In short
a16z Podcast: A Year of Unlocks - 2023 In AI, Healthcare, Housing, and More
Episode Overview As 2023 concludes, the a16z Podcast revisits significant discussions from the year, highlighting advancements in AI, healthcare, and housing. The episode features insights from industry experts and innovators, emphasizing the ongoing technological transformation across various sectors.
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Key Highlights
- AI and Its Impact
- Generative AI Explosion: 2023 witnessed a surge in generative AI tools gaining popularity.
- Autonomous Vehicles: The emergence of autonomous vehicles on urban streets, particularly in San Francisco.
- Electrification of Industries: Trends indicating significant electrification across various sectors, from transportation to healthcare.
- AI Hardware Insights
- GPUs as AI Accelerators:
- GPUs (Graphics Processing Units) are foundational to AI processing, with Nvidia currently leading the market.
- Comparison of GPU capabilities to CPUs, highlighting their parallel processing strengths.
- Discussion on the evolving GPU architecture and its role in processing large-scale AI models.
- Infrastructure's Role: The importance of robust infrastructure in supporting AI's growth, including the development of specialized chips by various companies (e.g., Nvidia, Intel).
- AI in Design and Creative Industries
- Dylan Field's Perspective on Design:
- AI's potential to transform the design landscape by automating initial drafts but not replacing human designers.
- The assertion that technological shifts historically lead to an increase in design demand rather than a decrease.
- AI in Law
- Joshua Browder from Do Not Pay:
- Exploration of AI's role in legal assistance and the potential for AI to draft legislation.
- The challenges and opportunities presented by AI integration into legal frameworks.
- Healthcare and Fintech Intersection
- Julie Yu's Insights:
- Discusses regulatory changes driving innovation in healthcare, particularly around payment transparency.
- The impact of laws aimed at publishing healthcare prices and their implications for consumer choice and company transparency.
- Housing Market Disruption
- Mark Andreessen and Adam Neumann's Discussion:
- The significance of housing as a fundamental component of the economy.
- Critique of Silicon Valley's focus on less impactful sectors and the necessity to address housing costs and accessibility.
- The bifurcation of the housing market due to geographic and economic disparities.
- Looking Ahead to 2024
- Steve Wozniak's Reflections:
- Discussion on the role of inventors in shaping the future and the unpredictability of technological advancements.
- The importance of focusing on immediate, actionable innovations rather than distant predictions.
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Conclusion The episode encapsulates a transformative year in technology, emphasizing the rapid advancements in AI, the evolving healthcare landscape, and the urgent need for housing reform. As we move into 2024, the podcast invites listeners to engage with upcoming topics and share insights on future episodes.
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Resources for Further Exploration
- [AI Hardware, Explained](https://a16z.simplecast.com/episodes/ai-hardware-explained)
- [Chasing Silicon: The Race for GPUs](https://a16z.simplecast.com/episodes/chasing-silicon-the-race-for-gpus)
- [The True Cost of Compute](https://a16z.simplecast.com/episodes/the-cost-of-compute)
- [AI Revolution: Disruption, Alignment, and Opportunity](https://a16z.simplecast.com/episodes/ai-revolution-disruption-alignment-and-opportunity)
- [When Two Giants Intersect: Healthcare Meets Fintech](https://a16z.simplecast.com/episodes/when-two-giants-intersect-healthcare-meets-fintech)
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Connect with a16z
- [Follow on Twitter](https://twitter.com/a16z)
- [Follow on LinkedIn](https://www.linkedin.com/company/a16z)
- [Listen on Spotify](https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYX?si=3E8B3qT9TyiwAHJ7JnaKbg)
- [Listen on Apple Podcasts](https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711)
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As we prepare for the new year, the a16z Podcast continues to explore the intricate relationships between technology, culture, and industry, shaping our collective future.
Written by AI. May contain mistakes. Listen to the episode to check what was said.
Transcript
Automatic transcript. May contain errors.0:00The speed at which we get to an AGI is dramatically faster. Your boat is charging your house. This is a great example of an act that actually incentivized a whole bunch of startups to come out of the woodwork. This requires a deep deep understanding and tremendous amount of machine learning. Your biggest asset is your current employee population. The engineer says, Feed on the Ground? What can I actually do and build and deliver to people? Usually controversial, there's still lawsuits in play. You could look across all these industries and find those opportunities where the technology that exists today delivers a better and more compelling experience to people.
0:38Whenever I tried to see the future a year ahead, I knew it one year ahead because I was working on it. We have about a five year window, I think, as a society to kind of figure out what this means and how to adapt. Hi, everyone. This is Dev. Welcome back to the A16Z Podcast. I cannot believe we are at the end of 2023, and I also can't believe it was about a year ago today that the A16Z podcast officially relaunched. And since then, a lot has changed. I mean, generative AI tools have absolutely exploded in popularity, fully autonomous vehicles, now roam the streets of San Francisco, and entire industries from the skies to the seas are being electrified as we speak.
1:16And those are just a few of the groundbreaking invasions that we did cover here on the podcast. We were also joined by some incredible guests, including a noble laureate who is now the lead scientist in the James Webb Space Telescope. We also had founders, hailing from the companies building some of the most important large language models today. We had founders trying to give billions of people internet access, the original creators behind one of the world's largest digital influencers, and even in our latest episode, Mark and Ben got to sit down with the one and only Tony Robbins. So as we round out this year, we wanted to recap some of our favorite conversations, highlighting how our collective future is being defined by the Builders Advancing Technology day by day.
2:00Today, you'll hear from the likes of Mark and Dresan and Steve Wozniak, and if any of these episodes do peak your interest, we'll include the links to the full episodes in our show notes. So, thank you so much for listening this year and without further ado, let's dive in. As a reminder, the content here is for informational purposes only. Should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16z fund. Please note that A16z and its affiliates may also maintain investments in the company's discussed in this podcast.
2:35For more details, including a link to our investments, please see a16c .com slash Disclosures.
2:472023 was a year dominated by AI. From health care to code, AI is in the midst of disrupting some of the world's largest industries, forcing even multi -billion dollar companies to rethink their strategy. But as AI software became more important than ever, the hardware running it has followed suit. Here in our first clip from our AI hardware series, you'll hear from Guido App and Seller, A16Z's very own special advisor on AI infrastructure, who also happened to be the former CTO of Intel's data center and AI unit. Here, Guido breaks down the GPU architecture and why it's so game changing for AI processing.
3:25Plus, why Nvidia has been leading the pack here so far. If you're running any kind of AI algorithm, this AI algorithm runs on a chip. And the most commonly used chips today are AI accelerators, which are in terms of how they're built, they should very close to graphics chips. So the cards that these chips are on that are in these servers often refer to as GPUs, which stands for graphics processing unit, which is kind of funny, right? They're not doing graphics, obviously. But it's a very similar type of technology. If you look inside of them, they basically are very good at processing very large number of math operations per cycle in a very short period of time.
4:03So very classically, like an old fashioned CPU would run one instructions every cycle, and then they had multiple cores, so maybe now modern CPU can do a couple of 10 instructions. But these sort of modern AI cards, they can do more than 100 ,000 instructions per cycle, so they're extremely performant. So this is a GPU and these GPUs run inside of servers. They think of them as big boxes. I have a power plug on the outside and a networking plug. And then these servers sit in data centers where you have racks and racks of them that do the actual compute. Let's quickly recap. CPU is central processing unit, and GPU is graphics processing unit.
4:39And while both CPUs and GPUs today can perform parallel processing, the degree of parallelization is what sets GPUs apart for certain workloads. So for example, CPUs can actually do tens or even thousands of floating point operations per cycle, but a GPU can now do over 100 ,000. The basic idea of a GPU is that instead of just working with individual values, it works with vectors or even matrices, or tensors more generally. The GPU, for example, is Google's name for these kind of chips, so I didn't call them tens of processing units, which is actually a pretty good name for them. The cores and these more than GPUs, often called tensor cores, like that's how it media calls them, because they operate on tensors.
5:20And basically, the core of their value propositions, they can do a matrix multiplication. So if you remember, matrix like the rows and columns of numbers, they can, for example, multiply two matrices in a single cycle. So in a very, very fast operation. And that's really what gives us the speed that's necessary to run these incredibly large language and image models that make generative A -A today. Today's GPUs are far more powerful than their ancestors, whether we're comparing to the earliest graphics cards in arcade gaming days 50 years ago, or the GeForce 256, the first personal computer GPU unveiled by Nvidia in 1999.
5:57But is it surprising that we're seeing this chip design applied so readily to the emerging space of AI, or should we expect a new architecture to evolve and become more performant in the future? In more way, I think it's very surprising. We would have thought that my gaming PC, my Bitcoin miner would eventually become a good AI engine. At the same time, what all of these problems have in common is that you want to execute many operations in parallel. You can think of a GPU as something was built for graphics, but you can think of them also just as something that's very good in performing the same operation at a very large number of parallel inputs.
6:33Very large vector or very large matrix. All right. So, perhaps it's not so surprising that in videos, prize GPUs are aligned to this AI wave. But they're also not the only company participating. Here is Gido breaking down the hardware ecosystem. The ecosystem comes in many layers, right? So let's start with the chips at the bottom. And Vidya's king of the hill at the moment right there. A100 is the workhorse that powers the current AI revolution that coming up with a new one called the H100, which is not the next generation. There's a couple of other vendors in this space. Intel has some called Gaudi, Gaudi 2, right?
7:07As well as that graphics card with ARC. They're seeing some usage, AMD has a chip in this space, and then we have the large clouds that are starting to build, or in some case have them building for some time, their own chips, we will have a TPU, you mentioned before, it is my popular, and Amazon has a chip called Trainiam for training and inferencia for inference, and we'll probably see more of those in the future from some of these vendors. But at the moment, NVD still has a very, very strong position as the vast majority of trainings going on on their chips. And when we think about the different chips, So you mentioned like the A100s or the strongest and maybe there's the most demand for those.
7:42But how do they compare to some of these chips created by other companies? Is it like double the performance or is there some other metric or factor that makes them much more performant? It's a great question. If you look at the pure hardware statistics, so how many floating point operations per second can these chips do? There's others that are very competitive, but Nvidia has. Nvidia's big advantages is that they have a very mature software ecosystem. So imagine you are an artificial intelligence, developer or engineer or researcher, you're often using a model that's open source, that somebody else developed, and how fast that model runs.
8:17In many cases, it depends on how well it's optimized for a particular chip. The big advantage in video has today is that there's software ecosystem, which is so much more mature. I can grab a model, it has all the necessary optimizations for Nvidia, to run out of the box. I don't have to do anything. But with all these other chips, I may have to do a lot more of these optimizations myself. That's what gives them these easy advantages of the moment. We just heard about the underlying infrastructure powering AI's exponential growth. But with this growth comes a lot of questions. Like how will AI change the way companies operate, will it automate creative tasks, and doesn't need to be regulated?
8:55Our next rewind is with Dylan Field, co -founder and CEO Figma, a browser -based web design tool tentatively acquired by Adobe for $20 billion. $1 billion. Dylan was one of the many speakers at our AI Revolution event alongside founders of OpenAI and Thropic, Character AI, Reblocks and more. Now as someone who helped reshape the entire design landscape over the past decade, here Dylan waits in on how AI may impact the design process and also answers the $1 billion question. Will AI eventually replace us? You know, it's kind of interesting to listen to the question is like, okay, there will be less things to design or is AI going to do all the design work, right?
9:35So it's like you're on one of those paths maybe. On the first one of, will there be less things to design? If you go get every technological shift or platform shift so far, it's resulted in more things to design. So you're like the printing press and then you have to figure out what you put on a page. You've got even more recently mobile. You would think, okay, less pixels, less designers, right? but no, that's when we saw the biggest explosion of designers. And so maybe if you'd asked me this like, beginning of the year, I might have said, okay, well, we'll all have these chat boxes. And people will be asking questions in them, and that's gonna be our interface for everything.
10:13You know, look at OpenAI. They're on a hiring acquisition spree, trying to get product people and designers right now, so that they're able to make great consumer products that turns out design kind of matters. The second one of Will AI, I'd be doing the design is I think pretty interesting. So far we're not there. Right now we're at a place where AI might be doing the first draft and game from first draft to final product actually turns out that's kind of hard. And you can take the team. But if you could get AI to start to suggest interface elements to people and do that in a way that actually makes sense, I think that could unlock a whole new era of design in terms of creating contextual designs and designs that are responsive to what the user's intent is at the moment.
10:57And I think that be a fascinating era for sort of all designers to be working in, but I don't think it replaces the need for human designers. Design isn't the only industry being redefined by AI. In 2023, Do Not Pay, an online platform and chatbot for legal help attempted to bring AI lawyers into the courtroom. Sparking a conversation about the role of AI in law. Our next clip is with the founder of Do Not Pay, Joshua Browder. Joshua has spent years building products that automate the role of lawyers, helping consumers fight legal battles and dispute unfair charges, from parking tickets to excessive medical fees.
11:34Now, with the growing accuracy of AI chatbots, the entire legal landscape looks pretty ripe for innovation. And Joshua is helping to pave the way. Here's his vision for the future. I want to ask maybe a far -fetched question, but just if we look super far ahead, How do you see this new technology AI in particular reshaping the legal landscape? And as an example of this, when might a robot lawyer actually write law that governs the implementation of AI within law? Like that seems like it hasn't happened yet, but something that maybe is inevitable. I think in the next year it's already secretly writing laws.
12:15A lot of laws originate from I'm an overworked staffer in Congress, and I'm sure it's 2am, they need to finish it, and they're probably typing in Chatchy PT asking it to help them a bit. And so I'm sure in the next year that's going to happen. In the future, I think a lot more court systems will be automated, and tools like do not pay will help people. So I think it's a very exciting time. Speaking of an exciting time, I feel like do not pay has done an excellent job of kind of keeping up with the times, and so something I saw you build recently is the chat GPT extension, which you can basically understand the terms and conditions of a website more effectively.
12:53So how are you thinking about the gaps that still exist for consumers and maybe what other opportunities there are to give consumers their rights back? When new technology comes out, we think how can it help people? And terms of conditions is a great example because people can't understand the changes being made, what they're getting up. So this is actually our first public product relating to true AI, where you can paste in any tons of conditions, upload a document, including Leases, and it will go through and tell you all the things that are non -standard and also things you should watch out for.
13:27And that's really important because people don't have time to read 100 -page document, which is what are some of these Leases are, especially in New York. And so that can be really helpful. Really, we're trying to be like David in David versus Goliath, so the AI can turn David into Goliath versus as Goliath and help ordinary people. Something that's coming up in my mind as you're speaking is a question around why the law is so complex. And maybe this is such a silly question because there's nuance to life and therefore there's nuance to law. But why are so many laws 100 pages long that no regular consumer can actually read through and understand?
14:06Should it not just be a rule system that you could even effectuate through code, for example? I think there's a lot of lobbying that goes on. The big companies spend huge amounts of money shutting consumers out their rights. And every year it seems to get worse. There are lots of positive laws that have come out like the CCPA and other laws around that. But I think big companies have a lot of influence in creating the laws. And one area that I'm worried about is anti -AI laws. So one could imagine a law that says that no bots are allowed unless they say they're a bot. And you already think companies implement these policies, and that could easily be a law.
14:43I could imagine members of Congress signing up for that idea. And that would hurt a lot of consumer rights and automated systems helping people. What makes you think that people will actually implement a law like that? Or are there other rules that you see on the horizon based on your experiences with companies, with lawyers who are pushing back against some of this new technology? The fear when any technology comes out is huge and it doesn't help that people like me are staring things up. And fear creates action on behalf of governments and big companies. And so that's very worrying. The good news is that there are a lot of things in the law we have over a hundred years of law to deal with that if you jump through the right hoop and follow the correct processes you can get results.
15:28Like one thing we all to make is credit report disputes. And they say that if you submit a correct letter and it's signed in the correct way and all of this stuff, you can dispute something on the credit report. And this allows us to kind of get old or inact her items off people's reports. And even if it's coming from AI and we have to tell the big company that this was written by AI, it's not going to be influenced. They still have to respond to that letter. And so there's all these rights that people have and I don't think that they can strip them all away with one law. but it is worrying about what kind of Congress is heading.
16:05As the industry continues scaling and refining these models, there are still many open questions on the horizon. In a recent episode with A16Z General Partner, Agene Mita, we explored some of the most important frontier research areas from synthetic data to self -play. But first, here, Agene clarifies why math problems, even at the grade school level, are a suitable testing ground for this kind of research. The reason great school matter so interesting is because it's pretty well -scoped and you can ask a model to break down a word problem. You know one of these classic, Jimmy has 10 apples and John has 16 apples.
16:46If they've combined their apples how many do they have in total. And you can ask a model to break down its reasoning step by step. And when it does that, it doesn't just give you the end answer. it gives you all the series of steps in the middle that it took to get to the final answer. And what you can do with reinforcement learning applied to sort of chain of thought prompting is you can say, hey, instead of traditional reinforcement learning from human feedback, where you just asked a human greater to grade the outcome of the model, the final step, or you asked another AI system to grade the final step and say, hey, is this answer right or wrong?
17:23you can actually have an AI now score every intermediate step. Yeah. And score whether each step of the way was correct or not. And then what you get is a much more set of granular rewards. And so in the case of the cake baking, instead of just scoring whether the end cake was good or not, you can start scoring the individual steps that they bake it at the right temperature. Did they put the right amount of dough in? And the big question is does that allow the model to start learning about reasoning, not just producing some end outcome. Right? And if you can get it to reason, then it can start solving, planning, and doing higher order sort of thinking of a kind that humans have.
18:04So that's where these sort of techniques all play together is reinforcement learning traditionally has been a way to get these models to improve. You know, Q learning or model free learning is a way to generalize reinforcement learning beyond just well -enumerated systems like games. And then lastly synthetic data and self -play come into relevance here because when you're having an AI score each individual step of your reasoning, then you're generating a bunch of really valuable data that then you can train the system on that synthetics. It's not generated by a human, it's generated by an existing system.
18:33So these are sort of the big components that are working together to potentially ultimately produce a model that's 10 ,000 times or 100 ,000 times better than GPT -4. Right. And let's double click on that element of self -play and potentially synthetic data. To underscore that, right now, many of these models do have an element of reinforcement learning, but that comes from human feedback, right? So RL, HF, is one of the methods that these different labs are using to orient the existing models, but obviously that requires humans in the loop. So what you're getting at is potentially the ability to obfuscate that and have AI's in the loop.
19:14Can you speak a little bit more to that idea of self -play and the ability to create synthetic data? What is that unlock other than just scale and speed or is that enough for us to get to those next levels? Yeah, scale and speed are pretty valuable. Yes, definitely. I think if you just go back to first principles on how these models are trained and get better, the rate at which they improve is remarkably predictable. They're predicted by a set of AI scaling laws that basically say, hey, you've got three ingredients to producing machine intelligence. You've got compute, you've got data, and you've got sort of algorithmic innovation.
19:49And I think what self -play and synthetic data do is they allow the scaling of these models much more rapidly because whereas with reinforcement very from human feedback, you're kind of constrained by the number of humans you can have, provide that feedback, synthetic data, and in particular, the types of AI feedback scoring that we're talking about here is sort of orders a magnitude more scale. It's not 2x or 3x. The sheer constraint is actually really how much compute do you have to run these AI models to do all this scoring. To make this a little bit more concrete, it might be helpful to just talk a little bit about reinforcement learning from human feedback.
20:28Yeah, please. The 1 .0 version of this world, a common example of reinforcement learning from human feedback as a system like chat GPT or dolly or mid -journey, when mid -journey generates an image or when chat GPT generates a message for you, there's a little thumbs up thumbs down button. And they track when users give you a thumbs up or thumbs down and they use the number of times people give thumbs up or thumbs down and use that as a signal for reinforcement learning. To improve the kinds of messages that gives you next time around. The reinforcement learning from AI feedback in this case replaces that thumbs up a thumbs up from users like you and me, and instead substitutes that with increasingly smarter AI models that these labs have built, to then provide much more granular scoring than just the thumbs up thumbs down.
21:16You can now start providing a score of one to ten on the intermediary steps. So the right scale is really the biggest unlock here. When you replace humans with models that provide the feedback 24 .7 at orders of magnitude larger volume than we could do with just humans, then the speed at which we get to any GI is dramatically faster. So any breakthrough in those systems represents I think a non -linear increase or acceleration towards this future. And while many people think of chatbots or personalized avatars when they think of AI, the world of applied machine learning is also catching steam.
21:52One way, self -driving cars. In 2023, Waymo vehicles covered over 700 ,000 trips. Yes, you heard that right. 700 ,000 trips and millions of driverless miles. They also received their final permit from the California Public Utilities Commission signifying a huge set forward. Up next is a clip from our episode with Waymo's Chief Product Officer, Saswatt Panagrahi. As CPO, Saswatt has been at the forefront of this industry's unique combination of technical, regulatory, and economic challenges, from ensuring rider safety to building defensible modes through hardware. Now in this clip recorded in April, Saswite discusses where we really are in the shift to autonomous vehicles and the challenges that still lie ahead.
22:38It's felt like this is coming for a while, like I almost feel like the future is here, like we're seeing this or sitting in a car with no driver, maybe you could kind of map out where we are in that kind of arc, the five levels of autonomy. Yeah. And also where we still have left to go. Yeah, certainly. We are in level four right now. So fully autonomous, as you can see, nobody in the front seat, no expectation of a human to take over. And in level two and three, it's really, really crucial to communicate the expectations to the driver. Yeah. Because it's very easy that during a normal situation, the driver feels, hmm, the car is kind of driving well.
23:14So I can pick up a book and start reading, no, by serious, and there is an expectation to take over. So we are in that level four with a certain scope. So right now, if we were to begin heavily snowing in SS, which it has, believe it or not, last season, there was a little bit of snow, we wouldn't have. But level five is truly defined as anywhere, anytime. Right, I mean, and even the idea of autonomy, people kind of view in this binary way. Right, like the whole idea of level puts that into perspective. Like level two is plain sensing, automatic breaking. is level three, we're basically what we're doing now, but there would be a human in the front, or how would you define it?
23:48No, no, I would say a vast difference even between level three and level four huge. It's almost a difference between driving and flying, I would say, that's a nice difference, because this concept still, that within seconds, you need to take over versus now we can have this conversation, we're not doing anything. They're challenging, right? But the assurance level you need to get to for level four is just a universe different. I would say. So tell me a little bit more about the barriers along the way to level four. What would you say was that the technology, the regulations and combination are the major factors that have really delayed us from getting to this point where we're now at level four.
24:27Yeah. On any journey when you're trying to do something that has never been done before, there will always be ups and downs. Some challenges you, for some, you didn't. The key is are you clear on how massive the price or the benefit to society's, on the other side of it. Because then that makes it all worth it. So we were super clear from the get -go that a fully autonomous car that does not get rousing, that we even have physical constraints, right? So even when you are alert, let's say you were looking for parking on this side of the street, your face would be turning towards that constantly looking for that parking spot.
Read the full transcript
25:02So you wouldn't be able to see. You don't have your sunglasses. Yeah, yeah, exactly. You can't keep turning back and forth. And so we're fundamentally convinced that a fully autonomous driver is going to be safer. So once we started that, yes, the largest challenge I would say was largely I would call it technology, but I mean two different things. One is building the driver itself that can drive under these conditions and having the high grade of performance. We're also measuring that is pretty hard. This smooth, sort of early stage drive under very tight constraints is now relatively with today's technology, not terribly hard to build.
25:37But to be able to do that at the scale of 24 -7, busy intersections at slow speed, or also high speed intersections of Phoenix. So in Phoenix, for example, the streets are wider, so you don't deal with these narrow situations. But the driving speed is 45. People are sometimes going 60 on that. That means you've got to see a lot further. So very different sets of challenges, very diverse ones. and the technology, it really required the full stack, right? We built the hardware, we built the software, because if you built just the software and waited for somebody else to deliver the hardware, the speed of learning, the speed of iteration that was necessary to build something like this was so steep that it was not feasible.
26:19So we had to build the lasers, the cameras, the radars, and the software on top, and the massive simulation infrastructure. I was gonna say there's so many moving parts, and actually maybe let's talk about that technology. I see a bunch of different kind of appendages to the car. So maybe you could just break that down. Like what is happening? How is all this technology coming together? And what are the bits and pieces that you've added onto the car that allow it to be autonomous? So fundamentally you can think of it like, is the car aware of what's happening around it? And then can it anticipate what the things around it are gonna do?
26:52And then reasoning on what it should do. So these are sort of the three components. In perceiving what's around us, think of the example we're just discussing, you're trying to look for a parking. So you're focused on that task. This car with those appendages, as you mentioned, can see three -football fields away, 360 degree, and it's getting a snapshot multiple times a second. Okay. And it's relying on a combination of the state -of -the -art laser, camera, radars, all strategically positioned. So to give you an idea, lasers give you a very precise understanding of everything around you. There's the smallest detail.
27:30So if there was a child, a inch out of this pole, you can be able to mark, oh, this is a demarcated child away from that pole. It'll get to see that. But, you know, the cameras are needed to distinguish between the red light and the green light. And the radars can almost see around corners, even when the laser and the camera are our human eyes can't because they can sense objects coming in. And so we took an approach that we want to combine the best strengths of each of these modalities to create the best picture of what you can see around the world that you're just incredibly better than a human possibly.
28:07It's both due to their attention span, the range, the fidelity and the combination of these sensors coming to either. So that's what we see. But then there's a harder challenge of anticipating what the person will do. Take a look at that pedestrian. They're standing pretty close to the crosswalk, right? We don't know if they're going to move. Exactly. Are they going to jump in? Or are they going to stay? Are they going to J -walk? Or are they going to obey the light? Yeah. This requires a deep deep understanding and tremendous amount of machine learning. Cars are not the only mode of transportation that have gone through a pivotal moment over the last year.
28:40Biden's inflation reduction act was the single, largest climate investment in US history. And its second -order effects are already becoming increasingly evident. Everything around us from the planes in our skies to the boats in our seas is in the process of becoming electrified. Our next clip is with Mitch Lee, co -founder of Arc Boats, a company manufacturing electric boats for water sports to start, and Gregory Davis, CEO of Aviation, a company building planes with an all -electric design. Together, they discuss the opportunities in our all -electric future. There's plenty of these product opportunities.
29:19You can look across all these industries and find those opportunities where the technology that exists today, the electric power train, delivers a better and more compelling experience to people. And I think, you know, the three of us are all doing that. There's also supply chain opportunities. It's we want our battery prices to be lower. We want our high voltage electrical power chain components to cost us less. There's a tremendous number of opportunities at that kind of base supply chain level. And then there's this third category of what I call second order opportunities. You're going to have an electric future, imagine what that looks like.
29:59Well, you're going to be towing around a bunch of electric boats in electric trucks or you're going to be towing around an RV and an electric truck. And what does that mean in terms of the new charging infrastructure you need to go support that or the new technology need to go make that happen? Maybe it's powered trailers that can actually help the vehicle or there's just a ton of opportunities and if you cast your mind forward of two years and Think about what are the new problems that are going to exist when The marine industry is going electric and aviation industry is going electric and the bus industry is going electric Yeah, and I think interoperability and standards industry standards is a challenge today.
30:40And we've left it largely to sort of standards making, you know, traditional industry associations to drive standards. And in my opinion, it's been too slow. We've done integrations with 15 to 20 utility companies in the last 12 months. And every single one of them has been discrete different. You just have to put the right team on it to know how to navigate the system and get through to the other end, but putting better standards in place would allow for what Mitch is describing. Your boat is charging your house when the power grid is down or for four hours every afternoon when the grid is strained and demand charges are kicking in.
31:23Or your trailer is charging your vehicle because it's got extra power. That's all interoperability that needs standards. I would flag one other challenge, Mitch mentioned it, but it is one of our greatest pain points, which is supply chains. It's not just about getting the cell cost down, so we can deliver cheaper product. It's being able to deliver product full stop. The last couple of years have been really tough on supply chains. It's led to soaring costs, but also poor reliability. We're one of the largest buyers of medium and heavy duty truck platforms in an electric format right now. So we're talking hundreds of millions of dollars per year of equipment purchases.
32:09We're not billions yet, but we will be quite soon. And even as a large buyer, it's tough to get product. And so you've got manufacturing sites that have thousands of completed vehicles and they are all short an air compressor So they can't complete these vehicles and deliver them to customers and so preach Yeah, we are buying switch gear 18 months in advance for projects We don't even have an or contract yet because if we don't have switch gear We're not getting the infrastructure completed and we can't get up and running we don't get paid and so we're solving problems that we shouldn't have to solve, in my opinion.
32:48And so I think for some entrepreneurs, there could be big opportunities in streamlining supply chains and creating better standards. I heard a very creative description of the supply base over the past couple of years, and it was called the surprise chain. That's all you need to say. Perfect. Yeah. You know, timing for what we're doing. You look at the outlook on the aerospace industry right now, particularly aircraft manufacturing. And a lot of the major aircraft manufacturers have already launched their next program. So they're into entering the service, they're delivering them. And if you take a look around at the large and company or aircraft manufacturers, there's no new programs on the docket right now.
33:30And the Tier 1 supply base, and even the Tier 2 and Tier 3 supply base, they're all hyper -weir of what they're going to be working on. And it gives us an opportunity to show, hey, we've got an aircraft, we've got a market, We've got good orders. The utility of the product is there. And I think what's going to happen is that the new technologies that we're bringing and our new products are actually going to garner the attention of the forward -looking supply chain. So I think we're going to disproportionately benefit us in the aviation industry, but I think the same is probably widely applicable.
34:00The new technologies, new companies are actually going to benefit from obtaining the focus of the established tier one, tier two, supply base. So I don't want to say the problems are behind us yet, but I think the outlook is actually great in Sunshinyi. Another massive opportunity lies in the biggest industry in America. That's healthcare. This multi trillion dollar industry responsible for 20 % of US GDP and climbing is widely referred to as broken. But there may be an opening to heal this problem with Fintech. In this segment from our mini series on A16Z's healthcare meets Fintech package, We're joined by A16Z General Partner Julie Yu, who exposes the huge opportunity at this intersection.
34:43And how changing regulation has created an opening for new generation -defining companies. It's rare that people talk excitedly about regulation, but healthcare is one of those domains where I think regulation can be a tailwind for innovation and category creation. There's lots of historical examples of this. I think the most traditional example that a lot of people point to is electronic health records, you know, did not really exist in a major adoption until the meaningful use law came into play where the government literally paid financial incentives to doctors to adopt digitized technologies for medical record storage.
35:18So that was really the sea change that drove so much of the digitization of our infrastructure layer of healthcare. Similarly, right now we have a number of regulatory tailwinds that are driving payment -related reform. And so we have things like a price transparency law that went into effect over the last couple of years that forced hospitals and insurance companies to publish their contracted rates. It was hugely controversial. There's still lawsuits in play. People are still pushing back, but the fact of the matter is we now have thousands of hospitals and hundreds of payers who have published all this data.
35:51Obviously, they're publishing it in forms that make it very very difficult to parse. And so entire companies exist to actually process that data where investors in turquoise health that's one of those players and actually make it actionable in the context of their contract and gochetians and the way that they engage with both providers and patients. But that's obviously a huge driver of change in terms of how we think about what used to be assumed to be opaque now just being out there and people not having a place to hide when it comes to comparing prices between to different providers who again are providing the same service, but at widely different prices.
36:24So that's been a massive change that is only just starting to play out, I would say. So I think we have still years for it to really see through the system and address a lot of the challenges that we described earlier. You mentioned lawsuits and some pushback just because I feel like listeners might be curious. What is on the other side of that? It seems like maybe an obvious reform that we should be able to see how much something costs if we're paying for it or someone is paying for it for us. So what was on the other side of that? Yeah, I think, you know, again, I have some degree of sympathy for these businesses.
36:53Effectively, what we're doing is taking proprietary contracts and publishing them on the web, right? So there's been a lot of pushback from the parties to those contracts, which are the payers and the providers, who say if it's proprietary data, and we should not be forced to publish such data in a public forum, because that's our competitive advantage. And our market is that we're able to negotiate special rates with our counter parties, and we lose all of that competitive edge if we're to put it out there. So that's the crux of most of the pushback from the incumbent lens is really that sort of propriety.
37:24The counter argument is that there had been previous laws that required things like upfront estimates for consumers if you called your hospital. You were, you know, sort of mandated to be able to present an estimate prior to coming in for a certain procedure or certain sort of services. And from a consumer lens, like those never really got implemented or enforced in a way that was reasonable in my opinion. I remember my past life when that law went into effect in the state of Massachusetts, we actually did a bunch of like secret shopper calls to hospitals to see what that user experience was like.
37:54And you basically got told, oh, you'll get a call back in an undetermined amount of time. It was generally like one monolithic number and there was no explanation as to what the range of assumptions that went into that number were. And again, you get the call back like a week later when you might have had the procedure like two days after that call. So the whole implementation was really poorly executed. So I think the argument on the consumer side of like listening, you guys promised this to us years ago. The form of that just didn't address any of our concerns about limiting the amount of financial exposure that we might have related to healthcare services.
38:24So those are kind of the siren calls on both sides of the argument. And so it sounds like we now have more transparency, but it also sounds like there were a couple other regulation changes that happened alongside that. Yeah, just maybe one more I would call out, which we've talked about before is just all of the movement towards value -based payment models. So this notion that a lot of the reason that healthcare business models were not resilient during the pandemic was that they were entirely fee -for -service oriented, meaning you only got paid for the specific services that you delivered. And therefore, again, if the service was not delivered, you didn't get anything.
38:56Value -oriented models tend to be much more bundled in nature. So either getting a set upfront rate or price for a set of services related to an encounter. So like a value -based orientation around a need for placement surgery would be rather than charge for every individual, you know, doctor who's involved in the procedure, the anesthesiology, all the pre -visit post -visit stuff. You'd actually just get charged one bundle for the entire journey. And then the providers at risk, right? So if they go above that budget, they are paying out a pocket effectively to cover their remaining cost. But if they stay under that budget and keep you out of the hospital and avoid errors and all that kind of stuff, then they get to pocket the difference.
39:34And so it aligns incentives for the provider and the patient in terms of staying within certain bounds, but also creates a lot more resiliency around the payment flows into the provider practice because they're not just rely on individual services getting built and paid for. So there's been a whole set of regulation around driving adoption of those payment models, Medicare advantage, I think it's been the most prominent form of that. It's very early days in terms of adoption across our industry, but it is a very, very promising means to align incentives in a fund in a way that results in much more transparent behaviors.
40:07Absolutely. And maybe before we jump into the Fintech side, just wanted to clarify the No Suppresses Act and the Cures Act. It feels like those also were pretty fundamental to maybe changing this incentive structure that you've talked about so far. Yeah, the No Suppresses Act is very close to tied to the Transparency Act, which basically limits the ability to do surprise out of network billing for patients. And you've probably read in lots of news articles, these incidents where you go get a surgery in an in -network hospital, the actual surgeon is in network, but low and behold, the anesthesiologist is out of network and all of a sudden you're getting a bill for just that slice of the service at a rate that's outsized relative to what you would have had to pay, had it been an in -network doctor as part of that procedure.
40:51This act limits the liability on the set of the patient and the provider in those situations. And then the Cures Act, it's a little bit orthogonal to Fintech, but certainly related in that the main provision that people care about is patient data access, enabling patients to readily access their full medical record data, and that sort of limit the cost of access. So it used to be the case that you'd have to potentially pay a couple hundred dollars somebody to get access to your own medical records on paper, you know, or even like a CD ROM. This is a great example of an act that actually incentivized a whole bunch of startups to come out of the woodwork and create apps that allow you as a patient to basically collect all your medical records from multiple providers in a single soup and really have agency over them.
41:34I think the way that it relates to the Fintech universe is that a lot of insurance adjudication acts, require access to patient data to approve certain reimbursements. This act allows much more data liquidity to support those decisions versus the traditional way of doing it, which is faxing literally medical records back and forth and having nurses look at them. in a manual fashion. And just as new regulation creates opportunity at the intersection of healthcare and Fintech, new regulation is also driving pay transparency. With over 10 states now, covering more than 25 % of the U .S. labor force being mandated to disclose salary ranges for job listings, companies are now being forced to adapt and adapt quickly.
42:19Up next is a clip from our episode on pay transparency with Shannon Schultz, an operating partner leading A16C's people team and Brandon Cherry, a partner on the same team. They not only share how companies are reacting to changing regulation but also touch on the importance of defining a consistent compensation philosophy. Companies are now having to react to some of these changes in law and I'm just so curious to hear what you're seeing in terms of the reactions are they abiding, are they struggling to catch up, are they kind of circumventing the system in different ways? How are you seeing companies react because it does sound like many were not quite in that place when these things started rolling out?
43:02Yeah, so I would say a couple things. I would say one, we've probably never had so many requests for compensation, councils, consultants, and how do I? Right. So one, I think people are reacting the right way. They just want to get their arms around it. And then I would say there's multiple ways that people are reacting. One, some are just saying we're going to be in a wait and see. Okay. We're going to see how enforceable these laws are, how much audits are taking place, what becomes of this new law? There's companies that are saying we're not going to do it. So we're not going to post our jobs because that's the most visible thing that's come out of it.
43:37Is you have to have a salary range posted with your jobs? Yeah. Yeah. And then the third piece is you have companies that are saying you could make between, you know, this role pays $65 ,000 a year up to $2 .5 million a year, right? And somewhere in there, we're going to pay someone. And so I would say those are probably the three things that we see companies doing. Absolutely. Those are three choices companies have. And there's new ones to it. And some states there's not laws. You know, there's minimum thresholds and things that people can explore around. What the requirements are in different locations.
44:07But, you know, I think there's just a lot of concern over making sure that not only do they understand how to comply from a regulatory standpoint. But also, what does this do to our employee population? Who's going to see this? How is this going to work? And if we do have $60 ,000 to $2 .5 million, are we prepared? And do we have the ability to say, no, for you, it's 60. For other people, it's 2 .5 million. How do you have that conversation? And so I think companies are navigating through all of that. And so I would say that those are the three choices. But for every company, it's going to be very specific to where they're located, the kinds of employee populations they have, how clear and how strong their infrastructure is, and how they feel like they're prepared to have those conversations.
44:47Yeah. And again, because back to the basics, right? If you have the structure, so for us, it was relatively straightforward. We have the structure here. Yeah. And so we could post roles and, you know, put a salary range out there. We set it up so we can defend it. The interesting thing that happens, though, is, again, And the number of times that somebody sees a job posted and thinks, well, that's what I do, but I don't make that. And so it does. You have to be ready to have those conversations and get your managers ready to have a conversation on why actually this is a level above you and you can progress into it.
45:24But that's not where you're at right now. And that's a hard conversation. I was gonna get into the hard conversations, but let's just talk about that now. Like, A, have you started to see that as you started to post roles? You're like, yep. both of you are sitting at home. And it helps us iterate on our process. Right. Right. No process is ever great. And so I feel like weekly, we're like, OK, are we checking this? Are we doing this? And we're iterating on it. Because your future hire is super important. But they're not actually going to be contributing to productivity and output for six to nine months in most situations.
45:58So you have to be able to defend it to your current employees. That's the biggest thing that people are missing is yes, there is this law that you're supposed to post a range. But your biggest asset is your current employee population. And isn't it true also that it's not just that current employees can see the new jobs, but they can also ask. And so what is the rule around that that I, Steph Smith can go and say, hey, I do this job. And what specifically can I ask before? You can ask for your pay range. And that's where your manager should be able to come back to you and say, here's the pay range.
46:28Here's where you're slotted. This is where you came in. Here's how long you've been in this role. we're seeing the progression. Here's how we think about compensation. Right? You would walk away thinking, they've been super thoughtful and how I paid. All right, let's start to think about how companies actually build this because a lot of companies don't have this in place or just in the early innings. And so if you are a company that, let's say, has, I think the California threshold is 15 employees and all 15 of those employees have been hired in disparate ways, paid in disparate ways. There's no sort of underlying strategy or structure Like how do you even go about thinking about how to start setting that up?
47:07Yeah, you know, the three core tenants are what we've talked about before. You need to be able to articulate your philosophy on compensation. Not everybody is going to be able to compete with a $10 billion in revenue publicly traded company on a cash basis. So you need to own the philosophy that you believe will drive the kinds of outcomes that you want as a company. So your compensation philosophy in terms of how you define your competitive market, companies of your size and scale. and where you position against that competitive market data is really important. That sets the tone for then how you think about driving market data through your comp philosophy to determine those ranges.
47:42The other element is your leveling architecture, how you define what it means to be an early career employee versus a seasoned individual contributor versus a first -time manager versus a seasoned leader. That's your leveling architecture. So between your philosophy and your leveling architecture and the third -party market data that you use to determine that relationship you can set your ranges very consistent with what you should be providing to candidates and articulate the current employees on the request. Another area that experienced a renewed focus among entrepreneurs in 2023 was the world's largest asset class housing.
48:18As the ripple effects from the pandemic have gradually played out, the housing industry has emerged as a space crime for disruption. And earlier this year, Mark and Dreson, co -founder of A16Z, sat down with A16Z General Partner and David Ylavitch, and Adam Newman, co -founder of Flow and WeWork. And together, they highlighted some of the biggest opportunities in housing that still lie ahead. Why is housing important? Why is it particularly relevant? Why is it even a part of American dynamism? There's a critique of Silicon Valley in the tech industry that, you know, I'm very staying sometimes I agree with, sometimes I don't.
48:54But there's a critique that basically says, look, you guys have done a great job. It's Silicon Valley's done a great job over the years that they're really like unimportant things. You've consumer electronics, and video games, and media, and e -commerce, and it's all great, it's all fine, it's all good. And by the way, we do those things we're very proud of our efforts in those bases. And we actually think they're very important. But they're basically their small slices of GDP. And then if you look at basically the GDP pie chart, sort of consumer spending pie charts, the things that people spend money on, they really matter their lives.
49:21They're basically three really big ones, health care education and housing. And if you look at kind of all the economic statistics by sector, health care education and housing are basically increasing in terms of cost, at a much faster rate than all the others. And there are the sectors where the tech industry has had the least impact historically. And of those, the biggest one is housing. The housing is the single biggest thing that most families have been money on. And so it's really central from that standpoint, it's very central from an economic standpoint in the life of a family because there's this very big question, which is if you own there are potentially big downsides because you might be stuck in one place where maybe you don't want your family to be for the next 30 years because maybe the opportunity shifts around.
50:00But if you rent, you don't necessarily build up equity by default with the way that most department offerings work. So there's big economic consequences. There's also big social consequences and political consequences, right? And this is something that's kind of been deep in the American character for many decades, but this idea that if you basically, if you have a sense of commitment, if you have a sense that you are actually invested in the place where you live, you were more connected to the society, you were more connected to the culture, and then ultimately you're more connected to the politics, to the political body.
50:29And so there's been long -running policies in the United States to encourage home ownership. Now, it turns out those could be overdone. They can end up with problems, and we saw a lot of the problems that came out of that approach. And the historical approach in the 2008 financial crisis. But nevertheless, there is a very big, deep, meaningful, kind of sense of identity that comes with housing. Why does this matter from a broad standpoint? point. So the reason I think this matter so much is because geography is so central to economic opportunities. So basically, take a step back, the role of cities in human society.
50:59Cities are a human invention. There were many hundreds of thousands, millions of years where people were running around, you know, in clients and tribes, not forming into cities. Then about 4 ,000 years ago, cities were basically invented and people started to cluster that way. Why were cities invented? A bunch of reasons. But one of the big ones is there's economic payoff after being in a city. And the way that it works is if you are, if you have a certain level of productivity in your inner rural environment, you don't have many opportunities to work with other people who are highly productive.
51:27If you move to a city all of a sudden, you're surrounded by lots of other people who are highly productive and all of a sudden, there's a catalytic effect basically of this positive feedback loop that forms where everybody becomes more productive. And so basically if the cities are basically the foundation of modern economic growth, economists call this the agglomeration effect, which is basically if you like slim people together, or you can get a lot of good things out of that. The West developed, the story of the West is the story of cities and the creation of all these amazing cities. And then over the last 50 years, that trend has gone into hyperdrive and you've had this emergence of what economists call superstar cities where you've got these specific cities like Washington, DC, New York City, Boston, San Francisco, Bay Area, Los Angeles, and then internationally London and Tokyo and Singapore and so forth and Paris.
52:11Then are these cities that are like irresistible draws for people who are highly ambitious and want to be a lot of other highly ambitious, highly productive people. It turns out as a result of that superstar city clustering thing, there's been a bifurcation in the housing market, where basically those superstar cities, they become basically occupied at some point, people end up with a political agenda, and then they stop building housing. And so the places where people want to go and want their kids to go are very difficult to get to. And the San Francisco Bay Area is like a classic example this.
52:39San Francisco last year, like authorized, there's I think 6 ,000 new housing units, right? Which is just like an absurdly low number relative to the demand for people who want to move there. And then the places that do not have this kind of clustering and glamouration superstar city effect, they've built plenty of housing, but there's not as much opportunity there. So heading into COVID, you just, you saw this incredible bifurcation, and you saw an American economy, and you see it in American politics, where basically you're either in a city, you buy home, you've kind of got that old commitment, you've got that level of opportunity for you and your family, or you're in a more real environment, and you're basically stuck, and you don't have access to good jobs, and your kids are not going to have high -tech jobs and you're going to have a lower quality of life.
53:15This was becoming a very big issue by the way for us, because we were basically getting stuck in the geographic confines of Silicon Valley. Our companies were like pinned in, and it was becoming very hard for companies to grow in that area. And then basically, a horrible tragedy struck that turned out to also be in some ways a miracle in terms of, I think, the longer and consequences which is COVID. And none of us would have hoped that COVID would have happened. And it has been a horrible tragedy for many people. and I think broadly for society. But nevertheless, COVID was a system shock that caused all big companies to basically instantly move online in the hard cutover in kind of a way that I never imagined was possible.
53:51It was also an instant proof that companies really can run, at least for some time, they can actually run online, basically like no big company actually stopped operating as a consequence of this dramatic shift from in person to online. And then all of a sudden, like that opened the door to every CEO, every manager, every entrepreneur, every investor thinking, oh, okay, the post -COVID world is not going to be like the pre -COVID world. The post -COVID world is going to be an opportunity to rethink and reinvent how companies are organized. It's going to be an opportunity to reinvent how industries are organized.
54:19It's going to be an opportunity to reinvent how geography works, right? What the role of the city is, it's going to be an opportunity to spread the economic activity from cities much more broadly, potentially throughout the country. Every employer, every company, every CEO here, you know, is having some version of this conversation with their own company. We talk about this with our founders all the time. And then there's a corresponding question that's opened up, which is how are people going to live? And some people are going to continue living the way that they were. Some people are going to undertake a radical change in how they live and they'll go remote.
54:47And all of a sudden, you go remote, and you have access to thousands of jobs anytime you want, which is a totally new phenomenon. And then you now have the opportunity to rethink how families live, right? And so, you know, is it necessary for ambitious kids to leave a place where they grew up in order to have access to first class as economic opportunity. In 2019, the answer to that was frequently yes today, maybe not. And it should also say like this is not a zero or 100 % thing. This is not like a hard cut over. It's not like the whole world goes remote or stays remote, which is not what's happening.
55:14But there's this moment in time that we're in right now, which is like we can actually rethink and reinvent how companies organize and how work happens. And then we can also reinvent how people live and get people a lot more options and how that happens. And so I think basically the presumptions that have underlie the whole structure of how the housing market works and how the industry works and how people live and work. I think it's basically all of the foregrabbs. If we have about a five -year window, I think it's a society to figure out what this means and how to adapt. Finally, we hear from Steve Wozniak, one of the most important innovators and engineers of our generation.
55:47And as you look toward the New Year and think about how you want to get involved in this increasingly dynamic world around us, I hope you find Steve's reflections on inventors and entrepreneurs inspired. One year at a time, people like Steve move us toward the future. Another word sometimes people use for inventor is visionary and I'm curious in the early days when you were just out of passion creating these computers, could you see the path to today? Of course you can't picture everything with so many advancements since those early days but like how far along were you actually and visioning. And I'm asking this partially because even if we apply this to space, a lot of the things that people talk about in the realm of space also sound kind of like science fiction, right?
56:32They probably won't be eventually. But I'm trying to understand also how far along you see or the extrapolation that maybe goes on in your brain when you're originally talking about, yes, a computer with 200 transistors and now we're talking billions and the applications that have kind of sprung from that. I myself, I was really a great engineer in certain field. and I was designing the hottest products in the world for you, a packer without even having a college degree yet. And then you talk about visionary, vision, see in the future. That's different than invention, though. And Ben really wants to actually go and create something today that didn't exist and not have a vision that's 50 years out or 10 years out because that's science fiction a lot.
57:10And everybody can talk about it and say later on, see, I proposed it, but it wasn't really possible to do with money. And the engineer says, feed on the ground, what can I actually do and build and deliver to people? Well, when we started Apple, you know, we had a great product that was going to be all the revenues of Apple for the first 10 years. We had such a great lead. We were comfortable and we could do what we wanted. But the amount of memory that would hold us song costs, you know, we were reckoning days of tape. It cost about a million dollars, a good fraction of a million dollars. Do you think we saw it today where you have a device in your hand with a thousand songs on it even?
57:44No. Steve Jobs is very instrumental in always taking us do what we can do today. Try to do something a little more tomorrow, a little more. You can have a lot of failures too if you'll have one great product bringing in the revenues. But the whole idea was we'll move towards the future and we'll be a part of it. We'll be in with it. After all, you'll look back and it was kind of invisible. The steps we took, but they all led to today. Then there was some of that invention stuff we got to. Steve Jobs's Apple II was really the iPod music. Music. That was the first time, oh my gosh, up till then our company valuation was the same as the old Apple two days.
58:20And then all of a sudden, we sold it to everyone in the world and our sales doubled, our profits doubled. And the board gave Steve Williams and stock options and jet airplanes. That was the turning point. And then the iPhone was even better. And it was based on the iPod, not the reverse. Not a phone and will include an iPod. And more like it's an iPod, but you get a phone with it. And so it's hard to say that you really see the future more than a year ahead when you're working a year ahead on your projects. Whenever I tried to see the future a year ahead, I knew it one year ahead because I was working on it.
58:50If I look two years ahead, made some guesses, oh my gosh, other aspects, other technologies, and all came out of outer space, and people's desire, which way they wanted to go, was different. It's very hard to predict even two years ahead, successfully, the way I work. Nowadays, we got huge big companies. So it's kind of like, you know, anything that work on is going to be successful. It's not as much a gamble, but, you know, real inventors like to gamble, like to prove the world that they can do more than you ever met. All right, that sums up our recap from the last year or so. Since our relaunch, we had over 75 amazing conversations and reach literally millions people.
59:30It's truly been a blast and I cannot thank you enough for listening. Plus, as we look toward the new year, this is your chance to let us know what you want to see us cover. You can leave your guest or topic selections in the comments on our YouTube channel or you can even email us directly at podpitches at a16seed .com. You can also find the email on the show notes. Again, I cannot thank you enough for listening. And with that said, we'll see you bright and early in the new year. Bye!
From the publisher
As we close off 2023, we wanted to revisit some of our favorite episodes from the last year. From AI hardware, to healthcare regulation, to disrupting the world’s largest asset class, to the electrification of nearly everything… there’s something in here for all listeners.
Resources:
AI Hardware, Explained:
https://a16z.simplecast.com/episodes/ai-hardware-explained
Chasing Silicon: The Race for GPUs: https://a16z.simplecast.com/episodes/chasing-silicon-the-race-for-gpus
The True Cost of Compute: https://a16z.simplecast.com/episodes/the-cost-of-compute
AI Revolution: Disruption, Alignment, and Opportunity: https://a16z.simplecast.com/episodes/ai-revolution-disruption-alignment-and-opportunity
The Robot Lawyer Resistance; https://a16z.simplecast.com/episodes/the-robot-lawyer-resistance
The Quest for AGI: Q*, Self-Play, and Synthetic Data: https://a16z.simplecast.com/episodes/the-quest-for-agi-q-self-play-and-synthetic-data
The Road to Autonomous Vehicles: Are We There Yet?: https://a16z.simplecast.com/episodes/the-road-to-autonomous-vehicles-are-we-there-yet-PstZz2G_
The Electrification of Everything: From Sky to Sea: https://a16z.simplecast.com/episodes/the-electrification-of-everything-from-sky-to-sea
When Two Giants Intersect: Healthcare Meets Fintech: https://a16z.simplecast.com/episodes/when-two-giants-intersect-healthcare-meets-fintech
Salary Transparency: Clarity or Chaos?: https://a16z.simplecast.com/episodes/salary-transparency-clarity-or-chaos
Disrupting the World’s Largest Asset Class with Adam Neumann: https://a16z.simplecast.com/episodes/disrupting-the-worlds-largest-asset-class
The Data Highway Above with Privateer’s Steve Wozniak, Alex Fielding, and Dr. Moriba Jah: https://a16z.simplecast.com/episodes/new-the-data-highway-above-with-privateers-steve-wozniak-alex-fielding-and-dr-moriba-jah
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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
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Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures.
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