Machines Taking Jobs: Why This Time Is Different

Will AI and robotics revolutionize human labor or not? 

More than half of all US jobs could be disrupted by automation in the next several decades; at least that’s our opinion. About half the people we talk to disagree. Those that disagree think AI will open up new job opportunities by enhancing human abilities. A common element to their argument is that we’ve always had technical innovation and human work has evolved with it. A few examples would be the cotton gin, the printing press, and the automobile. All of these inventions threatened jobs of their era, but they ended up creating more jobs than they destroyed. So why is this time different?

Because, for the first time in history, we don’t need to rely on human intelligence to operate the machines of the future. The common denominator among those three examples and countless other technical innovations is that they were simply dumb tools. There was no on-board intelligence. Humans using those tools provided the intelligence layer. Humans were the brains of the cotton gins, printing presses, and automobiles. If the human operator saw or heard a problem, they fixed it and continued working. Today, the intelligence layer can be provided by computers through computer vision, natural language processing, machine learning, etc. Human intelligence is no longer required.

You might say that machines aren’t nearly as smart as humans, so they aren’t as capable as humans. But in reality, they don’t need to be. AI required to operate a machine only needs to have very limited domain knowledge, not human level intelligence (a.k.a. artificial general intelligence). Think about driving a car. You aren’t using 100% of your total intelligence to drive a car. A large portion is thinking about other things, like disagreeing with this article, singing along with the radio, and probably texting. An autonomous driving system only needs to be capable of processing image data, communicating with computers from other devices related to driving, like other vehicles, traffic signals, and maybe even the road itself, making dynamic calculations based on those data inputs and turning those calculations into actions performed by the vehicle. Any incremental intelligence not related to those core functions is irrelevant for an autonomous driving system.

The magnitude of the technological change is also significantly different in this current wave of advancements in AI and robotics. This wave is more akin to the advent of the farm when humans were still gatherers, or the advent of the factory when we were still farmers. Farms not only organized the production of food, but also encouraged the development of community and trade. Factories organized the production of all goods, encouraged the development of cities, and enabled our modern economic system by institutionalizing the trade of labor for wages. Automation will result in equivalent fundamental changes to the philosophy of production by taking it out of the hands of humans. This could result in societal changes of greater freedom of location and a basic income. In a way, the Automation Age may be an enhanced return to the hunter/gatherer period of humanity where basic needs were provided, originally by nature, in the future by machines. Except in the Automation Age, our purpose will be to explore what it means to be human instead of simply survive.

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.

AGV Deep Dive: How Amazon’s 2012 Acquisition Sparked a $10B Market

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

Amazon’s 2012 acquisition of Kiva Systems was the spark that ignited the Autonomous Guided Vehicles (AGV) industry, which we believe will represent a $10B market by 2025. We’ve taken a deep dive into the AGV market, where we identify the different use cases for AGVs, market leaders and opportunity, as well as highlight specific areas where we see the best investment opportunity.

We believe the aggregate robotics hardware market will grow 17% y/y to $24.5B in 2017, and by 2025 we believe the market will eclipse $73B. When including supporting software and services, we believe the total robotics market will be more than $200B in the next ten years. Many categories within the 5 robotics domains (industrial, commercial, domestic, military and social entertainment) will flourish over this time frame.

We are particularly excited about the impact three categories will have on the world: collaborative robots (co-bots), domestic robots (aka robot vacuums, mops and lawnmowers), and Autonomous Guided Vehicles. While we have recently picked up positive data points in the Co-bot and domestic robot markets, the AGV market is a little bit harder to track due to the limited number of publicly traded companies in the space. However, based on the number of AGVs Amazon is deploying internally, as well as the amount of funding and M&A activity occurring in the space, we are convinced this sub-segment of the commercial robot market is inflecting.

What Is An Autonomous Guided Vehicle (AGV)?

AGVs are mobile robots used in manufacturing and other commercial industries to improve logistics efficiencies by transporting goods and other materials autonomously. The major benefits of AGVs are twofold: 1) these robots do not require human interaction when deployed; 2) AGVs do not require supporting infrastructure (tracks, floor sensors, etc), which are needed to operate legacy material handling equipment. Without the need for supporting infrastructure, these robots are more flexible and have a lower total cost of ownership. Advancements in Simultaneous Location and Mapping (SLAM) software and computer vision technologies allow these robots to understand their surrounding environment in real-time, which allows them to operate in variable surroundings and around people. Pricing on AGVs has come down significantly over the last 5 years, which has been a catalyst for the industry. Today, AGV pricing varies from $35 – 50K (not including supporting software and services). Below we highlight a few examples of AGVs in the market today.

Amazon Sparked the AGV Industry

The AGV market flew under-the-radar throughout the early 2000s, but in 2012, the industry became one of the most talked about sub-markets in the robotics space after Amazon acquired the AGV leader (Kiva Systems) for $775M. Amazon had no plans to sell these robots externally, only using them internally to improve logistics efficiencies within their fulfillment centers, which created a significant supply/demand imbalance and a massive opportunity for other companies to enter the space. Since deploying Kiva robots, Amazon has highlighted publicly the positive impact that robots are having on productivity and efficiencies. According to a 2017 Business Insider article, Amazon has deployed 15K mobile robots annually since 2014, and now has over 45K robots in operation throughout 20 fulfillment centers. These data points show the benefits of AGVs and validate that this market is real.

AGV Applications: Today Warehouses; Tomorrow Hospitals, Hotels, and Beyond

Today, most AGVs are deployed within warehouses and fulfillment centers to automate material handling and package logistics. Robots in these settings autonomously retrieve a shelf or bin and return to a packaging station where a human employee picks specific SKUs out of bin. Or, more commonly, a human will follow the AGV around a warehouse, and the AGV will stop in front of specific spots where a human then places the desired product in a bin. While most AGV products need to be fully purchased, there are a few companies capable of retrofitting legacy equipment with autonomous technologies and transforming them into AGVs. There are also a few companies that are taking automation to the next level by adding a robot arm to pick the desired object, taking humans completely out of the equation. While this is where the industry is heading, object recognition and grasping are two of the toughest challenges to solve in this space. Random pick-and-place is considered the “holy grail” of robotics, and it will take time for humans to be fully eliminated within a warehouse.

While we believe AGV adoption within warehouses and fulfillment centers will be a key industry driver, we believe the opportunity in other verticals will add meaningful tailwinds to this market. For example, AGVs are already being deployed in hospitals to autonomously transport food, prescriptions, or other medical supplies throughout a medical facility. In addition, manufactures in all different industries are adopting these technologies because of the cost advantages and flexibility over other legacy solutions. We also see a large opportunity for AGVs to be deployed in many commercial services settings such as delivering products to rooms in a hotel, as well as eventually companies such as Amazon using AGVs to deliver packages autonomously.

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Mcity Expert Weighs In On Autonomous Driving

“People tend to overestimate the amount of change that can happen in the near-term, and understate it in the long-term.” Huei Peng, Director of Mcity

Last week we made a trip to Ann Arbor, Michigan to hear more about Mcity. Mcity is a public-private partnership that focuses on the research, development, and deployment of connected and automated vehicles.  It runs the Mcity Test Facility, a 32-acre proving ground for advanced mobility vehicles and technologies, which opened in 2015 at the University of Michigan.  The Mcity Test Facility has eight connected intersections, a traffic control center, cameras, radars, and a small fleet of its own fully-automated driverless vehicles. The public-private partnership is comprised of more than 65 industry members including: BMW, Ford, GM, Honda, Toyota, Intel, Qualcomm, and State Farm.  It also leverages many government funded projects, from the U.S. Department of Transportation, and the U.S. Department of Energy.

Mock city approach. While there are other paved test facilities used for connected and autonomous vehicle testing, Mcity is the first purpose-built mock city in the United States designed specifically for autonomous driving research.  We see this mock city approach as an important avenue to advance autonomy. It’s important to note that WayMo and Uber’s projects in Phoenix and Pittsburgh are testing largely on public roads.  Testing in a controlled environment such as the Mcity Test Facility has many benefits: the tests are safer, cheaper, faster, and repeatable.

Importance of DSRC. One focus at Mcity is dedicated short-range communications, or DSRC. DSRC is designed specifically for automotive safety applications, and enables vehicle-to-vehicle or vehicle-to-infrastructure communications with an effective range of 1000 feet.  DSRC, like other wireless communications, does not need line-of-sight visibility to detect potential safety threats, such as an unseen vehicle ahead stopping suddenly in a snowstorm. DSRC can essentially serve as another sensor, providing useful vehicle and traffic information to support autonomous driving. We’re surprised to hear that many traditional auto manufactures believe DSRC makes cars safer, but WayMo and Tesla are not taking advantage of its benefits.

When will we have self-driving cars? Heading into our visit, we wanted to get a read for when consumers will be able to purchase a fully autonomous vehicle in the United States. Peng cautioned that while autonomous vehicles are ready for some niche and limited applications, anytime, anywhere driverless vehicles may take longer than we think, commenting that “developing a car that is 90% safe is relatively easy, 99% safe is harder, and the remaining 1% will take a very long time.” While Peng stopped short of predicting a roll out year, our sense is that 2025 will be the year when some autonomous vehicles are on par with, or better than, average human drivers in most driving conditions. It is clear that a lot needs to happen between now and the time we reach full autonomy. Peng illustrated this in an analogy that today self-driving cars are in the relatively early stages of development, much like planes before the airline industry invested a tremendous amount of time testing new technologies as it moved to fully automated planes like the fly-by-wire Boeing 777.   Getting to that level of readiness is necessary for autonomous driving to reach mainstream use, and will require much more evaluation and testing.

We took our findings from Mcity and applied it timing of Tesla autonomy, AI and auto, and quality of miles driven data. It’s important to note that the insights below are attributed to Loup Ventures.

We’re believers in Tesla, despite the fact we think they’ll miss their autonomy launch target. We expect Tesla to miss their 2019 autonomy launch target, and see 2022 as more realistic roll out year. Elon Musk has been clear that he expects each Tesla made today to be autonomous in 2019. What’s not clear about the 2019 target is what level of autonomy will be reached. At the recent Model 3 hand-off event, Musk made a reference to sleeping in your vehicle as an acceptable activity in autonomy, suggesting their 2019 goal is to reach the Level 4 or Level 5. Getting to Level 5 is a light year leap from Level 4. Level 4 autonomy is where no driver is needed, but the vehicle’s speed, range, and weather conditions (snow is a material problem) are limited. Level 2 autonomy is available today in some production cars as advanced driver assistance. It’s important to note, Tesla’s approach is evolutionary, moving Autopilot to Advanced Autopilot, and finally to self-driving. This is different than WayMo’s revolutionary approach of entering the market with a Level 4 or 5 autonomous vehicle.

AI’s fit. When we think about AI being better than humans, we think of cases where machines have defeated humans, like in chess or video games. These examples are in a world with a finite number of choices. On the other hand, driving in the real world has an infinite number of choices. WayMo, Uber, and Tesla are at an advantage when it comes to tackling these infinite choices, given that they’ve already logged significant miles to feed their respective self-driving neural networks. We believe the miles driven gap will be hard for new self-driving players to close. Because the problem is so complex, the neural network will need to learn from hundreds of billions of miles (today we are sub 2 billion miles). Also, consider that a car can only drive on roads that its trained on, so a U.S. autonomous car can’t drive in China.

Quality of data. While it’s true AI with more miles is better than fewer miles, it’s important to understand the distinctions between data captured by each player. We believe Waymo’s 3 million miles have saved most if not all of the critical data, and we have questions regarding how much data is stored and shared back from Tesla’s owners. Most Tesla data is discarded, since a Wi-Fi connection once a week doesn’t have enough bandwidth to share all of the data.

We left our visit to Mcity with a better sense of how traditional auto and tech companies are approaching autonomy. In addition, we reached a few new insights about the timing question, including the fact that the timing question is less relevant. We think back to Peng’s flight analogy, and believe we’re underestimating the significance of change autonomous vehicles will bring to the world.

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.

Tesla: Short Term vs. Long Term

People tend to overestimate what happens in short-term, and underestimate what happens in the long-term. We believe that notion will define the Tesla story over the next six years. We caution that it will take time for the Tesla story to unfold, and that there will be disappointments along the way, but Tesla’s Jun-17 quarter results and outlook around production and demand suggest the company is on a track to be a significant beneficiary in the global paradigm shift to EV and autonomy, all while producing affordable vehicles. Traditional auto is in a tight spot, dogged by legacy engineering (both on vehicles and manufacturing), and high labor costs. Tesla’s biggest challenge is ramping production, and, to a lesser extent, the threat of other tech companies (WayMo, Baidu, Apple).

Key Takeaways From The Quarter. Tesla maintained its outlook around key expectations including Model 3 production ramp, cap ex spend and profitability. The company also achieved slightly higher gross margins in Jun-17 than expected. In addition, there was commentary that in the past 5 days, they have not seen cannibalization of Models S and Model X from Model 3.  Instead, there has been an increase in both Model S and Model X orders, plus an uptick in Model 3 reservations to an average of 1,800 per day. Tesla clarified that there are 455k net reservations for Model 3, and that the “greater than 500k” reservations comment Musk made last Friday was 518k gross reservations.

3 Additional Gigafactories Are Coming. On the call, Musk mentioned that there are plans for Gigafactories 3, 4 and 5. One of these will likely be in the US, one in Europe, and one in China. We believe it will take 3-6 years to build these additional factories. This is significant for Tesla, as other automakers need to build factories at a similar scale and have yet to even break ground on their version of a Gigafactory. This underscores the structural lead Tesla is slowly building over traditional auto manufacturers as it relates to EV battery production.

Brace for Future Disappointments. As we’ve mentioned, this will take longer and be bigger than most people think. Our belief that it will take longer implies that there will be disappointments along the way. Here’s our best guess at the top 6 disappointments over the next two years: 1) Tesla may need to raise more money. 2) Tesla may miss on production targets by a quarter or two. 3) Full autonomy may come as late as 2021, not in 2019. 4) There may be a need for another factory to grow annual production above ~600k vehicles. 5) Demand may be negatively impacted when US tax incentives are reduced in 2019. 6) We’ll continue to see competitive announcements from other auto manufacturers and tech companies. While these disappointments will fuel controversy around the story, we believe they do not change the long-term potential of Tesla.

Tesla’s stealth advantage. Not captured in the quarterly results is something much bigger: Tesla stakeholders are on a shared mission to change the world. Tesla’s stakeholders include employees (all of which are shareholders, from the management team to the custodians), Tesla owners, shareholders, and suppliers.

Changes to our Model:

2017 Unit Shipments Adjustments

  • Vehicle delivery estimates increased from 101K to 105K.
  • Adjustment is due to higher Model S and X unit delivery assumptions based on positive comments on the call.

2018 Unit Shipments Adjustments

  • Vehicle delivery estimates increased 308K to 316K.
  • Adjustment is due to higher Model S and X unit delivery assumptions based on positive comments on the call.
  • Previously modeling 4% Y/Y decline in Model S and X unit growth, but changed to flat growth.

2017 Revenues, Margins, EPS Adjustments

  • Revenues increased from $11.2B to $11.8B; driven by higher model S and X unit deliveries.
  • Gross margins decreased from 23.4% to 22.7% due to lack of scale efficiencies related to Model 3 ramp.
  • EPS lowered from ($6.77) to ($7.06) due to lower gross margins.

2018 Revenues, Margins and EPS Adjustments

  • Revenues increased from $20.4B to $21.4B, driven by higher model S and X unit shipments.
  • Gross margins increased from 23.3% to 23.6% due to faster than expected scale efficiencies with model 3 ramp. We believe they can hit 25% gross margins in 4Q18, although it could be earlier.
  • EPS goes up from ($6.76) to ($5.93) due to slightly better gross margins.

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.

Tesla’s Stealth Advantage: Shared Mission

There’s been a lot written about the Model 3 and little more I can add about the car, but I’d like to share something I noticed at my first Tesla event last week: the incredible power of a shared mission.

Tesla’s Fremont factory is one of the world’s largest buildings, so it was no surprise that the Model 3 hand-off event was a 5 minute shuttle ride from where the guests first assembled. Twelve people rode in my shuttle, of which four earned their ticket to the event through their participation in the Tesla Referral Program, where Tesla owners receive credit towards their next Tesla purchase by referring a customer. In case you’re wondering, the other seven people were suppliers. The shuttle worked its way around the back of the factory past what seemed like an endless line of people wrapping around the building. My group wondered aloud where all the people had come from. We got dropped off on a black carpet next to a staging area for a ride in a Model S on the test track.

At 7:30PM, an hour-and-a-half before Elon Musk would take the stage, I took two Model S test rides with Spyglass Capital fund manager Jim Robillard, and talked to a wide range of Palo Alto-based Tesla employees. I started each conversation by asking, “What’s on your mind?” Each employee lit up, detailing their contribution to the Model 3, and why they believe it will change the world. Investors I spoke to were not concerned about the central bear case on the Tesla story, that the company will never make money and run out of cash. Instead, they talked about the importance of EV, Tesla’s head start in battery production, and Tesla’s mission to accelerate the world’s transition to sustainable energy.

At 8:45PM, the event kicked off with Project Loveday finalists, named after Bria Loveday, the 11-year-old who suggested to Musk that Tesla should host a user-generated commercial competition. The quality of the commercials was impressive, requiring a ton of effort given the modest grand prize of an on-stage introduction at a future Tesla event.

At 9:00PM, Musk took the stage in front of 6,000 vocal fans – all employees. The long lines my shuttle bus had passed on the way to the event was Tesla’s manufacturing muscle waiting to get into the event. The crowd’s energy suggested the rank and file share the same passion about Tesla as the optimistic Palo Alto-based employees I had talked to earlier in the evening. The main screen briefly switched to a live feed from Gigafactory 1 in Sparks, NV. Same thing: a huge crowd of hand waving employees.

During my trip home from the event I realized that I had gone to meet a car; instead, I met a group of Tesla stakeholders on a shared mission to change the world.

As an analyst, I’ve always evaluated companies based on unit forecasts, product road maps, competition, profitability, and management teams. As a venture capitalist, I’ve added to that list culture and the level of shared mission. During my trip home from the event I realized that I had gone to meet a car; instead, I met a group of Tesla stakeholders on a shared mission to change the world. Tesla’s stakeholders include employees (all of which are shareholders from the management team to the custodians), Tesla owners, shareholders, Project Loveday participants, suppliers, and even an 11-year-old fan from Michigan.

I was reminded of the famous anecdote about President John F. Kennedy. During a visit to the NASA space center in 1962, President Kennedy noticed a custodian at work. He walked over to the man and said, “Hi, I’m Jack Kennedy. What are you doing?” “Well, Mr. President,” the custodian responded, “I’m helping put a man on the moon.”

Tesla has this same stealth advantage: a shared mission at a scale greater than I’ve seen in my 20 years in tech.

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.