

00:08:54.720 | And we're just dealing with some of the limitations

00:08:58.920 | so that the model can work effectively.

00:09:01.680 | And what you see right now are glimpses

00:09:04.440 | of genius or greatness.

00:09:07.240 | If you feed the exact right chunks into the model,

00:09:10.320 | you'll get answers that can blow you away.

00:09:13.120 | But then sometimes you'll just get

00:09:14.560 | an answer that just kind of isn't that good.

00:09:17.160 | And again, the reason is because the model didn't really

00:09:19.600 | know where to look for the answer.

00:09:21.000 | So right now, it's like, again, glimpses of something amazing.

00:09:24.920 | And we just have to kind of get to more predictability

00:09:27.680 | around that.

00:09:28.360 | And there's a lot of effort that's

00:09:30.120 | going into solving some of these sorts of issues.

00:09:32.960 | It's not just LLMs.

00:09:34.120 | It's also-- although it is at the LLM level,

00:09:36.680 | it's also the infrastructure around it.

00:09:41.040 | And to put the two thoughts together,

00:09:42.840 | I think the people who embrace this

00:09:45.240 | are becoming bionic at work.

00:09:46.920 | I mean, we had many challenges in podcast production

00:09:50.560 | around things like transcripts or doing show notes

00:09:53.720 | and learning, hey, about this person who's a guest.

00:09:58.160 | They were on these three podcasts.

00:09:59.520 | Now, we have producers at our company

00:10:02.000 | who will take those three podcast interviews,

00:10:05.280 | download the MP3, put them into a chat GPT,

00:10:09.640 | and say, what are the key points in here?

00:10:11.440 | What are the key timestamps?

00:10:12.560 | And even, what questions would you ask?

00:10:14.240 | Or what are relevant topics this person is talking about?

00:10:16.640 | And these data sets are disparate,

00:10:18.160 | and they're not connected.

00:10:19.240 | So you can't say, well, what's David Sacks talking about,

00:10:21.840 | if you're going to have him on your podcast, on Twitter?

00:10:24.160 | Because there's no way to get that information.

00:10:26.860 | I could tell you what the topics are.

00:10:28.400 | It's pretty-- anyway.

00:10:29.760 | And those individuals are becoming

00:10:32.080 | so valuable in organizations.

00:10:34.320 | And it's allowing people to do so much work so fast

00:10:37.000 | and then unlocking things that were just not possible.

00:10:39.840 | And that, to me, is very obvious inside the startups

00:10:45.660 | we invest in, because when they're resource constrained,

00:10:48.240 | they look for the fastest, cheapest way to do things.

00:10:50.960 | And if you watch those startups, they're

00:10:52.640 | the same ones who, when other people were racking servers,

00:10:55.800 | they were using AWS.

00:10:57.240 | They were using cloud computing.

00:10:59.080 | And they figured it out first.

00:11:00.540 | And then they built businesses that you may have heard of,

00:11:03.100 | like Dropbox, YouTube.

00:11:05.080 | That whole wave was a group of people

00:11:07.200 | who just fundamentally said, the paradigm of cloud computing

00:11:10.700 | is going to work, even though it's not perfect right now,

00:11:13.040 | even though it's slow, or expensive, or it breaks,

00:11:15.200 | whatever.

00:11:15.760 | I'm just going to go all in on that.

00:11:17.220 | And Dropbox made this critical error.

00:11:18.760 | I don't know if you remember that, Sacks,

00:11:20.460 | where they stood up their own data centers instead of--

00:11:23.260 | they were just scared to have their data at, I guess,

00:11:25.920 | AWS or whatever it was.

00:11:27.160 | And so it's going to be unbelievable

00:11:29.880 | how many businesses become viable with just three, four,

00:11:32.480 | or five employees.

00:11:33.360 | And that's what I'm seeing on the field.

00:11:35.800 | Freeberg, you have any thoughts on AI right now?

00:11:39.760 | I think that there's a chance that SaaS

00:11:42.840 | gets kind of obviated.

00:11:46.240 | I've shared this in the past.

00:11:47.560 | I think that there's a chance that SaaS looks

00:11:49.400 | like this kind of temporary phenomenon that

00:11:52.400 | occurred between the ubiquity of the internet

00:11:54.320 | and the prevalence of AI for writing software.

00:11:58.760 | If you've worked with any enterprise,

00:12:02.600 | from a traditional industry, meaning

00:12:04.320 | they didn't grow up in software, and they're not software

00:12:05.940 | native, they have IT departments.

00:12:07.680 | And those IT departments are really

00:12:09.140 | good at procurement of software.

00:12:10.640 | And that's really been their primary function.

00:12:12.560 | But they all try to hire software engineers,

00:12:14.400 | and they generally suck.

00:12:16.320 | But they understand the needs of the business.

00:12:18.520 | And I think that if they can have software written for them

00:12:22.720 | at basically a cost of zero to improve their workplace

00:12:26.200 | productivity, which was the original thing that

00:12:28.200 | was enabled by software in the first place,

00:12:31.680 | it starts to really change how folks are sourcing and using

00:12:36.040 | software, that it can actually be written for them

00:12:38.760 | in real time by software.

00:12:41.600 | And so I think that the tooling and the capabilities

00:12:44.400 | to enable the enterprise to write their own custom

00:12:46.560 | workplace productivity tools and their own custom workplace

00:12:49.020 | software, whether that's customer-facing

00:12:50.840 | or internal-facing, really becomes kind of the standard

00:12:55.040 | maybe going forward.

00:12:56.040 | And you see this, what I think Doug Leone called

00:12:59.240 | the greatest business model in human history,

00:13:01.760 | which is SaaS, getting kind of blown up in that new landscape.

00:13:05.440 | So I think there's a chance that that happens, non-zero chance.

00:13:08.560 | And I've just seen this in a bunch of settings

00:13:12.240 | where folks are having software written for them

00:13:14.720 | by the software.

00:13:15.760 | And they kind of just really state clearly,

00:13:18.200 | here's the layout, here's what I want it to do.

00:13:20.320 | And with a couple of rounds of iteration,

00:13:23.300 | they can get it working pretty well.

00:13:24.880 | And they don't need to go pay some third-party per-seat

00:13:26.920 | license fee per year to use it.

00:13:28.440 | I have heard about your poker love through the podcast.

00:13:32.480 | I've been listening for a few years.

00:13:33.940 | And it was really fun to experience it

00:13:35.520 | with you last night.

00:13:36.880 | Could you comment a little bit on how

00:13:38.680 | you think about your utility function personally?

00:13:40.960 | Is it networking, strategy training?

00:13:44.000 | Is it fun?

00:13:44.940 | Could you comment a little bit on how you all think about it?

00:13:46.640 | Why we play poker.

00:13:47.720 | There is networking that has occurred at it.

00:13:49.560 | But I think-- also, you made a good comment about it.

00:13:53.360 | Or maybe Bobby Baldwin said it.

00:13:55.840 | He never saw-- or it was--

00:13:58.760 | a poker player get old and lose their mental facilities,

00:14:02.520 | right?

00:14:03.020 | Like, they--

00:14:03.600 | No, Bobby Baldwin ran the city center in Aria for a long time.

00:14:07.800 | He plays in our game.

00:14:09.240 | And he made this comment.

00:14:10.280 | He's like, have you ever seen a poker player

00:14:12.680 | get Alzheimer's or dementia?

00:14:14.480 | Right, that was it.

00:14:15.280 | And he was saying that about Doyle Brunson,

00:14:17.080 | because Doyle, when he died, was super sharp, sharp as a tack.

00:14:19.800 | His body failed before his mind failed.

00:14:21.940 | And it's true.

00:14:22.560 | It's one of these unique games where you can really

00:14:24.720 | stay mentally sharp as you get old.

00:14:26.320 | Yeah, absolutely.

00:14:28.000 | Sax, you still love it?

00:14:29.880 | Yeah?

00:14:31.760 | Definitely?

00:14:32.880 | Yeah.

00:14:33.360 | Look, I like it for the same reason that you guys like it.

00:14:35.920 | There was a phase early on where I would play in the World

00:14:39.160 | Series of Poker.

00:14:40.080 | I would go to that.

00:14:40.980 | And then I was just like, you know,

00:14:43.040 | I actually don't like sitting there for three days

00:14:45.560 | playing with strangers.

00:14:47.120 | It's actually not that fun to do that.

00:14:50.000 | And the thing that's fun is just playing with your friends.

00:14:53.440 | So now I just play in friend games.

00:14:55.640 | I first started playing poker when I-- did I ever tell you

00:14:58.000 | this?

00:14:58.480 | I worked at a pool hall in upstate New York.

00:15:00.560 | I was 16 years old.

00:15:02.520 | I got paid $4.25 an hour to work at this pool hall.

00:15:05.240 | I cleaned toilets and scrubbed down the tables.

00:15:08.320 | And the guy sat at the payphone all day

00:15:10.240 | was the bookie for upstate New York.

00:15:12.280 | And he would take all of the bets on the payphone

00:15:14.640 | and write them down.

00:15:15.640 | And one day, after he got to know me for a couple of months,

00:15:17.880 | he's like, come and play poker with us at our home game.

00:15:19.720 | And they played Limit Hold 'Em.

00:15:21.040 | $1.00, $1.00, $1.00, $1.00, $1.00, $1.00, $1.00, $1.00, $1.00.

00:15:24.480 | And these fuckers took all my money.

00:15:26.880 | And I was 16, going to college, like fucking cold.

00:15:29.800 | I had to walk through the snow.

00:15:31.080 | I couldn't afford boots or a jacket.

00:15:32.600 | It was like really shitty.

00:15:35.160 | And they took all my money.

00:15:36.640 | And I had to call my mom.

00:15:37.560 | And I lied to her.

00:15:38.240 | And I'm like, mom, I lost my jacket.

00:15:39.780 | Can you like loan me $150 Western Union

00:15:42.480 | so I can buy a fucking jacket?

00:15:43.840 | So that summer, I bought all the books

00:15:46.480 | from the back of the poker magazines

00:15:48.040 | to learn how to play poker strategy.

00:15:49.600 | And it was all Limit Hold 'Em at that time.

00:15:51.960 | And then my freshman year of college at Cal,

00:15:55.520 | I made $10,000 that summer playing Limit Hold 'Em

00:15:58.160 | at the Oaks Card Club, playing tournaments in 6-12.

00:16:01.680 | And after that, I was-- and I learned so much

00:16:03.600 | about investing and life and like perseverance

00:16:06.480 | through ups and downs.

00:16:07.480 | Because you sit there, you grind it out.

00:16:09.200 | As long as you have a positive EV decision,

00:16:11.840 | you know you made the right decision over time,

00:16:14.040 | the money will come to you.

00:16:15.320 | You don't need to like win every fucking hand.

00:16:18.040 | And to learn that at that age, I think,

00:16:19.880 | was really influential to me in entrepreneurship

00:16:22.440 | and investing and in decision making later on in life.

00:16:25.480 | It was very important to me.

00:16:27.120 | And so when I sold my company in 2013,

00:16:29.200 | I was introduced to Chamath and invited

00:16:31.160 | to come and play in the home game.

00:16:32.840 | And for me, it was like a very kind of nostalgia.

00:16:34.920 | Because I hadn't played really much since 2001, 2002.

00:16:38.660 | What's the most impactful advice you've been given?

00:16:41.880 | And how has it shaped your careers?

00:16:43.480 | Early on, I got two good pieces of advice.

00:16:47.800 | One was from Mike Savino, who's here somewhere,

00:16:50.200 | speaking tomorrow.

00:16:50.880 | There it is.

00:16:51.920 | And he said, the piece of advice his dad gave him

00:16:54.520 | was look to the left and come in an hour before that guy.

00:16:57.080 | And then look to the right and come in and stay an hour later

00:17:00.800 | than that guy, just basically the hard work stuff.

00:17:02.840 | And I had gotten that from my mom, as well,

00:17:04.400 | who worked three or four jobs to put us through school.

00:17:06.640 | And my dad, who worked really hard.

00:17:08.760 | And so the hard work ethic, I think,

00:17:11.320 | was the key to a lot of my success.

00:17:13.600 | Because I just decided, I'm just going to outwork everybody.

00:17:16.760 | Because I was coming not from Harvard or Stanford,

00:17:19.360 | but from Bayridge, Brooklyn, and going to night school

00:17:22.720 | at Fordham.

00:17:23.200 | So I had to use hustle.

00:17:24.640 | There was no connections.

00:17:25.720 | There was no network.

00:17:26.600 | I just had to literally kill what I ate.

00:17:29.480 | And then after I sold my first company,

00:17:31.120 | I was at TED at the Billionaire's Dinner

00:17:34.160 | with my book agent, John Brockman.

00:17:36.600 | And I still had a chip on my shoulder,

00:17:38.760 | but I had sold my company.

00:17:40.560 | And he said, hey, Schmuck, you made it.

00:17:45.400 | You stopped fighting with everybody.

00:17:47.920 | And I was like, OK, I'll stop fighting with everybody.

00:17:52.320 | So you took that advice?

00:17:53.600 | I took it sometimes.

00:17:54.520 | I flip a coin.

00:17:55.680 | When did you do that?

00:17:57.640 | You need to look at that memo.

00:17:58.880 | When did you get that advice?

00:17:59.720 | Last night, or--

00:18:00.720 | Yeah, I just got it.

00:18:01.600 | You're just texting me right now.

00:18:03.560 | But it was actually because I had to fight for everything,

00:18:07.000 | I think I just kept fighting to try to get the next level.

00:18:12.240 | And at a certain point, I decided

00:18:14.240 | I would be like super magnanimous

00:18:16.000 | and just be helpful and elder statesman-like.

00:18:19.440 | And I'm not perfect, but--

00:18:21.440 | What?

00:18:22.680 | Well, I mean, when you don't see me working,

00:18:24.280 | you don't see me working with startups.

00:18:26.520 | I am tireless in my ability with patience for them.

00:18:30.080 | And supportive.

00:18:30.680 | And supportive.

00:18:31.240 | And I just-- and I try to do that with my friends as well.

00:18:33.320 | It's just, what's the point of being successful

00:18:34.920 | if you can't support the people around you who you love

00:18:37.320 | and try to pay it forward?

00:18:38.400 | So those are just--

00:18:39.240 | You are like the most reliable wingman of all of us.

00:18:42.760 | Oh, thank you.

00:18:43.760 | You know, value your friends and your family

00:18:46.640 | is just such critically important advice.

00:18:49.160 | Because at the end of the day, all you have is your memories.

00:18:52.600 | And you make them with your friends and your family.

00:18:54.720 | And so I am on a mission to make great memories

00:18:57.720 | with my friends and family, as many as possible.

00:19:01.040 | And actually, this is part of it, you all being here.

00:19:03.560 | And that's why I always tell you, make a couple of friends.

00:19:06.240 | It's hard to make friends, especially as you get older.

00:19:08.560 | And people kind of tighten up their circles.

00:19:10.400 | Make a couple of new friends and go do things together.

00:19:15.320 | Anyway, some philosophy, I don't know.

00:19:17.360 | Advice that you got?

00:19:20.120 | The best career advice I got was from Peter Thiel,

00:19:23.360 | who advised me not to go to law school.

00:19:26.200 | And unfortunately, I didn't listen to him.

00:19:27.920 | I went anyway.

00:19:29.600 | But it ended up not mattering, because I ended up joining

00:19:31.920 | PayPal after I graduated from law school.

00:19:34.520 | So I wish I had a better story.

00:19:36.040 | The best career advice I've actually heard for Silicon

00:19:38.760 | Valley was the advice that Eric Schmidt gave Sheryl Sandberg

00:19:42.960 | when she joined Google, which is,

00:19:45.200 | when you get invited to take a seat on a rocket ship,

00:19:49.360 | don't ask which seat.

00:19:50.360 | Yeah, just get on board.

00:19:52.240 | I actually think that is perennially great advice

00:19:55.760 | for anyone in Silicon Valley.

00:19:57.320 | There aren't that many of these rocket ships.

00:19:59.200 | So when you get a chance to be on one,

00:20:00.960 | you should just take it and worry about the titles

00:20:03.480 | and all that kind of stuff later.

00:20:05.200 | Yeah, the details later.

00:20:07.200 | The biggest piece of advice that I still

00:20:08.840 | struggle to take every day--

00:20:12.600 | every time I have, though, it's had profound impact on my life--

00:20:15.520 | is to focus.

00:20:16.120 | I think the bigger a portfolio you develop,

00:20:21.760 | the less alpha there is.

00:20:22.880 | You try and minimize beta, but you take all the alpha away

00:20:25.280 | if you do that.

00:20:26.080 | In 2009, I focused my company on the agriculture market,

00:20:29.080 | which was crazy.

00:20:29.800 | We were in seven verticals and doing all sorts of stuff.

00:20:32.680 | When I focused on it, again, I went deep.

00:20:34.440 | I went on a long hike in Iceland and I came up

00:20:37.600 | with this crazy idea for the product for the app market

00:20:39.840 | and came back.

00:20:41.720 | We launched it and we did like $30 million in sales that year.

00:20:44.480 | And that made a huge impact in my life

00:20:46.240 | and the trajectory of the business changed.

00:20:48.040 | And every time I focused and avoided distractions,

00:20:51.200 | it's made a huge impact.

00:20:52.240 | I think my decision to become a CEO again back in November

00:20:54.960 | was a really important one for me.

00:20:56.600 | And I realized I was on lots of boards and doing

00:20:58.600 | lots of investing and thinking about lots of things,

00:21:00.760 | but now I can really dig deep.

00:21:02.280 | And I think that everyone assumes

00:21:04.000 | that there's this power law in the world

00:21:06.600 | that you are a passive participant in.

00:21:10.360 | You're either going to catch one of the power law winners

00:21:12.760 | or you're going to lose it.

00:21:14.040 | Therefore, you've got to portfolio your way to a power

00:21:16.720 | law to catch a power law.

00:21:18.400 | I don't give a shit about that because I think my job in life

00:21:21.280 | is to make the power law and to make that outcome.

00:21:24.280 | And I feel like that has made a huge impact

00:21:26.400 | on how I think about life.

00:21:27.520 | And it's really-- every time I've tried to act on it,

00:21:30.560 | it's actually paid back significantly.

00:21:33.000 | So focus is the biggest piece of advice

00:21:34.960 | I've gotten that I think matters.

00:21:36.440 | So the version of that that I tell founders all the time

00:21:39.400 | is always be all in on your best idea.

00:21:42.440 | Because founders, sometimes they have a whole bunch

00:21:44.520 | of different ideas.

00:21:45.200 | Sometimes they're doing multiple startups.

00:21:46.600 | Sometimes they're thinking about pivoting,

00:21:48.360 | but they're still hanging on to the old idea.

00:21:51.120 | And I always give them permission to pivot.

00:21:53.800 | I'm like, don't worry about what you

00:21:55.600 | said you're going to do whatever two quarters ago

00:21:58.280 | when you raised this money.

00:22:00.200 | Whatever you think the best idea is right now,

00:22:03.360 | let's go all in on that.

00:22:04.600 | Do not hedge your bets.

00:22:06.440 | I think that's just really important for founders.

00:22:08.800 | And it goes back to what you're saying.

00:22:10.440 | It's just so hard to execute any idea that you

00:22:14.400 | can't be hedged as a founder.

00:22:16.560 | Seven samurai.

00:22:18.440 | Akira Kurosawa did such an amazing job

00:22:21.880 | representing this ethos, I think.

00:22:24.680 | You strike, and you strike perfectly,

00:22:26.440 | and you strike with full force and with all your energy

00:22:28.720 | and everything you have, your whole character, everything.

00:22:31.320 | And you can accomplish incredible things.

00:22:33.120 | You have to put one foot in front of the other every day.

00:22:37.440 | And you have to focus on tangible progress.

00:22:42.480 | And where that fails is when most people--

00:22:46.040 | and I do it a lot, and I've tried

00:22:47.680 | to get better as I've gotten older-- is when I get

00:22:49.680 | comparative and I compare myself to the other person,

00:22:53.280 | the other company, the other funding round.

00:22:56.600 | There are so many reasons for you

00:22:58.160 | to feel like you're less than something else.

00:23:01.560 | And the reality is that has nothing to do with you.

00:23:03.960 | You're not in control of that.

00:23:06.240 | But it's so hard.

00:23:07.760 | And then if I don't take that medicine, I become insecure,

00:23:12.560 | and then I make mistakes that are entirely avoidable.

00:23:16.160 | So it's just tangible progress, the things that I can control.

00:23:20.160 | That's probably the most useful piece of advice

00:23:22.960 | that I try to remind myself of every day.

00:23:26.080 | And then I kind of have these two jobs.

00:23:29.200 | One is I'll incubate companies from time to time

00:23:31.360 | if I get intellectually curious enough.

00:23:33.840 | But the other part is just as an investor.

00:23:36.280 | And the best piece of advice I got as an investor

00:23:38.480 | is you are a big wave rider.

00:23:44.200 | And the swells that become ginormous

00:23:48.600 | are not visible when you paddle into them.

00:23:51.280 | And so you've got to commit, and you've got to go,

00:23:55.160 | and you are going to have less than 100% hit rate,

00:23:58.880 | and you will get massively washed out.

00:24:02.000 | And you just got to get to the surface and swim back out.

00:24:04.480 | And as an investor, that's been helpful

00:24:06.140 | because I've had some huge wins, but I've also

00:24:08.480 | had some huge losses and some total embarrassments

00:24:11.480 | and flame outs.

00:24:12.080 | And it's the totality of that that

00:24:14.520 | allows me to swim back out.

00:24:15.880 | So those are two different things.

00:24:17.840 | But the entrepreneurial one and the life one

00:24:19.840 | is more valuable because it's so easy to get distracted

00:24:24.960 | and feel insecure because of what somebody else is doing.

00:24:27.840 | And it always ends up screwing me up.

00:24:29.560 | I think that's a good note to end on.

00:24:31.240 | Give it up for my besties.

00:24:33.800 | And we're going to take a little walk over here

00:24:37.440 | for a little surprise.

00:24:38.440 | If I could have my four besties follow me over here.

00:24:40.800 | [MUSIC PLAYING]

00:24:44.160 | I'm going to walk over here because it's

00:24:46.800 | a very special day or a special time of the year.

00:24:51.160 | It's the Sultan of Science's birthday again.

00:24:54.040 | So come on around.

00:24:55.760 | And we got him a beautiful cake and then

00:24:57.880 | a tiny little vegan cake.

00:25:00.080 | And we're all going to sing the Sultan happy birthday.

00:25:05.040 | 3, 2, 1.

00:25:07.520 | Happy birthday to you.

00:25:11.740 | Happy birthday to you.

00:25:15.040 | Happy birthday, dear Caleb.

00:25:19.760 | Happy birthday to you.

00:25:23.400 | We love you, pretty bird.

00:25:24.560 | [MUSIC PLAYING]

00:25:27.920 | ,

The Besties Take Napa | All-In Special

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