Machines are Eating the World: The Data Gravity Investment Thesis in the Era of Autonomy

In 2007, I had my head buried in the sand as $AAPL and $GOOG set the end of BlackBerry in motion. At the same time, I was too busy to see the smartphone's ripple effects on $AMZN. I missed out on all three long investments. I'd later see that their success was entirely predictable.

Since then, I've studied historical patterns as critical indicators of future events in specific markets. The impact of the smartphone on companies like Google, Apple, and Amazon provides a blueprint for emerging events in the explosion of intelligent infrastructure in the Era of Autonomy catalyzed by the revolutionary industry innovations introduced by Tesla.

The following analysis lays the foundation for an investment thesis to capitalize on the emerging “Machine Economy” catalyzed by Tesla’s vision to make the Connected and Autonomous Vehicle (CAV) a powerful device platform.

What the Recalibration of Music, Movies, and Gaming Industries Teaches

Apple iTunes and other online content marketplaces have revolutionized the music, movie, and gaming industries. These platforms have transformed how these industries generate revenue and recalibrated their business models and profit strategies.

Platforms like Amazon, Apple, and Google sell not just music tracks and movies but also video games and game DLCs (downloadable content) as digital downloads. They take a percentage of each sale, which, while varying, can be substantial (often around 30%).

Beyond music and movies, gaming has also ventured into streaming with services that allow gamers to play games without downloading them, paying a subscription fee instead. This model offers a steady revenue stream and caters to users' preferences for access over ownership.

In-app purchases, such as virtual goods, extra content, or cosmetic items, are significant revenue drivers in games sold or distributed through these marketplaces. The platform typically takes a cut of these transactions, contributing to its revenue.

Some platforms and games offer free versions that come with advertisements. Revenue is generated through these ads, which can be particularly lucrative in popular free-to-play games that attract large audiences.

The digital marketplace has reduced the reliance on physical sales across these industries, initially causing a dip in revenue from physical goods but eventually leading to growth in digital sales and streaming.

By offering a convenient and legal alternative, these platforms have helped reduce piracy across music, movies, and gaming, recapturing revenue previously lost to illegal downloads.

The move towards subscription models for streaming music, movies, and games has created a more predictable revenue stream. This model benefits the platform with a steady income and the creators through ongoing royalties, although the distribution and calculation of these royalties have had to evolve.

Digital distribution through these marketplaces has made content more accessible worldwide, opening new markets and revenue opportunities for creators and publishers. As independent musicians and filmmakers can distribute their content directly to audiences, independent game developers have benefited from these platforms. They can publish their games without needing a significant publisher, reach a broad audience directly, and retain a larger revenue share.

At their core, these platforms shifted the focus from physical to digital, introduced new revenue models like streaming and microtransactions, and opened the door for a more global and diversified content offering. 

The introduction of smartphone device technology triggered the recalibration of these industries, along with many others.

In March 2007, Apple’s stock ($AAPL) was trading at an adjusted price of $12.31. If I had invested $100,000, my interest in the company would be worth around $1,218,521.53 today, representing an approximate return on investment of 1,118.52%. 

I didn’t buy AAPL for three reasons. First, I was a very early adopter of BlackBerry and a firm believer in the market dominated by Research in Motion. Second, I completely overestimated the architectural design of a physical keyboard. And finally, I found the idea of a camera on my phone as a fad. “It’s a little frightening,” confessed one Wall St expert. “There’s no way they can meet the hype.”

Since missing out on investing in the economy that formed after the introduction of the smartphone, I have been far more vigilant and attentive!

Software May Have Eaten the World, but a Machine Made it Possible

Marc Andreessen's "software is eating the world" thesis, posited in 2009, underpinning the launch of a16z, was that society was in the midst of a dramatic shift, and software companies were poised to take over large parts of the economy in a post smartphone world. Andreessen argued that more and more major businesses and industries are being run on software and delivered as online services. Every company must become a software company to survive and thrive in the current economic environment.

He was right. As a result, he created the most dominant venture capital firm of all time.

However, the smartphone was a prime mover in that 2009 investment thesis. As a highly versatile and widely adopted machine, it was the catalyst for the explosion of software development opportunities.

Smartphones' portability and all-in-one functionality mean that people can carry powerful computing devices. This convenience has transformed consumer behavior, with more people relying on smartphones for daily activities like shopping, banking, and entertainment. Retail and banking sectors, for example, have seen significant disruption as e-commerce and mobile banking apps challenge brick-and-mortar establishments.

Smartphones serve as platforms for many applications, completely disrupting traditional business models. For instance, ride-sharing apps have upended the taxi industry by offering a more convenient, user-friendly service directly from a smartphone. Similarly, health and fitness apps challenge traditional healthcare and fitness industries by providing personalized advice and tracking capabilities.

Smartphones are equipped with various sensors and tracking capabilities, allowing for data collection. This data offers personalized experiences affecting industries like advertising and marketing. Traditional mass media advertising was disrupted as companies can now target individuals with customized content based on their interests, behaviors, and location.

The ease of making purchases through smartphones has accelerated the growth of e-commerce, disrupting traditional retail. Consumers can compare prices, read reviews, and purchase anytime and anywhere, increasing competition and pressure on physical stores.

The mobile device industry has also transformed the way people consume content. Streaming services and social media platforms cater to mobile users with short-form content, disrupting traditional entertainment industries like television and film production. This shift has forced these industries to adapt by creating more mobile-friendly content and distribution models.

They have enabled new business models, such as the sharing and gig economies. Platforms like Airbnb and freelance job portals rely on smartphone apps to connect providers with users, disrupting traditional hotel and employment sectors.

Even the financial industry has seen significant disruption in mobile payments, mobile banking, and investment apps, which offer more convenience, lower fees, and enhanced features compared to traditional banking services. This shift enabled many old established financial institutions to innovate and adapt.

The Connected and Autonomous Vehicle (CAV) is the Next Big Device Platform Market Opportunity

As we look to the horizon of the Internet, I contend the CAV has become the next significant device platform, following in the footsteps of smartphones and, before that, personal computers. This transformation is rooted in several technological and societal trends converging to make cars not just a mode of transportation but a connected, intelligent platform for a wide range of services and experiences.

Not wanting to miss like I did on $AAPL and $AMZN, I began to follow and study $TSLA in 2015. In 2016, I test-drove a Model S. I was utterly blown away by the experience. The UX inside the cabin was raw, but the potential was obvious. And then, the company announced plans to make a mass-production affordable version—the Model 3. "We want to believe, but we simply cannot,” echoed the Wall Street armchair experts.

I knew most of the investing world was trying to analyze Tesla as a car company when, in reality, they are a technology company. Having learned my lesson from the smartphone market, I bought Tesla shares and a Model X soon after! 

Since 2016, the concept of the vehicle has undergone a radical technical overhaul.

Modern cars have advanced connectivity features, including built-in cellular connections, Wi-Fi, and Bluetooth. These enable them to communicate with other devices, infrastructure, and the Internet, turning the car into a mobile computing platform. As smartphones leverage cellular technology to offer a wide range of services, connected cars can provide real-time traffic updates, remote diagnostics, and more.

Automobiles increasingly incorporate intelligent technologies, including AI, machine learning, and advanced sensor technology. These technologies enable semi-autonomous driving, predictive maintenance, and personalized in-car experiences. Integrating these innovative technologies transforms the automobile into an intelligent assistant capable of understanding and anticipating the driver's needs.

Just as smartphones have app stores, automobiles are beginning to support their ecosystems of apps and services. Car manufacturers are opening up their platforms to developers, enabling the creation of automotive apps for navigation, entertainment, productivity, and vehicle management. This ecosystem expansion mirrors the smartphone app revolution, offering endless possibilities for customization and new functionalities.

Consumer expectations around technology and connectivity continue beyond their front doors or office buildings. People expect a seamless technological experience moving from their homes to their cars and beyond. Automobiles as device platforms will cater to this demand by integrating with other smart devices and systems, offering a unified user experience.

The rise of electric vehicles (EVs) and the move towards software-defined vehicles further position the automobile as a critical device platform. With their simpler mechanical systems, EVs rely more heavily on software for functionality, from battery management to driving modes. This shift means cars are increasingly defined by their software capabilities, much like smartphones.

The concept of Mobility as a Service (MaaS) envisions cars not merely as vehicles but as services. This approach, facilitated by connectivity and intelligent technologies, allows for innovative transportation models like car-sharing, ride-hailing, and subscription-based vehicle access. Cars become platforms through which various mobility services are offered, similar to how smartphones serve as platforms for communication, entertainment, and productivity apps.

Smart and connected technologies in automobiles offer significant improvements in safety and efficiency. Features like collision avoidance, lane-keeping assistance, and traffic flow optimization make driving safer and more efficient, reducing congestion and energy consumption. As these technologies evolve, the car's role as a platform for enhancing public safety and environmental sustainability becomes more pronounced.

If this all sounds familiar, it should.

The convergence of connectivity, innovative technology, evolving consumer expectations, and new business models positions the CAV as the next pivotal device platform. This transformation opens up new opportunities for innovation, reshaping how we drive and live, work, and interact with the world around us. As cars become more connected, intelligent, and integrated into our digital lives, they will offer unprecedented levels of convenience, efficiency, and personalization, marking a new era in the evolution of personal and shared mobility.

It’s about the Data, Stupid

Short sellers and critics of Tesla still point to the company's low gross margin on vehicle sales as a critical concern. They argue that this low margin indicates that Tesla's business model could be more robust and sustainable in the long term.

The gross margin is calculated by subtracting the cost of goods sold (COGS) from the revenue and dividing the result by the revenue. For automobile companies, COGS typically includes manufacturing vehicles' materials, labor, and overhead costs. If the gross margin is low, these costs consume a significant portion of the revenue, leaving the company with less profit.

Critics argue that Tesla's low gross margins result from high production and manufacturing costs and pricing strategies that aim to make the cars more affordable to a broader market. They contend that if Tesla can significantly reduce its manufacturing costs or increase its prices, the company will be able to achieve profitability, especially as competition in the electric vehicle market intensifies.

Furthermore, these critics point out that Tesla's reliance on regulatory credits to boost its profitability is another cause for concern. They assert that this is not a sustainable source of income over the long term as more car manufacturers produce their electric vehicles, reducing the demand for these credits.

This is why you can’t trust a financial expert to build more than a spreadsheet! :)

Data production from the CAV ecosystem will become the cornerstone of the next Internet software economy, where developing advanced learning models is poised to be one of the most lucrative sectors within artificial intelligence (AI). This trend is underpinned by the increasing sophistication of CAVs, which generate vast amounts of data.

For Tesla, it’s never been about the car. It has always been about the data. 

Modern vehicles have numerous sensors and systems that collect data on virtually every aspect of the vehicle's operation and environment. This includes engine performance metrics, driving behavior, GPS and location data, camera feeds for autonomous driving systems, and even information on the vehicle's surroundings. This data is invaluable for training machine learning models, as it provides a rich, real-world dataset that reflects the complexities of driving in diverse conditions.

One of the most direct applications of automobile data in learning models is the development of autonomous driving systems. These systems rely on deep learning models trained on vast datasets to recognize patterns, make decisions, and learn from outcomes. The data collected from vehicles about road conditions, obstacle detection, and driver behavior are used to train these models, improving their accuracy and reliability.

Machine learning models will predict when vehicle parts are likely to fail or require maintenance by analyzing data from sensors monitoring vehicle components. This predictive capability can significantly reduce maintenance costs and increase vehicle safety by addressing issues before they lead to failures, resulting in trillions of dollars in savings.

Learning models will use driver behavior and preferences data to personalize the driving experience. This can include adjusting the vehicle's performance characteristics, climate control settings, and entertainment system preferences to match drivers’ habits and preferences.

Aggregated data from multiple vehicles can be used to model and predict traffic patterns, informing traffic management systems and urban planning decisions. AI models will analyze this data to optimize traffic flow, reduce congestion, and improve road safety.

The insights from CAV data will also lead to new business models, such as usage-based insurance, dynamic pricing for shared mobility services, and value-added services for drivers and passengers.

Companies that can effectively collect, analyze, and act on CAV data will gain a competitive advantage in the automotive and mobility sectors. This will further drive investment and innovation in AI and data analytics technologies.

The 'Transportation Device' Economy Will Be More Significant than Smartphone Device

The CAV, now part of a much broader economy, is evolving into a sophisticated platform that will surpass the smartphone in terms of its total economic impact. While smartphones have undoubtedly revolutionized how we communicate, access information, and manage our lives, CAVs lead to more prosperous, more diverse data sets due to their complex operation, integration into our physical world, and the nature of transportation and mobility in society.

CAVs generate various data points that are inherently rich in context and utility. This includes operational data such as speed, acceleration, fuel consumption, environmental data (weather conditions, road conditions), behavioral data (driving patterns, route preferences), and even biometric data in some advanced systems. The multifaceted nature of this data, reflecting the vehicle's internal status and interaction with the external environment, provides a depth of insight that far surpasses the data typically generated by smartphones.

CAV data has a direct impact on safety and efficiency, two critical areas in transportation. For example, data on driving behavior and vehicle performance can be used to develop advanced safety features, predictive maintenance models, and efficiency improvements. These applications enhance the driving experience and significantly affect public safety, urban planning, and environmental sustainability. The direct impact on these critical areas adds substantial value to the automobile data.

CAVs operate within broader transportation and city infrastructure systems, and the data they produce can be integrated into these systems for comprehensive analysis and optimization. This integration can facilitate intelligent traffic management systems, optimize routing based on real-time conditions, and inform future urban development and planning. The potential for CAV data to contribute to more extensive systems analysis and improvement vastly increases its value.

The economic and societal benefits of CAV-generated data are significant. Beyond enhancing the personal driving experience, this data can contribute to more efficient transportation networks, reduced environmental impact through optimized fuel usage and route planning, and improved public safety through enhanced accident response and prevention strategies. The broad societal implications of these benefits contribute to the data's overall value.

As the CAV continues to evolve, it becomes a key enabler for new mobility services, such as ride-sharing, car-sharing, and autonomous vehicle services. The data generated by these services provides insights into user behavior and preferences and feeds into improving the services, creating a loop of continuous enhancement and value creation.

The continued advancement toward fully autonomous vehicles will represent a quantum leap in the value of automobile-generated data. Autonomous cars rely on continuous data from sensors to navigate and make decisions. The complexity and critical nature of this data exceed anything smartphones generate, with direct implications for safety, urban mobility, and the future of transportation.

While it’s evident in retrospect how and why smartphones changed our world and economy, it’s a little more challenging for most, especially investors, to see how that transformation could be dwarfed. However, the transformation of vehicles into connected, intelligent platforms promises to unlock unprecedented value for individual users and society, heralding a new era of mobility that will transform society.

The Data Gravity Investment Thesis

The concept of data gravity, theorized initially by Dave McCory, has significant implications for the future of the digital economy and provides the foundation for better understanding and rationalizing future economic prosperity as the Internet and the nature of commercial systems rapidly evolve.

McCrory's theory of data gravity, introduced in 2010, describes how large amounts of data attract additional applications, services, and users. It draws an analogy to the physical concept of gravity, where objects with more mass have a greater gravitational pull. 

The tenets of data gravity theory help visualize a framework for future economic wealth and prosperity, which underscores the importance of the emerging digital economy for investors seeking to capitalize on the future I have described.

Data production by disparate multi-tenant systems and the inherent market-based exchange of this data will permeate every value chain layer. Data's relative gravitational pull directly correlates to its potential for wealth and prosperity. The more data created and exists in the future Internet economy, the more applications and services will "gravitate" to it to produce commercially valuable systems.

The exponential expansion of these commercial value systems, insatiably dependent upon an ever-growing corpus of data, is the basis of my Theory of Economic Prosperity. Vibrant economies create prosperity, which acts as a subsequent gravitational field for talent, investment, and so on.

An investment thesis targeting the vertical integration of CAV intelligent infrastructure and software systems across manufacturing, robotics, devices, sensors, AI user experience (UX), and data exchanges focuses on capturing value across the entire spectrum of digital transformation in industry and society. This thesis recognizes the converging nature of technologies and the immense potential for synergies across different domains of the tech ecosystem. The core idea is to invest in companies and technologies that excel in their respective fields and contribute to and benefit from an integrated system of intelligent operations and interactions. 

The early stage investment opportunity is in diversifying across the many technology curves that are forming now as a consequence of the CAV breakthrough, or the macro curve.

We are at a precipice moment not unlike when a16z proclaimed its software investment thesis to capitalize on the economy created by the smartphone.

What is the prescription for the data gravity investment thesis?

Investments begin with the backbone of physical production: advanced manufacturing and robotics. Companies that innovate to make manufacturing processes more efficient, flexible, and scalable through robotics and automation form the base. This includes advancements in additive manufacturing, autonomous robots for warehouse and supply chain operations, and smart factories that leverage IoT devices for real-time monitoring and optimization.

The next layer focuses on the proliferation of devices and sensors that collect and transmit data. This spans from consumer electronics to industrial sensors embedded within infrastructure and machinery. The investment focus here is on technologies that enhance connectivity, durability, and precision in data collection, enabling a deeper understanding of operations, consumer behavior, and environmental conditions.

With the infrastructure and devices in place, the emphasis shifts to how users interact with this ecosystem through AI-driven user experiences. Investments would target innovations in natural language processing, computer vision, and machine learning models that make interfaces more intuitive, personalized, and efficient, enhancing decision-making and simplifying complex processes.

The final puzzle is this integrated system's secure and efficient data exchange. Investing in technologies and platforms that facilitate safe, transparent, and real-time data sharing is crucial. This includes solutions for data integrity, advanced data analytics platforms for insight generation, and edge computing technologies for decentralized processing. The goal is to ensure that data flows freely and securely across the system and is actionable and valuable.

This approach recognizes the blurring lines between sectors like manufacturing, technology, and services, focusing on the interplay and innovation at their intersections.

Central to this thesis is the understanding that data and the ability to leverage it intelligently are critical strategic assets. Integrating hardware and software systems maximizes the utility and value of data.

Investments in intelligent, interconnected systems contribute to sustainability through improved efficiency and resource use. Furthermore, these integrated systems offer resilience against disruptions by providing diverse, real-time data for responsive decision-making.

In echoing Marc Andreessen's decisive statement in 2009, "I know where I'm putting my money," the future lies in the convergence of technological advancements born out of the transformation of the automobile from a transportation device to a fully connected platform: Machines are eating the world. Manufacturing and intelligent infrastructure, devices and sensors, user interfaces powered by artificial intelligence, and the secure and efficient data exchange are all recalibrations of existing industries catalyzed by $TSLA and the CAV.  

I’ve seen this pattern before, only this time I’m paying attention.