Eight Fun Facts About Computer Vision

Our experience of the world is intensely visual. Researchers suggest over half of our brain power is devoted to processing what we see. We talk a lot about how artificial intelligence will transform the world around us, automating physical and knowledge work tasks. In order for such a system to exist, it’s clear that we must teach it to see. This is called computer vision, and it is one of the most basic and crucial elements of artificial intelligence. At a high level, endowing machines with the power of sight seems simple, just slap on a webcam and press record. However, vision is our most complex cognitive ability, and machines must not only be able to see, but understand what they are seeing. They must be able to derive insights from the entirely new layer of data that lies all around them and act on that information.

Despite being an important driver of innovation today, computer vision is little understood by those outside of the tech world. Here are a handful of facts that help put some context around what computer vision is and how far we’ve come in developing it.

1.)  Computer scientists first started thinking about vision about 50 years ago. In 1966, MIT professor Seymour Papert gave a group of students an assignment to attach a camera to a computer and describe what it saw, dividing images into “likely objects, likely background areas, and chaos.” Clearly, this was more than a summer project, as we are still working on it half a century later, but it laid the groundwork for what would become one of the fastest growing and most exciting areas of computer science.

2.)  While computer vision (CV) has not reached parity with human ability, its uses are already widespread, and some may be surprising. Scanning a barcode, the yellow first down line while watching football, camera stabilization, tagging friends on Facebook, Snapchat filters, and Google Street View are all common uses of CV.

3.)  In some narrow use cases, computer vision is more effective than human vision. Google’s CV team developed a machine that can diagnose diabetic retinopathy better than a human ophthalmologist. Diabetic retinopathy is a complication that can cause blindness in diabetic patients, but it is treatable if caught early. With a model that has been trained on hundreds of thousands of images, Google uses CV to screen retinal photos in hopes of earlier identification.

4.)  One of the first major industries being transformed by computer vision is an old one you might not expect: farming. Prospera, a startup based in Tel-Aviv, uses camera tech to monitor crops and detect diseases like blight. John Deere just paid $305M for a computer-vision company called Blue River. Their technology is capable of identifying unwanted plants and dousing them in a focused spray of herbicide to eliminate the need for coating entire fields in harmful chemicals. Beyond these examples, there are countless aerial and ground based drones that monitor crops and soil, as well as robots that use vision to pick produce.

5.)  Fei-Fei Li, head of Stanford’s Vision Lab and one of the world’s leading CV researchers, compares computer vision today to children. Although computers can “see” better than humans in some narrow use cases, even small children are experts at one thing – making sense of the world around them. No one tells a child how to see. They learn through real-world examples. Considering a child’s eyes as cameras, they take a picture every 200 milliseconds (the average time an eye movement is made). So by age 3, the child will have seen hundreds of millions of pictures, which is an extensive training set for a model. Seeing is relatively simple, but understanding context and explaining it is extremely complex. That’s why over 50% of the cortex, the surface of the brain, is devoted to processing visual information.

6.)  This thinking is what led Fei-Fei Li to create ImageNet in 2007, a database of tens of millions of images that are labeled for use in image recognition software. That dataset is used in the ImageNet Large Scale Visual Recognition Challenge each year.  Since 2010, teams have put their algorithms to the test on ImageNet’s vast trove of data in an annual competition that pushes researchers and computer scientists to raise the bar for computer vision. Don’t worry, the database includes 62,000 images of cats.

7.)  Autonomous driving is probably the biggest opportunity in computer vision today. Creating a self-driving car is almost entirely a computer vision challenge, and a worthy one — 1.25 million people die a year in auto-related deaths. Aside from figuring out the technology, there are also questions of ethics like the classic trolley problem: Should a self-driving vehicle alter its path into a situation that would kill or injure its passengers to save a greater number of passengers in its current direction? Lawyers and politicians might have to sort that one out.

8.)  There’s an accelerator program specifically focused on computer vision, and we’re excited to be participating as mentors. Betaworks is launching Visioncamp, an 11-week program dedicated to ‘camera-first’ applications and services starting in Q1 2018. Betaworks wants to “explore everything that becomes possible when the camera knows what it’s seeing.”

We’re just scratching the surface of what computer vision can accomplish in the future. Self-driving cars, automated manufacturing, augmented and virtual reality, healthcare, surveillance, image recognition, helpful robots, and countless other spaces will all heavily employ CV. The future will be seen.

Disclaimer: We actively write about the themes in which we invest: artificial intelligence, robotics, virtual reality, and augmented reality. From time to time, we will write about companies that are in our portfolio. Content on this site including opinions on specific themes in technology, market estimates, and estimates and commentary regarding publicly traded or private companies is not intended for use in making investment decisions. We hold no obligation to update any of our projections. We express no warranties about any estimates or opinions we make.

Robot Fear Index: 30.9

Like many in the tech space, we believe robotics is changing the nature of work; however, public perception of robots is still a question mark. We developed our Robot Fear Index to measure and track the average consumer’s perception of robots. We asked over 500 US consumers about topics ranging from their use of robots at home to their comfort level with self-driving cars. Then we distilled the data down to an index value that we will publish regularly. An index value of 100 suggests widespread and extreme fear of robots; an index value of 0 suggests minimal fear of robots.

Robot Fear Index: 30.9. Consumer adoption of artificial intelligence and robotics is already quite broad, and yet, fear of robots is also pervasive. We fear that they’ll replace our jobs or somehow overthrow us; and to be blunt, those fears are valid. That said, our 2017 survey indicates acceptance for these technologies continues to grow. Our most recent Robot Fear Index value of 30.9 (vs. 31.5 in late 2016) suggests that public perception of robots is essentially unchanged over the last year despite increased awareness of artificial intelligence, robotics, and the potential impact of these technologies. Notably, the related increase in media coverage of these issue does not seem be causing the rise in fear that we might expect. In fact, the slight year-over-year decline in our index value suggests slightly less fear of automation technologies.

Our most recent Robot Fear Index value of 30.9 (vs. 31.5 in late 2016) suggests that public perception of robots is essentially unchanged over the last year despite recent media coverage and increased awareness of automation technologies.

Survey Demographics. Of the 433 US consumers that responded to our 2017 Robot Fear Survey, 54% were male and 46% were female. Our survey population was also equally weighted across all age demographics, as shown in the exhibits below.

Use of Digital Assistants Growing Slowly. We continue to see digital assistants as an onramp to AI and robotics for many consumers. Our 2017 survey shows 69% of consumers have used a digital assistant (Siri, Google Assistant, Alexa) and roughly one-third use a digital assistant once a day or more, which is in-line to our results last year. When asked how many digital assistant consumers own, 21% said 1, while 14% indicated greater than 1.

Comfort with Robots is Up Slightly. We believe the comfort with AI is driving comfort with robotics. We asked consumers on a scale of 1 – 10 (1 being the most) how comfortable they are with using robots in many different settings including house cleaning (robot vacuums), healthcare (surgical procedures) and travel (self-driving cars). We were encouraged to see that 7 of the 8 categories we track saw a modest increase in comfort levels around robotics.

Domestic Robot Adoption Large Catalyst. We believe that consumer awareness of robotics is closely correlated to the rise of domestic robots within households. Domestic robots are classified as robot vacuum cleaners, mops and lawn mowers, and over the next 10 years we believe this category will be one of the fastest growing robot markets in the world. Our data shows that 75% of US consumers have yet to buy a household robot. Although we do not have the historical data to show y/y comparisons, last week, iRobot, a leading robotic vacuum and wet floor company, reported better than expected Q3 results and raised their FY17 revenue guidance for a third consecutive quarter (see note here). Given iRobot’s results, we believe the domestic robot market is seeing strong adoption domestically and internationally.

What Is Keeping Consumers From Using Robots? Many consumers have not yet adopted AI or robotic technology. When asked what has kept you from using robots, 41% (36% in 2016) of consumers said they are just not interested, while 29% (21% in 2016) believes robots are too expensive. That said, it was encouraging only 6% of consumers don’t use robots because it makes them nervous, which is down from 11% in 2016. We believe one of the the bigger fears when it comes to AI and robotics, is the risk of taking jobs. When asked when will AI and robotics cause significant job loss, 27% said within 5 years, 31% believe in 10 years and 24% anticipate significant job loss in 20 years. The remaining 17% of consumers did not believe robots would ever take our jobs.

Bottom Line. Following our 2017 Robot Fear Index survey, we believe consumer fear of robots is essentially unchanged, despite growing awareness of the potential risks of automation. We think our index value of 30.9 quantifies this cautious comfort with robots and we’re looking forward to updating the Robot Fear Index regularly as we track the progress of the robotics theme.

Disclaimer: We actively write about the themes in which we invest: virtual reality, augmented reality, artificial intelligence, and robotics. From time to time, we will write about companies that are in our portfolio.  Content on this site including opinions on specific themes in technology, market estimates, and estimates and commentary regarding publicly traded or private companies is not intended for use in making investment decisions. We hold no obligation to update any of our projections. We express no warranties about any estimates or opinions we make.

Do You Have What It Takes to Be a Great Founder?

Every VC says they only invest in great founders, but the majority of venture-backed businesses still end in relative failure. Does that mean we as VCs are just bad judges of founders or do we not know what great founders look like? This is a question we’ve obsessed over since we started Loup Ventures — trying to define what makes a great founder and how to test for it. It’s hard. Great founders come from a host of different backgrounds, educations, genders, ethnicities. We’ve identified 10 traits across two categories that make great founders at the seed stage: Innate and Dynamic.

Innate Qualities of a Great Founder

Innate traits are character elements that are difficult to impossible to learn — either the founder has them or they don’t. Regardless of the type of business a founder starts, there are five imperative innate traits for all great founders:

  • Intelligence
  • Integrity
  • Commitment to suffering
  • Focused curiosity
  • Resourcefulness

Warren Buffett has talked about a few of these traits as things he looks for in his managers. Naval Ravikant has also talked about a few of them, so they shouldn’t come as much of a surprise. Intelligence is probably the most obvious of the innate traits. To start a valuable company, a founder must have some kind of smarts because intelligence leads to interesting insights about a market (see the next section). These insights translate into vision, which is the only truly defensible element and most important asset of any startup business. Vision is how an entrepreneur attracts talent and creates strategy.


Pure book smarts matter, but emotional intelligence is important too. A founder has to deeply understand his or her customers to deliver products they want, not just products the founder wants to build. A founder also has to deeply understand his or her employees and what motivates them to sustain high levels of productivity.

Integrity is the current buzz word in the startup world. We used to think about integrity as honesty, but that doesn’t seem to fully encompass the spirit of the trait. Honesty is a requirement because it means the founder learns from his or her mistakes. Dishonesty assumes problems are someone else’s fault, which means it’s impossible to learn. Ownership is a popular modern term for honesty – taking responsibility for things that happen whether they’re purely in your control or not.

The interesting component about integrity as it relates to startups is that great founders need to be willing to break rules to build valuable businesses. However, there’s a line between what’s acceptable and what’s not, and sometimes it’s blurry. Salesforce.com hired fake protestors to disrupt a Siebel conference in its early days. Clever guerilla marketing. The cases of Hampton Creek, Theranos, and Zenefits are clearly in the unacceptable camp. The ride sharing legal disputes are blurrier, although we agree that the laws are outdated and ride sharing is a significant net positive to the world. In any case, dishonesty and unethical behavior are contagious, so integrity must come from the top and be a guiding light for any startup.

The third quality of great founders is a commitment to suffering for at least five years. This might sound more extreme than necessary, but starting a company is a rollercoaster of suffering. You need to be comfortable with hearing no over and over and not let that destroy your will. You need to be able to withstand low periods that are inevitable — unexpected customer or employee losses, investor rejections, tax bills, fights with cofounders. Entrepreneurs don’t necessarily need to revel in difficulty, but it helps. We like to track the number of times we hear no during the week to reduce the negative reinforcement of it.

Why five years of suffering? It usually takes at least two years before you have any reasonable traction to show that your business might be working, then another few years of driving growth to create something that looks like a moat. Then you can afford to breathe. A little.

Focused curiosity might seem like an oxymoron, but curiosity that is targeted at a specific market leads to a commitment to testing new things. Testing new things leads to new business opportunities and products. Curiosity may be particularly necessary for seed stage founders (our focus) because their businesses are so nascent and require constant iteration. A lack of curiosity at the early stage leads to stagnation, which leads to death.

An early stage startup is an unending series of challenges. This is doubly true for first time founders who not only have to figure out how to deliver their specific offering to market, but how to operate a business in general. The final innate trait, resourcefulness, gives founders the ability to thrive in the face of persistent tests. A great founder is not one that says he or she couldn’t do something because they didn’t have enough capital or it was too difficult. They figure it out and keep figuring it out.

Dynamic Qualities of a Great Founder

Where the innate traits are binary and fixed, the dynamic traits of a great founder are five qualities that exist on a spectrum and evolve over time:

  • Market insight
  • Operational capability
  • Product sense
  • Growth
  • Leadership

Market insight is our term for the popular “founder/market fit.” What we want to see from a founder is that he or she has spent a lot of time thinking about and experimenting on a problem they’ve identified. In that sense, market insights are a byproduct of the innate intelligence trait being applied to a specific problem over a length of time. Founder/market fit to us implies that the founder has spent time involved in a market, thus the fit; however, prior market experience isn’t necessary for great founders. Jeff Bezos didn’t have founder/market fit when he started Amazon. He never ran a bookstore before, but he had a market insight about the Internet changing the way people shopped. The founders of Uber never worked in the livery business, but they had an insight about mobile changing the way people arranged transport. Airbnb is another example, and there are many others.

The other four traits are relatively straight forward business-related qualities. Operational capability is the founder’s ability to deliver their product or service and serve customers. Product sense is the founder’s ability to create a product or service that unexpectedly delights consumers. Product sense is what enables a founder to reach product/market fit. Growth is the founder’s ability to market and sell the product or service. Leadership is the founder’s ability to organize his or her team to meet objectives.

All five of these traits work in conjunction with one another, and all five are necessary for an early stage founder to possess in some degree. However, numerous factors influence the relative importance of the dynamic qualities of a founder. In other words, some of the dynamic traits need to be more developed depending on type of the founder’s company. For example, in a highly social company, a founder’s product sense seems to matter more than any other trait because user growth will have to be organically rapid for the company to service. The immediate experience of the users will be what keeps them engaged and sharing the product with others. An enterprise founder should require stronger growth capabilities to directly sell their B2B product, software or otherwise. Hardware companies tend to need stronger operational capability given the manufacturing requirements of their product.

The above observations were specific to seed stage companies, but stage of the investment also impacts the relative importance of the dynamic qualities in a founder. At the A/B round, product should be somewhat established, so market insights and growth might matter more as the founder tries to leverage his or her unique vision into some sort of durable advantage. In a pre-IPO or public company, the importance of leadership matters significantly more because of the likely larger number of employees at the company.

If You’ve Got It, Go for It

It’s boring to hear every VC say they only fund great founders, but it really is true, and their criteria probably isn’t much different from ours. Early stage companies are extremely fragile. VCs obsess over the quality of founders because it’s one of the few variables we can control. Recognizing these qualities in oneself is also an important variable an entrepreneur can control. Whether you’re running a small business or hoping to build the next Google, you must have all the innate traits and the correct balance of dynamic traits to be great. If you know you have them, then focus on your goal and be great. Hopefully we can help you along the way.

Disclaimer: We actively write about the themes in which we invest: artificial intelligence, robotics, virtual reality, and augmented reality. From time to time, we will write about companies that are in our portfolio. Content on this site including opinions on specific themes in technology, market estimates, and estimates and commentary regarding publicly traded or private companies is not intended for use in making investment decisions. We hold no obligation to update any of our projections. We express no warranties about any estimates or opinions we make.

Auto Outlook 2040: The Rise of Fully Autonomous Vehicles

Special thanks to Austin Bohlig for his work on this note. 

Today, we are introducing our 2040 Automotive Model, available here, detailing our projections for electric vehicles, autonomous vehicles, and fleet services through 2040.

The global automotive industry is quickly approaching a transformation that should fully take shape by 2040. While 20 years doesn’t seem very far away, keep in mind that technology is advancing at an accelerating pace — the next 20 years of innovation will see changes equivalent to what we’ve seen over the last 50 years. We expect to see three major automotive themes emerge: 1) the transition to electric, 2) fully autonomous vehicles, and 3) a higher percentage of people relying on ride sharing services as their primary source of transportation. We believe these three themes will create enormous market opportunities. While some of the traditional auto players will capitalize on these emerging themes, the competitive landscape will change dramatically as more technology companies enter the space to bring these revolutionary technologies to market.

Theme #1 – Transition to Electric

According to Bloomberg New Energy Finance, 84.0M new passenger cars and light commercial vehicles were sold globally in 2016, up ~5% y/y. Of all vehicles sold, 81.5M were internal-combustion engine (ICE) vehicles, 2.0M were hybrid, and 440K were electric. While electric vehicles only accounted for <1% of new vehicles shipped in 2016, this segment of the market has seen tremendous growth over the past 4 years, and we believe we are nearing an infection point for demand of electric vehicles. By 2033, we believe electric vehicles will surpass 50% of total market share. By 2040, we believe 86% (87.9M) of new cars sold will be electric vehicles; from 2020 – 2040 the electric category will experience an ~18% unit CAGR, while ICE vehicles will decline ~13% over that same time period.

Over the next 20 years, electric vehicles will become more affordable, but due to the advanced sensors, onboarding computing processors, and other components that will enable fully autonomous driving capabilities, we expect electric car ASPs to increase modestly. That said, in order for ICE and hybrid vehicles to compete, these categories will see prices steadily decline. In 2040, we believe the global passenger and light vehicle automobile market will represent a $3.8T annual market opportunity, up from $2.9T in 2016. The bulk of the growth will be driven by electric vehicle demand, which we anticipate to increase from $20B in 2016 to $3.4T in 2040, representing a ~18% CAGR.

While affordability will be a meaningful catalyst to electric car adoption, there will be multiple additional catalysts driving the shift to electric vehicles:

  • OEMs Focus Towards Electric – Today, and for the next 20 years, we believe the leading electric car OEM will be Tesla, but we anticipate almost all other traditional car manufacturers will eventually switch their focus to electric vehicles. Volvo was one of the first to do so, and recently announced all new cars they manufacture will be electric or hybrid starting in 2019. We anticipate other OEMs to make similar announcements in the coming years, providing additional tailwinds to the industry.
  • Government Intervention – We believe we will see legislation over the next 5 – 10 years enticing consumers to buy electric vehicles through subsidies; gas powered vehicles may even from the road. France and Britain plan to ban the sale of gas and diesel vehicles beginning in 2040. Scotland recently announced similar plans but with an implementation date of 2032. While this may seem extreme, it’s not unprecedented, even in the US: In the early 1900s, when the Model T began shipping in volume, the government banned horses from operating on the same public roads as automobiles.
  • Transition To Fully Autonomous – Although autonomous cars can take the form of ICE or hybrid vehicles, the majority of autonomous vehicles deployed will be electric cars because there are many synergies between the technology implemented in electric vehicles and what will be incorporated in fully autonomous systems. In addition, given our thesis that most car OEMs will switch their focus to electric, it only makes sense autonomous cars will follow suit.

Theme #2 – The Rise of Self-Driving Vehicles

Today, 99.9% of all passenger and light commercial vehicles on the road have little to no automation capabilities. However, Tesla and a few additional OEMs have made great strides in introducing what the industry classifies as Level 2 (Partial Automation). By 2040, we expect that over 90% of all vehicles sold will be “Highly” and “Fully” autonomous systems, classified as Level 4 and 5 automation, respectively. Here’s a brief definition of the different forms of automation according the National Highway Traffic Safety Administration (NHTSA):

  • Level 0: No Automation – A human controls all the critical driving functions.
  • Level 1: Driver Assistance – The vehicle can perform some driving function, often with a single feature such as cruise control, but the driver maintains control of the vehicle.
  • Level 2: Partial Automation – The car can perform one or more driving tasks at the same time, including steering and accelerating, but still requires the driver remain alert and in control.
  • Level 3: Conditional Automation – The car drives itself under certain conditions, but requires the human to intervene upon request with sufficient time to respond, but the driver isn’t expected to constantly remain alert.
  • Level 4: High Automation – The car performs all critical driving tasks, monitors roadway conditions the entire trip, and doesn’t require the human to intervene. But self-driving is limited to certain driving locations and environments.
  • Level 5: Full Automation – The car drives itself from departure to destination, and the human is completely removed from the process.

This will not be a gradual transition from one level to the next; we expect most players to skip Level 3, going straight from Partial Automation to High or Full Automation. We also view Level 4 and 5 as very similar levels of automation; Level 4 has a steering wheel but Level 5 does not. So, in our forecast we combine Level 4 and Level 5 into one category labeled “Fully Autonomous.”

Self-Driving Car Rollout Begins in 2020, Inflects in 2028

We estimate that ~130K Level 2 vehicles will be sold in 2017; over the next few years, the industry will see a significant acceleration of Level 2 vehicles delivered, occupying a growing percentage of new vehicles sold through 2033. However, we believe 98K Fully Autonomous vehicles (Level 4 and 5) will enter the market in 2020, which is when the transition to self-driving will start to take shape. While some Level 1 and 2 systems will still be sold in 2040, the two groups combined will account for <6% of all new vehicles delivered, and >94% of systems will take the form of fully automated vehicles. It will take time for fully automated vehicles to gain meaningful traction, largely due to legislative hurdles, but beginning in 2028 we believe the industry will see an influx in demand for Level 4 and 5 automobiles. We expect the industry will go from shipping 98K Fully Autonomous vehicles in 2020 to 96.3M in 2040, representing a 41.2% CAGR over that time frame.

Leaders in Autonomy

While there will be many companies that will benefit from the transition to fully autonomous vehicles, a few companies are already positioning themselves to be early key players:

  • Tesla – Tesla has already established their dominance in the electric vehicle market, and we expect their commanding market position to prevail through 2040. We estimate that Tesla currently controls ~20% of the global electric vehicle market, and although we anticipate competition to increase in the years to come, we believe Tesla can maintain low-to-mid teens market share through 2040. Almost all Teslas today incorporate Level 2 driving automation, and while Tesla is hoping to get fully autonomous cars on the road by 2019, we believe their near-term focus will be ramping production of the Model 3 and less on getting fully autonomous cars on the road. That said, we view Tesla’s leadership around autonomous driving technology and AI is a step up from almost everyone else, and expect larger deployments to begin in 2020.
  • Waymo – It is still not completely clear what Waymo’s go-to-market strategy will be with regards to autonomous cars, but the company will have a meaningful presence. Waymo’s biggest competitive advantage thus far is the millions of miles their self-driving cars have driven and the terabytes of data gathered, which they can use to train their self-driving car algos. Waymo has already launched a ride sharing service in Phoenix, AZ, and we wouldn’t be surprised if they sell a Waymo branded self-driving car or develop a self-driving car OS that they license to third party car OEMs.
  • Traditional OEMs – It will be a significant challenge for traditional car OEMs to compete as we transition to electric and full autonomy, but there will be some legacy car brands that effectively transition by leveraging decades of car manufacturing expertise to compete with Tesla and Waymo. Ford is one traditional car company that has begun the transition, including promoting a CEO with deep autonomous experience and acquiring leading startups in the space (Argo). While some of these traditional car companies will be able to develop self-driving systems internally, we believe the more effective way will be to enter the space via acquisition.
  • Start-Ups & Others – The self-driving car industry is still in the very early innings, and other tech giants such as Apple, Uber, Lyft, and relatively unknown startups will deliver meaningful innovation. There are many technological gaps that still need to be solved before self-driving cars are fully deployed on public roads.
  • Apple – We continue to expect Apple to play in the self-driving car market, possibly bringing a self-driving car to market, or, more likely, developing an autonomous system for self-driving cars. We’ll cover this with more detail in a future note.

Theme #3 – Transition to Ride Sharing Services

In addition to the world transitioning to electric and autonomous vehicles over the next 20 years, we expect an increasing number of consumers will forgo owning a car and rely fully on ride sharing services for transportation. While we anticipate the number of cars sold will continue to increase through 2040, many of these new cars will go directly towards ride sharing services.

We estimate there were 1.3B passenger and light vehicle cars in use in 2016, of which 5% were dedicated to ride sharing services. In the coming decades, as ride sharing becomes more cost effective and reliable, the percentage of cars dedicated to ride sharing services will increase steadily. By 2040, we estimate that 68% of all vehicles in use will be dedicated to fleet services. As a result, the number of cars personally owned by individuals will decrease at a -2.0% CAGR from 2020 to 2040. Today, companies such as Uber and Lyft dominate the ride sharing market, but we anticipate other leading tech companies (Tesla, Waymo, etc.) and traditional car OEMs to introduce a ride sharing services as well. We also envision a future where individuals that own an autonomous car are able to deploy the system to a fleet service when they are not using the car (e.g., while at work), extracting value from the car’s dormancy.

Bottom Line

The global automotive industry is quickly approaching a paradigm shift, and the types of vehicles on our roads and the competitive landscape in the car market is going to change significantly by 2040. Level 1 and 2 automated vehicles will still be sold, we estimate that >94% of new cars sold will be fully automated in 2040. Some of the traditional auto players will successfully transition to these emerging themes, but several tech companies are most likely to become leaders in the space. Over the next several decades, the biggest headwinds to full autonomy will likely be legislative rather than technical, but the safety and efficiency benefits of autonomous cars will provide a strong tailwind to broad public acceptance and rapid market growth.

Disclaimer: We actively write about the themes in which we invest: artificial intelligence, robotics, virtual reality, and augmented reality. From time to time, we will write about companies that are in our portfolio. Content on this site including opinions on specific themes in technology, market estimates, and estimates and commentary regarding publicly traded or private companies is not intended for use in making investment decisions. We hold no obligation to update any of our projections. We express no warranties about any estimates or opinions we make.


The Future Perfect: Rediscovering Utopia

Technology ruined our utopia. When did humans have it better than the dawn of time? We were born with everything and nothing. The entire world was ours. Since everyone had nothing, everyone had everything. We had no property, no houses. We were free to roam and inhabit as we pleased. We only worried about survival — finding enough food and avoiding dangerous predators. We didn’t have to worry about 401(k)s or what car the neighbors just bought. There wasn’t a 1 percent or 99 percent. We didn’t have politics. We just had survival. Humanity at its purest. Then invention doomed us. It was innocent at first. Innovation made it easier to survive. Food and safety became essentially guaranteed, so we needed to find other things to define our lives. Then inventions became those things. Things not for survival, but for status. For having something someone else didn’t. For benefitting unequally based on that ownership. For handing down to the next generation so they didn’t start with nothing. Things became the new goal of survival and they defined our differences. People who had things treated people without them differently. We went to war with others who had things we wanted.

Now most of us are born with nothing. We have to earn everything, buy everything. We’re trapped in a system that forces us to chase things that maintain our differences. We innovated ourselves out of utopia and into industry, and innovation is the only way to get our utopia back.

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