The a16z Show - A Year of Unlocks: 2023 In AI, Healthcare, Housing, and More

Episode Date: December 31, 2023

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-explainedChasing Silicon: The Race for GPUs: https://a16z.simplecast.com/episodes/chasing-silicon-the-race-for-gpusThe True Cost of Compute: https://a16z.simplecast.com/episodes/the-cost-of-computeAI Revolution: Disruption, Alignment, and Opportunity: https://a16z.simplecast.com/episodes/ai-revolution-disruption-alignment-and-opportunityThe Robot Lawyer Resistance; https://a16z.simplecast.com/episodes/the-robot-lawyer-resistanceThe Quest for AGI: Q*, Self-Play, and Synthetic Data: https://a16z.simplecast.com/episodes/the-quest-for-agi-q-self-play-and-synthetic-dataThe 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-seaWhen Two Giants Intersect: Healthcare Meets Fintech: https://a16z.simplecast.com/episodes/when-two-giants-intersect-healthcare-meets-fintechSalary Transparency: Clarity or Chaos?: https://a16z.simplecast.com/episodes/salary-transparency-clarity-or-chaosDisrupting the World’s Largest Asset Class with Adam Neumann: https://a16z.simplecast.com/episodes/disrupting-the-worlds-largest-asset-classThe 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 Stay Updated: Find a16z on Twitter: https://twitter.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zSubscribe on your favorite podcast app: https://a16z.simplecast.com/Follow our host: https://twitter.com/stephsmithioPlease 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. Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg 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. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The 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, feet on the ground? What can I actually do and build and deliver to people? Hugely 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.

Whenever 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 Steph. 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 A16 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. And those are just a few of the groundbreaking innovations that we did cover here on the podcast. We were also

joined by some incredible guests, including a Nobel 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. Today, you'll hear from the likes of Mark Andresen 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 A16C fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see A16C.com slash disclosures. 2023 was a year dominated by AI.

From healthcare 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 Gito App and Cellar, 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, Gito breaks down the GPU architecture and why it's so game-changing for AI processing, plus why NVIDIA has been leading the pack here so far.

If you're running any kind of AI algorithm, right, 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're 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 in processing very large number, of math operations per cycle in a very short period of time. So 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 ten 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. You 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. And 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, right, or tensors more generally. TPU, for example, is Google's name for these kind of chips, right, and they call them tensor processing units, which is actually a pretty good name for them, right? The cores and these modern GPUs often call tensor cores, like that's how media calls them because they operate on tensors.

And basically, the core of their value propositions, they can do matrix multiplication. So remember metrics like the rolls 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 a speed that's necessary to run these incredibly large language and image models that make generative AI 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 G4th, 256, the first personal computer GPU unveiled by Nvidia in 1999. But 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 one way, I think it's very surprising. We would have thought that my gaming PC or 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, right? And so you can think of a GPU as something that's built for graphics, but you can think of them also just as something that's very good

in performing the same operation and a very large number of parallel inputs, right, a very large vector or very large metrics. All right, so perhaps it's not so surprising that NVIDIA's prize GPUs are aligned to this AI wave, but they're also not the only company participating. Here is Gito breaking down the hardware ecosystem. The ecosystem comes in many layers, right? So let's start with the chips at the bottom.

Nvidia is king off the hill at the moment right there. A100 is the workhorse that powers the current AI revolution. They're coming up with a new one called the H100, which is not the next generation. There's a couple of other vendors in the space. Intel has something called Gaudi, Gaudi 2, as 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,

in some case, have been building for some time their own chips, Google with the TPU you mentioned before. It is quite popular. And Amazon has a chip called Traneum 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, NVDA still has a very, very strong position as the vast majority of training is going on on their chips. And when we think about the different chips, so you mentioned like the A100s are the strongest and maybe there's the most demand for those.

But 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 performance? 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 with what Nvidia has. Nvidia's big advantage 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, in many cases, depends on how well is optimized for a particular chip. And so the big advantage that Nvidia has today is that their software ecosystem which is so much more mature, right? I can grab a model, it has all the necessary optimizations for Nvidia

had to run out of the box, right? I don't have to do anything. But with some of these other chips, I may have to do a lot more of these optimizations myself, right? And that's what gives them these strategic advantage at 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. Our next rewind is with Dylan Field, co-founder and CEO Figma, a browser-based web design tool tentatively acquired by Adobe for $20 billion. Dylan was one of the many speakers at our AI Revolution event, alongside founders of OpenAI, Anthropic, Character AI, Roblox, 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 and puts it in the question is like, okay, well, there will be less things to design or is AI going to do all the design work, right?

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 look at every technological shift or platform shifts so far, it's resulted in more things to design. So you got the printing press. And then you have to figure out what you put on a page.

And 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 ask me this beginning of the year, you might have said, okay, well, we'll all have these chat boxes. And people will be asking questions in them. And that's going to be our interface for everything. You know, look at Open AI.

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. It turns out design kind of matters. The second one of will AI 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. Right. And getting from first draft to final product actually turns out that's kind of hard.

And usually takes a 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, designs that are responsive to what the user's intent is at the moment. And I think that would 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 health, 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.

Now, 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. A lot of laws originate from an overworked staffer in Congress, and I'm sure it's 2 a.m.

They need to finish it, and they're probably typing in chat GPT, 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. So 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? In terms of conditions is a great example because people can't understand the changes being made, what they're giving up. So this is actually our phone. public product relating to true AI, where you can paste in any terms and 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. And that's really important because people don't have time to read a 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 and David versus Goliath so the AI can turn David into Goliath versus 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? Like should 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 of 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 and you're already seeing companies implement these policies. And that could easily be a law. I 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 100 years of law to deal with that if you jump through the right hoop and follow the correct processes, you can get results. Like one thing we automate 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 a credit report.

And this allows us to kind of get old or inaccurate 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 where kind of Congress is heading. As 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, Ageny Mita, we explored some of the most important frontier research areas, from synthetic data to self-play.

But first, here Angeena clarifies why math problems, even at the grade school level, are a suitable testing ground for this kind of research. The reason grade school math is so interesting is because it's pretty well-scoped. And you can ask the model to break down a word problem. You know, one of these classic, Jimmy has 10 apples and John has 16 apples. If 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 grader 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? You can actually have an AI now score every intermediary 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. Did 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.

So 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's synthetic. It's not generated by a human. It's generated by an existing system. So 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 GPD4. Right. And let's double click on that element of self-play and potentially synthetic data. And to underscore that, right now, many of these models do have an element of reinforcement learning, but that comes from human feedback, right? So RLHF is one of the methods that these different labs are using to orient the existing models, but obviously that requires humans in the loop. And so what you're getting at is potentially the ability to obfuscate that and have AIs in the loop.

Can you speak a little bit more to that idea of self-play and the ability to create synthetic data? What does 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. And I think what self-play and synthetic data do is they allow the scaling of these models much more rapidly because whereas with reinforcement driving 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 of 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. Yeah, please. The 1.0 version of this world, a common example of reinforcement learning from human feedback is 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 to learn to improve the kinds of messages it gives you next time around. The reinforcement learning from AI feedback in this

case replaces that thumbs up and thumbs down 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. You can now start providing a score of one to ten on the intermediary steps. And so you're right, scale is really the biggest unlock here. When you replace humans with models that provide this feedback 24-7 at orders of magnitude larger volume than we could do with just humans, then the speed at which we get to an AGI 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. One 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 step forward. Up next is a clip from our episode with Waymo's chief product officer, Saswat Panagrahi.

As CPO, SASWAT 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, Sasswat discusses where we really are in the shift to autonomous vehicles and the challenges that still lie ahead. It's felt like this is coming for a while. I almost feel like the future is here. Like we're seeing this.

We're 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. Because it's very easy that during a normal situation, the driver feels, hmm, car is kind of driving well, so I can pick up a book and start reading, no. That's 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 just big and heavily snowing in SF, which it has, believe it or not last season, there was a little bit of snow. We wouldn't operate there. 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 plane sensing, automatic braking.

Right. Is level three where basically what we're doing now, but there would be a human in the front? Or how would you define level three? No, 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. It's a massive difference. Because this concept still, that within seconds you need to take over,

versus now we can have this conversation. Yeah, we're not doing anything. That would be challenging, right? But the assurance level you need to get to for Level 4 is just a universe different, I would say. So tell me a little bit more about the barriers along the way to Level 4. What would you say? Was it the technology, the regulations, some combination? Are there major factors that have really done?

delayed us from getting to this point where we're now at level four. Yeah. 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 foresaw some you didn't. The key is, are you clear on how massive the price or the benefit to societies 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. So you wouldn't be able to see. You don't have your sense there. 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. But 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. But to be able to do that at the scale of 24-7, busy intersections at slow speed,

but also high-speed intersections of Phoenix. So in Phoenix, for example, the streets are so you don't deal with these narrow situations. Yep. But the driving speed is 45, people are sometimes going 60 on that. That means you got to see a lot further. So very different sets of challenges, very diverse ones. Yeah.

And the technology, it really required the full stack, right? We build the hardware, we build 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. So we had to build the software. the lasers, the cameras, the radars, and the software on top, and the massive simulation infrastructure as well.

I was going to 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 going to do? Yeah. And then reasoning on what it should do. 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. It's the smallest detail. So if there was a child an inch out of this pole, it will be able to mark, oh, this is a demarcated child away from that pole. It will get to see that. But, you know, the cameras are needed to distinguish between the red light and the green light. Oh yeah. And the radars can almost see around corners, even when the laser and the camera,

around the camera or 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 could, both due to the attention span, the range, the fidelity, and the combination of these sensors coming together. 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? But you don't know if they're going to move, right? Exactly. Are they going to jump in or are they going to stay? Are they going to jaywalk 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. Biden's Inflation Reduction Act was the single largest climate investment in U.S. 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. You could 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 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. Well, 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 in 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 you need to go make that happen. Maybe it's powered trailers that can actually have. help the vehicle or there's just a ton of opportunities. And if you cast your mind forward a few years and think about what are the new problems that are going to exist when the marine industry is going electric and the 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. And 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 and 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 it or 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 sell cost down so we can deliver cheaper product. it's being able to deliver product full stop. You know, the last couple of years have been really tough on supply chains. It's led to soaring costs, but also poor reliability. You know, 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. We're not billions yet, but we will be quite soon. And even, 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 switchgear 18 months in advance for projects. We don't even have under 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. And 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, timing for what we're doing. You look at the outlook on the aerospace industry right now, particular aircraft manufacturing. And a lot of the major aircraft manufacturers have already launched their next program. So they're into entry into service. They're delivering them. And if you take a look around at the large incumbent aircraft manufacturers, there's no new programs on the docket right now.

And the Tier 1 supply base and even the Tier 2, Tier 3 supply base, they're all hyper aware 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 broadly applicable.

The new technologies, the 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. It's unshiny. 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 A16C's Healthcare Meets Fintech package, we're joined by A16Z General Partner, Julie Yu, who exposes the huge opportunity at this intersection and 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 did not really exist in major adoption until the meaningful use law into play where the government literally paid financial incentives to doctors to adopt digitized technologies for medical record storage. So that was really the sea change that drove so much of the digitization of our infrastructure layer of health care. 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. Obviously, 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 is one of those players and actually make it actionable in the context of their contract negotiations 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 two different providers who, again, are providing the same service, but at widely different prices. So 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 seep 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. Effectively 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 it's proprietary data. And we should not be forced to publish them.

such data in a public forum because that's our competitive advantage in our market, is that we're able to negotiate special rates with our counterparties 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. The 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 a certain instead 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. And you basically got told, oh, you'll get a callback in an undeterminate 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, listen, 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. So 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'd call out, which we've talked about before, is just all of the movement towards value-based payment models. So this notion that, you know, a lot of the reason that, you know, 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. Value-oriented models tend to be much more bundled in nature.

So either getting a sort of 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 provider's at risk, right? So if they go above that budget, they are paying out of pocket effectively to cover the 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. And 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 relying on individual services getting billed and paid for.

So there's been a whole set of regulation around driving adoption of those payment models. Medicare Advantage, I think, has 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 fun and a different way that results in much more transparent behaviors. Absolutely. And maybe before we jump into the FinTech side, just wanted to clarify the No Surprises 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 Surprises Act is very closely 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 lo 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. This act limits the liability on the side 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, you know, sort of limit the cost of access. So it used to be the case that, you know, you'd have to potentially pay a couple hundred dollars

something 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 swoop and really have agency over them. I 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. Up 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. Yeah. 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. Right. So one, I think people are reacting the right way. They just want to get their arms around it. Yeah. 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. 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, is you have to have a salary range posted with your jobs. Yep, yep.

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 nuance to it. In 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. But, 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. 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. Yeah. And again, it goes 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, the number of times that somebody sees a job posted and thinks, well, that's what I do, but I don't make that. Yeah. And so, like, it does. Like, 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, but that's not where you're at right now. And that's a hard conversation. I was going to 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? Yep. Both of you are sitting up. And it helps us iterate on our process, right? No process is ever great. And so I feel like weekly we're like, okay, 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. So 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, right? 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 for? 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, here'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 in how I'm paid. All right. Let's start to think about how companies actually build this because a lot of companies don't have this 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? Yeah, you know, I think 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. The 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 articulating the current employees when they request. Another area that experienced a renewed focus among entrepreneurs in 2023 was the world's largest asset class, housing. As the ripple effects from the pandemic have gradually played out, the housing industry has emerged as a space prime for disruption. And earlier this year, Mark Andreessen, co-founder of A16Z, sat down with A16Z general partner David Ulovich and Adam Newman, co-founder of Flo 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, on various states, sometimes I agree with, sometimes I don't. But, you know, there's a critique that basically says, look, you guys have done a great job. You know, Silicon Valley's done a great job over the years that, like, the really, like, unimportant things. You know, you've consumed electronics and video games and media and e-commerce and, like, it's all great, it's all fine, it's all good. And by the way, like, we do those things.

We're very proud of our efforts in those spaces. And we actually think they're very important. But, you know, they're basically, there's small slices of GDP. And then if you look at basically the GDP pie chart, you know, sort of consumer spending pie charts, the things that people spend money on, they really matter in their lives. There's basically three really big ones, right, health care education and housing. And if you look at kind of all the economic statistics by sector, healthcare education, housing are basically increasing, you know, in terms of cost, you know, 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. Right, it's sort of the housing is the single biggest, you know, kind of thing that most family spend money on. And so, you know, it's really central from that standpoint, you know, it's very central from an economic standpoint in the life of a family because, right, there's this, you know, very big question, which is, you know, if you own, there are, you know, potentially big downsides because you might be stuck, you know, in one place where maybe you don't want your family to be for the next 30 years because maybe opportunity shifts around, you know, but if you rent, you don't necessarily, right, build up equity by

default, you know, 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're more connected to the culture, and then ultimately you're more connected to the politics, you know, to the political body. And so, you know, there's been long-running policies and, you know, 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 kind of came out of that approach and, you know, kind of the historical approach in the 2008 financial crisis. But nevertheless, there is like a very kind of big, deep, meaningful, you know, kind of sense of identity that comes with housing. Why does this matter, you know, kind of from a broad standpoint? So the reason I think this matters so much is because geography is so central to economic opportunities. So, right, so basically, take a step back, the role of cities, right, in human society. Cities are a human invention, you know, there were many hundreds of thousands, millions of years where people were running around, you know, in clans 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 to being in a city. And the way that it works is if you are, you know, if you have a certain level of productivity and you're in a rural environment, you don't have many opportunities to work with other people, right, who are highly productive. If 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 kind of everybody because we're productive. And so basically, these cities are basically the foundation of modern economic growth. The economists call this the agglomeration effect,

which is basically if you like slam people together, you can get a lot of good things out of that. The West developed, you know, the story of the West is the story of cities and the creation of all these, you know, these amazing cities. And then, you know, over the last 50 years, you know, that kind of trend has gone into hyperdrive. And you've had this emergence of what economists called Superstar Cities, where you've got these specific cities like Washington, D.C., New York City, Boston, San Francisco Bay Area, Los Angeles,

and then, you know, internationally, London and Tokyo and Singapore and so forth, and Paris, that are kind of these cities that are, like, irresistible draws for people who are, you know, highly ambitious and want to be around 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, right, where basically those superstar cities, they become, you know, basically occupied. At some point, you know, people end up with a political agenda, and then they stop building housing, right? 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 of this. You know, San Francisco last year, like authorized, I think, 6,000 new housing units, right?

Which was 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 glomeration superstar city effect, you know, they build 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 it in America, economy and you see it in American politics where basically you're either in a city you buy a home you know you've kind of got that old commitment you got that level of opportunity for you and your family right or you're in a more rural environment and you're basically stuck and you don't have access to good jobs and your kids are not going to have like high tech jobs and it's you know basically it's you're going to have a lower quality of life this was becoming a very big issue by the way for us because

we were basically getting stuck in the geographic confines of silicon valley like we were our companies were like penned 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 long-run consequences, which is COVID, right? 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, you know, broadly for our society. But nevertheless, COVID was a system shock, right, that caused all big companies to basically instantly move online in a hard cutover, you know, in kind of a way that I never imagined was possible. It was also an instant proof that companies really can run, at least for some time,

they can actually run online, right? 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, you know, 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. It's going to be an opportunity to reinvent, you know, how geography works, right? What the role of cities is. it's going to be an opportunity to spread the economic activity from cities much more broadly, you know, potentially throughout the country. Every employer, every company, every CEO here, you know, is having some version of this conversation, you know, 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 and all of a sudden, you know, you go remote and you have access to, you know, 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 economic opportunity?

In 2019, the answer to that was frequently, yes, today, maybe not. And I should also say, like, this is not a zero or a hundred percent thing. This is not like a hard cutover. It's not like the whole world goes remote or stays remote, right? Which is not what's happening. But there's this moment in time that we're in right now, which is like we can actually rethink and reinvent how companies are organized and how work happens. And then we can also reinvent how people live and give people.

people a lot more options than how that happens. And so I think basically the presumptions that have underlied 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 up for grabs. If we have about a five-year window, I think, as a society to kind of 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.

And 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 really. reflections on inventors and entrepreneurs, inspiring. 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 envisioning? 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? They 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 Yula Packard without even having a

college degree yet. And then you talk about visionary. Vision see in the future. That's different than invention, though. Inventor really wants to actually go in 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. And everybody can talk about and let's say later on, see, I proposed it, but it wasn't more possible to do with money. And the engineer says, feed on the ground, what can I actually do and build and deliver to people? 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 a great lead. We were comfortable and we could do what we wanted. But the amount of memory that would hold a song costs, you know, we were back

in the 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? No. 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. And we, you can have a lot of failures, too, if you 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. And we'll be in with it. And after all, you look back and it was kind of invisible, the steps we took,

but they all led to today. And then there was some, you know, some of that invention stuff we got to. Steve Jobs's Apple II was really the iPod music, music. And that was the first time, oh my gosh, up till then our company valuation was the same as the old Apple Two days. And then all of a sudden, we sold it to everyone in the world and our sales double. and 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 we'll include an iPod. 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. If I looked two years ahead and made some guesses, oh my gosh, other. aspects, other technologies and all came out of from 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 it's not as much of a gamble. But, you know, real inventors like to gamble, like to prove the world that they can do more than you ever imagine. All right. That sums up our recap from the last year or so. Since our relaunch, we had over seven be five amazing conversations and reach literally millions of people. It'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 A16C.com. You can also find that email in 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. Thank you.