Alphabet X's CEO Has A Vision For AI Moonshots — With Astro Teller

Astro Teller is the CEO of X, Alphabet's moonshot division. He joins Big Technology Podcast for a taping in front of a live audience at Summit at Sea to discuss the calculations society must make around advancing AI research and how the technology can help the world. Listen for a dynamic discussion that digs into Teller's family history, the current 'moonshots' that X is taking, and whether AI moonshots should've stayed separate from Google for longer. Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. For weekly updates on the show, sign up for the pod newsletter on LinkedIn: https://www.linkedin.com/newsletters/6901970121829801984/ Questions? Feedback? Write to: [email protected] Learn more about your ad choices. Visit megaphone.fm/adchoices

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Alex Kantrowitz?

  This episode is brought to you by Genesis. What does it actually take to put Agentic AI to work across an entire enterprise? I recently spoke with Genesis chairman and CEO Tony Bates about why orchestration is emerging as a competitive advantage in customer experience and what companies need to get right in the AI era. Then I sat down with Adam Mitchell, head of enterprise business solutions at Voya Financial, to go inside a large-scale Agentic CX transformation, including the business decisions, trade-offs, and lessons along the way. You can watch both conversations now on my YouTube channel today. It's never been more challenging to get frontier compute. How do you know where to look, who to trust, or how much to pay?

• 0:43Alex Kantrowitz?

  Akash is an open cloud that provides easy access, flexibility, and peace of mind. That's how companies like Razer were able to cut their inference cost by 15 times. Visit akash.network/alex for a free consultation. That's akash.network/alex for a free consultation.

• 1:03Speaker 01

  LinkedIn Presents.

• 1:23Alex Kantrowitz?

  Welcome to Big Technology Podcast, a show for cool-headed, nuanced conversation of the tech world and beyond. And we are here with Astro Teller, who's the captain of Moonshots and the CEO of X at Alphabet. Thanks for having me. All right.

• 1:41Alex Kantrowitz?

  Now, as you can hear, we're doing this in front of a live audience here at Summit at Sea. And folks, I think you just clapped a little bit, but I really want you to be on the recording. So let's hear it. You gotta be loud.

• 2:02Alex Kantrowitz?

  Doesn't it feel so good to be back in person again? Man. For sure. Is this your first time doing something in front of a live audience? I've done it a few times, but nothing as boisterous as this. This is amazing. So, thanks, everybody, for coming. You know, Astro, as I was doing my research for our conversation, which is billed as maybe an optimistic look at AI coming from you, I was looking into your background and saw that your grandfather, Edward Teller, is the father of the thermonuclear bomb.

• 2:32Speaker 02

  Yes.

• 2:33Alex Kantrowitz?

  This is gonna be a slow conversation. I'm kidding. So, I guess a bit of a curveball at the beginning, but here we are. So, is that weird? And also, what do you think about, when you think about AI, what do you think about in terms of our ability to develop technology that could be quite destructive?

• 2:59Speaker 02

  Let me give you two thoughts. I guess you can dig in further, but the first one is, I have always been inspired by the idea that getting really bright people into an intense environment to work on something really hard that really mattered could have sort of profound positive impacts for the world. And I think the NASA space program, the Manhattan Project, Bletchley Park in England, there are lots of these examples, and that was one of the things that inspired me when I was young. And we'll probably get back to getting a group of people together to work on something hard, in particularly hard ways.

• 3:40Speaker 02

  But the other thing is, the nuclear bomb is, makes sort of like a good headline, sort of the mushroom cloud. We all have emotions attached to that. But the emotions that we've attached to our fears, our frustrations, understandably about nuclear bombs, translated in the 60s and 70s into such a negative narrative about nuclear energy, that we, as a society, completely missed the boat. Pun intended.

• 4:20Speaker 02

  The disaster which is climate change right now would not be happening if we as a society had not let our fears about the first thing translate into an inability to use the upside of nuclear power to save us from what is now arguably the biggest problem in the world. So as we sort of move forward and talk about the technologies of the day, I would encourage us to think about that duality. Not that nuclear power has no downsides, but that if we're, if we weren't careful and we weren't in that case, we missed a lot of the opportunity for the upsides.

• 5:04Alex Kantrowitz?

  You could give it up. Okay. It's an almost a perfect precursor to a discussion about artificial intelligence because AI can help our society in countless ways. And in fact, it's already in place in certain ways helping us. But it also has this capacity for destruction. I mean, you have, I think it's an often cited statistic, but one in 10 AI researchers saying there's a chance that it could effectively turn civilization into paperclips. So my question to you is, when we create such powerful technology with the capacity for good, but also the capacity for bad, what calculation do you think needs to go through our head before we decide to move forward?

• 5:49Speaker 02

  Again, this is a very big conversation, so we may have to come at it from a couple different directions. But artificial intelligence, number one, is algebra on steroids. Just so we're clear. It is a very big field of study. And when you get out a microscope and you like look down inside the computer, you cannot find the AI in there anywhere. It's just math.

• 6:17Speaker 02

  And sort of depending on how you measure it, humans have been working on artificial intelligence by that name for 70 years. And it has been making progress over the last about 70 years. So it's not like we were at zero and then there was like a huge step function. The plane that flew you here flew itself almost the entire way using artificial intelligence. And we all rely on artificial intelligence every day, whether we realize it or not. So this is not to say no to your question, but just maybe for us to set the table that this is not like the lights just got switched on, or we're sitting there by the light switch, wondering whether to turn them on. Do I think that things are picking up speed?

• 7:02Speaker 02

  Yes, but this is part of a much longer narrative. And I think that we need to be really thoughtful about how, as we develop any technology, can we get the most benefit out of that technology? And at the same time, as wisely as possible, see potential downsides from that technology? And then find ways to mitigate against them or corral them into places where they won't be a big downside for society.

• 7:31Alex Kantrowitz?

  So you're a professional inventor, effectively, who manages professional inventors. And I wonder, what is it about us, about humans, that we'll go forward and create things that we know have the potential for great destruction? And kind of hope, kind of be optimistic that we're going to be using them for good? I mean, nuclear energy, nuclear weapons? Like, nuclear energy could help save the planet. But the fact that we have nuclear weapons increases the chance that we'll wipe ourselves out. Same with AI. AI is something that is, can do a lot of good, has done a lot of good. Glad that the plane made it here okay.

• 8:14Alex Kantrowitz?

  But also you have these potential downsides. So think, put your, like, put us all in the head of an inventor now. And, and explain sort of why the human spirit pushes forward with things and hopes that will do a good job taking care of them, even though there's risk.

• 8:33Speaker 02

  So inventing sounds like a monolithic effort, but let me separate it a little bit into two different things. One of them is learning, the discovery of new knowledge. If humanity can't survive the discovery of new knowledge, I mean, I don't believe that. Maybe you do, but I believe in humanity. I think it could be bumpy at times, but I believe in humanity and I believe we can survive discovering new knowledge. I don't want us to need to infantilize ourselves as a species by preventing new knowledge. That wasn't my intention. I know, I know, I know. That's the first half. Now, the second half of invention is what you do with the knowledge.

• 9:16Speaker 02

  And in instantiating the opportunity, a new thing, like, let's say, electricity, should we put electricity into the things around us? You can say, sure, generically, but when you start to do it in specific cases, you can say things like, who will benefit from this? Who will be harmed by this? You can say almost for certain with something like either electricity or artificial intelligence, we can't say for sure all of the ways in which this will play out. Great, so how can we sandbox this discovery, this invention process? So that as we try to instantiate it in products and services, we can put it out in the world before it's done,

• 9:59Speaker 02

  not to be irresponsible, but to be responsible. And to say to people, what do you think? How should we change this? What can we learn from this? And if you'll allow the metaphor, you know, for a long, long time, Waymo, the self-driving cars, which actually came from X, we had a person sitting in front, right by the steering wheel, with their hands really near the steering wheel, eight hours a day, just making sure that nothing bad happened. So the car was practicing driving itself, but there was a backup. I think there are lots of ways in which we can learn in the real world and do it responsibly by engaging the rest of the world in what's happening and getting that feedback

• 10:41Speaker 02

  so that we can get these unforeseen consequences out into the light so that we can design around them.

• 10:48Alex Kantrowitz?

  So I'm gonna get to the optimistic stuff, I promise, but I wanna keep going here just a little bit more. It's very interesting how you talked about how with AI, it's just, you know, a series of numbers, effectively. It's math and we're not gonna see God inside the algorithms. You could also say that with nuclear, right? It's just a series of equations that we figured out how to do some crazy stuff with. Sometimes crazy awesome, sometimes crazy bad. So is there ever a moment where we say, where we, like, you mentioned, we shouldn't stop, we shouldn't worry about surviving the discovery of new knowledge. But is there ever a point where we say, stop? Like, I think about the letter that Elon Musk and a bunch of others said about, we need

• 11:29Alex Kantrowitz?

  to stop researching AI, which seemed to be a bit of a pipe dream to me. But is there ever a point where we say, this type of stuff, we shouldn't keep going with? Or do we sort of, is it inevitable that we push ahead?

• 11:47Speaker 02

  I can't speak for the whole world. I think the reality is that lots of people who signed that letter, and lots of other people in the world, are going to keep working on it, no matter what letter.

• 11:59Alex Kantrowitz?

  No doubt. That's great for headlines for them, but...

• 12:01Speaker 02

  Yeah, for sure. I think what's, the only thing I can control is what we work on, and I wanna work on what we're working on responsibly, and so that we can get as much benefit to humanity as possible. I think that the world is overrun with serious problems. I would put at the top of that list, climate change. Nowhere close to second place, I would put nuclear weapons, just since we use that as an example, and I would put AI doing something particularly horrible for humanity, a very, very distant third. So instead of focusing on all of the bad things, and I'm willing to talk to you more about them, but I'm interested in why we're not talking about the downsides, because this

• 12:44Speaker 02

  is about us netting humanity to the positive.

• 12:49Alex Kantrowitz?

  The downsides or the upsides, why we're not talking about the upsides, right?

• 12:52Speaker 02

  Yes.

• 12:53Alex Kantrowitz?

  Okay.

• 12:54Speaker 02

  So, so I agree that...

• 12:57Alex Kantrowitz?

  We're gonna do that, by the way.

• 12:58Speaker 02

  Excellent. I'm looking forward to it. And... Is it possible that there will be complexity for humanity as we go through this? 100%. Do I believe that anything particularly extreme in our lifetime is going to happen? I don't. I'm sorry, but I've been working on robots trying to open doorknobs for like the last 30 years, and it's been a slog. So as someone who's been in the field for 30 years, I'm just a little bit maybe more sanguine than people who started learning about this recently.

• 13:33Alex Kantrowitz?

  I think I need to just clarify the question here because it's not do we stop? It's is there a point where we think about pausing? That's what I'm asking, really. It's not like, I'm not sitting here saying, Astro, we got it. This stuff is about to turn us into paperclips. Not the point at all. The point is philosophically, do we have a point where we say, maybe we don't want to develop those things? And if we don't, that's fine, and that's, you know, one answer. But I'm curious if you actually think that there is a place where we do say, no.

• 14:03Speaker 02

  Yes, I'm sure such a line exists. I argue by the time we've gotten to that line, it will already be too late. So this is actually me agreeing with you. I think way before that line, we should be saying, what are we doing? How are we doing it? And can we put intelligence into the things in the world around us in ways that benefit humanity? And how, as we're doing that, even if our vision is really well honed to be in that positive for humanity, can we be on the lookout for downsides and get ahead of them? Because I don't want to ever get to that line. And I really think if we get to that line, and then some half of humanity says, okay, we're out, the other half of the humanity is just going to keep going.

• 14:45Speaker 02

  So I think we need to be worried and thoughtful and responsible about these things, starting now, not starting when we get to that line.

• 14:53Alex Kantrowitz?

  Yeah, it's kind of, I mean, it is very interesting. And that's kind of the opinion that I share with you is that we don't really have as a species the capacity to stop. And that's very interesting. Same with the Manhattan Project, same with AI. So, so, okay, we're going to go ahead and build AI. Do you, do you think there is a similarly positive impact that artificial intelligence can have, the same way nuclear energy can have in terms of preventing, you know, we advertised this session? Energy climate change. So let's start there. Like, is there a place, is there a way that AI can have that impact? And is it the AI that we've been developing all along, meaning that the optimization technology, computer vision, stuff like that?

• 15:38Alex Kantrowitz?

  Or is it, does this generative AI wave have a role to play in this world as well? And let me add a third question, because this is now a two-part, or so might as well make it a three. What at Google X, or Alphabet X, right, is now happening to tackle these issues?

• 15:57Speaker 02

  I'm happy to go down all three of those. I think we're gonna get lost in the rabbit holes. We're gonna have fun getting lost in them, but you may have to bring me back to some of those topics.

• 16:05Alex Kantrowitz?

  I'm up to it.

• 16:06Speaker 02

  Awesome. So, I mean, let's start with, you know, artificial intelligence is a really big basket of things. Many people may have heard a lot about large language models recently. That is a particular piece. So, artificial intelligence has lots of baskets. Machine learning is one of those baskets. Deep neural networks is a subset of machine learning. And even within deep neural networks, there are ways of setting them up and training them that are what you've heard in public referred to as these large language models. Those will continue to have a lot of impact on the world, for sure. But I'd rather focus, I think it's actually more productive to go one or two steps back up to like, machine learning in general.

• 16:51Speaker 02

  And to say, how are we solving problems? We should be falling in love with the problems, not falling in love with the technology. And so, for example, one of the projects that's near and dear to my heart that's at X, I think it's a really big issue in the world. The world's electric grid is the world's most complex machine. That is by far the most complex machine humanity has built. The people who run the grid all over the world, and there are many pockets, obviously, these are good people, they're trying hard, but it is very complicated. They're regulators, there are private companies, there are these semi-public companies, the system operators, the people who own the wires, the people who own the generation, the people

• 17:35Speaker 02

  who own the distribution, the sort of last mile to people's homes, all often different companies. It is a very complex problem, and so when you go to the people who are supposed to make sure that the load, the need for electrons and the source of electrons are balanced on a millisecond by millisecond basis, they're trying really hard to do that for all of us, and they're barely hanging in there. And that's leaving the grid the way it is. There are now all of these solar fields all over the world that want to jump onto the grid, all these batteries that want to jump onto the grid, all these electric vehicles that want to jump onto the grid, and they have no way of figuring out how to maintain their system because, here's the crazy bit, they don't know what their system is.

• 18:20Speaker 02

  If you go to a system operator and say, "Show me the digital map of where every inverter, every transformer, every wire is in your grid," they will just say, "We don't have that." And that's why it takes them 10 years, when you wonder why there are 10-year waits in most states in the United States to get a solar field onto the grid. It's not because people don't want to put solar fields on the grid, they don't know what will happen to their machine if they plug that solar field into the grid. So, what if machine learning could help them to learn about their grid, virtualize their grid, and then answer in a minute, instead of in a year, in 10 years, what will happen

• 19:04Speaker 02

  if you plug this solar panel onto the grid? It's gonna be okay, give that one a yes. Think about the tsunami of renewables that are already waiting, they've literally already been built, they're just sitting there in the dirt, wind farms and solar panels, waiting because we don't have a virtualization of the grid. That is an example, right now, that X is doing, to try to use machine learning through the energy infrastructure to make the world better.

• 19:38Alex Kantrowitz?

  And let's talk about the wave of generative AI, large language models. Where do you see the potential there? And yeah, I'm curious if X has any projects underway that because, look, I if I had to put myself in your shoes, I think what you're probably going to say is that this is a little bit overhyped and we can actually do more with the technology we have. I guess that's what you just said in a sense. So, but like, yeah, where do you really think the opportunity is for generative AI? And is X working on anything there?

• 20:07Speaker 02

  So generative AI, as you've probably seen it play out in the media recently, leans more into things like asking Bard to write you a poem, going to one of these image producers and say, hey, make me a picture of a pony that's actually unicorn, it's at a birthday party, and it's wearing a purple saddle. And then it makes you that picture. That's what, you know, in the media right now, generative AI is typically being put forward as. Let me try to get a different, that's real, that's going to continue to be a thing. But that's the tip of the iceberg. So think about it this way.

• 20:48Speaker 02

  We're in the middle of, actually, this has been decades coming, and it will take decades more to move through society, of a process of lifting up people, moving them away from the craftsman mechanical detail work of designing and making things, lifting them up into guiding computers who help them make things. And this has been going on for a long time. If you're a photographer, you are familiar with and you have used Photoshop, for example. And that doesn't ruin your ability to be a photographer, it lifts up your ability to be a photographer.

• 21:30Speaker 02

  So if you work at a car company and you have a car strut, let's say, so it's sitting there, it's one of the main pieces that holds the car bits in place around the wheel to the frame. You want it to be really strong. When you pull it, but also when you smoosh it together, it's got to be really strong. It has to be really strong in torsion as well, because otherwise it'll snap like this. But you want it to be cheap to make, and you want it to be as light as possible. So instead of designing what you think would be the best one, what if you went to a system that could try millions of different possible car struts? So many that it started to hill climb in car strut design space.

• 22:14Speaker 02

  And you were watching it and telling it things like how much you value fast to make, cheap to make, low carbon footprint to make, low carbon footprint because it weighs less for driving it around afterwards. So you're guiding it, you're making the decision, but it's trying millions of things, and it comes out with a car strut at the end, which is better than any car strut a human could have designed. We're going to see this sort of thing play out in every discipline in the world over the next 30 or 40 years. And it will take a long time. X is really interested in some of these spaces where we can help the people of various industries to be inventing and designing much faster

• 22:59Speaker 02

  so that we get to much better designs, so we get much better solutions to the world's biggest problems.

• 23:04Alex Kantrowitz?

  So you're working completely on moonshots. What you just described just sparks a question for me, which is that if this becomes democratized, if this gets in the hands of so many people, then does the path from wild idea, moonshot idea, to production become much quicker and then also much more democratized? We don't have many invention houses like X in the world. So are you worried maybe that you're going to have a little bit more competition?

• 23:32Speaker 02

  Exactly the opposite.

• 23:35Speaker 02

  The world is not going to run out of problems. And the fundamental goal of X is to get a bunch of people together to work on those problems as efficiently as possible. The more people can work towards solving humanity's problems, the better off we'll be. So I hope it does democratize things. I'm watching it currently start to democratize things, and I'm super excited about that. I would also say, so there's democratize in the sense of more people can be at the starting gate and doing that work. It is also lifting us and people like us up in our ability to reach out even better to partners. And so we're now helping aquaculture experts in Norway in a way that 10 years ago, I'm

• 24:20Speaker 02

  not sure we could even have imagined. We're helping in the electric grid in Chile in ways that I just described to you. And so they're getting this same benefit. They're being lifted up by this technology as we are able to share it with them.

• 24:35Alex Kantrowitz?

  What are you doing with the aquaculture experts?

• 24:38Speaker 02

  Oh, aquaculture.

• 24:40Alex Kantrowitz?

  What is aquaculture?

• 24:42Speaker 02

  So let me take a step back. This is our, the first project I was described to you is called Tapestry. This project that I'm about to describe on ocean health, we call Tidal. And our, let me give you the context here. So humanity gets about two and a half, almost three trillion dollars a year from the oceans. And we are destroying the oceans, as many of you probably know, faster than we're destroying the land or the air. It is the sort of sink for humanity. It is pulling all of the worst bits that we're putting into the world into it, and that's why the ocean is dying. We, humanity is not going to stop using the oceans. We need to get more value per year from the oceans, and we need to do it in a way that

• 25:27Speaker 02

  is not only not destroying the oceans, but we need to regenerate the oceans. There is no possible way to do that unless we find a way to take automation to all of the things that we currently do in the oceans and can do in the oceans. So if that's the big picture, where are we gonna start? Well, one of the cool things about fishing, I mean fishing is, open sea fishing is actually really problematic, as most of you know, we're in the middle of overfishing all of the fish in the world. But aquaculture actually helps us not to overfish the oceans. And because the carbon footprint of a pound of fish is one-eighth the carbon footprint

• 26:12Speaker 02

  of a pound of beef, we are wildly better off as humanity moving to producing food through aquaculture. But when you go to a huge pen, even the, our partner, Moi, which is a sustainable aquaculture farm in Norway, it's the largest salmon farmer in the world, and they are very good at what they do. And the state of the art, when they wanted to find out, a year ago even, two years ago maybe, how well their fish were doing, how much their fish weighed, they would, in a pen with 250,000 salmon, they would pull 20 salmon out of the water, put them on a scale, weigh them, and be able, average that, and be like, well, that's probably what they weigh.

• 26:57Speaker 02

  And put them back in the water. If they wanted to find out if this fish were sick, they would pull 20 fish out, and like, mm, do I see any lice on the fish? Mm, put it back in the water. So what we're doing is we're enabling them through computer vision, and machine learning, and automation, and specialized sensors, to allow them to look at the health of the fish, the weight of the fish, we're helping them to automate the feeding of the fish, which makes it much more sustainable, because the runoff from overfeeding on these fish farms is actually one of the big problems in aquaculture. So we're making the farmer better while we're making the world better, using machine learning.

• 27:47Alex Kantrowitz?

  So Google X, is it called Google X anymore? X, it's called X. X is there to effectively try to insulate Alphabet from the innovator's dilemma, which is effectively, you're an incumbent, you have a business, and you do everything you can to protect that flagship business. And that might potentially head off your ability to do things that would disrupt your core business. We've talked about some very cool projects, self-driving cars. We've talked about things with climate and food. But there's been a big story recently, which is that these chatbots coming from places like OpenAI

• 28:30Alex Kantrowitz?

  and Microsoft have threatened Google. So now Google has its own, it has Bard. But I almost wonder, when we think about insulating Google from the innovator's dilemma, shouldn't X have been front and center, building the first ChatGPT? And not letting an OpenAI, for instance, run away with it?

• 28:53Speaker 02

  I swear, he's not a plant. But good setup.

• 28:57Alex Kantrowitz?

  I think, didn't you say that we shouldn't be afraid of the advancement of knowledge?

• 29:01Speaker 02

  Yeah, absolutely, absolutely. So, no, no, I love the question. Let me take a step back and remind you sort of how X functions, what our goal is. Our goal is to invent and launch moonshot technologies that if we do it right, help tackle some of the biggest problems in the world and lay the foundations for enduring sustainable businesses. One of the early things that we did was a thing, which, when we graduated it back to Google, was called Google Brain. Google Brain is the origin of much of what you now think of as Bard.

• 29:42Speaker 02

  And so that was an example in the earlier days of X, of us making something, realizing that it would be productive for that to be back in the mothership. We moved that back to Google, and it has gone on to do amazing things. And so the transformer paper and other things that you've heard about. Somewhat more recently, this is about five years ago, we said,

• 30:08Speaker 02

  We can feel on the horizon, we're going to get to the place where the ability is going to be there for us to be working in much tighter loops with software developers, like a partner to them, where we can complete code when they start to write it. If they write out the, an English language description of what they want, it will write out the code for them. It can even be like a pairs programmer and have a conversation with them and help them to be better programmers. And so that work happened for about five years at X. About seven, eight months ago, we moved it back to Google and it was just announced recently

• 30:49Speaker 02

  as Cody and that is now sort of the code making part of what you think of at Google.

• 30:55Speaker 03

  Ahoy sailors. At Virgin Voyages.

• 30:58Speaker 02

  Ahoy.

• 30:59Alex Kantrowitz?

  That was, the timing of that was so freaking perfect. Just finished the answer and then there we go.

• 31:06Speaker 03

  Familiarize yourself with the location and receive important safety information from our crew.

• 31:12Speaker 02

  So the real question is, is he going to delete this from the podcast or is he going to leave it in? What do you think? Should he leave it in?

• 31:25Alex Kantrowitz?

  Thanks, everybody.

• 31:27Speaker 02

  That's right.

• 31:33Alex Kantrowitz?

  Can someone shut this off?

• 31:38Speaker 02

  What?

• 31:39Alex Kantrowitz?

  It's required by law.

• 31:42Speaker 02

  One of our jokes at X is after we, you know, make time machines work and cure cancer, we're going to figure out how to get AV sorted out. That'll be like the hardest thing after we do the easy stuff like cancer and time travel.

• 32:00Alex Kantrowitz?

  So I want to ask a follow-up question, which is, you mentioned that Brain started in X and then graduated to Google. I want to question the graduation process because when you do graduate to Google, doesn't it become a little bit harder to say, "I'm gonna, we have, we've made all this amazing technology, now I'm gonna make something that's competitive with the bread and butter." For instance, you know, a chatbot that can sort of serve as something that people might want to use instead of search. And once it goes into Google, doesn't it have the constraints that it might not have in X, which is that now all of a sudden it has to worry about the quality of search

• 32:41Alex Kantrowitz?

  and the quality of response, where it might not in a more experimental unit?

• 32:49Speaker 02

  Ramp anything up to serve more than a billion people a day is super complex. We're talking about, you know, hundreds-plus languages. There is a lot, technically, legally, ethically, just to do this, practically at all, but also doing it ethically, that X is totally not set up to do. For the same reasons that X may be a particularly good place to do rapid prototyping and learning of new things, we are not the right place to move something from, "Yeah, we have something special here," to "It's ready in a really thoughtful, responsible way to serve a billion people a day." So, I hear you, but I would actually say that that would have been irresponsible for

• 33:32Speaker 02

  us to, like, try to keep that to ourselves. That the goal of X is to create really good seed crystals. We don't want to do it all ourselves. We want to get the ball rolling where, you know, either sometimes the rest of the world in the form of an other bet at Alphabet like Waymo is, or back at Google. The world says, ah, got it now. See why that could be really big before it's done. But it can survive on its own because it's left behind that like, what are you talking about? Why would we do that? That's definitely not possible when those things are still on the table. That's what X should be for is to actually work through and often be wrong about whether there's something awesome there.

• 34:17Speaker 02

  And so we try 100 things and 99 of them don't work out. And our job is to be wrong about those 99 as efficiently as possible to move past the ones where we're wrong about them with evidence as quickly as we can so that that one, which we can double down on over time, can sort of go on to have a really productive future.

• 34:40Alex Kantrowitz?

  So here's my uneducated argument to switch things up. Yes, it makes sense to have a prototyping lab. But also couldn't you, for instance, you know, a billion users, that'd be building something like Bard into Google Chat. But OpenAI's ChatGPT started with 100 million users. That's the fastest growing consumer product in history.

• 35:02Alex Kantrowitz?

  It seems like you might even be in a great position to, you know, say, hey, we have this chat thing. Keep developing it with InX and then call Sundar up and be like, hey, man, can we have some of that cloud infrastructure? The same way that OpenAI called Saya up and said, can we have some? And that's sort of what's enabled them to power ChatGPT. And then you don't really have to worry about the competitive elements of coming up against the core business.

• 35:28Speaker 02

  Let me use a related example, see if this at least partially satisfies you. So one of the things that we've worked on at X for a very long time is factory automation. The world spends sort of pushing $10 trillion a year making stuff. And if you can automate the making of stuff, you can do it faster. You can do it cheaper. You actually have less waste. It's more reliable because it's been automated. But it is fiendishly complex and expensive to automate the making of almost anything. Because until very, very recently, there was a lot of bespoke-ness, artisanal-ness to programming robots. And so

• 36:14Speaker 02

  X worked on that for many years. Created, it was a project called Intrinsyc. It graduated. It's now in Other Bet at Alphabet. They just announced a few days ago that they have a platform called Flow State, which is a developers arena in which you can develop new capabilities and new skills for robots to have with a seamless ramp from early investigations in simulation to actually landing them in robots in factories doing real work. And that creates more jobs for developers, makes it easier for people all over the world

• 36:57Speaker 02

  to help make robots more capable, and it helps the makers of the world, and it democratizes the making of things. Because right now, it costs a billion dollars to seriously automate a factory. So that's an example that started at X. It's not exactly a chatbot, but it is, you know, making robots much more capable, much more flexible, much more nuanced and dynamic in their ability to solve problems on the fly. And then we've laid out the early infrastructure, but are allowing the rest of the world to build on top of that, and which I think is going to be really good, yes for Alphabet, but for the whole world as well.

• 37:36Speaker 02

  So that's something that didn't go to Google, that lives in this region post-X, but still inside Alphabet, called an Other Bet. So do you want Brain back? What was that? Do you want Google Brain back? No, no, no, no. Our job is to catch new waves for Alphabet, to catch new waves for the world. What excites us isn't sort of empire building, or getting to sort of like have been the one who rang the bell. That is not our goal. We want, in a really efficient way, keeping this balance of audacity and humility just right. Audacious enough that we'll try almost anything, but then humble enough that we're honest

• 38:21Speaker 02

  right after we start out on each of these investigations, that we're probably wrong, and that we need to spend our money figuring out that we're wrong, rather than trying to prove that we're right, since mostly we are wrong. And by doing that in lots of different domains, what rises to the surface are these new things like inverse design or generative design that we were talking about. It's not that I said there will be generative design seven or eight years ago. It's that we've tried a thousand things over the last decade. And the generative design stuff is some of the stuff that's bubbling to the surface in a way that's looking really promising over and over again. That's what excites us is catching those new waves, doing it efficiently and doing it responsibly.

• 39:06Alex Kantrowitz?

  Astro Teller is here with us. He's the captain of moonshots at X and the captain of moonshots at Alphabet and the CEO of X. We'll be back right after this to talk a little bit more about how Alphabet and X actually create the products they do. This episode is brought to you by Avpoint. Everyone's racing to roll out AI right now. Co-pilots, chatbots, agents doing real work. But here's the part nobody loves talking about. All that AI runs on your data. And most teams have no single way to see it, secure it and prove it's under control. That's exactly what Avpoint does. For 25 years, they've been the trusted layer beneath the world's most demanding data, now extended across your entire AI estate,

• 39:51Alex Kantrowitz?

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• 41:28Alex Kantrowitz?

  And we're back here on Big Technology Podcast with Astro Teller. He's the captain of Moonshots at Alphabet, CEO of X. So Astro, I'm actually curious how you decide what to fund inside of X, because, you know, I might think of this as like you need to predict the future, but that's not the way that you think about it.

• 41:46Speaker 02

  Exactly. I, one of the core axioms at X is I do not believe that anyone, certainly including myself, is any better than random at predicting the future. I know that that's like, not the cool thing to say in Silicon Valley, but I just don't believe that any of us are better than random at predicting the future. I think we can discover the future a good bit more efficiently than maybe other people discover it, but that's very different. So the core kind of map to beginning a journey at X is we make these three circles and we talk about X efforts living at the intersection of those three circles. The first one is

• 42:30Speaker 02

  there has to be a huge problem with the world that you want to solve. If you're proposing something for X, you have to tell us what that huge problem with the world is you want to solve. If you can't say that, we're not starting on the journey. Number two, there has to be some radical proposed solution for how to fix that problem, some science fiction sounding product or service, however unlikely it is that we could make it, that if we made it, would make that huge problem go away. And then there has to be some kind of core technology opportunity, some breakthrough technology that gives us the opportunity to start on that quest. And those three things together are a moonshot story hypothesis.

• 43:14Speaker 02

  That doesn't mean you're right. In fact, you're almost certainly wrong. But if you can propose those three things, it has the form of a moonshot. And then the answer is, great, gorgeous moonshot story hypothesis. What is the smallest amount of money and shortest amount of time you think it'll take to kill your idea? Because your idea has a 99% chance of being wrong. And there's no way to avoid that because if that wasn't true, it wouldn't be radical. We're only interested in the over the horizon stuff. And so as soon as we sign up for that, we are explicitly signing up for being wrong most of the time. And that's why the humility has to kick in. So it doesn't work like we will work in these areas.

• 43:59Speaker 02

  We won't work in those areas. It's, it mostly is what are huge problems with the world? So maybe not coincidentally, more than half of what we're doing right now is in the climate change space. But not because I mandated that, but because that's what people were excited about. And that is legitimately some of the biggest problems the world is having right now. And then it's actually evidence that kills off most of these things over time. And the ones that survive that we double down on. So it's that winnowing process that looks at the end like we planned each of these things. But we didn't plan these waves to catch. It was just a filtering process that weeded out all the ideas who are just bad sometimes.

• 44:44Speaker 02

  But more often we're like, just wrong time, technology wasn't right. Or maybe even the technology was right, the timing is right, and we just couldn't figure out how to make it great enough. Which happens sometimes.

• 44:55Alex Kantrowitz?

  It's a very interesting process. I'm curious, most people who are listening to this are not in the position to take moonshots. You must hear this all the time. Is there anything from your process they could put into place inside their companies? That might help them achieve those 10x moments that you're going for?

• 45:12Speaker 02

  For sure. I would actually argue what I just described is the most efficient you can be in trying to find something new. And even if you've pre-decided, you know, you're working on flying cars or whatever that is. Inside of your effort, there are lots of things that you might be thinking, should it be gas powered? Should it be batteries? How many propellers are we going to have? There are going to be lots of things for you to figure out over time. What should the airframe be shaped like? And so there are lots of risks to take, lots of discoveries to have. And for each of them, you can do exactly what I just said. So one of the things that we say at X is, if you're starting out on a journey, I know it's no fun to kill your ideas.

• 45:57Speaker 02

  I get that. But let's say, for argument's sake, that the idea you have isn't going to make it. Would you like to find out now for $1? Or find out three years from now for like $10 million? And everyone, of course, says, I guess I'd like to find out now for a dollar. Great. Or, welcome to X. Thank you for working on the propellers or the time machine or whatever it is you're working on. How are we going to do this? Are we going to be intellectually honest or intellectually dishonest about our discovery process? You're like, oh, I guess intellectually honest, Astro. We all know in our hearts what to do. What I just described is not rocket science. It's not like X invented this.

• 46:42Speaker 02

  That's not what's going on. It's just so hard to do it. All of human nature drives us in the opposite direction from what I'm describing. So what X spends all its time doing is trying to create the infrastructure, the social norms, reward systems, et cetera, so that people actually do these things. But what I've described, for sure, if you work in a startup company, if you work in a huge conglomerate, anywhere in the world, this is still the right way to explore new ideas.

• 47:14Alex Kantrowitz?

  Let's check in on a couple of famous X product projects. Waymo, right? I think I'm seeing more progress in self-driving cars now than, in like maybe the past year than, because I was in San Francisco 2015, 2016. People were like, this thing is going to happen next year. We're 2023. Not quite there yet, but it seems like there's a lot of progress being made. What's your, what's your perspective on where we are right now in self-driving cars? And how far are we from realizing that original vision?

• 47:47Speaker 02

  For sure, it was harder than it turned out to be. One of the things that I think caught Waymo by surprise, got us by surprise, is that there are a lot of things that human drivers do that if we want to follow the laws, we can't do. Those humans just kind of like are in the gray area a lot about how they choose to drive their cars, where they pick people up, where they drop them off. If it's like a transportation as a service, obviously Waymo can't do that. That is incremental hard stuff for us to figure out. So there are a lot of edge cases to make sure that this is super safe, it's a fun experience for people.

• 48:28Speaker 02

  But I also think that the way it tends to work is any exponential ramp up, it's the same ramp up every year, assuming it's gradual ramp up, but if we're not paying attention to it, it feels like it went like this. And I think most of you will experience self-driving cars like this. There are three cities in the United States where you can get a ride, you know, with nobody in the front seat, in Los Angeles, in Phoenix, and in San Francisco, from Waymo. And I don't know exactly when, but I pretty much guarantee there will be more cities over time. And so if you live in one of those cities, it probably feels more real.

• 49:11Speaker 02

  If you don't, it feels like, nah, it's not going to be a thing. And then all of a sudden, it will be a thing, and that's natural because, you know, unless you're really in that industry, you aren't watching really closely as the number of rides per day, per city, is going up from a group like Waymo.

• 49:28Alex Kantrowitz?

  I'm legitimately pumped to ride in a Waymo for the first time, hopefully this year. Another project that I want to talk to you about is Project Loon, which was, was, is these balloons that are supposed to beam internet down to everyone, no matter where you are. Now, I'm curious, like, people now, if they think about accessible internet, they immediately go to Starlink. How did Loon, which was, you know, a great idea early on, sort of, I don't know, is it right to say, but I think lose ground to Elon Musk and Starlink?

• 49:58Speaker 02

  First of all, I mean, so Loon was a goal. Could we make a worldwide stratospheric layer of balloons, they're like cell towers, but floating at 65,000 feet, that were talking to each other in an ad hoc mesh network, and beaming LTE or 5G to the ground so that people in rural communities around the world, particularly the people who don't have good internet connection, all of a sudden would. Very inspiring, and it took a long time. We built it. It was working. We were beaming LTE and 5G to hundreds of thousands of people in multiple countries.

• 50:37Speaker 02

  We couldn't figure out how to get the business to close with the owners of the spectrum that we had to work through in those different companies. So we turned it off. It made us very sad. Starlink is a cool company, but they're solving a different problem. There's a fixed amount of bandwidth you can land when you beam something from a satellite in any one region. So it works very well in rural places, but you couldn't have like a huge number of them parked very close together. So that wasn't really the goal that Loon was trying to solve. But let me tell you a sort of fun side story. It was crushing for all of X for us to kill Loon. It had gone on to be another bet. It was really one of the inspiring things that had come from us, and so it made everyone

• 51:22Speaker 02

  really sad for us to stop doing it. And we have this saying at X, we're very focused on moonshot compost. Whenever we stop something, the people, the code, the patents, the know-how, we try to keep them all at X. They recirculate, working on new projects. And so in this particular case, one of the technologies that was allowing these balloons at 65,000 feet to communicate between the balloons at very high bandwidth were lasers.

• 51:56Speaker 02

  And so when Loon ended, someone said, "Well, how come we couldn't put those lasers on the ground, you know, like on a pole?" And that sounded almost embarrassingly too simple after all of the work we had done to get them up to 65,000 feet. But fast-forward five years, we have these things about this big, smaller than a traffic light. It shoots a laser up to 20 kilometers. It's eye-safe. You could just put your eye right up against it. Nothing bad would happen. It's unregulated. It's near-visible light. It's about one order of magnitude outside of visible light, but in the EM spectrum, that's basically visible

• 52:39Speaker 02

  light. And it's received by another box. It's, you know, two feet tall. You strap them on two poles. If you plug the internet, like a fiber optic cable into one, you have 20 gigabits per second, up to 20 kilometers away, for less than 1 one-thousandth the cost of trenching fiber. And we're rolling these out right now in mainly Africa and India. And that project is now moving more data to real customers per day than Loon moved in its entire history. So, go Moonshot Compost. That project is called Tara, if you want to look it up.

• 53:22Alex Kantrowitz?

  Pretty cool. Okay, let's see. We have maybe two or three that questions to get through in nine minutes. Let's see if we can do it. In the break, I asked some people in the audience to shout out different big problems that maybe X could get working on. And we've talked a lot about hardware, for instance, but what about some metaphysical issues? Is that ever something that you'd want to... or, I don't know, societal issues? Is that ever something that you'd want to take on? So, I'll just give one example. So, one person in the front here said, "Community." I mean, we know that community, building community is one of the things that people struggle with the most right now. I mean, we're here with one. So, it's like, it's nice to see that there, it persists, but most people would say, or many...

• 54:05Alex Kantrowitz?

  Many people, many more people than usual, would say that they're lonely, they don't have friends and stuff like that. Is that something that an ex would ever take on? Or is that even too moonshot-y for the moonshot factory?

• 54:15Speaker 02

  No, no, not at all. As long as we could be proud about the output and there is a technology solution to the problem, we would be excited to work on it. You know, we have had various explorations, for example, in the social justice space. Because the temptation is to think that something like belonging or systematic bias in society is just not amenable to technology helping. And maybe that's true, but that's not written in stone somewhere. Like, shame on us for not at least trying. So, we don't have some huge news to give you on that front, but we certainly haven't given up. I'll give you another one that's sort of like that. Education.

• 54:59Speaker 02

  Like, I will consider it a failure if X doesn't eventually have a great moonshot in the education space. We've tried like thirty things. It's so painful. But I also don't want to be kidding ourselves that something is a solution if it's not really going to be a significant phase transition for society. And some things may either just not be at all amenable to technology or have so many sort of other complex human issues around them, like pedagogy in education. If you take the teacher out of the process... Again, nowhere in is it written in stone, you can't help kids. But it might be fundamentally different and harder.

• 55:42Speaker 02

  So, there are lots of those kinds of things where we continue to go back to the well. And, technology is what we're good at, so we're not gonna sort of do things that don't have a sort of core technology piece to them. And some of these, hopefully, will have great news for you in the future, but, you know, haven't solved yet.

• 56:01Alex Kantrowitz?

  Have you thought about building an app? You could make it blue, where people would post their personal profile and connect with the people they know in life, and maybe share small updates?

• 56:11Speaker 02

  Like 140 characters, just very succinct.

• 56:14Alex Kantrowitz?

  Well, darker blue.

• 56:15Speaker 02

  Okay. That could be something. No, I feel like the world's already got one of those. And, um, you know, what we're best at are the things where in the early days, it doesn't look at all reasonable or possible. And usually, because we're not scientists per se, it's less that we're doing kind of basic research. It's more like taking something from column A and something from industry B and some observation from sort of field C and putting them together in a really unexpected way. And again, often we're wrong, but if those things come together, then at our best,

• 56:59Speaker 02

  I think we're system integrators and we're really good at getting mud on our boots, getting out in the world, working with partners, prototyping quickly, trying and learning, and using humility as a superpower to be wrong, admit we're wrong, learn from being wrong, and get better faster. Like, I think that's us at the best.

• 57:20Alex Kantrowitz?

  Okay. And then we also had someone yell out carbon sequestration. I think that was what it was. Yes. Okay. He's not correcting me. So thoughts on that?

• 57:29Speaker 02

  Yes. We have done a bunch of interesting work in carbon sequestration, in green hydrogen. We're doing explorations in lots of other parts of the space. We're excited about the possibility for making much lower carbon footprint cement. So I can't tell you that we've absolutely cleaned any of these problems off yet, but these are spaces that we're excited about. And, and by the way, spaces where inverse design can be really helpful because these tend to be large machines, chemistry problems, electromechanical problems. They have sort of a lot of the techno economics of these things are really unforgiving. Ultimately,

• 58:14Speaker 02

  the number of dollars it costs for you to sequester a ton of carbon is like the only thing that really. Really matters at the end of the day. Green hydrogen, of course, everyone will take it. But the number of dollars per kilogram is just going to determine, because it's a commodity, whether people buy it or not. And so trying to come up with something very different, but also have an eye on the sort of long term, like the Rust Belt engineering side of this work, the project finance side of this work. What would this really be at scale? Because it's it's actually kind of easy to make any of these things look great in the lab. It's having that sanity check on top.

• 58:57Speaker 02

  What is this really going to be at scale? Would this actually change the world? You know, we had this project, it was one of my favorite. We had a device, it was about as big as like the area where we're sitting, a little bit bigger. And it was taking seawater and producing methanol you could burn in a gas tank. There are four billion internal combustion engines in the world. Tears were running down the faces of the people, like capturing the first couple of drops here. This project was called Foghorn. Like, that felt like a real save the world moment. It was actually working. And we could not convince ourselves we were going to get it cheaper than about $15 gallon of gas equivalent. And that's just not going to save the world. So we turned it off.

• 59:42Alex Kantrowitz?

  So tighter economic conditions, interest rates around 5%. Do you worry that now that we've exited zero interest rate environment, that you might not be able to get as much funding from the mothership as you had in the past?

• 59:58Speaker 02

  I think Alphabet needs to be very thoughtful about how it spends every dollar. And we have spent the last 13 years working hard on our efficiency and on our rigor. So, you know, if Alphabet stopped being interested in sort of like the 10 year plus, that would be one thing. But as and Alphabet is very serious about the long term. And as long as Alphabet is serious about the long term, I am sure that You know, making sure that we spend every dollar wisely would be very important. And I think that Alphabet is still excited about having a part of itself that goes and explores these other spaces.

• 1:00:35Alex Kantrowitz?

  Let me ask you one last question. We started about talking about where invention could go wrong. I actually am kind of curious from your perspective, I know we only have like a minute left, but why do we continue to try to invent and build? Because it does seem like in some ways that like if we all put our heads together, we could have enough on this planet for everyone. But we don't do that. So I'm kind of curious like what you think, where are we trying to get to?

• 1:01:04Speaker 02

  I'll give you two answers. The first answer is humans are fundamentally explorers. I think we all have some pioneer spirit inside of us, wanting to learn, wanting to grow, wanting to find new things and do new things. It's a very fundamental part of who we all are. So I don't think humanity is going to stop doing that anytime soon because I think it's part of what makes being a human great. And, don't worry, I'm going to end on a positive note here.

• 1:01:46Speaker 02

  There is enough pain and complexity in being alive, enough reasons to think short term, that people are going to by and large do what is in their somewhat narrower self-interests and what solves best for their pocketbooks. So if we want to save the world, if we want to make the world a radically better place, we have to find ways for doing the right thing to be cheaper than doing the wrong thing, especially when it comes to climate change. And the only way that we're going to get to the place where doing the right thing is cheaper than doing the wrong thing, when we can dig the problem out of the ground and

• 1:02:28Speaker 02

  burn it, is going to be radical innovation. So that's why I believe we're working on it, and I think that's why the whole world is working hard on it right now.

• 1:02:45Alex Kantrowitz?

  Astro, thanks so much for coming on. My pleasure. Thank you for having me. Thank you, everybody, for listening. Thanks, everyone. Thank you so much to our live audience. I want to thank Summit for having us here. Thank you, Nate Watney, for handling the audio. LinkedIn for having me as part of your podcast network. Everybody in the audience, if you find Big Technology Podcast in your app of choice, we have the product manager on Bard, Google's chatbot. That's going to be on the feed within the next week as well. Thanks again for being here. Thank you, Astro, and enjoy your time on the ship.

• 1:03:24Speaker 04

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