Podcast transcript: a16z AI, Robotics & the Future of Manufacturing
Marc Andreesen and Ben Horowitz talk AI, Boeing CEO and corporate governance, manufacturing
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This episode is AI, Robotics & the Future of Manufacturing, from the Ben & Marc Show at a16z. It was published on Youtube on May 16, 2024.
Episode link: AI, Robotics & the Future of Manufacturing
Marc A.: The only prospect for rebuilding US manufacturing is advanced manufacturing. The only potential is to climb the tech stack and build new kinds of factories that are fully robotic and fully AI-enabled and where they are extremely advanced, sophisticated systems. I think there's a path here. You might almost call this sort of like an Operation Warp Speed for manufacturing, where you just basically lean hard into this and you say, look, America will once again be the number one manufacturing company in the world, not because we are opposed to automation or robotics or AI, but precisely because we're going to embrace all of this new technology as hard as we possibly can.
Welcome back everybody to the Ben and Mark show. We are getting underway with part two of our two-part series on AI and startups, talking about all the new developments in AI and everything that they have to do with company building. But I wanted to start with a nascent meme, which should be showing right now. We have a nascent meme from the most recent show we released, which was a conversation that Ben and I were having about the topic of Boeing's travails came up and Ben sort of asked the question of like what, basically, if you pick an accountant for the CEO of an airplane company, what exactly do you expect to happen? And it cut to a shot of me drinking my tea, trying to not burst out laughing.
So in the spirit of, and it's a nascent meme on X. And so in the spirit of seeing if we can fan the flames of a new meme. And it's also a very interesting topic because the topic of who runs these companies is like an incredibly important topic both for the companies and shareholders and for the public in terms of the products that get built. Maybe let's do a slightly longer version of that discussion. Because Ben, as you know, it is very common in the Fortune 500 for the CEOs of companies like Boeing to be people with a variety of backgrounds.
By the way, drug companies, another example of this. So car companies, accountants, CFOs become CEOs, lawyers, general counsels become CEOs, marketing people become CEOs. By the way, also operating executives become CEOs who have not necessarily designed new products or created new content or whatever, but have run the production lines. And then every now and then somebody who actually kind of came up actually like creating the product is the CEO. Although for the big companies, that is a very minority position. It's very rare in American business these days that you'd have a drug designer run a drug company, or a car designer run a car company, or an airplane designer run an airplane company.
And so, Ben, maybe give a longer form kind of version of like from your perspective, what issues does this raise? And then what questions does it provoke?
Ben H.: Well, I think that, look, I mean, the big issue - the big question is, like, what is the company's core competency? What's their core competency? And if Boeing's core competency is not building airplanes, then, you know, what is it? Like, is it really just keeping the cost of the airplane down? That's what they're really good at or that kind of thing. And I think that if you hit that's what you're optimizing for, kind of that's what you get and it seems like really exceedingly obvious and, by the way, you know, in airplanes, it's not like there's not a lot of new technology and new ideas that can be applied and, in fact I mean I think the first Boeing incidents were on like the autopilot technology which of course is you know computer technology, AI technology, these kinds of things.
And so if you don't understand, you know, if you don't even know how to build a plane, if you have no idea, if you've never done it, if you've not even been in those meetings, then the decisions you make as CEO are very likely to be not only wrong, but potentially dangerous. And I think this is, you know, kind of certainly true of a lot of these businesses. I can imagine businesses where you could take, you know, a finance person or a legal person and have them run it. But if you want to build new things, and the things that you're building are complex, then it seems quite obvious that you should have somebody who knows how to do that.
I feel like a positive example of this was, you know, Microsoft eventually got to such a point who, look, his background was building stuff. And so you know, a lot of decisions that I think had consultants completely baffled about Microsoft were obvious to him because he's like, well, we can't build that, but we can build this, you know, like just that kind of simple ass fucking thing.
And so one question that I had, which maybe you understand because you've been on more big boards than I have, is like how does a board make that decision like how am I on the board of Boeing and I go well you know like we're building airplanes and we're building new airplanes but that's not really the tough thing that we need the decision maker in charge to do the chief executive has to really know you know public accounting how do you how do you get all the way to that point.
Marc A.: Yeah, so there are several answers to this, and let's walk through each of them because this is precisely for people who, you know, most people have not been in the room for these decisions. And so what I'm going to represent to you is the discussion that actually happens because I've seen it and I've been part of it a few times.
So one is just like, look, and I'm going to steel man these, Ben, so that you can respond. It's like, look, These are not just product development things. When Boeing started, it was the main thing with building airplanes. But today, it's basically like a nation-state. It's this incredibly large, sprawling operation with many different aspects of the business, many different plates that have to be spun to keep the thing running.
And then the CEO has to be everything all in one. But a big part of the CEO is they got to be like a diplomat, you know, they've got to be able to represent the company, they've got to have extraordinary general management skills, they've probably, I don't even know, a 200,000 employee workforce, they've got to understand how to navigate everything from employment law to safety law to this to that.
They've got to, and then the shareholder thing, you know, look, the shareholders that we have are not interested in our 10-year plan. We don't have Elon shareholders. They're not interested in our 10-year product roadmap. They're interested in our quarterly annual earnings, and the company has to be optimized financially. And so we need somebody who's going to be able to do all that.
And the kind of person who can do all that is somebody who has come up in a business or general management or maybe finance role. The problem with putting a product guy in charge is they've basically like they spent their first 20 years in a lab.
Ben H.: And then, you know, they've spent maybe 15 years or something being in charge of product development, but they just don't have the breadth of what's required to run a company like this.
Yeah, so this actually, this is a really interesting point because this is one of the things that we get into a lot in the firm and that it's probably the core thing that I advise CEOs on what to do and what not to do, and it comes back to this thing that you and I talk about all the time, which is: Do you hire for magnitude of strength or do you hire for lack of weakness?
And I think in hiring executives or CEOs, you really have to start with, okay, there's 30 things they need to be able to do. But what do we need them to be world-class in? What do they have to be better than anybody else in the world in? And then, like, how do we mitigate the things that aren't that? And I think if you don't start from there, you always get it wrong. Because if you don't start from there, then you end up in exactly what you're talking about, which is, let's find a guy with no lack of weaknesses.
Oh, he knows how to do this. Oh, he's talked to Wall Street. Oh, he's done this. Oh, he's done that. Oh, okay. But how hard is it really to talk to Wall Street? Can you not hire somebody to help you do that? Or does that really have to be the CEO? And I think that if you don't start there, then you're just going to end up with this kind of lack of weakness. They're not really great at what you need them to be great at, etc., etc., etc.
And this is, you know, we find this so often and it's not just in CEOs. It's really in every executive position, but we do it a lot. Like in our business, we look at companies this way. You know, if a company has nothing wrong with it, that's not a reason to invest. It has to have something truly great about it.
And I think that, and I think, you know, part of it is a board isn't any competent person who has experience in hiring would think about it this way. But I think when you get into a committee, the problem with committees always is this gets to another important point about hiring is that they always go for lack of weakness because it's the easiest thing to say, "Oh, I want to talk. I know what I can say. I can see this thing that's wrong with that guy." Right? Like, but it's much harder to be so expert that you say this person is better than anybody in the world at this. And this is the thing that we actually need, which is also why I think that for CEOs, you can't do consensus hiring because you end up with lack of weakness.
That is the output. At the end of the day, you can take input. But there's got to be a single decision-maker who knows what the person has to be world-class in and knows how to assess that. And then can build the plan to mitigate, complement, deal with the things that the person isn't world-class in. But those have to be the things that you don't need them to be. You can hire a CFO.
Marc A.: So that actually goes to a second thing I would say directly on point with what you just said. So the second thing is incentives at the board level. So the incentive at the board level, the sort of board decision matrix for this kind of thing kind of looks, I won't draw it out, but it's basically, I make a good decision, bad decision, it goes well, it goes poorly. Kind of thing and it's so it's like basically if I go for somebody who has no weaknesses to your point, um, you know who's optimizing for lack of weakness then I'm making a quote safe choice that you know they may not be great, but you know they're probably not gonna destroy, you know, there's no clear weaknesses then they're not gonna destroy it.
So I'm, you know, and you know sometimes you hear a term for this like steward, right? It's I'm gonna get somebody who's gonna steward the thing right, um, yeah, right. Well, that's a big part of it because it's like, okay, look, our days of inventing the next widget are over. It's now a question of we gotta keep the wheels on.
By the way, look again, steel manning it, we have responsibilities. Like we have responsibilities, right? And we got like regulators and investors and all these people and like we have responsibilities. And then like, because if we take a little bit more risk, like if we take a little bit more risk and we get somebody who's spikier on the strengths and weaknesses, you know, kind of grid and they have stronger strengths, but maybe they don't have, like for example, they haven't been in a finance line job before.
And then they like screw up the Wall Street part, like then we're all just gonna look like complete losers, yeah, right. And so you see what I'm saying is it's a very strong orientation towards risk aversion at that point at the board.
Ben H.: Chris, you also look like a loser if the airplane, you know, the doors fall off the airplane while it's in the sky. Thanks.
Marc A.: Yes, correct. Or if you doubled out, if you do the various moves, if you do the boneheaded strategic moves.
Ben H.: I actually think this is a general organizational psychology problem. Andy Grove remarked about it, and I think it was in "High Output Management" when he talked about, well, there's a need for people with ambition. Ambition is critical to get anything important done, but they have to have the right kind of ambition, meaning they have to be ambitious for the enterprise itself. It has to be like, you need people who want Boeing to be the best airplane manufacturer in the world and do important new things. You can't have it, "I want prestige for myself for being on the Boeing board, and I don't want any kind of heat about that, and I don't want to get criticized."
Like once you get into that kind of thing, and I think this is, you know, true in any organization, you really end up with bad decision-making because now you're no longer optimizing for the thing you should be optimizing for, which is the company. You're optimizing for the kind of individual ambition of the various members of the board or the members of the executive team or whoever it is.
And, yeah, this is probably one of the most dangerous things in business when personal incentives start to override the goal of the organization. And, like, it always happens to some degree, right? Because nobody is like a perfect, you know, all for the team in a sense, right? Like everybody cares about themselves to some degree. And when those interests start to diverge, it's problematic.
But I think the job of leadership, the job of whoever the chairman of the board is, whoever is driving who goes on the board, has to be, you know, how do you really get alignment between whatever anybody's ambition is and the ambition of the company? Because when that gets misaligned, you get exactly what you're talking about. Right, right.
Marc A.: Okay, so then the third thing I would say, and again, this is a very explicit conversation that will happen in the boardroom. By the way, this is almost a given that just everybody just kind of accepts the following at big companies, which is basically, look, we have a career path for CEOs and it starts with MBA, right? Like we have this concept in American business that basically being a CEO, being a general manager, it's a general manager, you know, Masters in Business Administration. Business like these are generalized things, these are generalized skills. Yeah, um, and if I know how to run a soup company, I know how to run a car company, I know how to run a plane company, I know how to run a computer company, these are because it's the running the company part that's the generalizable skill, what the product is. Whatever, like the whole point of being an MBA or a general manager or CEO is you can adapt to those things.
And then I would say linked to that specifically, you asked the question up front, which is like, if Boeing's not in the business of making airplanes, what business is it in? And like Boeing is actually for the most part, arguably not in the business of making airplanes, right? Like any more than like, you know, PC companies are in the business of making PCs, right? Or car companies in the business of making cars, which is, it's more of a supply chain integration company at this point than it is a primary airplane maker.
I would talk about the car companies are like this. Most of what goes into a car is coming from what they call their tier one supply chain, which are these integrated systems of everything in the car. The engineering design is done by other people, and then you put it together and put your name on it. The PC industry, as you know, works the exact same way, which is you're buying parts from the... Half the time PC companies now when they ship a PC, they haven't even designed the PC. They actually had what's called an ODM design the PC, which is outsourced design. These companies are basically supply chain integration and then financing sales and regulatory machines.
Ben H.: This is precisely why GM is totally vulnerable to Tesla and BYD. This is why HP ended up being totally vulnerable to Apple because Apple was still building computers and HP was assembling computers, and GM is assembling cars and Elon completely kind of re-engineered how you build a car. Uh, and you know, the product difference is dramatic. If you are actually doing the thing as opposed to kind of, um, you know, basically milking the cow.
So yeah, I think that's right. By the way, I think that general management the way you describe it is fake, and I don't think that in my view, if you can manage a soup company, you cannot manage meta. That's just not true. And by the way, if you can manage meta, I'm not sure that you can or would even have enough interest to manage a soup company. Those are different things.
The products really, really matter in terms of how you organize the operation, how you run it, the kinds of employees you have, how you can and can't talk to them. There are things tech workers won't live by because they're in very high demand that maybe a manufacturing employee would deal with. You know, some companies have unions and like union negotiations are a very different kind of skill set and so forth. So I think that the whole idea of that kind of general management is wrong.
I think that there's one true kind of part of general management, which is knowing you have to learn how to manage. If any CEO has to learn how to manage people who are doing a job that they haven't done, so like, okay, now I'm, you know, I'm a product person I'm managing an HR person, or a salesperson, so like, that's a skill you have to learn. But the decisions that come to a CEO always kind of relate back to what the company does, and so, at a very deep level, the CEO has to understand what the company does, like what the hell does it do.
And look, you could be coming from a finance background and understand at a deep, deep level what the company does, but that's the requirement where making a high-quality decision is very difficult because, yeah, you do run into these things. Okay, I'm running GM and I'm going to run it like a car assembly plant and I'm going to be caught with my pants totally around my ankles when electric cars come and there's nothing I can do about it. I'm stuck because I don't even know how to build cars really. I just know how to run this thing that assembles cars.
Marc A.: By the way, look, and again, I'm a steel man, but look, the new CEO might say, look, this company stopped making cars 30 years ago.
Ben H.: Yeah.
Marc A.: They stopped designing cars 30 years ago. I can't transform it into all of a sudden a different kind of company. I can't build a time machine, take it back to 60 years ago when it used to design cars.
Ben H.: Yeah, I mean, I think that, you know, at that point, like that's what John Madden used to call 'brown shoes and black socks', you know, it's a give up.
You know, you were getting dressed and you just gave up.
Marc A.: Of course, these days, brown shoes and black socks would be a real overachievement in the fashion arts, but it'd be the opposite of that. But the metaphor is still good.
Okay, then the fourth, there's five of these. So the fourth of these is what I call the people issues. And there's two and they're often related. So I'll just hit the two. So one is you often have a long-suffering number two. And so you're often in a situation where if you've had a successful CEO and now you're doing a handoff to somebody else, that successful CEO has had a key lieutenant.
And that key lieutenant is kind of the person who kept the trains running on time. And so in the best case scenario, that's a Sheryl Sandberg or a Tim Cook. Where it's somebody who's basically been the partner to probably a superstar, highly visible CEO. They've been in the back. They've been in the back office. They've been keeping all the trains running and they've been doing all the work and they've been doing that for some cases, ten, 15, 20 years. And so it's quote unquote, it's their time. And so there's some pressure there.
And then there's a related thing because everybody's like, okay, why don't you just wave that off? There's this related thing that happens in the case where you have that person, or even if you don't, or even if you go, sometimes this pushes you to recruit from outside the company when you shouldn't, which is there's the phenomenon where if we appoint anybody on the current executive team, the peers will all quit. If we don't appoint the long-suffering number two, then the internal candidates are all then peers on the executive team and there's probably 10 or 20 of them running all the different functions in the company.
Then if we elevate one of them, all the other peers are going to be like, hey, why not me? They're all going to punch out and then the company is going to collapse. By the way, that's also the reason often you go outside, is because you may actually have candidates inside at that level, but if you appoint any one of them, the team's going to quit. The theory is if you hire from the outside, you get somebody in who's not, and then all of a sudden you can keep the team coherent. How do you think about that?
Ben H.: Yeah, it's interesting. I wrote about this. I have a thing that I wrote called "Ones and Twos." The challenge with, often not always, but often, if you are a super high qualified CEO who makes great decisions and knows how to run the company, then you're particularly good at a lot of the creative aspects and particularly setting the direction of that company and figuring out where it needs to go. And then you may not be as interested in keeping track of all the OKRs and KPIs and designing all the detailed processes and other kinds of things that just end up happening in a company.
And so you'll have somebody very close to you who can do that. The problem with that model is when you get to succession, that person does not have the qualifications to run it. It's hard to get people who are good at running the company enthusiastic about working for a person like that because they're not kind of setting the direction in interesting ways. It is a trap.
Um, look, I think the thing about people threatening to quit or quitting actually is true. And I also think that them quitting is a little overrated in the following sense. I think that, you know, we're talking about big companies in this scenario where I think, you know, very large company so there's a couple things about big companies.
One is if you have the right leader, they'll be able to identify talent at the next level, they'll be able to recruit in talent, so they'll be able to deal with the fact that like people's feelings are hurt and that kind of thing. And I also think that if you pick the right one, everybody knows they're the right one. And that mitigates it as well. But I definitely think that causes people to make that mistake, that dynamic. Yeah, that's right.
Marc A.: That's right. And again, it's sort of scary for a board, right? Because it's just like, wow, we're the board that unraveled the management team, right?
Ben H.: And the board has such an interesting view of the company, right? Because they only know the company through the management team. So to them, it's like, oh, if an executive leaves, oh.
But probably there's some key engineer somewhere in the company who's actually way more important than whatever the head of partner channels or whatever the hell you're worried about leaving.
And so, yeah, I mean, I just think it's a little bit of a misunderstanding of how a company works.
Marc A.: And you're right. The way the boards get information is it's the information to a board is really stovepipe through the management team, and particularly the CEO. And so it's very hard for boards, in practice, nearly impossible to have an independent kind of read on things happening inside the company, which actually takes me to the fifth and final thing on this topic, which is board composition.
And this is not a steel man. This is the thing that's very important that will never be discussed in the boardroom, but it is very important, which is um big companies, the boards are not selected for who knows who understands the business- like that's not really very high up on the priority list- um it is on the, you know, the boards I'm on like this is always the thing I push on is like do they understand what we do.
Ben H.: Or am I just gonna be on this board talking to myself? You know, cause I do feel like I am on enough big boards that like I do find myself in that situation at times.
Marc A.: But like, if you go on the website, pick a typical Fortune 500 company, basically pick anyone. And you just, you know, all public board members are public. Just look at the board members. They all have bios, smiling photos in the bios, and you just read the bios.
Basically, what you see for every single one is the board is selected for. And you write down the list. You know what they all are as well. We need a head of the audit committee. So we need a CPA or CFO. And then we need to staff the audit committee. We need finance experts for that. So you've got three people on the board who are mainly there for their finance skills.
And then you write down the list. We need a voice of the customer. Maybe it's for Boeing or something, the head of an airline or something where, you know, good news is their customer, bad news is they don't build planes. We need CEOs, people who can advise, mentor the CEO, especially if it's a younger CEO. So we need retired CEOs. By the way, we're under incredible scrutiny by the SEC, FTC, and DOJ on antitrust. We can't have conflicts.
I was on a board once where we could not add a board member who was otherwise completely qualified. It would have been great for the board because they had a line of business that 2% of their total revenue was advertising, which was the business of the company that was recruiting. So that small, nascent stub business on the other side basically meant that it was a conflict and would be a Sherman Act antitrust violation to add.
For legal reasons, we actually can't have other people from the airplane industry. By the way, we can't have someone who grew up inside one of the other airplane companies. It would be resented inside our company. And would they even want to do it or would it be an act of betrayal?
Let's say we have other political diversity requirements that are now legal requirements, depending on the state. We have requirements on board composition imposed on us by the stock exchange, banks, and independent.
Ben H.: We have like the you need to be independent from ownership stakes and these kinds of things.
Marc A.: Exactly. If you're a big shareholder, that's a potential problem. By the way, if you're a former executive at the company, it's a problem because you might not be independent.
If you're not independent, you can't be on committees. You can't actually staff it. You can't necessarily be on votes.
Ben H.: Yeah.
Marc A.: So anyway, there's this long laundry list of basically, it's like a Jenga puzzle that you're trying to fit and assemble the board.
And then basically, by the time you get all the pieces together, you have like two, or one, or zero people on the board who are actually from the business.
Yep.
Ben H.: And I think this is just... this is just... this is... I was just saying... this is just reality. Yeah, um, yeah. No, it's funny because, um, it's actually the only reason that I ever end up on public boards. Because, you know, as a venture capital firm, we're kind of better off not being on the public board.
Um, but what keeps happening to me is the CEO will go, "Ben, we kind of need you because you're the one who understands the product. Um, so can you stay on?" And even though it's not really in my interest, you know, I end up doing that from time to time. Um, yeah, yeah.
No, like, I do think it's a very hard product. So I think it's a real problem with what's happened with corporate governance in the US. I mean, I think that, particularly like the ownership requirements, one, I think is actually pretty stupid.
In that the board is there to protect shareholders and this kind of thing, at least as one of its main functions. And as a shareholder, I'd rather have somebody on the board who owns a lot of the company and has skin in the game with me and is representing me, than somebody who owns no shares and is like literally a professional board member that sits on eight of these things and is kind of yappity-yap. And then writes the product things and so forth. But it is what it is.
I think the laws have evolved in a way that is really detrimental to the shareholders. We have poor laws around boards in this country, for sure. I don't know enough about international various laws on this to know who's better, but we're certainly suboptimal.
Marc A.: It's generally worse.
Ben H.: That's what I think.
Marc A.: Overseas, because you end up with, especially when you end up basically with, you essentially bring the unions onto the board at a lot of European multinationals.
And then you basically bring European, sorry, union politics onto the board.
And that is, let's just say that, you know, like maybe the Europeans, they think that's important.
And I defer to them on that.
But let's just say that's not going to enhance the discussion, for example, on product design.
Ben H.: Like if you want the company to make great products and kind of make a lot of money and grow big and innovate and, you know, and we always think in those terms, 'cause that's, I guess, our job.
Um, but like, that's what you're optimizing for.
It's not a good board structure.
Marc A.: So, and then, you know, just, it's just worth noting, Ben, on your point of laws, like the laws and the regulations and the pressure and the activist campaigns -- it's all enough.
Not, I don't mean shareholder activists. I mean, like the social activists who come in and, you know, bear the governance activists who come in and really bear down on these policy issues.
You know, it's basically all pushes you further and further away from people who understand the business. Like that's where all the pressure is headed.
Ben H.: Yeah, it gets into politics. There's this much larger issue, which is, what's a better idea for humanity: to grow the pie and create more resources and more abundance, or to focus on dividing up the pie and making sure everybody gets an even slice?
And I think that we're obviously way in the camp of creating more abundance, but as soon as you get into politics, it seems to always flip the other way and being much more about like, okay, can we make sure whatever we have is divided fairly?
Marc A.: Come get a slice. Okay, good. Well, let's get to part two of our AI discussion. We have some fantastic questions from X as usual. So I actually have four questions in one that are all related.
So our friend Beth Jesus asks, "Do you see a resurgence of hardware startups on the horizon, given that the current scaling of AI is going to be limited by the scaling of compute and energy?" And of course, our friend Beth Jesus has a hardware startup directly trying to address that.
A Block asks, "Is energy production a limiting factor in the future of AI?" Jeremy asks, "As AI, crypto, and electric cars rise, tackling energy challenges is key. How do we address those?" And then Tara asks, "Energy being a likely bottleneck, how much more importance do you see in the more algorithm-aware hardware innovations like thermodynamic computing, which is the kind of thing that Beth Jesus is working on?"
AI energy and then hardware. Are we going to see a boom times for new AI chip startups? Is it boom times for new energy startups? Is it boom times for entirely, you know, companies doing entirely new computer architectures? Yeah, and data centers, by the way, another hardware thing.
Yes. So I think the one that is probably
Ben H.: Most likely, just because I think we're stuck without it, is on the energy side, which is we've funded, of course, portable nuclear energy. And I think both on the efficient side and the fusion side, if we don't have energy innovation, I think we have a huge problem with AI. Because when you look at once we get by the chip bottleneck, like we had unlimited chips, um, the AI power consumption would be like over 10% of global power consumption.
I think it's pretty clear, um, just on AI, uh, which, you know, people were like you know campaigning against crypto because of the like Bitcoin mining, which is like tiny, tiny, tiny compared to what people are using on AI now. And that's tiny compared to what people want to use on AI, but can't yet because there aren't enough chips. So I think power hardware power for sure.
And then you get into, okay, well, what's the next thing that you run into? And it's, well, if you are having like a gigawatt data center, um, cooling becomes a very hard problem. Like even if you like, you know, stick it in the middle of the ocean and use the ocean light, like you're, you're starting to boil all the water around the thing. It is so, I mean, this is really, really, really high power consumption. So then you say, "Okay, well, chips that are kind of that throw off less heat or consume less power and so forth become like super, super interesting for sure."
On the chip side, you know, there's been a lot of we've looked at a lot of things that are okay. Can you be like AI optimized and You know, it's, I think on a general chip, it is really hard to, like the current chip makers, like NVIDIA is really good at innovating and AMD is really good at innovating and so forth. So to start a new chip maker, you need a pretty novel angle. We've seen ones that are, that are quite interesting.
Like one is like, "Okay, we'll make a chip for a specific like model. So we will take a model and the whole process is designed to make a chip for that model. And given the cost of training that you have to amortize across all the inferences that you do over time, the math actually works for that idea where you would have basically a model on a chip. But then the question is, how long in the market does that architecture last and how long can you count on it and then does that work so so that's another point of innovation.
And then you know like you know data centers we have not built data centers like this that require this level of power and cooling um and You know, I think this is something that every, I know, you know, on the meta board, I know they've been talking about this and, you know, Zuck is very focused on it. And I know that, you know, Satya is very focused on it at Microsoft and I'm sure Sundar is focused on it. So like, I think that's going to be another big area.
So there is a lot of hardware opportunity. You know, what goes to new companies and what goes to big companies is still probably an open question. Although I think energy is very likely to go to new companies, particularly in the area of nuclear.
Marc A.: So, you know, we've talked a lot about in the past, and I've talked a lot in the past about, you know, we have funded many hardware companies of various kinds, consumer hardware, you know, systems hardware. And as you said, we're now doing, you know, we have many drone investments. We have self-driving car investments.
We have energy investments now, you know, energy systems companies. We talked in the past about how hardware companies are harder than software companies.
The way I always describe this is there's just a lot more ways for a hardware company to fail because not only can you design the thing, can you manufacture it? Is it going to work in the field? Is it going to get recalled? Can it work in harsh conditions?
Is it safe? Is it regulated? You need a way better CFO if you're building a hardware company.
Ben H.: What's another one?
Marc A.: Yeah, exactly. And then, um, yeah. And then also your cycle times are, are lowered. Software cycle times are usually very fast. Hardware cycle times are slow just because it takes a long time to get, you know, something all the way through the production process.
And then if something goes wrong, there's a recall, the recall can kill the company. Supply chain issues, to make a hardware thing, you need every component all the time. If you run short on one component, you're stalled out. There's 10 different reasons that hardware companies are harder.
However, look, when one works - less competition, get it to work. So if it's an important problem in a big market and it works, you can really have something amazing and you can really change not just the world of bits, but the world of atoms.
And many of the legendary companies in the history of the world have been the ones that have been able to do this. And so anyway, how should entrepreneurs think about us as a firm in terms of what we will do, what we won't do, how we process through that thought process as we think about it internally?
Ben H.: Yeah, so we invest in hardware companies. As I mentioned, I do think the bar is higher. And some of the things that we'd look for in a hardware company that we don't in a software company is the first thing I think is we don't take the financing risk for granted at all. So like if we invest in a software company, we, whatever we do in a round at, um, you know, 10 on 40 pre or something like that, whatever it is, we're not really like, if it works, like it's definitely getting financed, right? Like that's no problem.
With a hardware company, even if it's working, you're going to likely hit multiple valleys where you're both low on cash and it's not working well enough yet to justify the valuation. And in that becomes existential and of like if you're not familiar with this you can read like Elon's the book that Walter Isaacson wrote on Elon because it kind of goes through a lot of the crises he ran into in both SpaceX and Tesla and that's like Elon Musk who's like as good at hardware company building as anybody in the world. But they all have that characteristic. And so the CEO has to be, as one of the things that they do, has to be like a world-class fundraiser.
Like they can't be, you can't be, you know, like, I don't like to raise money. You know, like, look, we have a lot of software CEOs who are like, I don't like to raise money. I don't want to talk to investors. Like, F them. Like it that doesn't really work on a hardware company I think that you know getting back to like what are the world-class strengths you need in the hardware CEO like one of them is like you got to be able to get the money because it's going to happen and you know We've seen that and we try and be helpful, but we can only help to a certain degree. The CEO has to be massively compelling on that.
Then I think the precision with which you run the company, quality ends up mattering a lot. The details around the metrics, the optimizations, the you know and this is you know again this is where I think to me the most interesting thing in the Elon book was just like how focused, psychotic, creative he was about ways to save money, ways to make things more efficient, etc.
It's so, so critical in hardware to have that attitude. And you can't just build things fat and happy the Silicon Valley way. You can't focus on free lunches and organic juice and all that stuff. You really have to focus on cost. Um and you know and then uh like inventory things and and that kind of stuff becomes just fundamental to the business and and so you have to be focused on that so it's just a yeah I would say a more complicated thing you have to be a great recruiter because you have to bring in people who are world-class at things that you know at a really broad set of skills as you said Mark you know like manufacturing like you need a team of finance people that that's really really good you know all that kind of stuff so it's a certainly a higher bar in terms of just the complexity and the competency that you need in the company for it to work.
Marc A.: Yeah, this is the sort of thing that I try to tell entrepreneurs.
Maybe important for people to know is, you know, there's this meme. There's this meme of like, you know, basically VCs are scared of hardware.
You know, not bold enough.
True meme. Well, partly true.
Ben H.: Yeah, I mean, we are scared of it, like not that we won't do it, but like it is scary.
Marc A.: Okay, but then why is it scary? But I want to double down on something you talked about. So a big difference. So basically the way an entrepreneur, right, the entrepreneurs are trying to get through the next phase, right? And so when an entrepreneur talks to VC, the entrepreneur is thinking, I need to raise this round from this VC right now. And if he tells me yes, that's great. And if he tells me no, that's a pain in the ass. It was a waste of time. And like, you know, right. And so it's like this, you know, I'm focused on this round. And of course, entrepreneurs have to think that way because you have to raise money. You have to get to the short term to get to the long term.
The Cs, the way we think about it is, okay, Ben, to your point, like, okay, we fund you for this round. What happens in the next round? Right? What happens? We fund you in the series A. What happens in the B? And by the way, what happens in the C, the D, the E, the F, the G, the H, and the I? The more complex thing that you're doing, and the more of its hardware, the more of it is complicated, the more of it is fundamental advances, the more of it is complex integrations, the more of it is all these things. It's just like, okay, where is that money going to come from? Are there going to be other capital partners down the road for this company? Or by the way, are we yet?
And if we're yet, we need to go into that with our eyes open. And by the way, what does it say about the company if we're the only possible kind of funder of it? So we're thinking as we're sitting in the meeting, we're thinking in our heads like, okay, what happens next? And I think it's important for entrepreneurs to know that because I think as an entrepreneur, if you really wrap your head around that, it's like, okay, that needs to be part of my plan.
Ben, this goes to your point of the entrepreneur has to be a great fundraiser. As the entrepreneur, you have to take it upon yourself to be like, okay, I really have to think this through beyond just this round, but to the construction of the company over time. And like is my story and my plan and my team and everything else that I'm doing, is it good enough and does it hit the bar and is it or you know kind of organized orchestrated in the right way so that it's going to I'm not so that I'm not only going to be able to raise an A but also all the following rounds.
And you're not going to have all the answers for that, but you can have you can have a plan. You know, we find entrepreneurs who have really thought this thought this through and then this goes to what I always say about the venture process, which is the venture getting yes on a venture series A is the easiest thing you'll ever do. Like we're in the business.
Ben H.: Well, by the way, I think we cause problems because that is true.
So if you're an entrepreneur, your experience in fundraising is you raise your Series A and you're like, "Oh, that was easy."
And then you think the next round is better. That's always the easiest round you raise.
Marc A.: And by the way, everything else you do, recruiting is harder, sales is harder, partnerships are harder, government relations are harder, and marketing is harder, right? And getting a good press story written or not getting your ears ripped off by the press is harder. So like everything else that happens, basically everybody else you deal with is harder than we are.
And the reason for that is just very straightforward, which is like our entire business is to sit there and let people sweet talk us into giving them money. It's like our entire existence is that. Everybody else you deal with down the line, that's not their job. Their job is something else.
And then whether they are going to work for you or buy your product is like they've got many options of who they go to work for or what product they could buy or whether they buy any product. And so if you can't clear the Venture Series A test, if you can't get through us and firms like ours, maybe we're the idiots. Maybe we're making a huge mistake, but also maybe it's because your plan has not yet hit the bar that's necessary, not just to raise from us, but to do all the downstream things that you're going to have to do to succeed.
And I think the smartest founders understand this and they think ahead on this and they use this as a catalyst to make sure that their plan is good enough to succeed throughout the company, throughout the life of the company, and with all the constituents that they're going to need to appeal to. And they understand that we're just a small part of that.
Ben H.: And I think that's right. And I think that, you know, now that we're talking about it, I think that if we weren't a multistage firm, I probably wouldn't want to do hardware deals because I think that they do get into these states where I'm thinking, you know, we've got a space company, I won't name the name, but like, you know, they just got into the, it's a great company, they've got great contracts, they're growing.
They got their stuff to work, but look, they hit a point where everything was working, and they had one bad part from a third party. And by the way, that would have ended the company or potentially could have ended the company. But because we knew so much about the company and our multistage, we could step in and go dip. Like, we'll do this round. Give us a good deal. We'll do the round. And we'll keep pedaling and be on to the next thing.
I mean, knock on wood, but it looks like this company will be a great success. But if we weren't multistage, we would have lost the company, I think. And I think that is often true with hardware companies. And so that doesn't mean we would always be the funder of last resort. But if the thing is kind of genuinely working and they hit one of these incidents, which hardware companies always hit, I mean, you seem like Tesla who had to go back to its existing investors, right? They could get new money.
And that seems to be, I don't think that we've done anything in hardware where that hasn't been the case. I mean, the one one was Oculus, but Oculus got, they sold relatively early to Meta. And so, But had they not, they definitely would have been in that situation at some point. That's right.
Marc A.: And then let's go to a related topic. Sunshine Vendetta, which is a great name, asks, by the way. That's a great name if it's a real name and a great name if it's a pen name.
Will nations specializing in low-cost power data centers for AI emerge, similar to oil-rich countries?
Are these nations good investment areas for buying land to expand or build such data centers?
Ben H.: I think nations are definitely looking at this. And I think it's not just, so there's a regulatory aspect, there's an aspect of, do they have like oils popping out of the ground? So there's a lot of pieces to it.
I think, I don't know the answer to that yet, because I do think it depends on like, okay, how fast, does um how fast does do the nuclear options uh the nuclear options funny how that sounds emerge and then um you know how do countries think about the regulatory environment and does the kind of climate agenda kind of help the countries that are more flexible on that.
So we're getting into you know one of the things that um I think you and I learned not to predict is things that involve like large governments jumping into the economics of it because we learned this in the financial crisis of 2008 where I think when we were looking at it we're like well if this plays out um we're gonna have like a pretty serious depression and of course what happened was the global government just poured enormous amounts of money on the situation which is causing, by the way, you know all kinds of problems now uh but uh yeah it didn't it was unpredictable in how it's gonna unfold.
And I think that uh yeah this one is similarly unpredictable because it involves government policy knowing like a really major kind of way and then also the arbitrage of that uh you know government policies against each other.
But yeah I i think it certainly you know if I had to bet on it I'd say that probably will happen.
Marc A.: That makes sense. And then there's, you know, for people who haven't tracked this, there's this interesting thing where training particularly can be placed sort of anywhere.
Ben H.: And that's the biggest cost in terms of power, for sure.
Marc A.: For people who haven't tracked this, Ben, see if I have this right, there are two parts to AI the way we have it today. There's the training phase and the inference phase.
The training phase is getting the dataset assembled to be able to answer questions, and then the inference phase is when you actually ask a question and provide the answer.
The training phase is what used to be called a batch process, where you do it all at once. By the way, it may take months. Some of these training runs for these big models now may be -
Three months, yeah.
Ben H.: It's not- Yeah.
Marc A.: Yeah, three-month run, but you're not responding to user queries in real time, and so it doesn't matter when that happens. You need a data center big enough with enough power and cooling to do it.
Then, the other interesting thing that has come up in some of these discussions is training runs can be paused.
You can shut a training run off for two weeks and then start up again, which you can't do if you're running a Gmail service or a search engine or something.
Ben H.: You don't need whatever five nines.
Marc A.: Or you could run it with intermittent power. For example, if you had a nuclear reactor that needed to be shut down every now and then for maintenance, that would be fine.
Whereas you couldn't serve Gmail off of that because you can't have the service interruptions. There is a lot of flexibility on where the training runs go.
Having said that, Ben, there's also a fair amount of scrutiny. Governments are putting a fair amount of scrutiny. Number one is like some governments don't want training runs to happen other places or will have concern about that.
Ben H.: Yeah, that's another regulatory layer, right? Like where are you allowed to train? Who's an enemy? Do we allow export of AI at all or is it like some kind of munition?
Marc A.: And the more you're talking about these giant runs, with, you know, there are, and you're talking about these deployments of capital and these, you know, kind of what are viewed as strategic assets.
And then of course, God knows there's also plenty of regulation, regulation around nuclear reactors and all the other energy, you know, permit, you know, kind of everything around all that, you know, do you have, do you allow civilian nuclear power?
So there's a lot of. They say this would intersect with government policy, even if it wasn't considered to be AI, wasn't a topic that was itself relevant to government.
But then on top of that, AI is itself a topic that's now relevant to government, which makes this something of heightened interest.
So it's complicated.
Ben H.: Super complicated. It's a great question.
Marc A.: So anyway, the reason to go through that is, yeah, are such nations good investment areas for buying land? Maybe, but you want to consider the economics of that, but you also want to consider the geopolitics of that.
And if you're going to be an investor like that, you want to think in both layers.
Ben H.: You almost have to be like a Stan Drunken Miller type, like a super macro mega type investor, to figure that one out, I think.
Well, the other thing is, right, there's this whole AI alignment issue where different governments have very different requirements on that.
Exactly.
Marc A.: So, from a US perspective, a lot of other countries may not want the AI that runs in their country to be an AI that has been "aligned" by, let's say, a crop of millennials in San Francisco.
Ben H.: If your country follows, say, Sharia law.
Marc A.: For example, or by the way, vice versa, right? We may not want that in reverse. And so, or in any given direction. So yeah, there's an increasing number of questions on that front.
Okay, Ori asks, I love this question.
Ori asks, I love this question. What do you think of selling work produced by AI versus selling software itself? Will AI enable service businesses to become the new norm? Will their margin structure converge with that of software-only businesses?
And so Ben, this would be the idea, which I know this idea has been around for a long time, including before AI, but it's a particularly potent question now with AI, which is okay. If you want to do legal AI, are you building AI software that you sell to law firms or are you trying to run like literally a robot AI lawyer service? And then same thing for accounting, same thing for every other sort of aspect of any sort of services business.
Ben H.: Yeah, so it's interesting because, like, it's already emerging as a real pricing model in many ways.
So examples being, like, Waym sells an AI driver. That is basically the product that's an AI driver. Turns out the AI driver is, like, reasonably expensive compared to, like, a human so far.
So, you know, we'll see. You know, this thing is, you know, and all this hardware is real. You know, all these GPUs and whatnot.
Marc A.: But Elon, to your point, Elon has declared, I think, that the future of Tesla is that. He's building robo taxis. That's the end state for the Tesla car business.
Ben H.: Well, I mean, I tell you, Waymo takes it to a further thing because they're not selling. They're like, we'll sell you the driver for your car or for your truck. We'll install it. We'll fit it to it.
And then Devon, the new kind of programming tool that just got the wonderfully high valuation, they're basically kind of charging you for an engineer is the way they think about it. And by the way, that engineer at least looks to be like she may be fairly expensive. Or he, I guess, Devon. No, could be either. It's a dual use name. That's a dual use name. Could be he or she, and it could be a human or a robot.
And then we have a company, Hippocratic AI, which is a really interesting situation because it's sort of an AI nurse. And they similarly price it like a nurse. And you may think, oh, my God, an AI is going to replace a nurse. Well, obviously, an AI can't really, at this stage, replace a nurse fully. Nurses do a lot of things like wash you and stuff. There's no AI that can do that.
But there is a massive nursing shortage, and there's a lot of nursing work that nurses don't necessarily want to do, like ask you, what medications are you on? When were you born? Or call you later and say, did you take your medicine? These are important tasks but things that can easily be done by an AI. And Hippocratic's got an AI that can kind of augment your nurse workforce, which is, as I said, I have a huge shortage of nurses right now.
And so, and they in their model for charging, and it works because the people who hire nurses where you would sell this product into are used to buying that way. They're like, okay, this is what we pay for a nurse. Okay, you have like a very um tireless but less functional nurse, how much, you know, does she cost or he cost and away they go.
So I think that idea um is a good idea. I don't think it works in the way or it's not yet working in the way that kind of people fear in the dystopian sense where like this thing's going to come in and replace my job. It's more this thing is going to come in and replace the parts of my job that I really don't like doing and then it's not going to be um as cheap as you know running a software program over and over again because the you know, the power cost of these inferences and to get them right and so forth is actually still pretty high right now.
Marc A.: By the way, the economic numbers came in today and actually productivity growth in the U.S. economy was super low in the last quarter. Yeah, yeah.
Ben H.: Always, always. Yeah, yeah, yeah. Yeah, like, I mean, that's the most overblown fear ever, right?
Like, so we've now had this AI revolution started in 2018, and I think unemployment has gone nowhere but down since then.
And... They're going to take all our jobs. Every automation has this total crisis of fear and we'll see. It may happen yet, but it certainly hasn't happened yet.
Marc A.: Well, what's happening right now is the number of people being employed, building AI, and deploying it is rising really fast, right?
So, like AI developers and AI consultants are exploding as a job category.
I think far faster than any workers should be replaced by AI.
Ben H.: Yeah, yeah. And I think, you know, you and I know why, because like in order to actually, if you're really going to replace actual work and people, you need to start a new company, basically.
I mean, it's very, very hard to go in and re-engineer the way.
Good luck re-engineering the way Boeing works given it's run by an accountant.
Marc A.: Or, good luck re-engineering a European manufacturing company that has the union occupying half the board seats. Good.
Let's jump right into the, and I think we're over an hour, so maybe this will be our final topic, but it's a big one. Three-part question.
Dr. Ali asks, how long before AI is fully integrated in robotics? Polynumera asks, do you think this latest robotics hype cycle is going to generate enough data? This is a complicated question, so I'll go through it. That the bitter lesson will overcome Moravec's paradox. Basically, effectively, what that means is AI is going to get good enough where the robots are actually going to work and create generally useful robots, or there will be at least one robotics winter, you know, death zone down cycle before they achieve general usefulness.
And then Nag asks if data is the only thing that matters, which we don't say but the premise of the question, then why aren't we seeing more AI agents or robotics companies going end-to-end ML like Tesla? And the reference there is Tesla: every Tesla car, whether you have the autopilot turned on or not, is gathering sensor data from the cameras that is being brought back to Tesla HQ and used to train the Tesla self-driving capability AI. Why aren't we seeing more AI agents and robotics companies going end-to-end ML like Tesla and building data flywheels by acquiring large amounts of data?
Ben, AI and robotics.
Ben H.: Yeah, this is a super interesting question. So just kind of to frame it, if you, the successful robots today, in the kind of large scale like the auto manufacturers and so forth, are very rigidly programmed to do very specific tasks. So, put this bolt on this wheel or whatever kind of thing. And then if the wheel is off by like two millimeters, it'll put the bolt right through the wheel or whatever. It's not very adaptive AI or like the current deployed state of the art.
And so there's been kind of a movement for a while to do kind of intelligent robots where the robot can learn the task and then do it more flexibly. So if something's out of place, you use computer vision to see it and then plan the task and execute the task and so forth, but that's only worked okay so far, it's not been like a big breakthrough where all of a sudden you can have basically robots walk in and substitute for humans, like the Optimus Prime thing that Tesla's building hasn't really worked actually yet.
And so then, with LLMs and these great generative models, like, why is that? And it turns out there does seem to be a missing technological piece, which is that to build an effective robot that can just operate in the world like a human, it needs to really understand physics very well to not end up being either dangerous or just clumsy and wrong and these kinds of things.
And there's varying theories on how a robot might learn physics. One being, well, if I just watch enough video, then I'll basically infer, I'll just figure out how physics works, because in order to predict what happens in a video, videos of the real world, I'll follow the laws of physics, and so I'll just follow the laws of physics too. That's been kind of a big theory, and that would kind of enable also generated video and robotics and all these kinds of things.
Now there's a different theory that says that's actually never going to work because you're never really going to get physics down and the only way to do it is you have to build a new kind of model that may have some transformer capabilities or whatever but also has a fundamental understanding of how the real world works in three dimensions with full physics and that kind of thing, and that is work that is being worked on.
We've got a cell startup that's doing that and then Elon is certainly doing that in exit.ai pursuing that kind of avenue whereas OpenAI with Sora is kind of going the other route and not necessarily explicitly teaching physics but implicitly learning physics through video and video games and these kinds of things.
It'll be interesting but I do believe that the age of robotics ought to emerge certainly in the short to medium term as these things are worked out, but it's not tomorrow or in three months, I don't think because the AI doesn't work well enough yet. That would be my short opinion.
Marc A.: So the theory for optimism, I think, would basically be what's happened at Tesla. So let me kind of describe that for people who haven't studied it. There was a reference in the question to this thing called the bitter lesson. The bitter lesson is this famous thing in AI that somebody wrote a paper on years back. Basically, it said the bitter lesson of AI is that the thing that works is more data. Over the course of 80 years of trying to get AI to work, efforts to do what you might call top-down directed explicit AI, where you're trying to teach a machine the laws of physics, or by the way, common sense or language, and you're doing that in a top-down way, just doesn't work.
The reason that doesn't work is because there are always edge cases and the edge cases always get you, and you never actually finish the job. It's never actually that useful. And you know, Ben, you'll remember in the 80s there were all these attempts to do expert systems for medicine and so forth that didn't work for very similar reasons. It could diagnose you if you had the common cold, but if you had diabetes and a broken foot, it would get very confused, and there might be consequences to the combination and so forth.
Anyway, the breakthroughs that have happened, especially since 2012, have essentially all been breakthroughs where you just get these giant data sets. And why did image recognition start working? The big thing was you could train an AI off the giant data set of images off the internet that didn't used to exist. Why did self-driving cars start to work? Why did text recognition start to work? It's because you could train them on the entire corpus of texts on the internet.
And then basically what Tesla has found, they're very public about this. What they found is the self-driving car capability that they had when it was a top-down system sort of worked. But the one that they have now that's working really well is entirely based on neural networks and large amounts of data. And the more data they get, the better that performs. And the whole thing on the giant data is like, somewhere in the giant data set, if you have a sufficiently large data set, somewhere in the data set is every single scenario you could conceivably imagine running into.
So a construction zone with police cars and flipped over in a crash and a fire, okay, if the data set's big enough, it has examples of that. And so you can train it on what to do. And so it can adapt to the real world in a way that you just can't if you're trying to anticipate everything. That's the bitter lesson. The bad part of the bitter lesson is there are lots of AI techniques that don't work. The good news is the bitter lesson is that more data does seem to work. It does seem to solve these problems.
So what Elon set out to do with Tesla is basically just, as we said, turn every Tesla car, it's got like a million cars on the road, turn every car into basically a roving sensor that's pulling in all this data and sending. This is all imagery outside the car. So there's no privacy issue. You're just driving around in the world. So you've got a million rolling sensors with, whatever, 32 cameras pointed out, and you just stream all that back and keep training the self-driving car algorithm and it gets really good.
In theory, if you believe all that, then in theory, that is the answer for how to train humanoid robots and how to train every other kind of thing that's coming along. We have a large part of the problem, at least in theory, solved that we didn't realize before.
Ben H.: I think that's right. And then there is a kind of a bit of a question on sort of how, well, two things. One is like, okay, which neural net, like which architecture, is going to work for this particular class or do we need some change in the architecture to make it really work because you know we had image recognition working really well before we got to language kind of prediction, which required, you know, not a dramatic new architecture, but a new architecture.
And there's a question on, OK, do you need a different model for this? And then I think the other thing with robots, as well as with self-driving cars, and why we don't have, you know, why all cars aren't already self-driving cars, is the edge cases end up being really, really important when it's life and death.
And so, you know, an LLM hallucination is really cute and funny, and kind of a Tesla auto-driving hallucination would be really horrifying and deadly. And so the amount of engineering to get from 99% working to 100% working is a lot.
So those would be the things. But I think what you said, I think that's right. I think that's exactly right.
Marc A.: Let's say it's reason for optimism that maybe we didn't have five years ago.
Ben H.: I do think robotics are going to work. It's going to be amazing. I think people get into fear about these things because they underestimate what's possible in terms of work for humans.
The original manufacturing jobs, which we all like, those were good jobs, like they're really in some ways were very, very bad jobs in that, uh, you know, Henry Ford very famously doubled the minimum wage and everybody, you know, like it's funny he's celebrated by socialists now because he doubled the minimum wage voluntarily on his own, um, for seemingly no reason at all but there was a very core reason which was all his workers were quitting because they hated going like this all day.
And they wanted to go back to the farm and be like, let me milk a cow and wake up at the crack of dawn and that kind of thing. That's way better than coming here for eight hours and numbing my mind. And if you look at Detroit, you know, Detroit quickly became a drug capital, and a lot of it was, you know, like so many of the people on the assembly lines were just on drugs because the job was so damn boring.
And so I think, you know, there are more interesting jobs for humans than jobs that robots can do. And so it should be a real breakthrough. And like, there's a lot of these jobs we do for free, like, you know, the laundry.
Marc A.: Right, right, right, exactly, right. And then actually, Ben, that takes us to the final question, which is a big one and something I feel strongly about.
So Dominic asks, could AI plus robots plus automation be used to reboot US industry and also reboot old industrial towns with capacity for factories? So let me frame the answer negatively first, which is the only prospect for rebuilding U.S. manufacturing is advanced manufacturing.
The only process is, the only potential is to climb the tech stack and build new kinds of factories that are fully robotic and fully AI enabled and where they are extremely advanced, sophisticated systems that run in a very different model than a manual labor system. And you pour as much technology in them as you possibly can.
And the reason for that is just very straightforward, which is the big thing that caused U.S. manufacturing to a lot of it to move offshore was the cost of people. It was just flat out cheaper to hire people in, you know, Korea or Vietnam or China or India or other, you know, wherever you go, Mexico. It's just flat out cheaper. And it just the cost of labor was just too much. It's just a high. It was a high percentage of the thing.
Um, and as you said, Ben, like these weren't, you know, a lot of these actually were not like very appealing jobs. And so you had to pay people a lot of money for them. And there were lots of, you know, issues involved and people were getting mad at you for all kinds of things. And it just was like, it's just difficult.
Um, I mean, Elon, quite frankly, you know, deals with a lot of these issues in his business today, you know, just has a large number of manufacturing, uh, workers in, in the U S and like a lot of people are really mad at him about it.
Ben H.: In a, in a sort of, you know, for these kind of perverse reasons, um, probably hired more new manufacturing jobs than anybody. Um,
Marc A.: And he gets no end of pain because it's like, is it unionized? Is it not unionized? Is it this? Is it that? It's Jen's fault.
Ben H.: We need more manufacturing jobs. He's like, okay, not only do we create manufacturing jobs, I'm going to give them all stock in Texas and a lot of them are going to become millionaires. Screw you, Elon.
Marc A.: Basically, if you get out of California, it's a lot to have a manufacturing company that is based on human manufacturing labor, traditional blue-collar labor. It's just a lot to do that in the US. And it's been more cost-effective for 30 years for manufacturing companies to do that offshore. Those jobs are never coming back if there's not a step function change in technology and the ability to use technology to get leverage and transform those jobs and transform the economics like they're just they're never coming back. It doesn't matter. By the way, it doesn't matter. Trade policy, whatever tariffs, like whatever, it doesn't matter. Those jobs are coming back.
And so the only way that those jobs come back is if you basically close your eyes and imagine, actually I'll use an Elon phrase, an alien dreadnought, fully automated, AI-enabled production facility, factory, that you just go in and it's just like a marvel of technology. And like, it's just got incredibly smart robots running around doing things. And it's got all AI everywhere and everything is instrumented and being run through software. And it's amazing.
The control system for that factory is going to look like somebody playing a video game. Cause it's going to be just like this amazing marvel of fused technology and then real-world actions. That is how you could get these things back. By the way, the US is in many ways the best place to build that kind of factory because the US is the technology leader of the world. We have the best engineers here building all the enabling technology for that, including all these great AI companies and they're going to do their best working here. Just because of proximity. That's the only way it's going to happen.
By the way, those jobs would not have as many assembly line jobs, but they would have many, many, many, many jobs around that factory. Um, and in fact, building all these factories, running these factories, fixing everything when it breaks, optimizing everything, improving everything, upgrading everything, and then all the downstream things, like if you have an alien dreadnought advanced AI manufacturing facility in Alabama or something, you're going to have tons of jobs that are going to be around that.
And you're going to have, and this goes to the thing I like factory towns, factory towns that whole town that is going to have an ecosystem, service providers, everything from restaurants, hotels, everything else that comes with that to be able to service that. So, I actually think there is a path here towards revitalizing and rebuilding the American manufacturing sector.
The geopolitics of our world kind of point us in that direction, because for a variety of reasons, like we've discovered that it would be a good idea to make more stuff in the US. Yeah, for example. Or, by the way, did not have American quote-unquote manufacturing companies actually just be supply chain integration companies that they may actually need to get good at building, the world's leading airline company in 10 years used to be good at building planes.
And, maybe that's a new company, but maybe those planes are made in the US in this new method. And they're made in a way that is better from a cost standpoint, and from a quality standpoint, and from a speed of improvement standpoint, and having integrated engineering, you have engineers on site at the company, at the factory, constantly making everything better. As opposed to just having the factory off in some remote location that your engineers never go.
You might almost call this like an Operation Warp Speed for manufacturing where you just lean hard into this and you say, look, America will once again be the number one manufacturing company in the world, but not because we are opposed to automation or robotics or AI, but precisely because we're going to embrace all of this new technology as hard as we possibly can.
Ben H.: Yeah, I think that's right. I also think, you know, we underestimate how good robotics might be for job growth.
Robots are like, where do jobs come from? They come from companies. Where do companies come from? They come from entrepreneurs. For entrepreneurs to have a whole new tool set, like "oh, I've got robots that can do things and that can create new products and services," is a huge boon. And I think possibly a boon for things outside of Silicon Valley.
We're like, look, if software is the tool and that's the tool you've got, and that's it, then that's a bit limiting. You have to be able to have those kinds of people to build those kinds of companies. We know that better than anyone. But like what we're seeing with hardware manufacturing and robotics and so forth is that those companies are much more spread out than the software clusters that we see.
So I think generally it could be very good for the country and very good for jobs, provided we don't outlaw it all before it happens, which is certainly a possibility.
Marc A.: Well, that's the current plan. The current plan is everything I just described is illegal.
So we would need a national campaign. We would need a major, major initiative. Um, on the part of both government and industry to do this, but I do think it is, it is a possibility.
It's more, it's more a possibility now than it's been for at least 30 years, yeah. Um, and so it is, it's worth pulling on that thread and maybe we can spend more time on that down the road.
The aperture is reopening, um, and you know, in a really, I think exciting way because, uh, yeah, you know, it's always much better when you have kind of innovation that can be done.
Ben H.: by a broad swath of skill sets and people than just a very narrow base. And I think we're kind of getting back to that through AI and robotics.
Marc A.: Yeah, that's right.
Ben H.: Okay, good.
Marc A.: I think that's a great note to end on. Benjamin, thank you again. Yeah, that was fun. Thank you, everyone. Great to see you.