a16z Podcast - a16z Podcast: High Growth in Companies (and Tech)

Episode Date: July 20, 2018

with Elad Gil (@eladgil) and Chris Dixon (@cdixon) There's a lot of knowledge out there -- and networks of talent (especially in Silicon Valley) -- on what to do in the early stages of a company, going from 0 to 1, and even in going from 1 to 100... but what about beyond that? It's not as simply linear as merely doubling or tripling resources and org structures; it's actually much more complex on many levels, communication to coordination. Because with great scale comes great complexity... and many, many more places for things to break down. So how should founders/CEOs of growing tech startups think about everything from hiring (including key executives) to product management (what is it, really, beyond common myths/misconceptions around the role?) to thinking about late-stage financing, M&A, and other key aspects of building a company? This episode of the a16z Podcast shares both specific answers to -- and general mindsets for thinking about -- these questions. Chris Dixon, general partner on a16z crypto, interviews Elad Gil, investor/advisor to numerous tech companies; co-founder of Color Genomics; formerly of Google and also co-founder and CEO of Mixer Labs (acquired by Twitter, where he also became a VP). He's the author of the new book, The High Growth Handbook, on scaling companies from 10 to 10,000 people. But the two also explore the growth -- and evolution -- of market and tech trends, including the continuation of mobile/cloud; machine learning (and silicon); crypto; and finally, longevity -- both in the near term and further out in the future. Should people -- and even companies for that matter -- really live longer?

The content here is for informational purposes only, should not be taken as legal business, tax, or investment advice, or be used to evaluate any investment or security and is not directed at any investors or potential investors in any A16Z fund. For more details, please see A16Z.com slash disclosures. Hi, everyone, welcome to the A6NZ podcast. Today's episode features Chris Dixon, now a general partner on A6NZ crypto, interviewing Elad Gill, the author of a new book. just out called the high-growth handbook, scaling startups from 10 to 10,000 people. Elod is an investor or advisor to numerous tech companies, as well as co-founded color genomics. He was also co-founder and CEO of a company that was acquired in the early days of Twitter, where he also then became a VP, and he also worked at Google, where he started their mobile team and worked on AdSense. The discussion that follows covers everything from executive hiring to product management,

to late-stage financing, and more. The two also then discuss broader market and tech trends, ranging from the continuation of mobile cloud to machine learning, to crypto, and finally to longevity, both near term and further out in the future. But they begin with the challenges of scaling. There's sort of famous books like Peter Thiel's book, which is from zero to one, kind of the early stages of a startup. And I think a lot of blogging and other kinds of stuff has been out there from like Paul Graham and Fred Wilson and things about fundraising and product ideation and things like this. but there hasn't been much written about all the later stages. I mean, to your point, I think there's, you know, Peter Thiel's book is zero to one,

and really the hope is to have a book that's talking about when you go from 10 to 10,000, you know, something's working, you have product market fit, and then what are all the different things that break? If you think about it, the surface area of an early stage company is actually really simple. It's don't run out of money, don't fight with your co-founder, and find product market fit,

and that's all you have to do, and you'll be successful to some extent. Although, of course, very hard to do. It's incredibly hard, incredibly hard, but, you know, those are the three things. At the late stage, there's all sorts of things that can break, executive hiring, buying companies for the first time, whether you should go public, how do you do late stage fundraisers, how do you build a product management org? Like, there's all this complexity that starts to kick in. And so this book is really meant to try and address those leader stages, something's starting to work. What do you do? You know, it's interesting because

I used to be based in New York and now based in Silicon Valley. And there's a lot of, you know, the companies that get to product market fit, they're not just in Silicon Valley, right? There's plenty of companies in New York and other cities, other places. What I've noticed is different about Silicon Valley is there's a whole bunch of people who have done the scaling the one-to-end phase. And so I kind of describe it as like the second stage rocket, you know, that sort of the first stage is finding product market fit and then can you get the second one layered on there?

And one of the advantages of Silicon Valley, I think people talk about how there's more engineers and there's more investors, but those are frankly everywhere. But the big difference is VP marketing, VP sales, VP engineering. and then all the product managers and just sort of all those layers in between. Somebody was telling me, a European founder was telling me that they felt that often European technology companies will cap out at a certain stage. And it's in part market dynamics around the EU market, but in part it's just driven by the fact that you tap out in terms of where the executive team is capable to take things given their experience. So there's a lot of misconceptions or myths around skilling startups. What do you think are the biggest changes in the company as the organization grows?

I mean, the naive view is you have 50 employees, you have 100 people. that's just twice as many, and you've got to do some, you know, more management. But of course, what really happens is the communication patterns break down and a whole bunch of new things have to be developed. There's three or four big shifts. So you touched on a really key one, which is the way that you communicate is fundamentally different, particularly if you're high up in the company. And part of the reason is, you know, if you're 10 people, you can just sit in a room

and talk to everybody. If you're 100 people, there's two to three layers between the CEO and every other person. But as you start hitting 500 or 1,000 people, the layers multiply. and the ability for information to move up and down the stack shifts. And that also means that the modes of communication has to change, right? You can't go and have as many skip level meetings. Not everybody's going to shift with all hands anymore. You have people in international offices.

And so you really have to move the modes of communication in a pretty deep way. The second thing is you really have to focus on building out a strong executive staff. And not just that, you're going to have entirely new functions that you as the CEO have never dealt with. And you have to figure out how do I hire a CFO for the first time. how do I think about customer support at scale? And so you have to start understanding how are you going to hire all these really talented people and convince them to come in

and build out their respective orgs. The third is that you start adding new processes and you have to have a certain set of lightweight processes for strategic planning, for deciding how to budget. At 1,000 people, it's more about how do you start allocating resources to the right areas. And then lastly, the surface area of what you're doing expands so dramatically that you have to really learn

how to delegate and manage at scale. So if you're internationalizing and you're dealing with different time zones and a much larger orb, you have to do with that. If you're buying companies, you now have to have people who can deal with the integrations of those companies. If you're launching multiple product lines,

how do you think about deprecating the old ones? How do you do cross sales, bundles, etc.? And so the complexity keeps sort of notching up across the board. One thing I've noticed, so for example, if you take your typical former programmer, engineer, founder, and let's say they're doing a B2B kind of sales company,

and then they have to at some point hire a sales leader and marketing and all these other things. They have never done that before, right? And so they just have no idea what greatness looks like. And so, you know, how do they do that, number one? And number two, there's a tendency, I think, to apply some of the same filters that one might use in the first 10 people when you're hiring product. So, for example, I've heard multiple times engineer founders wanting salespeople who know how to program or something, right? Which is probably not compatible with generally great enterprise sales skills. And so what kind of advice do you give for people who are hiring in these roles for jobs I've never frankly even worked maybe with, let alone hired for?

I think there's basically two things that are sort of good generic advice. And obviously the only good generic startup advice is that there's no good generic startup advice, right? So take everything with a grain of salt. But the first is don't reinvent everything. I think if you're an innovative, especially technical founder, you want to rethink what's my org structure. Ultimately, you know, there's a reason that corporations have been around for hundreds of years and actually work. and there's certain org structures that you can innovate on, but ultimately, you should find certain experience people to do certain things where the function needs that experience.

So that's sort of a meta point. I think in terms of finding an executive, there's a few steps that you can take. The first one is you should go and meet all the best people of that function. It's not necessarily people to hire. It's people to learn from. So what does a great CFO look like? And then I would just ask them, what would you look for in a person that would fit my company? And what sort of experience would you look for?

what interview questions should I ask them? How should I get to know this person? How will I know if they're good in the function? Second, once you get that feedback, I'd write up a job rack and I'd circulate it to all the people who are going to be interviewing this individual because they don't have any context either and they have no idea what to look for. And so I found, for example, when you're hiring a BD person or somebody to run BD, an early company will have a mix of people from engineering and product and other orgs. And they may have no idea what to actually look for and they may think it's a salesperson versus a BD person versus something else. And so just to defining that tightly so everybody knows

these are the questions we're going to ask, these are the characteristics we're looking for, allows both clarity of interviewing, but then it also allows for clarity of discussion of the candidate. Once you make that higher, and then the last thing is, you should figure out how to onboard them effectively. And part of that will be working with them closely

to figure out what they think their role should be and what the function should be, what they think it should evolve as, and then mixing in your own opinion and making sure that you don't feel overwhelmed by their expertise, but you're willing to defer when they really understand something you don't. One thing, one process I've observed is companies that don't think they need, let's say a CFO. And what we like to do is we do what we call calibration where we introduce

them to three great CFOs who probably aren't looking for jobs. And almost invariably, the founder comes back and says, oh my God, I didn't know that someone could do those things and like all those problems that would solve for me. And it's completely eye-opening, right? Because if your background is engineering or whatever, you've just never seen greatness in CFOs, BP sales, et cetera, right? Yeah, absolutely. I think the services model that Andrew Sin Horace has built is really valuable for founders as they're scaling in the CEO role because to your point, otherwise, they'll have no idea. Founders have a bunch of questions about hiring execs. When should they hire them? What should they look for? There's lots of debate about how high the bars should be and what kind of athletes versus veterans or something. Where do you come down on those issues? So there's a few different sections of the book that talk about both executive hiring as well as managing your executive team and how do you go about it. And I think there's a few key tenants in terms of bringing on execs or alternatively promoting. people from within. I think Ben Horowitz may touch about the inside cameras in his book or in other articles. You should always be thinking of the person as the right person for the next

12 to 18 months, not the person for the next five years. And so I think a common mistake is that people will end up hiring, you know, they'll have a 20 person engineering team and they say, hey, in five years, we'll be 500 people. So let's hire that 500% engineering team person now. They hire way too far ahead. And that's a person who's out of a big company, used to managing a big organization. They'll basically show up, be incredibly bored and either try to overhire and hire a super senior staff or just turnout. Second, people tolerate good versus great hires for too long. And so they'll have a good VP marketing versus the person who's truly exceptional and a great fit for what they're doing. And it's almost like being on a life raft. And you're like, I have

five spots. And who are those five spots? It's not this giant yacht, you know, cruising down the Mediterranean that you can have hundreds of people on. And so really your executive team should be this life raft. And you should ask if this person's good, but not great. How do I find great? Sometimes, by the way, those people will be co-founders, other people who were early, you're loyal to them, they were really helpful in the beginning, and they may still be great in the organization, but maybe they're not the best VP, right? I don't know. That's the tension I've seen sometimes. I think in general, dealing with sort of what are called old-timers in the book is a key question because they've proven their loyalty to the company. They were essential in their early days. They have all the cultural context. They have all the trust of the founders, which is really important and valuable currency in terms of getting things done. And a subset of them are going to

act really badly when all said and done. They're going to drag their feet. They're going to argue with new hires. They're going to effectively throw sort of a prolonged mini-fit. and part of that is just driven by the fact that they used to have enormous influence and that influence is diminishing. Part of that is it's a very chaotic environment and part of that is not understanding what's coming. And what's coming is enormous growth potential for everybody who's involved with the company. And so I remember when I joined Google, it was about 1,500 to 2,000 people and over three and a half years, I grew to 15,000 people. And the first time I went through hypergrowth, I was caught up in all the chaos. If I had a peer who got promoted above me, I'd worry

what about me? And the second time I went through it was at Twitter, and they bought my startup when Twitter was about 90 people. And over two and a half years, I went to 1,500 people. And the second time through it, I just thought, you know what, the company is going to grow in all these different directions. And that means that from a career perspective, I'll be fine. And I should just do good work and keep my head down and try and be helpful. And so there was a product reorg that happened on the Twitter team that was a little bit messy. And Dick was asking different product managers what they wanted to do. And I said, well, I just want to help the company. Like, put me wherever you want. I don't care. I just want to help the company.

And then two months later, I got promoted to be a vice president there. And I think it was because I just put everything else aside and said, where can I help? So I think that's a really important thing. Put aside the angst, focus on the big picture, and realize that every six months, if the company is in hypergrowth, it's a different company. If it goes from 50 to 200 people to 500 people over six to 12 months increments, it's a completely different company. There's new opportunities. Somebody's going to go and need to open international offices. Somebody's going to need to launch new product. Somebody's going to be needed for new functions. And you can be that person if you do good work and keep your head down. And the other thing you should expect

as an old timer is your role is going to shrink and then it'll expand. And you need to be okay with that shrinking period. And you need to be okay with you losing responsibility before you suddenly gain tons of responsibility. Because if you're the only designer, you were designing everything. But you're going to start hiring other designers and you're going to have to give things away. But a year later, they're going to have to promote somebody to run a chunk of the design team, and that may be you if you act the right way. And so I think really keeping that in mind is crucial. So let's talk a little bit about product management. What are some of the key learnings that people should know about there? There's very few, very strong product organizations

in Silicon Valley. Google has a great product org. Facebook has a great product org. There's a handful of them. And part of that, I think, is because often technical founders either create a suitor role for that and other functions are basically filling the gap, or in some cases, it's back to the earlier point around you never see excellence in the role so you don't really understand how to build it out. And so often the evolution of a product organization will be one of the founders will be really the product manager for the product. And then it'll evolve into a state where there's gaps and they're trying to fill the gaps around product. And so they'll pull in somebody from another team. It could be marketing. It could be sales. It could be

business operations. It could be something else. And those people may be very exceptional at running process and running schedules, but they may not have the product insights and the experience to really know, okay, this is how you think about product road mapping. This is how you think about a PRD or writing a spec for the product for the first time. And here's a set of processes that help me engage all the stakeholders around the company to really sort of drive a product forward. So eventually what happens is if those processes start to break down or that approach starts to break down, they'll end up eventually hiring in somebody who has deep product experience to build out an org and then it sort of gets professionalized. Facebook and Google, they obviously

are hiring great people. But I think it's also the way they, empower those people and design the organization that makes some great product organizations. Yeah, they ended up hiring Jonathan Rosenberg and externally to run product for Google. And what he ended up doing is, I think, three key things. Number one is he really started mentoring a lot of the key product individual contributors who went on to actually run most of the product org at Google in the early days. So Susan Wichiki, who now is the CEO of YouTube, Salar, who similarly had run YouTube. been before that, the ads team,

Marissa Meyer, who became CEO of Yahoo. So you have these people who were sort of all-stars in terms of the things that they've accomplished. And very early on, they were basically individual contributors on the product org, and they were really learning the ropes. And some came from engineering backgrounds, and some came from marketing or other backgrounds, back to that mix of people sort of joining.

And so one, is he mentored people actively. Two, is he really focused on the process side of it. What are the checkpoints for launching a product? How do we approach it? And then lastly, he actually built a training program called the associate product management, product manager program where they basically recruited really, really smart people out of, in the Google case top universities, and they basically said, we're going to train these highly technical, really smart people to do product

management. And we're going to select for people who, in our interview process, seem to exhibit very strong product instinct. And so that group ended up, including a variety of people who've really gone on to impact product organizations all over Silicon Valley. So this will go to the question of what is a great product manager. Just first, what is product management. I think you get confused sometimes with project management on the one hand, design on the other hand. Yeah, it's a good point. Ben Horowitz has a good product manager, bad product manager from sort of the 90s that tries to encapsulate enterprise product management. Ultimately, the role of a product manager is fourfold. Number one is to really

own the generation of the product requirements and what's the user need, how will this actually get used, how is it differentiated, what are the key components that make this a good product. So one is sort of owning that view of the product and roadmap and sort of... And depending, for example, in the enterprise company, typically they'll sit between sales and engineering and kind of mediate the two. Yeah, there's typically a natural tension between product and other organizations. And sort of a second role that they often play particularly in enterprise companies is there almost a buffer between engineering and the rest of the world, but they're also a communication device both in and out. In other words, they'll help synthesize a lot of the different feedback from sales and directly from customers. I want this feature or want that.

And the salespeople are kind of incented to say yes. The engineers, on the other hand, want to keep the product focused and get their job done. And so there's a tension, right? And the product manager sits in between and kind of prioritizes and figures out what really matters. Exactly. And on the engineering side, there's tension with product because engineers may think a feature is stupid because they're not necessarily hearing the direct feedback from the customer. And they may not believe, oh, the customer really needs this,

or they may want to go down the road of building something that may be more complicated than what's really needed to future proof it. for other reasons. And if product pushes back on that, there may be tension there as well. And so the role of a product manager in part is also to navigate all that tension between the different organizations and end up with a view of what's really needed for the product despite all these various inputs.

Great. So let's talk about, in your book you talk about late stage financings. There's been a lot written and sort of blogged and other things about early stage and I think people, at least in the startup community, kind of understand Series A and B, but there's a whole kind of new, new I say, because in the old days 10 years ago you'd go public or something. And now

There's just a whole kind of other set of investors. What are the things, the main things that founders should know about that? Yeah, I think there's a few key things that founders should consider when doing a very late stage round. And many of these rounds, in some cases, are tied to secondary, in some cases are not. So the first is there are some value-added late-stage investors. And so just like in the early stages, not all money is equivalent. In the late stages, there may be people who will continue to buy stock as you go public and therefore help support the price of the stock in the early days as things start.

She's going to be the so-called crossover investors. Yeah, Tira Price, Fidelity, and the like. They may have interesting insights in terms of how public markets will think about them, and therefore how they should be thinking about navigating their business early on or the sets of reporting or controls or other things that they want to build in. Or they may have real insights into markets or differential networks that you normally wouldn't get access to. And so I do think that there's investors who can help in a more traditional sense.

They just get involved later in the life of a company. So the first question is just whether or not this money will be helpful. It doesn't have to be, frankly, because in some cases, in many cases, you have escape velocity, but optimally you have capital on board that's always going to help. The second thing is that you don't want to get too far ahead of yourself in valuation, and I think often at the later stages, there's the temptation to do that because the odd thing is the higher the valuation of the company

and the better known the brand, the more sources of capital exist. And so what you see is on the late stages, some companies start building in very complicated structures because they want that very high valuation and a firm may be willing to give it, but then they build in extra preference or participation for the investor or things that effectively are really a debt instrument

masquerading as equity. They're really locking in a return. Participating preferences, multiple liquidation preferences, ratchets, all sorts of kind of quote structure. Yeah, yeah, exactly. The last thing that I think is interesting about late stage capital is the degree to which it allows companies to keep going whether they should

or not. And so I would posit that about half of the unicorns, are not worth a billion dollars or more. In other words, 50% of the companies that are valued in the private markets as being worth more than a billion shouldn't be worth more than a billion. And that day of reckoning keeps getting pushed out because of so much capital in the markets and so much liquidity sort of pushing things ahead. So there's been big shifts in terms of late-stage financing and how you get that done.

There's also been big shifts in terms of M&A. What do you attribute that to? I think it's maybe three things. One is private market valuations continue to go up. Second, I think that there is a generation of founders who haven't really made the transition mentally that often as a company scales, you're moving from a product-centric organization to one that's both product and distribution-centric. And this is actually something that Mark Andreessen talks about a little bit in the book

in terms of a discussion that we have where, you know, if you look at the old model, you look at Cisco or Microsoft or any of these companies, what they would do is they'd have over than anything else. They'd get distribution due to that. And then what they do is they'd go and buy companies or build things. things and push it against that distribution channel and continue to sort of iterate. That also happened with the next wave. I mean, Google did that really aggressively with everything from Google Maps to Android to Chrome. Facebook did it. And so the traditional model is you buy stuff

and you distribute it to your channel and you realize you're also a distribution company. And I think that a lot of folks today have less of that mindset of, hey, we're also a distribution company and therefore we should be buying things aggressively to distribute. I think a third area is internal pushback. But maybe it's stronger now around two. things. So the argument is often, well, you should wait, or we shouldn't buy it because it'll take us two years to integrate it. And often the answer may be don't integrate it. You know, it's okay. Maybe if it runs independently, you have a series of APIs talking to each other. And then secondly, there's often arguments around comp. If we had $10 million

instead of buying this company, we could hire 50 engineers and therefore we should hire those engineers. But it's often really hard to find 50 incremental engineers and then allocate them to that thing that you'd buy. Often you allocate them elsewhere. I think I agree with all that. and I would add to it that some of these big tech companies have shifted their salaries so dramatic. Like they basically broke, like traditionally incumbents would have sort of salary bans, and that was one reason startups would leave, and then they would also, then they get re sort of aqua-hired. But now you look at the salaries, the companies like Google, some of this came out in like the court documents of what they were paying, like the Waymo engineers, and it's like, you know, it's like the price of, an annual price of a startup acquisition or something.

So they've also just dramatically rethought their pay scales, which has hurt the kind of aqua-hire market. I think there's probably different analysis of different stages like Aquahire, and then sort of tech tuck-ins would be more of the distribution. I think the takeaway, though, for founders, I think it was probably never a good idea to build a company to sell, but it's even more so not a good idea today. You need to first hit product market fit, and then you need to figure out of scale, and you just have to deal with those issues, right? Absolutely. But I also wonder how much of it is cyclical or sort of secular change, right? Is it like a fundamental change in the market? I think you're raising two really good points.

One is, what's the why now of why to buy a bunch of stuff? and often those mirror technology waves. And so since we just went through cloud mobile and social as three separate waves, you actually saw companies subsequently buying talent for those areas. I remember when Twitter bought my startup back in 2009, shortly thereafter, there's a big wave of buying mobile companies

and every company was trying to buy mobile teams because iOS was new. Yeah, yeah. And then it was AI and then it was, you know, or whatever. Exactly, yeah. So maybe the only area where people are very aggressively buying right now is machine learning. And so maybe there's a lack of why now.

I think part of it too is people aren't really hiring corporate development people early in the lifecycle of these breakout companies of this last batch or last generation. And I think that may also be a reason. Like there's nobody who's just thinking about it nonstop. And often you see these transitions where if you hire a great corporate dev person selling the company, we'll go and buy a bunch of companies. And it's a little bit chicken or egg, but I actually think having somebody who's thinking about it strategically and can aggregate all the input from the different teams in the company and take it to the executive team and say, hey, here's the five things we should buy in why actually makes a big difference. And so Coinbase is a good example where over the last,

you know, 12 months, I think they've made five or six acquisitions. And in part that may be driven by them hiring Emily Choi to come in and, you know, really drive that function in a smart way. So one thing I like to think about a lot is sort of where are we in the history of tech. We're still in the middle, I guess, of the sort of mobile revolution, but obviously sort of mobile phones and all that. And cloud computing and SaaS, social. What's the big thing over the next 10 years? Clearly those trends are going to continue. But we'll be. we layer on more trends. We do seem recently to be headed for this kind of consolidation around the big five tech companies. Will that continue? Will there be new things that kind of come out?

Obviously, you and I both have an interest in crypto as an example as kind of this new thing that's kind of potentially quote unquote disruptive. Yeah, it definitely feels to me like there's four areas that are really interesting right now. The first, to your point, is a continuation of the trends of the last decade around cloud and mobile and then sort of social being a primary consumer platform at this point. The second area really is the crypto world. And at least for me, a lot of the really interesting areas in that are around forms of money, store of value, privacy tokens, et cetera, as well as sort of the smart contracting world securitization of everything. And then lastly, the NFT or the non-fungible tokens. So persistent digital goods that you can mix

and match in different ways across media, games, et cetera. So crypto is super interesting. Third area is the sort of systems and semiconductor layer of machine learning. So if you look at Nvidia and Nvidia GPUs are really the primary basis for a lot of sort of performant machine learning models. And Google obviously launched TPUs a couple years ago, a tensor processing units, a specialized ASIC that's really meant to help accelerate certain types of machine learning models as well from a hardware perspective. And so I think that's a really interesting, pretty wide open area. And I think that's pretty transformative. and it's a way to effectively index machine learning because I think it's very hard to invest in

quote-a-quote a machine learning company. And then lastly, I'm personally very interested in sort of longevity and anti-aging technologies. And by that, I mean true biopharma, which is translating existing science that's existed in labs for 10, 20 years and is trying to turn it into drugs that can extend lifespan pretty dramatically.

So the continuation of mobile and cloud, we've gotten through obviously kind of the first wave of that. What would wave two? What would be the most exciting things about Wave 2? I think there's two or three components of Wave 2 that are really interesting. Number one is on the SaaS side. I still think there's a dozen billion dollar plus companies to be created where you're just taking something that everybody's building internally over and over again

and it was turning it into an API. So factor out a thing that people are building over and over and make it an API? Exactly. And I think if you took apart a Fortune 500 company and asked what are all the different things that they have to do repetitively, you could come up with a dozen of these. So I think that's super interesting. I do think there's a lot of survey. that are sort of mobile first in really interesting ways.

I think there's, again, reinvention of a lot of different areas. You and I could talk about crypto for a full podcast or more, so I won't go too much into that. But I guess one big question is, where do you think we are in the evolution of it? Like, what are the next kind of key milestones that you'd want to see? I think of it is there's two things. There's sort of the prices, and then there's the real innovation. And the real innovation has been strong, but I think there's a lot of things that need to happen. Yeah, it's a great point.

You know, I think the primary use case that is clearly working as some form of store of value, slash speculative asset. And there's lots of things that are claimed to be working that aren't. So, for example, nobody's really building scalable apps on Ethereum right now. And I think the Ethereum community is clear that they need to be able to scale that platform. And so one key thing is infrastructure and scalability. And so a lot of people are obviously focused on that right now. You know, ultimately, I guess the way that I would think about it at a macro level to just abstract out a level is it reminds me a lot of the late 90s, where there's a few fundamental

core things that people are clearly doing on the internet. and then there's tons of speculative stuff that's going to go to zero. And so in the early days of the internet, you needed PayPal to be able to buy things. You needed to be able to start doing commerce, Amazon and eBay and some of the early sites. You had Yahoo and then later Google for media and things like that. But most dot-com companies didn't work out. The interesting thing is that you basically had a speculative bubble couple to real value creation. I mean, Amazon, the most controversial company in that era, of course, is worth far more than its peak back then.

So it took a long time to get back there. Yeah. And then there was new companies like, Google that ended up going public much later, Facebook didn't even exist, and then ended up becoming a major force. And to your point earlier, it said you had to have both apps and infrastructure. In that case, the infrastructure was things like broadband and mobile phones and things like that.

But, you know, you couldn't have had Netflix and YouTube and a lot of just the rich experiences that people expect today without a lot of infrastructure build out, right? I mean, Netflix was literally mailing DVDs, right? And so it's shifted pretty radically to becoming a major content producer. And I think in crypto, we're going to see the same thing where, A, most crypto projects from today are going to go to zero. There's a few that are going to be incredibly valuable in ORDR, and that's going to grow. But I think most are going to go to zero.

But second, I think as you have this technology built out, you're going to see ideas from today that fail 10 years later work. There's this game, I think it was called Dot Bomb. It was a board game that was popular, I don't know, popular, but somewhat popular among certain people in like the early 2000s. And they had cards that were all of the, they were called Bad Idea cards. And they were all the bad ideas of the 90s. And it was literally like, if you go through the cards, it's literally every top. It was like ride sharing, like selling pet food online, internet money, you know, group deals, like mobile gaming. It's literally all the quote unquote bad ideas actually turned out to be really good ideas.

They were just way out of their time and not built right. Yeah, I think we're going to see the same thing in crypto where there's all these ideas that are early and there's no infrastructure. And in 15 years, they're going to be amazing. And then the third category. I was machine learning. Silicon machine. That's interesting because historically you have some big new trend and what is, startups do you build infrastructure software

etc. Now all of this stuff is either open source or like the algorithms are published openly as papers. A lot of the code implementing those algorithms is very high quality kind of open source stuff that's in turn monetized through the cloud service providers, right? So it's kind of hard, you know, now as a

startup you can go and you can take this stuff and you can apply it so you can build machine learning power and accounting software which is a very interesting area. We've got a bio fund that's doing a lot of AI meets bio investing but you're right that there's no way if you want to sort of bed on as an investor or as an entrepreneur, bet on machine learning. It's very hard to. Yeah, and I think every major technology wave also had a major silicon company created in it.

And right now, to some extent, that's Nvidia for not only graphics processing, but also for machine learning purposes. But if you look at what a machine learning ASIC really needs, it's really fast IO, and then it's a lot of matrix multiplication. And if you look at the surface of a GPU that's actually dedicated to those functions is quite small relative to the overall chip. And so there is room to create things that are 10, 100 times better. or faster or more performant from a power perspective by creating custom A6 for ML. And so I think that's really exciting. Well, the other thing with ML, right,

is there's the training side, which usually happens in a data center, and then the deployment side, which will happen at some edge device and may have, you know, size and power constraints and other kinds of things that require specialized hardware.

That's a great point. The inference side of machine learning will have its own specialized chips potentially, and if you look at the companies in the market, they often segment themselves by whether they're involved with the training to your point or the inference. and if you get into a world where you really have broad-based self-driving cars or robotics or other things,

then these chips become really important. And then your fourth category of longevity. There's 20 years of evidence that aging is a biological process you can perturb. And there's three or four main lines of evidence. Number one, there's caloric restriction. So if you calorically restrict, you extend lifespan in multiple organisms. Two is there's certain genes that if you knock out and model organisms, they'll live two, three times longer, they'll be healthy adults a whole time, and then they'll crush out.

So for example, in Cialigans, there's a pathway called the Daff pathway that is involved in regulating lifespan. And I actually worked on that directly for my PhD. Third, there's what's known as peribiosis, which is you exchange young blood into old animals and vice versa. And it's clear that you can increase the rejuvenative capacity of older animals by giving the young blood. And actually, it looks like those factors in old blood that are screwing up functions. So if you give old blood to a younger animal, it'll actually be less functional in terms of regenerating a damage liver or muscle or the like. And then lastly, there's at least two FDA-approved drugs that in multiple organisms will extend

lifespan between 5 and 30 percent. One of them is rapamycin, which in mice, for example, will extend lifespan 10 to 30 percent, and it also extends lifespan and flies and worms and other organisms. So it's evolutionarily conserved. The other one has metformin, which is the 10th most prescribed drug in the world, the antideabetes drug. So it does look like there's clearly things that can modify the pace at which different types of organisms, including mammals, actually age. I think a lot of people think that's kind of science fiction or something. They make jokes about the blood transfusions from young people or whatever. I think it's as near term as any biotech product is in some sense because it takes time to

develop. There's a lot of factors that have been cloned out of blood that are actually really valuable drugs today. Two examples of that would be insulin, which again is just a factor in blood, which impacts a biological process that people now inject as a standalone. And then there's epigen, which is Amgen's big blockbuster drug, which helps you generate red blood cells. And similarly, the way that was discovered was they literally looked for what's the factor in blood that lets anemic cows be non-anemic because something they have red blood cells. And so these aging molecules are the same thing.

It's not some magical thing about blood. It's just there's proteins floating around and maybe some of them do good stuff. The hard part has been that because you're looking at things like aging and longevity, it takes a really long time to measure it. And so the pace at which people have been innovating on these things is slow, but also biopharma in general tends to be focused on oncology or cancer and a few other areas.

And they're not really thinking about how can I, push disease back in general, you know, with a drug. So I do think there's a big market gap, which creates a really interesting opportunity. What do you think about the societal implications of, if we do develop longevity technology? The societal implications are massive. And I think there's all sorts of ethical conundrums, because if you look at the Kuhnian paradigm of scientific change, it basically says you need the old guard to die in order for scientific progress to happen. And so imagine if no professor at any university ever retired, or imagine if any of these characters who, you know, have had a very negative impact on their country around the world.

You could flip it around, of course, and find all the positive characters. Yeah, yeah, yeah. But it does lock in societal structures, and it does lock in power structures. And so from that perspective, one can argue that it could really hurt self-renewal in a global system. And therefore, is it, you know, quote-unquote bad. The other sort of really interesting societal dynamic is around how you think about your own life. So say that you had another 100 productive years where you're basically 30 something for 100 years You'd think about marriage and having kids differently

When would you do that? Would you do it early? Would you do it late? Would you do it multiple times? People would stay in grad school until they're like in their 60s? Yeah, I mean there are already people who do that So the people who stay in grad school to their 40s where they stay in old are 140 Do you try to make money early in life so you could spend it through the rest of your life Or do you wait until late and gather experiences? Do you start thinking about having multiple careers Over the course of your life? And you also think about social mobility So, you know, if you're disadvantaged early, does that extend for 200 years and what does that mean?

And so I just think there's these really deep societal implications of the ability to extend life span. Fascinating. Well, great. Well, thanks a lot for joining us, Elad. Elad's the author of the new book, High Growth Handbook. Great. Thanks so much. Thanks so much for having me.