Imagining Our Future Through Tech

AI-powered toilets. Iron Man-esque exoskeletons. Robot furniture. VR-based offices. Actual dream-recording machines. If these all sound crazy and futuristic, well, that’s because they are! And yet, what if the technology behind many of these is more science than science fiction? a16z Operating Partner Frank Chen takes us on a tour of what daily life could look like in 2030 and beyond — and what innovations in both technology and business model need to happen to get us there. He can’t promise flying cars (yet), but the future might be closer than we think. Highlights Kicking off the day, the Morning routine of 2030 [1:20] What the future of work looks like in 2030 [04:44] Practicing a sales pitch enabled by advances in VR technology [7:10] New ways to invest and engage with fans enabled by blockchain [9:10] Executing smart contracts using Blockchain [11:05] What will we do after work for entertainment? [12:12] What will public transportation look like? [13:22] Building up our longitudinal health data [18:51] Bioengineering more sustainable materials such as, Spider-silk [21:00] How we get to the smart coach [27:34] How we get to more efficient public transportation [31:10] How we get to a dream recorder [34:19] *** The views expressed here are those of the individual AH Capital Management, L. L. C. (“a16z”) personnel quoted and are not the views of a16z or its affiliates. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by a16z. While taken from sources believed to be reliable, a16z has not independently verified such information and makes no representations about the enduring accuracy of the information or its appropriateness for a given situation. This content is provided for informational purposes only, and should not be relied upon as legal, business, investment, or tax advice. You should consult your own advisers as to those matters. References to any securities or digital assets are for illustrative purposes only, and do not constitute an investment recommendation or offer to provide investment advisory services. Furthermore, this content is not directed at nor intended for use by any investors or prospective investors, and may not under any circumstances be relied upon when making a decision to invest in any fund managed by a16z. (An offering to invest in an a16z fund will be made only by the private placement memorandum, subscription agreement, and other relevant documentation of any such fund and should be read in their entirety.) Any investments or portfolio companies mentioned, referred to, or described are not representative of all investments in vehicles managed by a16z, and there can be no assurance that the investments will be profitable or that other investments made in the future will have similar characteristics or results. A list of investments made by funds managed by Andreessen Horowitz (excluding investments for which the issuer has not provided permission for a16z to disclose publicly as well as unannounced investments in publicly traded digital assets) is available at https://a16z.com/investments/. Charts and graphs provided within are for informational purposes solely and should not be relied upon when making any investment decision. Past performance is not indicative of future results. The content speaks only as of the date indicated. Any projections, estimates, forecasts, targets, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others. Please see https://a16z.com/disclosures for additional important information.

• 0:00Speaker 0

  So, what data do we have? We have the data that tracks her urine. We've got smart speakers that listen to your voice and changes in your voice over time. We've got what you look like. We call these selfies. So we've got data, lots of data. We also have algorithms that can take this data and predict health outcomes. We can use selfies to predict skin cancer. We can use changes in your voice to predict depression or early onset Alzheimer's. So what's missing? We've got the data, we've got the algorithms. What's missing is a business model and a privacy model and an ethical framework to tie these two pieces together.

• 0:53Speaker 0

  What will life look like in the 2030s and how will we get there? What do we need to build? What social acceptance do we need? What business models do we need? And I've been thinking about this because my house is starting to empty out. Please meet my daughter. This is Katie. She is a high school junior, which means that when we hit the 2030, she'll be in her late 20s, heading into her mid-30s. And so I imagined what her life might be like. And so we're going to spend a day with her. So who's ready? Let's go. Like many of us, Katie has a morning routine to get ready for her day. And she starts with this piece of technology. Now, it doesn't look like it's changed much, but like most other things in her house, this has become a smart toilet. It measures 10 properties of her urine, including glucose, and it looks for health problems before they become actual health problems.

• 1:45Speaker 0

  After she's done here, she's training for the marathon, so she's ready for her run. Sorry, even in the 2030s, there is no train for a marathon pill that you can buy. So she has to log her miles. So she slips on her exercise gear. This shirt is made out of spider silk, engineered by DNA. It's got sensors and actuators right in it. So the sensors pick up her health stats like temperature and EKG and heart rate. And it's got actuators so it can bumper on the wrists, right? Turn left, turn right, slow down. While she is out running, the room is reconfiguring itself. As more and more of us crowd into the world cities, we need to be smarter about the space that we use. And so while she's out running, the room is reconfiguring itself from sleep mode to eat mode. It's also got change mode, work mode, and party mode.

• 2:32Speaker 0

  We'll see party mode later. Now Katie is back from her run and she's ready for breakfast. This meal is personalized specifically for her. It understands her genetic makeup as well as the makeup of her microbiome. So it knows exactly how much lactose her body can break down. It's also tailored specifically for her training regimen. So when you're training for a marathon, you need more protein. The amount of carbs that you need depends on how many miles you're running that day, and you need a baseline amount of iron to make sure you're carrying enough oxygen in your blood. So we take her workout plan and her health stats and we send them to the kitchens who are gonna build meals specifically for her. Once they've built the meals, they'll deliver them to her autonomously via delivery robot.

• 3:19Speaker 0

  And we're gonna use kitchen robots inside the kitchens to keep the cost of all this personalization down. Now, one intriguing place where she could get protein, because she needs protein, she's training for the marathon, is with something called cultured meats, meats that we're gonna grow in a bioreactor. I'll have more to say about that later. All right, after breakfast, Katie is done, is ready to pick a look. And she does that in conjunction with her smart mirror. Actually, the smart mirror is gonna help her to pick two looks a physical world look, that's hair, makeup clothes, as well as a virtual world look. As Katie spends more and more time in virtual meetings, the way her avatar look has become as important as her physical world look. So the mirror can preview both the avatar look and the physical world look, and she can swipe through a couple options and select the look that she wants.

• 4:11Speaker 0

  The last piece of her wardrobe, augmented reality contact lenses. So we'll have tiny projectors and cameras built into contact lenses that she can just pop into her eyes, and they'll work in conjunction with nearby computers to basically give her a heads-up display on the real world. As people come into her field of view, she'll get reminded about their names and the last time that she had contact with them. All right, so now Katie is fueled up and ready to go to work. Now she doesn't go to an office, she's going to a bunch of virtual meetings, but we'll join her there. She's got a big day at work today. She works in sales. Yes, there are still salespeople in the 2030s. And they've been preparing for a big sales call. Now, Katie works at a company called Influencers Inc., and what they do is they build products for influencers to sell to their fans.

• 5:02Speaker 0

  So think of it as an entire supply chain that can get behind somebody who's becoming popular on social media. So Katie starts her day with a smart coach. So today, if you are a CEO or a five-star general or maybe director of the CIA, you probably have a chief of staff who will follow you around to all of your meetings and help you be the best you. Katie's chief of staff is software. It listens to every meeting that she has and gives her real-time feedback. So, for instance, her smart coach will listen to her meetings and say, hey, you might have wanted to ask a question in that situation instead of stating an opinion. Smart coach is also listening to the entire uh call so that it can automatically extract action items and block calendar slots for them. Now, when those calendar slots get booked depends on what kind of action it is, when it's due and how important it is.

• 5:53Speaker 0

  Smart coach knows things like, hey, Katie is a morning person. And if you're a morning person, you're best off doing your focused analytical work in the morning and your creative work in the afternoon. But if you're a night owl like my wife, it's actually reversed. You're best off doing your creative work in the morning and your analytical work in the afternoon. Smart coach knows all this and can block all the right slots on your calendar. Okay, so as I mentioned, Katie's been preparing for a big sales call. The team has been preparing by doing a bunch of customer research and product development. So think about today if you wanted to build custom clothes to sell to an influencer and you're trying to figure out what kind of clothes to build, that would probably be a lot of summer interns, a lot of Excel spreadsheets, a lot of focus groups, a lot of conversation, stretches out over possibly months.

• 6:46Speaker 0

  In the 2030s, this is gonna be automated with software in a matter of hours. We can ingest the social media feeds of the influencer to get a sense for her style. And then we can ingest the social media feeds of her fans to figure out what's popular in your neighborhood. So we can take all of that and automatically generate candidate clothes to build. So we can put those candidates in front of a human clothing designer who can pick the best and then put their own finishing touches on it, which is exactly what the team has done. So the result is they have three outfits that they're ready to pitch the influencer when they get the audience with her. Okay, so now we're ready to demo. Katie wants her team to practice for the sales pitch. They head into a VR conference room.

• 7:32Speaker 0

  So Katie's team is global. She lives in Portland. Her clothing designer is in Paris. The clothing manufacturer is in Phnom Penh, Cambodia, and the data analyst is in Lagos, Nigeria. So it's a global team. But the VR room, like all VR rooms, will do real-time translation, so they all hear each other's native languages. This is the product that the CIA director was hoping for. It's gonna be mainstream in these VR rooms. But even though the room is translating, it doesn't automatically translate cultural nuance. And so what they're doing in the VR room to practice is to build their cultural understanding. So the influencer self lives in Tokyo, and none of the team has spent enough time in Tokyo to really understand the nuances of the culture. So what they're doing in the simulation room is having the influencer talk with various tones of voice, various body language, and the team's trying to figure out what is her state of mind.

• 8:26Speaker 0

  Is she into it? Is she not into it? Are we in the right price range? Does she like these designs? When she says yes, does she mean I love it and I want it, or does she mean yes, I understand you, but I disagree? So they're practicing all of these things in VR simulation so that when they're meeting the real influencer, they have a much higher sense of cultural understanding. Okay, time for the actual call, which of course they do in VR as well. In VR, they can preview the clothes in uh 3D, see what that looks like. They can preview how the supply chain would work, how would we actually manufacture these clothes? And then Katie's AR contact lenses are superimposing an engagement meter over everybody in the room. And when somebody's engagement meter has fallen too low, Katie knows to prompt them to lean in and speak up.

• 9:15Speaker 0

  So the influencer seems pretty excited about the clothes, but she's got some ideas that she wants to put in of her own. But instead of just incorporating that feedback, the team offers to run an instant poll. So if we again, if we were trying to do something like this today, set up an A B test, especially with a global audience. This might take weeks. We have to set up the A-B test, we have to find the people to show it to, we have to figure out how to distribute, and we have to figure out how to reward them. But in the 2030s, all of this software automates this process, so we can literally run the process the same afternoon. So the data scientist finds people who have bought clothes before and their lookalikes. So these are people who might buy the clothes. And it gets them an A B test, surfaces in the social media app, and gives them the opportunity to provide feedback instantly.

• 10:07Speaker 0

  So we can reach hundreds of her fans instantaneously and get their feedback. Now, the fans can get paid instantly for giving this feedback. And that you can they can choose to get paid in a cryptocurrency like. Bitcoin or Libra, or they can also choose to get paid in influencer coin. So if the fans are convinced this influencer is only going to get more popular over time and her influencer coin appreciates over time, we've basically given the fans a way to participate in the upside of an influencer's rising popularity. In exactly the same way that startup employees get to participate in the upside of their startups through equity. But because we're doing this with cryptocurrencies powered by the blockchain, we can do this globally and instantaneously. As soon as the fan gives feedback, they get rewarded.

• 10:54Speaker 0

  So once the feedback is back, software automatically updates all of the documents associated with getting the supply chain to build the clothing, starting with the sales contract. So the sales contract goes to the influencer. She sees the results of the instant poll, and she's excited to see that a lot of her fans agree with her own taste. So she's excited to have these clothes get made, and she digitally signs. That digital signature unlocks a chain reaction of smart contracts getting built and distributed throughout the supply chain. So everybody's going to have a hand in creating these clothes from the fabric supplier to the button supplier to the dye supplier. Everybody will get a smart contract that basically stipulates the terms of their participation. Hey, if you make one of these or if you ship this much fabric, we're going to pay you.

• 11:44Speaker 0

  All of these smart contracts are essentially pieces of software that tie the supply chain together, replacing essentially word process documents that we've been using for our century. Once the orders start coming in, the supply chain will get rewarded. And they too can choose to get paid in Bitcoin or influencer coin. So the supply chain can also participate in the rising popularity of the influencers and experience the upside of her growing popularity. All right, Katie's excited. She's closed a new influencer. She's super excited to make these clothes, and she's gonna celebrate with her favorite activity, which is drone racing. So she heads to the mall. With the new style of entertainment, audience participation is woven into the game itself. So as the fans cheer for their favorite pilots, how loud they cheer and how coordinated their cheering are determines whether, as they fly the as the pilots fly their drones through the obstacle rings, whether they get a double or triple point bonus.

• 12:43Speaker 0

  So your participation in the live event is influencing the way the game unfolds. Katie's super excited because her pilot, Wonder Woman, has come in second, and because she's such a high finisher, when Katie logs in to the drone racing game on her phone, her drone will have new capabilities and a new look. Again, integrating online and offline play. Katie's helping a friend move over the weekend, and so she needs to get fitted for an exoskeleton that will help her move bigger and bulkier items. She's looking forward to getting the help. And she goes to the store to get calibrated and comfortable with the controls. When she's ready, she schedules the suit to be delivered to her friend's house when it's time to move on Saturday morning. And then Katie's ready to come home.

• 13:29Speaker 0

  It's been a long day. So she summons an autopod. Her city, Portland, has recently replaced their light rail system with an autonomously summoned mass transit system. So you can summon a ride just like on a lift. You get into a private one or two-person pod that's half the width of a normal car. And it turns out that if you do the math, packetizing mass transit in this fashion is an excellent idea, just like packetizing data for the internet was. You can actually move more people from point A to point B, even during rush hour in these private one and two person passenger pods compared to big long trains. All right, now Katie is back home and ready for some sleep. But before bedtime, she's got one shopping errand and two important connections to make. So last shopping errand, Katie offered to host the after move party at her place, and so she needs to rent some furniture, and in particular, she needs a cocktail bar.

• 14:23Speaker 0

  This is not something that normally lives in her house, so she's gonna get one delivered for the weekend. She fires up her AR shopping app to see what's available. The AR contact lenses render exactly what the furniture will look like in her space, so she knows if it'll fit and if she likes the look and feel. So she scrolls through some of the options and settles on one that she likes. And that'll get delivered for the move party. All right, two important connections for her to make. First is with her pet kitty. So sadly, Kitty's been having a little tummy troubles. And as soon as the AR contact lenses see Kitty, they remind Katie that Kitty needs her antibiotics. She's on day five of a 10-day course, and that'll remind Katie to put the antibiotics in with her food.

• 15:08Speaker 0

  You notice the Katie, the kitty glows in the dark. This is a CRISPR-engineered pet, and this is awesome because when Kitty needs her antibiotics, like the non-glow in the dark kitty was very good at hiding. So now there's no more hiding for Kitty. She glows in the dark thanks to jellyfish genes that produce a protein called the green fluorescent protein and is now integrated in with the cat. Yay, kitty, no more hiding. All right, one more important connection. So my father-in-law lives in San Francisco and he loves living independently. But lately he's been having trouble as he ages doing some things, like getting out of the couch or bending over to pick up a box. So he's recently got himself a robotic tail inspired by a seahorse that knows what he's trying to do and automatically counterbalances to make it easy for easier for him to do all of those things.

• 15:59Speaker 0

  Get up and downstairs, pick up boxes, get out of the sofa. And look, I know this thing looks really weird, but if it was my choice between weird looking and being able to live independently for a few years longer, I know which choice I'd make. So the tail needs to be calibrated every few weeks. And my mother-in-law has gone to visit our sister, so she's not there to calibrate it for him. Calibration involves changing the counterweights in each of the vertebrae. So Katie offers to do it for her. A lot of us help our parents digitally today by logging on to their laptops and helping them with Gmail problems, right? With remote desktop. Katie does that with remote robotic arms. The kitchen robot that's in dad's kitchen has a remote control mode. So Katie can dial in and basically control the arms.

• 16:45Speaker 0

  Because the robot was programmed to make food and not calibrate tails, Katie needs to do it by hand, and she's glad to be able to do it.

• 16:54Speaker 1

  I love the fact that she can connect

• 16:57Speaker 0

  with my father-in-law this way, and he loves being able to live independently for a few years longer.

• 17:02Speaker 1

  All right,

• 17:03Speaker 0

  last thing to do before Katie, in her very full day, it gets to sleep. She's been having these incredibly vivid dreams where people are wearing awesome outfits. And she wants to remember the details of those dreams so she can share them with their coworkers. So just before she falls asleep, she slips on a headband that's connected to her dream recorder, which will record her dreams in a way that she could share with her friends. Wow. What a day. So what have we seen? We've seen better health through personalized food through richer data sets through shorter feedback loops. We've seen a flipped supply chain that allows people with big fan bases to actually make their own lifestyle brands, make clothes for them. We've seen easier ad hoc global collaboration that spreads the benefits of ownership much farther than today.

• 17:52Speaker 0

  We've seen tech that gives us superpowers, the ability to adjust dad's tail and not even be in the same room, deeper empathy for cultures that we haven't experienced before. And we've seen a set of new experiences. Going to a mall to watch drone racing or having a glow in the dark pet. And look, I know that predicting the future is a bit of an uncertain enterprise. I may have gotten some of the details of the specific products that I talked about wrong, but I'm pretty confident in the direction of these products, and that's because I've seen a lot of work today in either startup form or corporate innovation form that points in this direction. So what I want to do with the rest of this talk is basically describe how will we get there? How do we build it?

• 18:37Speaker 0

  What technology do we need to build? What business models do we need to build? What privacy frameworks do we need to create to usher in all of this awesomeness? I had to leave a lot on the cutting room floor, but I hope the ones that I saved you will find as intriguing as I did. So let's go on a tour and see what we need to build to get to Katie's life in 2030. So you, as you look at the smart toilet, it's really part of a broad trend towards longitudinal health data, which is a fancy way of saying, let's use cheap sensors and get your health data over time and use that data to predict health outcomes, predict whether you're getting kidney stones or diabetes. So what data do we have? We have the data that tracks your urine. We've got smart speakers that listen to your voice and changes in your voice over time.

• 19:25Speaker 0

  We've got what you look like, we call these selfies. So we've got data, lots of data. We also have algorithms that can take this data and predict health outcomes. We can use selfies to predict skin cancer. We can use changes in your voice to predict depression or early onset Alzheimer's. So what's missing? We've got the data, we've got the algorithms. What's missing is a business model and a privacy model and an ethical framework to tie these two pieces together. So imagine you were just using Instagram or your Smart Mirror, and all of a sudden alert pops up and says, hey, do you? You want me to send all these pictures to your doctor?

• 20:03Speaker 1

  Hmm,

• 20:04Speaker 0

  I'm not sure. I'm not sure I want you to do that. Is it any better if it offers to send it to an insurance company? Still not sure, right? So it's not clear what the business model that ties these two things together is. Let me give you an edge case that shows how tricky this will be designed. So imagine, like many people, Katie takes selfies with her BFF all the time. Now imagine that Katie's insurance will pay for the skin cancer detection service based on our selfies. Now imagine further that our BFF's insurance doesn't cover that. So what happens when the algorithms spot potential melanoma in our BFF? How do we design the right business models, privacy framework, ethical framework to get the best health outcomes to everybody? It's gonna be tricky.

• 20:50Speaker 0

  All right, how do we get to spider silk shirts? So before I describe the technology, can I sing the praises of spider silk for just a second? This is an amazing biological material. Ounce for ounce, this is lighter than cotton, tougher than Kevlar, not derived from petroleum, and biodegrades naturally. So why aren't we all wearing spider silk shirts today if it's so awesome? The short answer, it's way too expensive. So spiders themselves don't generate that much usable silk. And then you can't really farm it because if you put enough spiders together close enough to each other, they will definitely start eating each other. So no good. So bioengineering to the rescue. Using techniques we've recently discovered, like CRISPR, we can copy and paste DNA that codes for spider silk proteins and stick them inside E.

• 21:39Speaker 0

  coli and turn E. coli into a factory to build the materials that we want. Until very recently, a problem we had with this approach is if you look at the DNA sequences that code for spider silk proteins, they're very long and repetitive. So if you just do a copy and paste of that long repetitive sequence and then stick it inside E. coli, E. coli will break the sequences down, and you won't get the proteins you want. Recently, a team at the Washington University in St. Louis figured out a way to recode the DNA into shorter sequences and in a way that E. coli won't reject it. So they literally have E. coli producing spider silk proteins that, when combined with other ingredients, make spider silk fabric. This is a perfect illustration of where we are with bioengineering today.

• 22:27Speaker 0

  We've discovered the basic techniques like copy and paste DNA. And what we're now working on is the higher level engineering design principles that allow you to do things cost effectively that nature wouldn't do. So we could get spider silk without building spider farms. All right, how do we get to robotic furniture? Actually, no technology breakthrough needed at all. You can actually go buy this today if you wanted. So we know how to make robotic furniture. But this does illustrate a challenge that we will have, which is our homes are turning into data centers. Martin and David both talked about this problem in that you probably have 10 going to 20, maybe going to 100 IoT devices in your house, each of which wants software updates.

• 23:12Speaker 0

  So I'm guessing most of us do not want a second full-time job as a data center engineer just managing this fleet of software and equipment that we have in our house. And so we're gonna need a breakthrough in usability design that makes the experience of upgrading software for all of the devices in our house, every light bulb, every plug, every shirt, so that we're not getting prompted all the time. And we're definitely not there today. You probably have 10 devices that are all prompting you for soft updates. That's not gonna work when we get to hundreds of devices in the house. All right, how do we build customized foods? And in particular, how do we get to the protein in these meals? So, one super intriguing possibility for where the protein comes from is what's called lab grown or engineered or slaughter-free meats.

• 24:03Speaker 0

  So here's the basic idea. We take a tissue sample from a pig or a cow, we harvest its stem cells, and we put those stem cells in a bioreactor in a nutrient bath where when cells are in a healthy environment, they'll do what cells do, they'll multiply. If we can do that, we have this intriguing possibility that we could literally grow the meat that we want without raising and slaughtering an animal. Growing meat in this way in a bioreactor instead of on the field would have several advantages. One is definitely better for the environment. So, little known fact about global warming, climate change, is that 17% of all greenhouse gas emissions are caused by cows burping and farting. 17%. So if we could actually not grow cows, but still get hamburgers, uh, it'd be better for the environment.

• 24:51Speaker 0

  Obviously, it's better for the animal. And this the jury's still out on this, but we suspect that it would be better for us. I suspect that it's easier to keep this supply chain cleaner than it is if you think about raising a cow, managing a slaughterhouse and managing the cold chain all the way through to your refrigerator. So this would be great. So if we could do this, what should we do with it? Probably what we should do with it is start with the species that are under the most pressure today, right? So think of the overfished Pacific Bluefin tuna. Everybody loves sashimi, turned into a global thing. As a result, we're overfishing the tuna. So why don't we grow it instead of fishing it? All right, how do we build a smart contact?

• 25:36Speaker 0

  Well, it turns out that VR supply chain has already done a great job of shrinking projectors and cameras into very, very small form factors. So the questions here are can we shrink it even smaller? And can we shrink it into a form that we could embed it into a transparent flexible material, like what contact lenses are made out of? And then can we get them high resolution enough so that even though they're tiny and embedded in a contact lens, we see full color text and images? And then if we can do all that, how do we get power and data to it? So believe it or not, there are already tons of companies working on each one of these problems. There's a Silicon Valley startup called Mojo Vision that demonstrated one piece of this puzzle, and that is the high resolution tiny projector.

• 26:24Speaker 0

  So made out of micro LEDs, this thing is half a millimeter across and has a pixel density of 14,000 pixels per inch. The latest greatest iPhone clocks in as just under 500. So this thing has 30, 40 times the pixel density of the latest greatest iPhone, and is half a millimeter wide. So the way to visualize that, it's like a tenth of the size of one of the black dots on a ladybug. So now we've gotten it small enough and probably pixel dense enough. Now we're on to the other problems. Can we put it in to a contact lens? Can we get it power and data? So incredible engineering going into packaging all of these components in contact lens form. I think I have a confession, which is to say like once we build all this intergalactically awesome technology, the thing I'm going to use it for is to remember your name because I can't remember anybody's name.

• 27:16Speaker 0

  I've already forgotten your name, so sorry. This will be awesome. Okay. AR contact lenses. All right, how do we build a smart coach? If you think about what we want out of a smart coach that we're not getting from Alexa today, it's the ability to understand us much better and answer much higher level questions. So instead of answering what's the weather, we want how could I have been more effective in that last meeting? So here's an observation. There is already an analog device, weighs about three pounds, draws about 20 watts of power, that can answer those questions already. And it can answer those questions despite the fact that it has enormous handicaps compared to modern computers. Its components are a million times slower, and it has a terrible memory subsystem.

• 28:04Speaker 0

  So that device is our brain. So the possibility is that we can build things that are more explicitly brain-like to get to the smart coach. Now it's certainly possible that we can get to the smart coach on our current trajectory, which is more data fed to better machine learning algorithms. But this might be a shortcut. On the hardware side, this is called a neuromorphic chip. And it basically explicitly models the brain. What's happening in your brain is neurons are connecting to other neurons and adjusting their weights over time based on stimulus. That's what these neuron neuromorphic computers do on the inside too. So instead of using logic gates and zeros and ones, they have connections with neurons that have different strengths over time.

• 28:49Speaker 0

  So from a hardware point of view, we might be able to build a more brain-like computer. Now on the software side, we also have a lot to learn from brains, even some teeny brains that are young and relatively underdeveloped. So I want you to watch this cognitive psychology experiment on a toddler. And what you'll see is Felix in the upper right corner in the red sweater, he's the experimenter. He's going to uh try to complete a task and watch what happens. He's trying to hang the clothes. And then uh, oops, he drops the clothespin. He's like, uh. And the baby reaches over and hands the clothespin back for him. So if I were to write the Twitter caption for this video, I would say, humanity for the win. Because, like, look at what this one year old baby has done.

• 29:36Speaker 0

  No prior training has never been an experience like this. And yet he's figured out that Felix has a problem and is gonna need the clothespin to solve his problem. And not only that, he's gonna help, even though there's no promise of reward. You saw the whole experimental setup. He wasn't being offered candy or a bribe of any sort. So there's so much going on in the base software of this toddler that we would love to reverse engineer and put into our smart coach. The ability to form what psychologists call a theory of mind, in other words, a prediction that of other people's desires and goals and emotions. The ability to break a task down into subtasks without seeing thousands of similar examples. And third, and maybe most importantly, the built-in desire to help when it's actually helpful.

• 30:25Speaker 0

  So Felix does a fascinating variant of this experiment in which he basically just throws the clothes pin on the floor and doesn't look at it. You know what most babies do in that situation? They don't do anything. They don't pick up the clothes pit because they've somehow figured out the difference between needs help and doesn't need help. So if you've ever been in a room where the smart speaker all of a sudden interrupts your conversation with somebody because it couldn't figure out the difference between needs help and doesn't need help, you understand how valuable this is. And it's in the base operating system of a one-year-old brain. Let's go figure that out, and maybe we can teach our smart devices when not to interrupt us.

• 31:16Speaker 0

  All right, the autopod. I'm ready to ride the autopod. It turns out that most of the technology that we need to build the autopod already exists because it's a subset of what we need to build for self-driving cars. Because the autopods have their own right of way, they don't have to deal with crazy pedestrians and bad drivers, right? They have a dedicated right of way just like LightRail does today. What we will need though is a lot of people to say yes. We need regulators, mayors, regional plant uh transit planning authorities, state and federal regulators to all say yes to this technology. So a lot of people to say yes. I'm still hopeful though that my region would be among the first to deploy this type of technology. For those of you that have visited the Bay Area, you know that we have a train connecting San Francisco and San Jose, and it runs so infrequently because it's so expensive to operate that it's only once an hour on the weekends.

• 32:11Speaker 0

  So you have to plan your entire day around when the train runs. Compare that to pushing a button and like Lyft, you have a one or two person pod waiting for you at the station. That would be a huge upgrade. Now the good news is we don't have to replace Caltrain all at once. We can basically incrementally add to it. So my humble proposal was we start with the link between the San Jose Airport and the San Jose Caltrain station, which is currently served by a bus. Definitely ready for the autopod there.

• 32:45Speaker 0

  All right, we're still doing a lot of shopping in the 2030s, mostly online, of course. And so one thing that's needed is we need something called post quantum cryptography. And the reason we need this is because you might have read Google's recent announcement that they have a quantum computer that has achieved quantum supremacy. In other words, this quantum computer can do things that a normal computer might take 10,000 years to do. One of the things that people have been itching for quantum computers to do is brute force attack, the encryption keys that protect all of our messages and credit card numbers today. So we're on the hunt for new algorithms that will withstand brute force attacks against crypto algorithms. The good news is that a worldwide community of cryptographers has been working hard on this problem.

• 33:32Speaker 0

  And NIST, the National Institute of Standards and Technologies has actually organized a survivor like process to identify all the candidate algorithms and successively vote them off the island until we get to the standard in post quantum crypto. They've been running this process since 2015, and we're already in the second round of uh votes. We've gone from 59 candidates to 26, and the hope is that we'll standardize sometime in the mid-2020s so we can all continue to shop online, even when there are quantum computers that can brute force the encryption keys we currently use. So the last technology I want to talk about is admittedly the most speculative and potentially furthest out of all the products that I talked about, and that's the dream recorder. But I want to share two amazing research projects that demonstrate we're on our way.

• 34:20Speaker 0

  We have a path forward. If you're going to record dreams, you need two things. You need to interpret brain signals, the brain's electrical activities, and turn that into audio and video. So let's take each of them in turn. So researchers at Columbia, led by Nima Mezgarani have literally turned brain waves into speech. So here's what they did. They found epilepsy patients that were already getting brain surgery. So we had the skull open. They inserted probes into the brain to capture brain activity, and they had people listen to people talking. They used the brain waves that resulted and trained a voice synthesizer. So exactly the same thing that's in Alexa today. So we're gonna listen to this interpretation. So before I hit play, let me just say look, this is the very first start of it.

• 35:06Speaker 0

  The voice doesn't sound all that great, and it sounds a little robotic, but but we're listening to brain waves interpreted directly into an audio stream. So I'm gonna hit play, and you're gonna listen to the brain wave driving a voice synthesizer, listening to somebody read the numbers zero through nine. Here's what that sounds like.

• 35:35Speaker 0

  Awesome. That's the audio stream from your brain waves. Now, could we do the same thing with video? Researchers in Japan published a paper this April doing exactly that. So instead of epilepsy patients with probes inserted to their brain, we're basically taking brain scans of people inside FMRIs, and then we're showing them lots and lots of images. Might be pictures, it might be a plus sign, it may be letters of the alphabet. We record the brain activity, and then we use a popular popular technique today in machine learning called a deep generated network to basically build an image generator. It's kind of the same technology that's behind the deepfakes today. So we capture the brain waves, we build the brain generator. Let me show you the pictures. So if we show somebody the picture at the top, there's an owl, a plane, a stained glass window, and a plus sign, and then we try to reinterpret those images from the brain activity, you see three different patients below.

• 36:31Speaker 0

  And to me, those images look a little like a first-year art student trying to paint a Monet, right? It's kind of impressionist, like you got the rough shape right and you get the right color family, but there aren't really details. On the other hand, that's clearly a plus line. So it's amazing that we've gotten this far, and it points the direction that one day we would be able to interpret the brain in a way that we could literally record your dreams. So how awesome is that. So the future is awesome. And as a dad, I don't even know if Katie knows how much awesomeness is headed her way. Because to her, I think it's just gonna feel like you know, a product announcement here or a product announcement there, and she learns a new product, and all of a sudden she's got these superpowers.

• 37:17Speaker 0

  But from this vantage point, from the point of view of when we haven't built those technologies yet, I would say it's amazing. It's awesome. I'm ready for it. I'm ready for personalized food. I'm ready for a healthier me. I'm ready for a healthier planet. I'm ready for a smart coach. I'm ready for AR contact lenses, so I don't forget your name. I'm ready to ride the autopod. I'm ready for superpowers at work. The future is awesome. I hope this talk and the rest of this conference has made you more and more excited about the future that is coming. Thank you.