The Race for the Mechanical Human

Elon Musk stands on stage, arms folded, as a silver humanoid robot walks hesitantly toward him. The crowd holds its breath.

This isn't just another Tesla stunt. It's the beginning of what might be the most consequential technological race of our lifetime: the sprint to create the first commercially viable humanoid robot.

Four years ago, humanoid robots were science fiction props, research curiosities, or embarrassing failures that toppled over during demos. Today, billions of investment dollars are flowing to startups and tech giants alike, all betting that mechanical humans will transform our world.¹⁵

Why now? Three forces have collided to create this moment.

First, AI finally works. The same breakthroughs powering ChatGPT are being embedded into robot brains, giving machines the ability to understand commands, adapt to new situations, and learn from mistakes. Boston Dynamics' robots no longer need pre-programmed routines; they can figure things out on their own.⁵, ⁷

Second, we're running out of people. Factories can't find workers. Nurses are in short supply. The global population is aging, with fewer young people entering the workforce each year. Japan, Germany, and Italy are already feeling the demographic crunch. China's working-age population shrinks annually by millions.² ,⁸

Third, manufacturing is coming home. Countries want secure supply chains after pandemic disruptions. But reshoring factories to high-wage nations requires automation on an unprecedented scale.² ,¹⁵

The result? A frenzy of activity.

Tesla aims to build 500,000 Optimus robots annually by 2027, more than the entire industrial robot market today.¹⁵ Figure AI just raised $675 million from tech leaders to push its humanoids into factories.²⁴ Agility Robotics opened a "RoboFab" to mass-produce its two-legged Digit robots for warehouses.²

In China, companies like Unitree are designing $16,000 robots that can perform gymnastic feats.³ ,⁷⁰ Germany's Neura Robotics positions itself as Europe's champion in the race.⁷⁸ Even carmakers Hyundai⁴ and Honda²⁹ have placed major bets.

The cost targets are stunning. Most industrial robots cost $50,000 to $150,000. But Tesla claims its humanoids will eventually sell for under $30,000, less than many cars.¹⁰ At that price, the economics of replacing human labor becomes tempting in many industries.⁴⁷

Prominent analysts call this one of the most significant tech developments of the 2020s.¹⁰² Market projections for 2030 range from a conservative $4 billion to an eye-popping $40 billion.²⁵, ¹⁸

But investment slides and press releases don't equal real-world performance. Can these machines actually deliver? Boston Dynamics spent decades perfecting walking robots, yet still hasn't created a commercially successful humanoid. The gap between demonstration videos and daily reliability remains vast.

And that's what makes this race so fascinating. We're witnessing the messy, chaotic birth of an industry that could fundamentally alter human work. What steam engines did for muscle power, humanoid robots might do for physical labor.

The question isn't if humanoid robots will affect our world. It's when, and whose vision will prevail.

Metal Athletes: What Today's Robots Can (and Can't) Do

Watch a video of Boston Dynamics' Atlas doing parkour or Unitree's G1 sticking a backflip, and you might think robot overlords are imminent.⁷⁰ Their ability to walk, run, balance, and even recover from pushes is genuinely astounding.³ It’s the result of sophisticated control software (like Model Predictive Control, or MPC) and relentless AI training in virtual worlds before deployment in the real one.⁵

But look closer. Those slick demos often happen in controlled spaces. And while mobility has improved dramatically, other crucial abilities lag far behind human capability.

Take hands. Your hands are marvels of engineering – strong, fast, sensitive, adaptable. Robot hands? Not so much.⁶⁶ While companies like Figure AI show robots handling metal parts with precision²⁶ and Sanctuary AI focuses on tactile feedback,³⁶ most robotic hands are clumsy. They struggle with soft objects, complex tools, or anything requiring nuanced touch. Replicating human dexterity is arguably the single hardest mechanical challenge.

Then there's the battery problem. Today's humanoids are energy hogs. Most can only operate for 2-5 hours before needing a recharge – not even close to a full workday.² The power needed for dynamic movement pushes current lithium-ion battery technology to its limits.⁹⁸ Achieving all-day operation will require breakthroughs in battery energy density, much better efficiency, or clever solutions like fast charging or autonomous battery swapping (which Agility Robotics is developing for Digit).⁵³

And the brain? AI is advancing fast, letting robots recognize objects, map rooms (using techniques like Visual SLAM⁷⁴), and learn tasks from demonstration or simulation.⁴ Integrating large language models (LLMs) allows them to understand spoken commands.⁵ But true, adaptable intelligence – the ability to reliably handle unexpected situations in messy, unpredictable human environments – is still far off.¹⁴ Today's robots are easily confused by novelty. Ensuring they can perform tasks safely and reliably outside the lab remains a massive AI challenge.⁵⁶

So, the reality is mixed. Impressive athletes? Yes, increasingly. Ready to cook dinner or perform surgery? Absolutely not. The gap between what looks cool on video and what works reliably hour after hour is enormous.

The Robot Makers: Key Players and Their Strategies

Who’s actually building these mechanical humans? The field is crowded, but a few key players define the major strategic bets being placed.

• Tesla (The Vertical Integrator): Elon Musk's vision is typically grand: leverage Tesla's existing strengths in AI (from self-driving efforts), batteries, motors, and mass manufacturing to build Optimus cheaply and at enormous scale.¹⁰, ⁴⁰ The bet is that vertical integration and automotive production discipline can crack the code where specialized robotics companies haven't. Initial focus: Tesla's own factories.³⁴ Big promises, high execution risk.¹⁵

• Unitree Robotics (The Agile Disruptor): Emerging rapidly from China and leveraging its experience in agile quadrupeds,⁶⁸ Unitree develops highly dynamic humanoids at remarkable speed. Their strategy emphasizes physical capability, showcased by robots performing complex gymnastic maneuvers,⁷⁰ alongside aggressive pricing, such as the G1 model's reported $16,000 starting price.³ Aligned with China's national robotics ambitions,²⁴ Unitree's combination of high agility and low cost positions them as a significant force, relying on their in-house AI efforts.⁶⁹

• Agility Robotics (The Warehouse Specialist): Agility has focused laser-like on logistics from the start, getting its Digit robot into real-world warehouse operations with partners like GXO faster than most competitors.⁴², ² Their strategy is pragmatic: target a clear industry need (material handling), build a dedicated factory for scale,² and create the cloud software (Agility Arc⁴²) needed for easy integration. They aren't trying to build a general-purpose human replacement (yet), but a practical tool for a specific job.¹⁰

• Boston Dynamics (The Mobility Pioneer): With decades of DARPA-funded research behind its legged robots, Boston Dynamics possesses unmatched expertise in dynamic motion and control.²³, ⁴ Now owned by Hyundai and shifting to an electric Atlas designed for work,²⁴ their strategy plays to their strength: tackling tasks where extreme mobility and balance are paramount. Partnerships with Hyundai (manufacturing, deployment)⁴ and Toyota (AI for manipulation)²⁴ aim to commercialize their advanced research.

• Figure AI (The AI-First Challenger): Backed by a who's-who of tech investors (OpenAI, Nvidia, Bezos),²⁴ Figure believes intelligence is the key differentiator. Their strategy focuses on embedding advanced AI reasoning and learning, using partners like OpenAI to give their Figure 02 robot sophisticated language and problem-solving skills.¹¹ Early pilots in demanding environments like BMW manufacturing aim to prove the AI-driven approach quickly.¹¹, ²⁶

Beyond these, other notable strategies include Apptronik's partnership model (leveraging Jabil for manufacturing),⁴³ Sanctuary AI's deep focus on complex manipulation,³⁶ 1X Technologies' early bet on the consumer market,³⁷ and the rapid hardware iteration seen from Chinese players like Unitree and UBTECH, often benefiting from strong state support and aiming for aggressive price points.³ ,²⁴

Crucially, almost everyone relies on partnerships. Whether it's for AI software (Nvidia's Isaac Sim⁴, foundation models like GR00T N1⁷), manufacturing scale,³² or market access through early customers,³¹ collaborations are essential fuel in this race.⁴²

Table 1: Comparative Overview of Leading Humanoid Robots (Select Players, circa 2024-2025)

From Prototype to Production Line

Cool demos are one thing; building hundreds of thousands of reliable robots is another challenge entirely. The transition from lab prototype to mass-produced product is where many robotics dreams die.

The production targets being thrown around are staggering. Agility aims for over 10,000 robots annually.² Tesla talks about scaling to potentially over half a million Optimus units per year within just a few years.¹⁵ Figure AI has similar ambitions.¹⁵ These numbers represent an exponential leap from current boutique production levels.

Achieving this scale requires mastering complex manufacturing and navigating fragile supply chains. Humanoid robots are intricate machines packed with specialized, high-performance components: powerful, compact actuators; sophisticated sensors (cameras, LiDAR, force sensors, tactile grids); powerful onboard computers; and high-density batteries.²⁸ These aren't off-the-shelf parts yet. Ramping up production volumes relies on suppliers being able to scale their output of these critical components just as quickly – a significant uncertainty. Bottlenecks in specific materials or manufacturing equipment could easily derail ambitious timelines.²⁸ The heavy reliance on Asian suppliers, particularly for electronics, also introduces geopolitical risk.²⁸

Cost is the other side of the manufacturing coin. Prices are falling, with BOM estimates dropping¹⁵ and target prices heading towards the sub-$30k range for some models.¹⁰, ⁴ This is crucial for making the economics work against human labor or existing automation.⁴⁷ But hitting those targets at scale demands relentless design optimization⁴³ and finding major efficiencies in assembly and component sourcing – easier said than done.

Finally, don't forget the Total Cost of Ownership (TCO). A low purchase price means little if the robot is unreliable, consumes excessive energy, or requires expensive maintenance and software subscriptions.⁴⁷ Demonstrating not just an affordable purchase price, but a compelling lifetime operating cost is essential for widespread adoption, and that data is only just starting to emerge from early deployments.¹⁴, ¹⁶ The manufacturing challenge isn't just about volume; it's about volume, cost, and reliability.

Where Robots Will Win First

Humanoid robots won't replace everyone everywhere overnight. Like most transformative technologies, they'll find initial success in specific niches where their unique advantages provide the clearest value. Where are those early beachheads likely to be?

The consensus points strongly towards logistics and warehousing. Why? These environments often involve repetitive material handling tasks (moving totes, boxes) in spaces designed for humans (shelving aisles, loading docks). Labor shortages for these physically demanding jobs are acute in many regions.² Robots like Agility's Digit are explicitly designed for tasks like loading/unloading conveyors or moving items between processing stations – jobs difficult to fully automate with fixed systems or wheeled robots alone.⁴², ⁵²

Manufacturing is another prime target, especially for tasks requiring mobility around existing production lines. Examples include machine tending (loading parts into CNC machines, as targeted by Apptronik²⁰) or delivering components to assembly stations. Pilots at automotive plants (Figure at BMW,²⁶ Apptronik at Mercedes-Benz,³¹ potentially Tesla's internal use³⁴) suggest this is a key focus. The ability to work in spaces designed for people without major retrofitting is a significant advantage here.¹¹

Other potential early niches include tasks that are dangerous or unpleasant for humans, where the cost of automation is more easily justified by safety improvements (reducing injury costs) rather than just direct labor savings.¹⁰ Think inspection in hazardous areas or handling potentially harmful materials.

The common thread? These early applications leverage the humanoid form factor's core strengths: mobility in human spaces and the ability to perform relatively simple manipulation tasks currently done by people. They focus on augmenting existing workflows and filling labor gaps, rather than attempting highly complex, dexterous tasks where humans still vastly outperform current robots. Success in these initial deployments will be crucial for building confidence and justifying investment in tackling harder problems later.

The 2030 Reality Check

Given the immense potential and daunting challenges, what's the realistic outlook for humanoid robots by the end of this decade?

Market forecasts, as we've seen, are all over the map – from $4 billion to $40 billion – reflecting fundamental uncertainty.¹⁸, ²⁵ Goldman Sachs analysts predict over 250,000 units shipped in 2030, mostly for industrial use, implying a substantial market is forming.²⁸ But achieving even the mid-range forecasts requires significant progress on multiple fronts.

The bull case rests on continued rapid AI improvement,⁵ successful manufacturing scale-up driving costs down,¹³, ¹⁵ and persistent economic pressures (labor shortages, reshoring) creating strong demand.², ⁸ The core promise is a flexible, mobile automation platform that can adapt to various tasks in human environments.¹¹, ²⁰

The bear case highlights the formidable remaining hurdles. Dexterity⁶⁶ and true, robust autonomy⁵ remain far from solved problems. Battery life is a practical constraint.² Achieving industrial-grade reliability¹⁴ and proving a compelling TCO⁴⁷ at scale are major unknowns. Safety validation and regulatory frameworks are still nascent.⁹⁴ Societal acceptance and ethical considerations also loom large.⁸⁶, ⁹⁴

The most probable 2030 scenario? Humanoids become an increasingly common sight, but primarily within specific industrial settings (logistics centers, large factories) in high-wage economies. They won't be ubiquitous general-purpose assistants. Instead, they will likely perform targeted tasks like material transport, machine tending, and simple pick-and-place operations, working alongside humans and other automation systems. Their key advantage will be flexibility within existing infrastructure.¹¹ Widespread deployment in homes or highly dynamic public spaces seems unlikely within this timeframe.

What does this mean for businesses and individuals? Companies with large logistics or manufacturing operations should be actively monitoring pilot results and evaluating potential use cases. Understanding the integration requirements and potential ROI is becoming strategically important.¹⁵ For workers, the impact by 2030 is likely focused on specific manual labor roles, potentially augmenting human capabilities or taking over the most strenuous/repetitive tasks, rather than causing mass displacement across the board. Investors face high risks but potentially enormous rewards, needing to assess not just the technology but also the manufacturing prowess and go-to-market strategies of competing firms.¹⁰² Policymakers need to engage with safety standards and the longer-term socio-economic implications.⁹⁴

The next three to five years will be critical. Success depends on moving beyond demos to demonstrate consistent, reliable, cost-effective performance in real-world operations.

What to Watch: Key Developments in the Next 6-12 Months

The field is moving quickly. Keep an eye on these specific developments over the coming year (from May 2025):

1. Pilot Program Outcomes & Expansion: Look for public data from major industrial pilots (Figure/BMW, Agility/GXO, Apptronik/Mercedes). Quantitative results on task rates, reliability, and ROI are key signals. Announcements of expanded deployments or initial commercial orders will indicate growing market confidence.

2. Manufacturing & Cost Milestones: Track progress against early production targets (late 2025/early 2026) from Tesla, Figure, Agility. Verifiable data on actual unit costs versus targets (e.g., Tesla's sub-$30k goal) will test scaling feasibility. Closely watch Chinese players like Unitree entering mass production now, and adoption by companies such as BYD.

3. Demonstrated Capability Advances: Watch for demonstrations beyond controlled settings, especially in:

   1. Dexterity: Robots using common tools or performing complex assembly autonomously.

   2. Autonomy: Robots reliably completing multi-step tasks in unstructured environments, showing practical gains from foundation models like GR00T N1.⁷

   3. Endurance: Concrete proof of longer runtimes or effective battery swapping in operation.

4. New Models & Specification Updates: Anticipate potential next-gen robot unveilings or major updates from key players (Unitree, others). Specification releases for models like Boston Dynamics' electric Atlas will clarify the competitive positioning.

5. Safety Standards & Regulatory Movement: Monitor industry groups (ISO, A3) and government bodies for progress on safety standards or testing protocols specifically for mobile humanoid robots in workplaces. Draft guidelines would be a significant development.

Share

A significant technological shift is occurring, with humanoid robots holding considerable potential. The journey towards 2030 involves primarily laying critical groundwork: proving technological reliability and value, refining the economic proposition, and managing complex integration and societal challenges. While widespread, general-purpose deployment may be a vision for the following decade, the advancements and investments occurring now are setting the stage for this technology's future impact. The next few years will be pivotal in determining the pace and scale of humanoid robot integration.

IMPORTANT DISCLAIMER

This article represents my personal opinions and analysis only, not statements of fact. All views expressed are speculative, subject to bias, and should not be construed as factual representations of reality. Content should be treated as entertainment and general information only.

I am not a registered investment advisor, financial analyst, securities broker, or financial planner. I have no professional relationship with any readers of this content, and no fiduciary duty to you. Nothing in this content establishes any form of advisor-client relationship.

I expressly disclaim all responsibility for any investment outcomes that may result from following ideas, concepts, or commentary presented herein. Past performance is not indicative of future results, and all investments involve risk of loss, potentially including the loss of all invested capital.

For full disclosure, I may currently hold positions (long or short) in securities of companies mentioned or related to the subject matter of this article. My positions may change at any time without notice. I may have business relationships or seek to do business with companies mentioned. I may receive compensation directly or indirectly connected to these companies or the subject matter discussed. These actual or potential conflicts of interest may influence my opinions despite my best efforts to remain objective.

Despite my best efforts to ensure accuracy, this article may contain errors, inaccuracies, or outdated information. The subject matter covered may involve rapidly evolving fields. Data points, statistics, timelines, and projections cited were based on information available around the time of writing or the stated access dates of sources, but may have changed by the time you read this. Strategies, plans, and market conditions are subject to change without notice.

Always consult with a qualified financial, legal, or investment professional before making investment decisions. This content should not serve as the basis for any investment actions.

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Welcome back to Prdctnomics, the economics of technology products newsletter that's been on a much longer hiatus than intended. It's been a while - most of my writing was published back in 2021, and now we find ourselves in 2025 with the tech landscape dramatically transformed. Today I'm diving back in with a look at perhaps the most consequential technology megatrends happening right now: autonomous vehicles. Thanks for sticking around, and let's jump right in.

The Great Self-Driving Race: Who's Leading in 2025?

We're in the middle of one of the most expensive technology races in history.

Companies have poured over $100 billion into figuring out how to make cars drive themselves¹. And the prize? A market projected to be worth nearly $1 trillion by 2040².

But here's what most people don't realize: there isn't just one race. There are three separate battlegrounds with different leaders in each.

The Three-Way Battle for Autonomous Dominance

Battleground #1: Robotaxis

Current Leader: Waymo

Waymo - The future is now (Video)

Waymo is absolutely crushing it. They're completing over 200,000 paid driverless rides EVERY WEEK across Phoenix, San Francisco, LA, and Austin³. That's not testing - that's real people paying real money to be driven around by robots.

Alphabet just poured another $5.6 billion into Waymo⁴. They're doubling down on their investment as competitor Cruise (GM's robotaxi effort) suspended operations following a serious safety incident that led to regulatory action⁵.

I have used Waymo in San Fransisco several times now and mostly loved the experience. I personally would not use a human driver via ride sharing anywhere Waymo is available, it is that good. Waymo drove significantly better than any of my recent human Uber drivers, had cleaner vehicles, and was less expensive. This observation will become a consensus view I believe as more people try robotaxis and they continue to improve.

In China, Baidu's Apollo Go is the dominant player, with over 1.1 million rides in Q4 2024 alone⁶. They're expanding aggressively into Dubai and Abu Dhabi⁷.

As of April 2025, Baidu's Apollo Go robotaxi service operates a fleet of over 400 fully driverless vehicles in Wuhan, with plans to expand to 1,000 vehicles by the end of the year. In Dubai, the service plans to deploy 100 fully autonomous vehicles by the end of 2025, with an expansion goal of at least 1,000 vehicles by 2028. In Abu Dhabi, initial trials have begun, with full commercial operations expected by 2026.

Meanwhile, Zoox (Amazon's $1.2 billion bet) is preparing to launch its first commercial service later this year⁸. Their vehicle looks like a living room on wheels - no steering wheel, no front or back, just seats facing each other. Details on their exact fleet size are hard to come by.

And Tesla? Their plan is robotaxis in Austin by June 2025⁹ (build on modified Model Y vehicles with FSD). But we've heard "robotaxis next year" since 2019, so... ¯_(ツ)_/¯

​As of April 2025, Tesla's Cybercab: a fully autonomous, purpose-built robotaxi, is not yet operating on public roads, but mass production planned to start in 2026.

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Battleground #2: Autonomous Trucking

Co-Leaders: Aurora and Kodiak

While everyone's distracted by robotaxis, the trucking industry is poised to get disrupted.

Aurora Innovation is set to launch driverless commercial trucking between Dallas and Houston in April 2025¹⁰. Think about that - next month, 18-wheelers with no humans will be delivering goods on Texas highways.

Kodiak Robotics has already logged over 750 hours of completely driverless commercial operations in West Texas¹¹. Atlas Energy Solutions has ordered 100 autonomous trucks from them¹². Let that sink in - a real company is buying 100 trucks with no drivers.

Torc Robotics (owned by Daimler Truck) is targeting 2027¹³. They're taking longer, but when the world's largest truck manufacturer puts their weight behind something, pay attention.

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Battleground #3: The Tech Platforms

Co-Leaders: Nvidia and Mobileye

The companies making the brains behind self-driving have the best business model of all - they sell pickaxes in a gold rush.

Nvidia dominates computing hardware with their DRIVE platform (now upgrading to their "Thor" chip based on Blackwell architecture)¹⁴. They're the arms dealer selling to everyone in the war.

Mobileye has deep relationships with nearly every carmaker on the planet¹⁵. They've built a brilliant ladder of products from basic driver assistance to full autonomy¹⁶.

Qualcomm is the challenger, leveraging their smartphone chip dominance to move into automotive¹⁷. Late to the party, but catching up fast.

The Great Technological Fork in the Road

There's a massive philosophical divide in how to make cars drive themselves.

Almost everyone (Waymo, Zoox, Aurora, Mobileye, etc.) believes you need a combination of cameras, radar, AND LiDAR (those spinning things on top of self-driving cars that cost $10,000+ each)¹⁸. This multi-sensor approach creates redundancy and safety.

Then there's Tesla.

Tesla is the only major player betting everything on cameras alone - no LiDAR, not even radar anymore¹⁹. Elon's reasoning? "Humans drive with their eyes, so cars should too"²⁰.

It's like bringing a knife to a gunfight... but if the knife turns out to be Excalibur, Tesla wins everything.

Existential Threat to Traditional Automakers

Clayton Christensen famously described three types of innovation:

1. Efficiency Innovation

2. Sustaining Innovation

3. Disruptive Innovation.

This framework explains not just why traditional automakers are struggling with self-driving tech, but why their entire business model faces existential danger.

Traditional automakers are masters of Efficiency Innovation - squeezing more profit from existing processes. Look at Toyota's relentless lean manufacturing or Ford's automated assembly lines. They're obsessed with reducing the cost to produce largely the same product. Even their EV strategies reflect this mindset - "How do we efficiently make electric versions of our existing lineup?"

They also excel at Sustaining Innovation - incremental improvements to existing products. Slightly better engines, marginally nicer interiors, a few more MPG each year. This worked beautifully in a world where transportation meant personal car ownership and human drivers. Their entire organizational structure, supplier relationships, dealer networks, and R&D processes are optimized for this approach.

But self-driving technology represents classic Disruptive Innovation - creating entirely new markets while making existing ones obsolete. The true disruption isn't just autonomous driving; it's what happens next:

1. The death of individual car ownership. When robotaxis become cheaper per mile than owning a vehicle (projected by 2035), why would consumers tie up capital in depreciating assets that sit unused 95% of the time?

2. A dramatic shrinking of the total vehicle market. One autonomous vehicle in constant operation could replace 5-10 personally owned vehicles. For manufacturers built to sell millions of cars to individual consumers, this means a catastrophically smaller market.

3. A regulatory environment increasingly hostile to human drivers. As self-driving vehicles prove statistically safer, governments will likely create incentives to get humans off the wheel. We're already seeing automotive insurance costs skyrocket; this will accelerate as human drivers become a higher-risk pool.

4. The complete disruption of adjacent industries - traditional auto insurance, repair shops, parking garages, and auto dealers all face massive upheaval.

Traditional automakers are responding predictably - investing primarily in efficiency innovations (cheaper EVs) and sustaining innovations (marginally better driver assistance). But they're structurally incapable of the disruptive leap needed. Their dealer networks actively fight direct-to-consumer models. Their unions resist the dramatic workforce changes autonomous tech requires. Their shareholders demand quarterly returns incompatible with the decade-long investment needed.

As Christensen observed, being great at operating in an existing paradigm often prevents you from succeeding in a disruptive one. The skills, processes, and values that made traditional automakers successful are precisely what will kill them in the autonomous age. Their expertise in building cars for human drivers to own becomes not just irrelevant but a liability in a world of robotaxis.

Strategic Approaches and Market Dynamics

The players I've mentioned are pursuing wildly different strategies:

Integrated Full-Stack Players like Waymo and Zoox are developing everything - sensors, compute, software, and in Zoox's case, even the vehicle itself. This gives them incredible control but requires massive investment.

Vision-Centric Tesla is betting it all on cameras and AI, deliberately excluding LiDAR and radar. They're leveraging their fleet data and Dojo supercomputer to train neural networks that can match human vision.

Platform Providers like Nvidia, Qualcomm, and Mobileye focus on providing the building blocks - SoCs, software development kits, and reference designs to OEMs and suppliers.

AI-First/Software Licensing is Wayve's approach - developing adaptable, data-driven AI software that can operate without HD maps, aiming for rapid scalability through licensing.

So Who Wins?

If I had to guess today:

Robotaxis: Waymo is the clear leader with unmatched scale and technology. They'll dominate the US market while Baidu owns China and broader Asia / Middle East.

Trucking: Aurora and Kodiak are neck-and-neck for the near term, but Torc/Daimler is the sleeping giant for the long haul (pun intended).

Platforms: Nvidia and Mobileye will continue to power most of the industry, with different companies choosing different partners.

The Wildcard: Tesla. If they actually solve FSD with cameras only, they could leapfrog everyone. But that's a massive IF. They may be forced to add LiDAR/radar for true L4 capability.

Why this matters

The next 24 months will be the proving ground²⁵. We'll see whether these companies can scale beyond their initial deployments and start generating actual profits.

The companies that succeed here won't just disrupt transportation; they'll fundamentally alter human civilization's relationship with distance, time, and space.

Cities will decentralize as commute time becomes productive time, transforming two-hour drives from burden to opportunity. The weekend map will expand dramatically: go to sleep in your autonomous vehicle on Friday night and wake up 500 miles away for a Saturday morning hike in the mountains. Those destinations that once required flight bookings and airport hassles become accessible with nothing more than packing a bag and falling asleep.

Meanwhile, autonomous trucking will collapse shipping costs, operating 24/7 without rest breaks, union contracts, or hours-of-service limitations. This will reshape supply chains, reduce consumer prices, and bring production closer to demand.

Above all, these vehicles will save lives, potentially millions of them. The 1.3 million annual global traffic deaths, mostly caused by human error, could plummet. The elderly and disabled will gain unprecedented mobility independence. Parents will no longer fear their teenagers driving home late at night.

In that new world, our lives will expand in ways we've only begun to imagine.

IMPORTANT DISCLAIMER

This article represents my personal opinions and analysis only, not statements of fact. All views expressed are speculative, subject to bias, and should not be construed as factual representations of reality. Content should be treated as entertainment and general information only.

I am not a registered investment advisor, financial analyst, securities broker, or financial planner. I have no professional relationship with any readers of this newsletter, and no fiduciary duty to you. Nothing in this newsletter establishes any form of advisor-client relationship.

I expressly disclaim all responsibility for any investment outcomes that may result from following ideas, concepts, or commentary presented in this newsletter. Past performance is not indicative of future results, and all investments involve risk of loss, potentially including the loss of all invested capital.

For full disclosure, I may currently hold positions (long or short) in companies mentioned in this article, including but not limited to: Alphabet/Waymo, Tesla, Nvidia, Mobileye/Intel, Qualcomm, Baidu, and Amazon/Zoox. My positions may change at any time without notice. I may have business relationships or seek to do business with companies mentioned. I may receive compensation directly or indirectly connected to these companies. These actual or potential conflicts of interest may influence my opinions despite my best efforts to remain objective.

Despite my best efforts to ensure accuracy, this article may contain errors, inaccuracies, or outdated information. The autonomous vehicle industry is evolving rapidly, with new developments occurring weekly. Data points and statistics cited were current as of publication, but may have changed by the time you read this. Company strategies, deployment timelines, and market projections are subject to change without notice.

Always consult with a qualified financial, legal, or investment professional before making investment decisions. This content should not serve as the basis for any investment actions.

References

¹ "AV Investment Trends: Who's Pouring the Most Money into Self-Driving Technology? (Funding Stats)" - https://patentpc.com/blog/av-investment-trends-whos-pouring-the-most-money-into-self-driving-technology-funding-stats

² "Autonomous Vehicle Market Size to Reach $980.7 Billion, Globally, by 2040 at 22.3% CAGR: Allied Market Research" - https://www.globenewswire.com/news-release/2025/02/04/3020166/0/en/Autonomous-Vehicle-Market-Size-to-Reach-980-7-Billion-Globally-by-2040-at-22-3-CAGR-Allied-Market-Research.html

³ "Waymo - Wikipedia" - https://en.wikipedia.org/wiki/Waymo

⁴ "How to invest in the second wave of autonomous driving" - https://globalventuring.com/corporate/asia/invest-autonomous-driving/

⁵ "NHTSA closes preliminary investigation into General Motors' Cruise robotaxis" - https://apnews.com/article/cruise-gm-nhtsa-general-motors-odi-48591349c24c6f969c07c65f1ed4e9d8

⁶ "5 Autonomous Vehicle Leaders Revolutionizing Transportation (2025)" - https://www.securities.io/autonomous-vehicles-companies/

⁷ "Baidu Expands Robotaxi Service Globally with Major Dubai Partnership" - https://www.stocktitan.net/news/BIDU/baidu-s-apollo-go-enters-strategic-partnership-with-dubai-rta-to-yq6kbug9se16.html

⁸ "Zoox Expands Autonomous Vehicle Testing to Los Angeles" - https://www.therobotreport.com/zoox-expands-autonomous-vehicle-testing-los-angeles/

⁹ "Tesla's Bold Move: Model Y Robotaxi Service to Hit Austin's Roads by June 2025!" - https://opentools.ai/news/teslas-bold-move-model-y-robotaxi-service-to-hit-austins-roads-by-june-2025

¹⁰ "Aurora, Continental & NVIDIA Partner to Launch Driverless Trucks by 2027" - https://texasborderbusiness.com/aurora-continental-nvidia-partner-to-launch-driverless-trucks-by-2027/

¹¹ "Autonomous trucking developer Kodiak Robotics to go public via SPAC" - https://www.therobotreport.com/kodiak-robotics-autonomous-trucking-developer-goes-public-via-spac/

¹² "Kodiak Robotics and Atlas Energy Solutions Launch First Commercial Driverless Truck Service in Permian Basin" - https://www.ctol.digital/news/kodiak-robotics-atlas-energy-first-commercial-driverless-truck-permian-basin/

¹³ "Autonomous Driving: Daimler Truck delivers latest iteration of autonomous-ready truck platform to Torc" - https://northamerica.daimlertruck.com/news-stories/2025/autonomous-driving-daimler-truck-delivers-latest-iteration-of-autonomous-ready-truck-platform-to-torc

¹⁴ "NVIDIA DRIVE Hyperion Platform Achieves Critical Automotive Safety and Cybersecurity Milestones for AV Development" - https://investor.nvidia.com/news/press-release-details/2025/NVIDIA-DRIVE-Hyperion-Platform-Achieves-Critical-Automotive-Safety-and-Cybersecurity-Milestones-for-AV-Development/default.aspx

¹⁵ "Will Mobileye be the winning supplier of autonomous driving? Or Waymo?" - https://www.reddit.com/r/SelfDrivingCars/comments/1gmjone/will_mobileye_be_the_winning_supplier_of/

¹⁶ "Mobileye | Driver Assist and Autonomous Driving Technologies" -

https://www.mobileye.com/

¹⁷ "BriefCASE: Revving up - Qualcomm's automotive business hits high gears" - https://www.spglobal.com/automotive-insights/en/blogs/2024/11/briefcase-qualcomms-automotive-business-hits-high-gears

¹⁸ "Why Tesla's camera-only approach may be a mistake" - https://www.fastcompany.co.za/design/why-teslas-camera-only-approach-may-be-a-mistake-6e572ced-d725-4c39-ae21-f74c29ef9900

¹⁹ "Tesla's Vision-Only Strategy Hits a Speed Bump as NHTSA Investigates FSD" - https://opentools.ai/news/teslas-vision-only-strategy-hits-a-speed-bump-as-nhtsa-investigates-fsd

²⁰ "Tesla's FSD Struggles: Is Vision-Only Enough?" - https://evhype.com/teslas-fsd-struggles-is-vision-only-enough/

²¹ "Partially autonomous cars forecast to comprise 10% of new vehicle sales by 2030" - https://www.goldmansachs.com/insights/articles/partially-autonomous-cars-forecast-to-comprise-10-percent-of-new-vehicle-sales-by-2030

²² "Self-Driving Truck Market Set to Hit $41.2 Billion by 2035" - https://shipping.einnews.com/amp/pr_news/793081719/self-driving-truck-market-set-to-hit-41-2-billion-by-2035-key-trends-driving-growth-robotics-inc-plusai-inc

²³ "Waymo - Wikipedia" - https://en.wikipedia.org/wiki/Waymo

²⁴ "Autonomous vehicle technology for self-driving cars can cost up to $100,000 per vehicle, but is expected to drop to around $3,000 by 2035" - https://www.itskrs.its.dot.gov/2020-sc00454

²⁵ "2025: The Defining Year for Autonomous Vehicle Adoption" - https://investorplace.com/hypergrowthinvesting/2025/03/autonomous-vehicles-why-2025-will-usher-in-the-self-driving-car/

The world of AI and machine learning has been captivated by the rapid advancements of large language models like GPT4 (my coauthor and editor for this article), but beyond the limelight lies an under hyped, yet groundbreaking technology, which is quietly poised to reshape the future of AI and your life.

Picture this: a world where AI systems become seamlessly integrated into your daily life, enabling intelligent devices that consume minimal power and respond with lightning-fast speed. Smart cities with connected infrastructure optimize traffic flow and energy usage in real-time, making urban living more efficient and sustainable. Wearable devices offer unprecedented battery life, monitoring our health and providing personalized recommendations, potentially extending our healthspan and reducing healthcare costs.

In this world, robots learn and adapt to their environments as naturally as humans, assisting us in healthcare, manufacturing, and beyond. Autonomous vehicles navigate the streets with ease, reacting to obstacles and changing conditions faster than any human driver could. Edge computing becomes the norm, empowering local devices to process vast amounts of data with unparalleled efficiency, transforming industries like agriculture, retail, and security.

This future won't be brought to life by ever larger data centers of power-hungry GPUs. Instead, an unassuming, groundbreaking technology harkens back to a bygone era, blending the old with the new, and redefining daily life as well as the winners and losers of the AI race. 

Analog: From Vintage to Vanguard

This technology is analog computing, a once-forgotten approach now experiencing a renaissance in AI research.

Like fashion trends that cycle through decades of styles, updating and refining them for modern tastes, AI research is revisiting analog computing, a technology that was once overshadowed by digital advancements. Analog computing now offers unique advantages, such as energy efficiency and faster computation times, complementing and enhancing contemporary digital techniques. 

What is analog and digital exactly?

You can think of analog signals like a flowing river, with its water level constantly changing as it meanders through different terrains. Analog signals use this fluid nature to represent information through continuous variations, such as the pitch of a singer's voice or the changing shades of a sunset. However, like a message in a bottle floating downstream, analog signals can be affected by external factors and lose some of their original quality when traveling far distances or through various channels.

An analog signal: source

Now, imagine digital signals as a series of on-off light switches, where each switch represents a bit of information in the form of 0s and 1s. Instead of the smooth flow of a river, digital signals communicate information through distinct and separate values. It's like sending a message using Morse code, where each combination of dots and dashes conveys a unique meaning. Digital signals hold up better against external disturbances, ensuring that the message remains clear and accurate even when traveling long distances or through challenging conditions. This resilience is why digital signals are the backbone of modern electronic devices like computers and smartphones.

A digital signal: source

In the context of circuits, analog circuits are like an orchestra, with each component working harmoniously to process and amplify the continuous waveforms of analog signals. These circuits involve various components, such as resistors, capacitors, and operational amplifiers, all fine-tuned to work together in processing the fluid and ever-changing analog signals. However, like an orchestra playing in a busy city square, the performance of these circuits can be affected by external noise and interference. Thus, maintaining the fidelity of the information in analog circuits can be challenging, especially when dealing with complex signals or when the circuits need to perform under varying conditions.

Digital circuits, on the other hand, are more like a precision marching band, with each member executing specific and discrete commands in a synchronized manner. Digital circuits consist of components like logic gates, flip-flops, and microprocessors that work together to process the on-off nature of digital signals. Their binary language allows for a clear and consistent performance, even in the face of external disturbances. Just as a marching band can maintain its formation and rhythm despite distractions, digital circuits can reliably process and transmit information with minimal degradation. The robustness and predictability of digital circuits have made them the go-to choice for modern electronics, enabling the rapid growth and innovation of computing and communication technologies.

Analog’s AI Advantages 

Here is what you need to know:

1. Analog Mimics the Human Brain: Analog computing for AI is heavily inspired by the structure and function of the human brain, making use of continuous signals to process information in a way that resembles the brain's neural networks. This brain-inspired approach, known as neuromorphic computing, aims to create AI systems that can learn and adapt in a more natural and efficient manner.

2. Analog is Energy Efficient: One of the key advantages of analog computing for AI is its energy efficiency. Analog and neuromorphic systems can perform complex calculations with significantly lower power consumption compared to traditional digital systems, such as GPUs. This efficiency is particularly valuable for edge computing applications, where energy constraints are critical.

3. Analog is Well-Suited for Real-Time Processing: Analog and neuromorphic systems can process information faster and with lower latency than their digital counterparts, making them well-suited for real-time AI applications. The ability to perform multiple calculations simultaneously using continuous signals enables faster response times, which can be crucial for applications such as robotics, computer vision, and autonomous vehicles.

4. Analog is Noise Tolerant and Robust: Analog computing is inherently more tolerant of noise and imprecision, allowing for more robust AI systems that can handle uncertain or noisy input data. This characteristic is especially beneficial in situations where data quality may be compromised, such as sensor data from IoT devices or robotics applications.

5. Analog is not a Silver Bullet: While analog and neuromorphic computing offer several advantages for AI, there are still challenges to be addressed. The design and fabrication of analog and neuromorphic chips can be complex, and the field currently lacks standardized software and programming tools. 

Deep Dive on Analog in AI

Why now?

The resurgence of analog computing in AI stems from the limitations of digital computing, particularly in terms of energy efficiency and the ability to handle certain types of problems. As AI applications have expanded, researchers have been seeking new approaches to improve performance and efficiency, paving the way for analog and neuromorphic computing technologies to re-enter the scene.

A Brief History

Analog computers have a long history, dating back to ancient times with devices like the Antikythera mechanism and the astrolabe. In the 20th century, electronic analog computers were widely used for various applications, such as solving differential equations and modeling physical systems. However, with the advent of digital computers and their rapid growth in capability, analog computers gradually fell out of favor.

The interest in analog computing for AI started to re-emerge in the 1980s with the development of neural networks and the idea of using analog components to simulate brain-like behavior. This eventually led to the concept of neuromorphic computing, which aims to emulate the structure and function of biological neural systems using electronic components.

During the 1980s, the foundations of neuromorphic computing were established with pioneering work by researchers like Carver Mead, who coined the term "neuromorphic." He believed that the future of computing lay in building systems that closely mimicked the architecture and efficiency of the human brain. In this decade, the development of neural networks and the exploration of brain-inspired models of computation led to the creation of early neuromorphic systems and laid the groundwork for future innovations.

The 1990s saw a surge in research on artificial neural networks and the development of new learning algorithms, such as backpropagation. As the understanding of biological neural networks grew, researchers began to design more advanced neuromorphic systems. During this period, the first silicon retina and silicon cochlea were developed, paving the way for more sophisticated neuromorphic sensors and processing systems. However, the computational power and memory limitations of the time posed significant challenges to the development of large-scale neuromorphic systems.

In the 2000s, advancements in VLSI (Very Large Scale Integration) technology enabled the development of more complex and efficient neuromorphic chips. Researchers focused on creating specialized hardware to address the limitations of traditional digital computing for artificial intelligence tasks. The development of the IBM TrueNorth chip, which featured one million programmable neurons, marked a significant milestone in neuromorphic computing during this decade.

The 2010s witnessed a rapid expansion of interest and investment in AI, driven by the success of deep learning and the availability of large-scale datasets. Neuromorphic computing continued to advance, with researchers developing more efficient and powerful hardware, such as Intel's Loihi chip and the SpiNNaker project by the University of Manchester. This decade also saw the rise of analog AI companies, like BrainChip and Mythic, who began to commercialize neuromorphic technologies for various applications.

The current decade has seen the growing convergence of analog and digital technologies, with researchers exploring hybrid computing models to leverage the strengths of both approaches. As the limitations of digital computing become more apparent, particularly with respect to energy efficiency, the potential of analog AI to address these challenges has garnered significant interest. This decade is poised to witness further advancements in neuromorphic computing, the development of novel materials and technologies, and the widespread adoption of analog AI in various industries.

Different Categories of Analog AI Technologies

Neuromorphic Computing: This approach focuses on building hardware that imitates the structure and function of the human brain. Neuromorphic systems often use analog components to represent neurons and synapses, enabling more efficient processing of information compared to traditional digital systems.

Analog Accelerators: Analog accelerators are specialized hardware components designed to perform specific AI tasks, such as neural network processing or optimization, using analog circuits. These accelerators can be integrated with digital systems to improve overall performance and efficiency.

Mixed-Signal Computing: Mixed-signal computing combines analog and digital components in a single system, leveraging the strengths of both technologies. This hybrid approach can offer greater flexibility and efficiency, particularly for applications that require both continuous and discrete data processing.

Memristor-Based Computing: Memristors are a type of analog component that can store and process information simultaneously. They can be used to build neuromorphic systems or other types of analog computing architectures, offering potential advantages in terms of power efficiency and processing speed.

Specific examples and applications

Neuromorphic Computing

IBM's TrueNorth chip: This neuromorphic chip consists of 1 million programmable neurons and 256 million programmable synapses, offering high efficiency and low power consumption.

Intel's Loihi chip: Loihi is a neuromorphic research chip designed for energy-efficient AI applications, featuring 130,000 neurons and 130 million synapses.

Specific Applications

Neuromorphic Engineering for Robotics: can be used to develop more efficient and responsive robots that can learn and adapt to their environment in real-time.

Neuromorphic chips in Edge Computing: can be utilized in edge devices, such as smartphones and IoT devices, to enable AI applications with low power consumption and minimal latency.

Computer vision: Neuromorphic vision sensors can process visual data faster and more efficiently, enabling applications like real-time object recognition and tracking.

Analog Accelerators

Mythic's analog compute-in-memory technology: Mythic's technology performs AI processing using analog circuits within memory components, significantly reducing power consumption and increasing processing speed.

Hailo's Hailo-8 chip: This AI accelerator chip is designed for edge devices and incorporates analog techniques to optimize performance and power efficiency.

Specific Applications

Image and video processing: Analog accelerators can be used to perform image and video processing tasks more efficiently, such as image recognition or video compression.

Natural language processing: Analog accelerators can improve the performance of NLP tasks like sentiment analysis, language translation, and speech recognition.

Autonomous vehicles: Analog accelerators can be integrated into autonomous vehicle systems to perform real-time sensor data processing and decision-making with minimal power consumption.

Mixed-Signal Computing

Eta Compute's low-power neuromorphic computing solutions: Eta Compute combines analog and digital techniques to create AI processors that consume ultra-low power while maintaining high performance.

Specific Applications

Wearable devices: Mixed-signal computing can be used in wearable devices to perform AI tasks with low power consumption, enabling longer battery life and improved functionality.

Environmental monitoring: Mixed-signal computing can be employed in sensor networks for real-time environmental monitoring, such as air quality or water level measurement, with minimal energy requirements.

Memristor-Based Computing

Knowm Inc.'s memristor-based neuromorphic computing technology: Knowm's technology leverages memristors to create energy-efficient neuromorphic systems capable of learning and adaptation.

Specific Applications

Pattern recognition: Memristor-based computing can be used for pattern recognition tasks, such as facial recognition or handwriting recognition, with higher efficiency than traditional digital systems.

Data storage: Memristors can be used to develop non-volatile memory systems that retain information even when power is lost, offering potential advantages for energy efficiency and data retention.

The Fusion of Analog and Digital: A Glimpse Into the Future

Standing at the cusp of an AI revolution, it's essential to monitor the developments that will shape the industry and pave the way for a new generation of AI applications. Here are some key things to be on the lookout for:

1. Emerging Research and Breakthroughs: Stay informed about the latest research findings in the field of analog and neuromorphic computing. Follow leading research institutions, such as MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) or Stanford's Brains in Silicon Lab, to stay up to date with their work and explore potential applications of their discoveries.

2. New Collaborations and Partnerships: Watch for collaborations between academia, startups, and established tech giants that could drive innovation and accelerate the adoption of analog AI technologies. Strategic partnerships can facilitate the sharing of resources and knowledge, helping to overcome the current challenges faced by the industry. To do this check out Venture Beat AI, or this tech crunch query. 

3. Industry Adoption and Integration: Keep an eye on how industries begin to adopt and integrate analog AI technologies into their products and services. As these technologies become more mature and accessible, a wide range of applications will emerge, transforming industries such as agriculture, retail, security, and healthcare. To accomplish this look at AI Business, IoT World Today, Towards Data Science, and O'Reilly Radar.

4. Policy and Regulatory Developments: As the analog AI industry grows, it's important to monitor any policy or regulatory changes that could impact its development. Government support and investment can play a crucial role in driving the growth of the sector and addressing challenges related to standardization and intellectual property rights. Check out the Global Partnership on Artificial Intelligence, and the UNICRI Centre for AI and Robotics. 

5. Advances in Complementary Technologies: Analog AI is poised to benefit from advances in other areas of technology, such as materials science, sensor development, and quantum computing. Keep an eye on these fields, as breakthroughs could directly impact the performance and capabilities of analog AI systems. Monitor this at IEEE Spectrum, Quantum Computing Report, and Nature’s Nanotechnology section. 

6. Educational and Training Opportunities: As the analog AI industry expands, demand for professionals with expertise in both analog and digital technologies will grow. Stay informed about educational programs, workshops, and online courses that can help you develop the necessary skills to excel in this exciting field. Coursera, and the firehose of directly reading research papers are both good candidates for staying up to date. 

Like a surfer waiting for the perfect wave, staying informed about the developments in AI can prepare you and your friends for the opportunities its creating. Change is constant, fortunately so is our free newsletter! Subscribe and stay informed:

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From Farnam Street’s “The Red Queen Effect”:

In Through the Looking Glass, Alice, a young girl, gets schooled by the Red Queen in an important life lesson that many of us fail to heed. Alice finds herself running faster and faster but staying in the same place.

Alice never could quite make out, in thinking it over afterwards, how it was that they began: all she remembers is, that they were running hand in hand, and the Queen went so fast that it was all she could do to keep up with her: and still the Queen kept crying ‘Faster! Faster!’ but Alice felt she could not go faster, though she had not breath left to say so.

The most curious part of the thing was, that the trees and the other things round them never changed their places at all: however fast they went, they never seemed to pass anything. ‘I wonder if all the things move along with us?’ thought poor puzzled Alice. And the Queen seemed to guess her thoughts, for she cried, ‘Faster! Don’t try to talk!’

Eventually, the Queen stops running and props Alice up against a tree, telling her to rest.

Alice looked round her in great surprise. ‘Why, I do believe we’ve been under this tree the whole time! Everything’s just as it was!’

‘Of course it is,’ said the Queen, ‘what would you have it?’

‘Well, in our country,’ said Alice, still panting a little, ‘you’d generally get to somewhere else — if you ran very fast for a long time, as we’ve been doing.’

‘A slow sort of country!’ said the Queen. ‘Now, here, you see, it takes all the running you can do, to keep in the same place.

If you want to get somewhere else, you must run at least twice as fast as that!’

Increasingly it seems the borders of slow sorts of countries are shrinking.

Jobs requiring education often evolve or are disrupted. Trades (plumbing, mechanics, electricians, carpentry) has proven resilient but even portions of these fields could be disrupted. Extremely high quality prefab homes are being built by startups driven to make housing affordable, fast to build, and more accessible. It seems like the loser in this transaction is the labor that went into the much slower conventional house.

Another example is mechanics, electric vehicles do not require oil changes, do not require replacement spark plugs, do not require transmission fluid changes, and do not require lubricants like ICE vehicles [1]. In fact hybrid and electric vehicles require ~3x less brake replacements as wear on pads, rotors, calipers is instead used to engage an electric motor and turn that lost momentum into electricity to recharge the vehicle [2]. This might result in fewer mechanics over time. This is before we factor in self driving and vertical integration of these hypothetical self driving car companies.

My world view has been biased by my own experiences.

My career hasn’t been conventional. Depending on how we define career I started in the less desirable areas of the service industry, replaced those jobs around 18 with freelancing in web design, then web development, then an emerging area now long taken for granted: mobile development. The latter was actually inspired by an article I read that impacted me so strongly me that I persuaded a few friends that we should incorporate a company to try to get into this mobile thing. I wish I could say we were a runaway success and retired early, this isn’t the type of story. We had the technical chops but not the business chops and ended up shutting down a few years later.

While working for myself or companies I founded I also interned every year I was in school. First as IT / web design for a small derivatives broker, also doing odd part time jobs including trying my hand at cold calls, and finally moving onto automated trading systems at the same firm. I also held a couple software engineering internships at a startup and a fortune 500.

All of the above before I had a degree.

At some point I ended up becoming a more stable salaried software engineer. Not knowing how the world works I didn’t move to a tech hub and joined a large well respected but shrinking tech company. There I experienced quarterly layoffs and watched many very smart and overly loyal people get pushed out of a company they spent their life building.

A few years later seeing the writing on the wall I was back into startups, this time at a fintech bringing the power of expensive financial terminals to the retail investor. Despite failing to raise money and shutting down after 6 months we made substantial progress and that led me to pivot into a new career, this time in a major tech hub: PM.

Back in a large company I worked harder than I ever worked to learn an entirely new job, and to be held to standards far higher than I ever had before.

Honestly it kicked my ass.

I learned a ton, and adapted, but within my individual team turnover was extremely high, and frankly everyone was fungible. We were all just one bad month from being replaced. I realized I really wasn’t getting a good return on investment for putting in 60-80 hour weeks, what I was doing (mainly project management at that point) was just too common of a skill set.

During college and before I was always very interested and involved in security, but I didn’t want to pigeon hole myself opting for being a generalist software engineer instead of a penetration tester or researcher. Now we were overhauling some areas of our product with a security focus and I took that as an opportunity to dive back in. My studying outside of work of course went far beyond the above and a couple months and maybe 50-100 cold emails later I’d be in the job I’ve had the past 5 years: Product Management with a security focus.

At the cost of less optionality and going deep into one or two specializations I found much more job stability, and a place to grow. The last ~5 years have in many ways been better than I could have imagined for what a job could be. As I reflect on what I want to do next my world is still wide open. I could focus on security and become an applied researcher, stick to product and work on some of the most challenging security and machine learning problems in the world, try to ride what I speculate will be the next big secular trend in tech (Web3), or as many readers of my blog might have gathered I could get involved in finance or fintech again.

I don’t know what I will do next but thus far taking risks and trying new things has at worse cost me some bruises, some time, or some money, but at best they’ve been radically life changing for the better.

We live in a world of rapid change that is both punishing and rewarding at times. The more popular a path is the more competition there is and this applies in your career, business, and life.

Looking at how the tech landscape has transformed during my career the spoils have gone to those willing to invest in human capital and research. As every company becomes a tech company this increasingly applies to all industries. As I’ve discussed before Clayton Christensen describes 3 types of innovation: Efficiency Innovation, Sustaining Innovation, and Disruptive Innovation.

What I experienced at my first job was efficiency innovation and sustaining innovation at a company that used to invest in disruptive innovation. Failure to stay efficient can lead to both a failure to attract investment and squeeze margins lowering the ability to invest in future product improvements or potentially disruptive new lines of business, but it is possible to be “too efficient”. Efficiency innovations around doing more with less works really well until it doesn’t, but from the perspective of Wallstreet it often looks great when reducing costs while ideally profits continue to grow. I find companies overly focused on efficiency often lose the plot of what is important going through down sizing, right sizing, re-organization, and re-structuring over and over in an effort to squeeze out another drop of profit from cost cutting. Worse case is when this is in a shrinking industry or market.

Sustaining innovation is the world of the Red Queen. If a car manufacturer doesn’t release a better car with comparable new and existing features to their competitors they are left behind. This is the default state of business. Simply to maintain existing market share you must continuously run faster just to stay in the same place. If you can offer a product better than the last that is cheaper or obviously differentiated from your competitors maybe you can even gain market share next year.

The investment in sustaining innovation is most essential the more alike you and your competitors are.

Warren Buffet recognized the futility of staying on certain hamster wheels of sustaining and efficiency innovations. It is one thing to gain market share in a business with a growing market and durable competitive advantages, it is another thing to fight for market share in a commodity business. Emphasis mine:

Over the years, we had the option of making large capital
expenditures in the textile operation that would have allowed us
to somewhat reduce variable costs. Each proposal to do so looked
like an immediate winner. Measured by standard return-on-
investment tests, in fact, these proposals usually promised
greater economic benefits than would have resulted from
comparable expenditures in our highly-profitable candy and
newspaper businesses.

But the promised benefits from these textile investments
were illusory. Many of our competitors, both domestic and
foreign, were stepping up to the same kind of expenditures and,
once enough companies did so, their reduced costs became the
baseline for reduced prices industrywide. Viewed individually,
each company’s capital investment decision appeared cost-
effective and rational; viewed collectively, the decisions
neutralized each other and were irrational (just as happens when
each person watching a parade decides he can see a little better
if he stands on tiptoes). After each round of investment, all
the players had more money in the game and returns remained
anemic.

Thus, we faced a miserable choice: huge capital investment
would have helped to keep our textile business alive, but would
have left us with terrible returns on ever-growing amounts of
capital. After the investment, moreover, the foreign competition
would still have retained a major, continuing advantage in labor
costs. A refusal to invest, however, would make us increasingly
non-competitive, even measured against domestic textile
manufacturers.

His conclusion, emphasis mine:

My conclusion from my own experiences and from much
observation of other businesses is that a good managerial record
(measured by economic returns) is far more a function of what
business boat you get into than it is of how effectively you row
(though intelligence and effort help considerably, of course, in
any business, good or bad). Some years ago I wrote: “When a
management with a reputation for brilliance tackles a business
with a reputation for poor fundamental economics, it is the
reputation of the business that remains intact.” Nothing has
since changed my point of view on that matter. Should you find
yourself in a chronically-leaking boat, energy devoted to
changing vessels is likely to be more productive than energy
devoted to patching leaks.

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The above doesn’t mean not to invest, but to be wise or lucky on where to invest. If we look at the most successful companies of the last decade they invested considerable amounts into R&D and it appears to have paid off.

From KnowHow Capital:

But simplistically, if you invest well and have a lot of money to invest, you will generate the most returns. No surprise then that the top r&d spenders in the US are also some of the best performing stocks of the past 10yrs.

…

Of course, as this chart from Strategy + Business shows, spending big doesn’t always lead to returns. Without embracing an innovative culture, it can just be money down the drain. Hence, make sure you bring your own qualitative overlay to the analysis we are about to run you through.

The most successful companies are swinging for the fences with new lines of business and attempts at disruptive innovations, improving their existing offerings with sustaining innovations, and investing in lean operations and efficiency improvements. Increasingly all 3 are required to be competitive if you’re competing with the best.

Tech is not a “slow sort of country” to quote the Red Queen. This came up with a discussion last month with Clueless on Amazon’s huge and ever increasing Capex and was the inspiration for this article:

Will they continue to make wise investments and find opportunities as good as their past opportunities? That is anyone’s guess, but companies that are highly innovative and willing to invest heavily are where I’ve been most successful as an employee and as an investor. Anecdotally I’ve seen the type of people who keep a beginners mind and try different things without a fear of failure have been the most successful. Trying more things and being willing to fail across many survivable bets seems to lead to a bigger “luck surface area” for those taking risks in business and in life.

Have you used the red queen mental model to improve your life, career, or investments? Feel free to share.

China has some of the most innovative companies in the world. Tencent in particular might be one of the best capital allocators of any company in history. Unfortunately for investors who are not Chinese citizens the civic laws of China appear to be pretty clear that they ban foreign ownership in a variety of industries. Wall Street has never met an issuance fee it didn’t like and worked hard to come up with structures to get around this called Variable Interest Entities. 

As always this is not investment or legal advice. 

I am simply documenting my thoughts publicly for my own records and to get feedback. It appears a few things are true:

1. Non-Chinese Investors are buying companies in the Cayman Islands with the same name as the Chinese mainland company

2. The Cayman entity owns no direct equity in the mainland company

3. There are agreements to profit share with the VIE and the mainland company but these might be both illegal and have been shown unenforceable multiple times in practice

4. While the Cayman and China entities are legally two separate companies, the SEC allows the VIE to report the balance sheet and income as if it was the mainland company but its claims to those assets and incomes appears to be weak at best, nonexistent in practice

5. The spirit of the VIE seems aggressive if not outright illegal under Chinese law

6. Even if you could buy the mainland shares directly the accounting and properly law standards of China are significantly lacking 

Let's look at each of these individually. 

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Non-Chinese Investors are buying companies in the Cayman Islands with the same name as the Chinese mainland company

From Didi’s F1:

I personally am not smart enough to understand each part of this corporate structure or understand what the US listed Cayman Didi owns relative to the Beijing Didi entity. This level of convolution is not uncommon in the world of Variable Interest Entities. 

What is clear is an investor would be buying some portion of a Cayman Island shell company which owns or has contractual relationships with a variety of other Cayman and Hong Kong entities which owns or has some sort of contractual relationship with the onshore entities.  

The more general form of this VIE structure is below:

Source: Council of Institutional Investors 

2. The Cayman entity owns no equity portion of the mainland company

“In the VIE structure, American stockholders don’t own the Chinese company. They just own a company that has some contracts that are supposed to mimic what it would look like to own the Chinese company. You’re not buying stock in Alibaba when you buy Alibaba on the NYSE. You’re buying a holding company called Alibaba Holdings registered in the Cayman Islands with a claim on some of Alibaba’s profits but no actual ownership stake. You don’t own shares in the real Alibaba and you don’t get a vote in how Alibaba is run, although you do get to share in Alibaba’s profits.” - The Diplomat

“The VIE structure could be deemed to contravene Chinese laws that restrict foreign investment in strategically sensitive industries. VIEs operate using contractual arrangements rather than direct ownership, leaving foreign investors without the rights to residual profits or control over the company’s management that they would otherwise enjoy through equity Ownership.” - Council of Institutional Investors 

3. There are agreements to profit share with the VIE and the mainland company but these might be both illegal and have been shown unenforceable multiple times in practice

“Frankly we do not think that the SEC (US), the FCA (UK) and ESMA (Europe) have done what they should do when it comes to VIEs and regulating them. There is little to nothing made of the fact that shareholders in these VIEs own no real assets and have no legal recourse should the Chinese decide to void the whole situation, or the Chinese parent company decide to move assets elsewhere (as with Alipay).”- GCI Investors

What were the practical consequences of this for Shareholders? Let’s look at Yahoo and Alipay. 

“Yahoo is apparently losing the faith of some of its investors as its dispute with China’s Alibaba drags on: Greenlight Capital head David Einhorn has unloaded his stake in Yahoo just a few months after acquiring a sizable holding in the company. And Einhorn’s reason for getting out of Yahoo is nothing other than Alibaba’s transfer of online payment service AliPay to a new entity with full Chinese ownership.” - Yahoo loses major investor Einhorn over Alipay dispute

“Internet giant Yahoo and China’s Alibaba have, in conjunction with Japan’s Softbank, reached an agreement (PDF) in their lingering dispute over the Alipay online payment service. Under the agreement, Alibaba will receive no less than $2 billion and no more than $6 million when (and if) Alipay experiences a “liquidity event” like being sold or going public. The exact amount will be 37.5 percent of Alipay’s value at the time—and, of course, with Yahoo being a 43 percent stakeholder in Alibaba, the American company will reap some of the rewards.” - Yahoo, Alibaba, and Softbank reach agreement on Alipay

Note two things, the above quote by Yahoo has a typo, it appears they mean up to 6 billion, which at the valuation Ant Group attempted to go public at last year would be 2% of Ant Group / Alipay. Significantly less than the 126.85 billion or 43% they would likely have been entitled to under common law and a more normal shareholder rights structure. 

“Naturally, Yahoo shareholders were furious, and the CEO of Yahoo was fired shortly afterwards.” - GCI Investors

Finally Yahoo still has not received any money from a liquidity event as the IPO was blocked by regulators. Other investors in the private Ant Group such as Fidelity have marked down their shares by about 50% in response to the regulatory uncertainty: 

“The Boston-based asset manager, which was among an elite group of global investors that bought into Ant three years ago, marked Ant shares in several of its funds at prices that implied a $144 billion valuation for the company at the end of February, according to regulatory filings.

The prices were below what Fidelity originally paid for Ant’s shares, suggesting the firm believes it could incur a loss on the investment.” - WSJ

4. They are legally two separate companies, the SEC allows the VIE to report the balance sheet and income as if it was the mainland company but its claims to those assets and incomes appears to be weak at best, nonexistent in practice

“Investors are also let down the by global accounting standards of both US GAAP and the IFRS. Current accounting standards make shares in the VIEs appear to investors as if they are in fact ownership of the Chinese company. Under both US GAAP and IFRS, VIEs are allowed to consolidate the financial statements of their domestic counterpart into their own reporting. So, when any prospective investor looks up Alibaba stock (which is of course the VIE), they see the balance sheet and income statement of the Real Alibaba, despite the fact that what they’re actually buying is a Cayman’s shell company with no real assets and no legal claim to the assets or income streams of the Real Alibaba. We believe that presenting the information in this way is hugely misleading.” - GCI Investors

5. The spirit of the VIE seems aggressive if not outright illegal under Chinese law

“U.S. investors have invested over $70 billion in Chinese companies. Many of those investments—by some estimates most—are illegal under Chinese law. Some of China’s largest companies, including nearly half of the Chinese companies listed on U.S. stock exchanges, are participants in this scheme to facilitate illegal investment. The illegal status of these investments is abused by corporate insiders in China who steal assets owned by U.S. shareholders with impunity, and there is a constant threat that the Chinese government may crack down on these illegal investments at any time. The situation is unsustainable, and the threats posed to U.S. investors and the global economy are colossal. The mechanism enabling this dysfunctional system of international investing—the “variable interest entity” corporate structure—is at the root of the problem and must be replaced. This can be accomplished through a robust U.S.-China bilateral investment treaty, which will allow the U.S. and Chinese governments to fix this broken system before more U.S. investor money is lost and the global economy is placed in greater jeopardy.” - China’s Variable Interest Entity Problem: How Americans Have Illegally Invested Billions in China and How to Fix It

6. Even if you could buy the mainland shares directly the accounting and properly law standards of China are significantly lacking 

Luckin coffee proceeded to fall over 80%. 

This is not a terribly uncommon phenomenon:

“In total, regulators suspended 46 IPOs and bond offerings, based on filings made at the Shanghai and Shenzhen stock exchanges, including Shanghai’s Star Market, as of Monday, according to the South China Morning Post. The reason: these companies had chosen Ruihua Certified Public Accountants as their auditors.

Ruihua, the second largest audit firm in China, has been embroiled in scandals involving large amounts of fake data, including fake cash, on its clients’ books. The fakeness of this cash became obvious when these companies defaulted on debt that they could have easily serviced with the cash they claimed to have on their books but didn’t. And Ruihua had just signed off on those fake books.” - Wolfstreet

“…Our ability to promote and enforce these standards in emerging markets is limited and is significantly dependent on the actions of local authorities—which, in turn, are constrained by national policy considerations in those countries. As a result, in many emerging markets, including China, there is substantially greater risk that disclosures will be incomplete or misleading and, in the event of investor harm, substantially less access to recourse, in comparison to U.S. domestic companies.” - Public Statement by SEC Chairman Jay Clayton “Emerging Market Investments Entail Significant Disclosure, Financial Reporting and Other Risks; Remedies are Limited”.

What are the implications of this? 

“Such a complex, deceptive practice as investing through the Chinese VIE structure naturally comes with significant risks beyond those associated with ordinary investing. There are two potential hazards in particular that raise serious concerns for U.S. investors: (1) the potential for insider misappropriation that leaves the company with no legal recourse; and (2) the possibility of a government crackdown on the VIE scheme.” - China’s Variable Interest Entity Problem: How Americans Have Illegally Invested Billions in China and How to Fix It

To me it appears investing in a VIE is a 3 pronged bet on things outside the realm of typical stock investing. The first is that the founder of the company only acts in the best interest of the VIE shareholders. We have already seen this is untrue for even some of the best Chinese companies. The second bet is that China does not crack down on VIEs, which appear to be obviously illegal under Chinese law. This type of risk is Nassim Taleb’s Turkey where 1000 days of the Turkey getting fed does not imply that the Turkey will not be eaten on day 1001.  

The final bet investors are making is that not only will China avoid a crack down on the VIE structure, but they will also open up geopolitically sensitive areas of their economy to foreign investment, and grandfather in the current investors in Cayman Island entities to Chinese equity ownership. This seems significantly unlikely to me and I am not enough of an expert to dig deeper when there are alternative opportunities with greater simplicity. 

Due to these factors, investments in any company or fund with exposure to the VIE structure is unacceptable to me personally and I exited the already very small exposures I currently have.

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Disclaimer

In no event will Prdctnomics or any of the Prdctnomics parties be liable to you, whether in contract or tort, for any direct, special, indirect, consequential, or incidental damages or any other damages of any kind even if Prdctnomics or any other such party has been advised of the possibility thereof. 

The writer’s opinions are their own and do not constitute financial advice in any way whatsoever. Nothing published by Prdctnomics constitutes an investment recommendation, nor should any data or Content published by Prdctnomics be relied upon for any investment activities.

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I believe the rise of the Metaverse will be about as impactful as the rise of the internet itself. 

Disruptive technology is uniquely interesting. Only here in this pocket of innovation do we see technology go from obscure toys and hobbies to being absolutely necessary to function in modern society. More shocking is that the rate of adoption of new products and whole new patterns of human interaction are increasing in speed and frequency.  

Source: Visual Capitalist

Previously I’ve written about the Unbundling of Banks, a theme I speculate has just begun and of which I am an investor. However there are any number of emerging technological themes that could be vaporware or could change almost all human interaction which I’d like to dive deeply into. How fast any one of these technologies could change how we do almost everything is hard to overstate. 

Buckle up because things are about to get weird. 

Entering the Metaverse

This will be the first article of several on the Metaverse. 

The Metaverse can be thought of as a stack of immersive technologies that give access to virtual worlds with “characteristics such as infinite persistency, ubiquitous synchronicity, unlimited concurrency, wide interoperability” to quote Matthew Ball.

Pong and Galaxy are to Fortnite and Minecraft what Fortnite or Minecraft are to what the Metaverse can be. 

Imagine virtual worlds accessed via mind machine interfaces, VR headsets, AR headsets, and even the glowing rectangles we know and love today. Because commerce and private property exists within these worlds there will be people who work entirely within the Metaverse and are able to make a living within it. 

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As far fetched as this sounds Atari has already launched a virtual casino in Decentraland complete with a DJ hosted launch party and thousands of attendees.

More strange for those outside of the blockchain loop is this is a casino in a virtual world governed by a distributed autonomous organization which receives 88% of the casino profits, but we will cover the decentralized finance world in another post.

Decentraland is just one of many attempts to build a blockchain backed metaverse, others include an obviously Minecraft inspired world called The Sandbox, and a virtual world called Cryptovoxels. While none of these are examples of what I believe a Metaverse will be, they are great examples of what the future of commerce and ownership will likely look like. 

You could be forgiven for saying this is crazy and closing this article, but some of the leading tech companies are also hyper focused on enabling, building, or owning the Metaverse. 

Big Tech enters the Metaverse

According to Forbes everyone on Facebook’s management team already must read Snow Crash, and yesterday Mark Zuckerberg announced why Facebook was becoming ‘a metaverse company’ in an interview with The Verge. 

A couple highlights to show Mark’s head space: 

Clearly Mark sees AR and VR as new interaction methods for a new kind of internet and a new kind of collaboration. More on this below: 

Finally like the game examples earlier this will enable entirely new jobs and areas of commerce:

All of the above snippets are from the Verge, and I highly recommend digging into the whole article here. 

This isn’t science fiction either. 

This is already reality, it's just not widely distributed yet. 

A couple video examples then I promise I will move on from Facebook. 

Spatial the new Virtual Office

Infinite Office

This is by no means an exclusive Mark Zuckerberg vision. 

For example take a look at what Microsoft is doing with the HoloLens and Microsoft Mesh:

Or NVIDIA’s Omniverse platform for building virtual worlds: 

Disney is looking to build “The Theme Park Metaverse”

https://www.linkedin.com/pulse/how-physicaldigital-driving-present-future-disney-parks-tilak-mandadi/

“As we look to the future, connected park experiences that transcend the physical and digital barrier and unlock new layers of storytelling are a very exciting focus of ours. I call this concept the “theme park metaverse” – this is where physical and digital worlds converge, with wearables, smartphones and digital access points immersing the guests in the metaverse experiences. These experiences are perennial, and the guest engagement is inside and outside the parks. They are unique to you, but are also social and connected. They are constantly changing so there is always something new to discover.” - Tilak Mandadi

EPIC has created Core Games which has been described as an early Metaverse:

Tomorrowland has launched a year round digital entertainment venue for concerts and events: 

https://naoz.live/

And held Tomorrowland 2020 in a virtual space:

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For anyone not following this corner of tech closely it can seem other worldly but I encourage you to go back and remember how people viewed the early internet. We are witnessing the birth of the early Metaverse.

Where to learn more

Matthew Ball has one of the more comprehensive sets of writing on the Metaverse and I would say reading his 9 part primer is probably the best source of info on getting acquainted with the Metaverse. Packy McCormick writes frequently about the Metaverse and Web3, two emerging areas of tech near and dear to my heart. Dave Baszucki, CEO of Roblox, has done a podcast discussing the Metaverse here. 

What's next for Prdctnomics

If you’ve been following me so far I’ve mostly written about and discussed investing. I am not a professional investor by any means, but I am a professional technologist, having been a software engineer, product manager, and entrepreneur. I will still be writing about investing (especially in the context of book reviews, articles, and mental models) but will be mostly writing about technology, business models, and the future at least as much going forward. 

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The writer’s opinions are their own and do not constitute financial advice in any way whatsoever. Nothing published by Prdctnomics constitutes an investment recommendation, nor should any data or Content published by Prdctnomics be relied upon for any investment activities.

Prdctnomics strongly recommends that you perform your own independent research and/or speak with a qualified investment professional before making any financial decisions.

Today I will be sharing some interesting content from other authors with a focus on:

• The defense industry and geopolitics of cyber attacks

• Innovations in hardware for machine learning computation

• Intangible Value when Investing

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The defense industry and geopolitics of cyber attacks

Quotations and graphics from Elad Gil’s Anduril Interviews

There is a lot to unpack here but I am going to highlight two things. One is the defense industry consolidation essentially into just 4 conglomerates, the other is top of mind for everyone interviewed is cyber attacks. In addition to my usual disclosures I should mention I work on a computer security product so I may be biased regarding the importance of cyber security. 

“Just as old incumbent institutions with little to no organizational renewal impacted our ability to respond to COVID, the defense industry has undergone significant consolidation over the last 30 years. There has not been a new defense technology company of any scale to directly challenge these incumbents in many decades (SpaceX and Palantir are obviously sizeable ~20-year old “newer” entrants in adjacent areas).”

It is remarkable actually the degree of rollups and consolidation that the defense contractors have accomplished. Is this good for innovation? Most likely not. Monopolies while funding R&D generally are not incentivized to self disrupt in the Clayton Christensen sense of the word disruption. 

Of these interviews there was tremendous attention given to cyber attacks:

“Elad: How should we think about that in the context of security at home? We've had a number of different ransomware attacks that have shut down oil pipelines that have shut down other core capabilities in the U.S.. How do you weigh those different challenges and how do you think the tech industry can get involved?

Katie:

...

I think that's so much in the cyber world operates similarly to nation state adversaries who operate in the gray space - they are getting away with so much because it's really hard to attribute who they are. It's hard to hold people to account whether it be criminally financially or just in the court of public opinion. When it comes to the cyber realm the hope is advanced technologies can help us respond and thwart threats more rapidly.”

“Elad: Where in particular do you think the U.S. is behind or on par? What sorts of technologies are capabilities? 

Vincent: Recently we’ve seen a number of cyber attacks in the news. Many more similar attacks go unreported every year. At times, criminal hackers are extensions of their governments or are permitted to operate within their borders for a price. Our enemies don't have the limitations of laws and legal bureaucracies as we do. This enables them to move faster and act with plausible deniability.”

“Vincent: Getting back to cyber, there is no doubt we have a very fragile infrastructure, when it comes to our cyber backbone. There is risk in our control systems, water and power plants, and energy grid. The Colonial pipeline was turned off by some thugs using malware, sending the Northeast into chaos with long gas lines. Think about the vulnerabilities of hospitals, banks, and other critical services we take for granted. Go back to the California blackout from a few years ago. That was a mistake made by a single person that sent California into the dark for over 24 hours. That type of cyber attack in conjunction with military action somewhere in the world is most problematic. ”

“Elad: How has the nature of threats to our armed forces and national infrastructure shifted over the last decade?

Tina: Geographic distance and natural borders have provided nearly unbreachable defenses for the United States as a whole since its inception. 

…

What has seemed to change is the relative ease of launching these attacks and their outsized impact - particularly through cyberwarfare. A recent example of this is the United Pipeline ransomware attack that brought gas lines to a standstill on the East Coast last month. The disruption of that incident is comparable to the scope of an airstrike. Which brings the overwhelming implications of strengthening our national security not only through our armed forces, but on the domestic “fence lines” of our cyber infrastructures and networks.”

Innovations in hardware for machine learning computation

Cerebras Wafer Scale Hardware Crushes High Performance Computing Workloads Including Machine Learning And Beyond

I found this article by Dylan Patel fascinating. Instead of chasing more transistors per mm^2 for a given chip size, why not just make a really big chip? It seems stupidly obvious as a potential way to scale compute power but as far as I know Cerebras has been fairly unique in pursuing this horizontal scaling of a single chip size. 

Their goal appears to be direct competition with Nvidia based on their comparisons to the Nvidia A100: 

“Semiconductor manufacturing has limited die size has been limited by the reticle limit for a long time. The reticle limit is 33x26 meaning that this is the largest size a lithography immersion stepper from ASML can pattern on a wafer. Nvidia’s largest chips are in the low 800mm^2 range mostly because going beyond this is impossible.

The Cerebras WSE is actually many chips on a wafer within the confines of the reticle limit. Instead of cutting the chips apart along the scribe lines between chips, they developed a method for cross die wires. These wires are patterned separately from the actual chips and allow the chips to connect to each other. In effect, the chip can scale beyond the reticle limits.”

The whole article was a very interesting read to me, worth going deep if you are an investor in the semiconductor space or interested in the implications of hardware accelerated machine learning. It is worth calling out the whole newsletter SemiAnalysis as being worth a subscription.

Also on my radar for machine learning hardware acceleration is custom analog chips. 

From semiengineering.com: 

““The digital AI ASIC might not be the ideal solution for IoT edge computing due to its high-power consumption and form factor,” says Hiroyuki Nagashima, U.S. general manager at Alchip. “Mixed-signal machine learning, inspired by nature like the human brain, should play an import role in the future world. Are we able to build a machine that can sense, compute and learn like human brain, and only consume several watts of power? It is quite a challenge, but scientists should aim to this direction.”

We already have some of the building blocks. “Analog-dot-product can make use of using analog filters, op-amps, etc.,” says Microchip’s Lee. “For example, you can compare two signals or mix them, and you can make a decision from the results. There are many cases that analog computation is much faster than digital computation.””

I do suspect that analog based machine learning will become commonplace by 2030 assuming of course they achieve the same compute results with significantly lower power consumption and heat output.  

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Intangible Value when Investing

Quotations from Intangible Value by Kai Wu

Here is the executive summary: 

“Value investing has struggled over the past decade. We believe this is due to its failure to incorporate intangible assets, which play an increasingly crucial role in the modern economy. We consolidate our prior research to construct a firm-level measure of intangible value. We find that expanding intrinsic value to include intangibles can help restore value investing to its former glory.”

Kai used a natural language processing technique called topic clustering on his datasets to research individual companies and created “pillars” of what type of intangible value an investor is buying (if any) when purchasing these company’s stocks. He then sliced and diced these outputs to generate insights as well as find companies to backtest. 

“The sections naturally fall into nine broad themes. The most central clusters form around the four intangible pillars: innovation, human capital, brands, and network effects. Five other research topics radiate out from this core.” 

“Importantly, most of these metrics are scaled by price. Thus, they do not measure the total quantity of intangibles owned by a firm but instead quantify the share of intangibles we obtain by buying a fixed dollar amount of the firm’s equity. For example, we don’t care about how many total patents IBM has, but rather how many patents we obtain per dollar invested in IBM. Think of it like the “dividend yield,” except that instead of buying dividends, we buy patents.

Value investing is all about getting value for your money. Traditional valuation ratios measure the quantity of tangible assets obtained for a given dollar of investment. Our metrics are conceptually identical, except they focus on intangible sources of value (e.g., # patents, # PhD employees, $ brand equity per dollar invested). Our hope is that this metric helps us find efficient ways to obtain intangible assets.”

With that background let's look at some of the research. 

Notable Intangible Companies

Are some forms of intangible value more conducive to performance than others? As an investor should I be diversifying across different types of intangible values? Some interesting questions to consider if you buy into the premise of these intangible factors being quantifiable and represented by these buckets. 

While academic in consideration Ken suggests that the value factor if expanded in definition to consider intangibles would not have had a post 2008 financial crisis drawdown and would continue to outperform through the 2010s. 

Where do these high intangible companies exist when looking at traditional sectors? 

It would appear mostly in software, hardware, pharma (patents), media, and semiconductors none of which is surprising. 

Kai backtests this concept to show it appears to outperform both value and growth style indices and the S&P500: 

“We find that the Intangible Value portfolio would have outperformed the S&P 500. Interestingly, despite currently having factor exposures in between those of Russell 1000 Value and Growth, it would have also outperformed both.”

In my opinion Kai is finding companies that skilled qualitative investors traditionally find using some cleverly applied machine learning. He also has an ETF that focuses on these intangible factors. I look forward to his continued research. 

If you want more (or less) link roundups vs long form articles please let me know in the comments. 

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Disclaimer

In no event will Prdctnomics or any of the Prdctnomics parties be liable to you, whether in contract or tort, for any direct, special, indirect, consequential, or incidental damages or any other damages of any kind even if Prdctnomics or any other such party has been advised of the possibility thereof. 

The writer’s opinions are their own and do not constitute financial advice in any way whatsoever. Nothing published by Prdctnomics constitutes an investment recommendation, nor should any data or Content published by Prdctnomics be relied upon for any investment activities.

Prdctnomics strongly recommends that you perform your own independent research and/or speak with a qualified investment professional before making any financial decisions.

Banks are truly awful. Ignored or forgotten most of the mega banks of 2021 only exist due to the socialization of their losses in 2008, and being propped up by preferential lending from our central bank today. Despite or more likely because of this preferential treatment by the government, banks have largely failed to innovate while abusing their customers with high unnecessary fees for increasingly commoditized services. Interacting with the mega banks summons a feeling of nostalgia as you’re plunged into an antiquated era where it is still acceptable to do commerce via dead trees and fax machines while other companies are regularly launching self landing rockets into space. Many interactions with these dinosaurs will require you to enter one of their many no doubt expensive locations where despite being near empty will provide you a level of customer service that would make a DMV employee blush. 

By 2030 we won’t ask why traditional banks are gone, we will wonder how they lasted so long. 

If banks are the dinosaurs then fintechs are the mammals. Fintechs are usually small and specialized businesses undergoing adaptive radiation while the current dominant lifeforms fail to adapt to a changing world. Welcome to the Eocene Epoch. 

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After reading the above it should surprise you that banks do still exist and provide a variety of services people voluntarily consume. Today banks are essentially a bundle, a bundle being a convenient form of distribution for goods and services. It made sense prior to the microprocessor and internet that you’d have a relationship with your local bank where they would know both your character and finances. This bank would provide a one stop shop for earning money on your savings, investing for retirement, and financing your home or car. 

Bundling and unbundling

“I said, really just to end the conversation, Gentlemen, there’s only two ways I know of to make money: bundling and unbundling.” - Jim Barksdale

As mentioned above bundles are just a convenient form of distribution for goods and services. My hypothesis is that bundles largely arose out of distribution restraints not out of customer preference. Media bundles such as broadcast and cable television, newspapers, or music are all classic examples of bundles that have been unbundled, unlocking massive value to both producers and consumers. In a world of the microprocessor and internet you can slice and dice the media you want to consume. We are no longer subject to a newspaper editorial, now we can consume the news in a curated feed of niche interests. The same holds true for music via iTunes or Spotify, niche education and entertainment via Audible and Podcasts, and specific shows that interest you via streaming services and YouTube. 

Even internet companies get unbundled by new internet companies with laser-like focus on a single vertical, the classical example being Craigslist below. Capitalism’s creative destruction hates stagnation and encourages ever better user experiences. 

Craigslist Unbundled

Banks Unbundled

This brings us back to banks, banks have historically been less subject to the above due to a protective regulatory environment and the structural advantages that provided. Despite that there has been an explosion of new highly focused companies attacking every vertical that banks profit from. 

Banks and Brokerage services have begun to unbundle in ways that seemed impossible just a couple years ago as shown by this fantastic graphic above. 

What is currently bundled into banks? 

From Chase’s Website:

• Credit Cards

• Checking and Savings accounts

• Home Loans

• Auto Loans

• Commercial Loans

• Credit Scores

• Merchant Services (Point of Sale, Payments)

• Business Lending

• Investing Services

• Retirement Plans

What does this look unbundled? It looks like a series of hyper focused companies going after each vertical above. 

If you are interested in the specific players disrupting banking incumbents you can dig into the list below, otherwise I will leave you with these parting thoughts: 

The time is now to disrupt these institutions, low rates and a lack of technological innovation actually tilt the advantage to new entrants. If you are starting a company in this space you should aim to provide excellent UX and customer service with a focus on a single vertical at least at first. You should also reach out to me, a former fintech founder passionate about the space. Likewise if you are a VC or investor interested in fintech you should also reach out to me. I would love to help both parties above remove the frictions and end the dark patterns banks monetize today via competition. 

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The fintech list

Debit / Credit Card Issuance

Non-bank players: VISA and MasterCard are non-banks themselves and manage the infrastructure around payments. These cards were historically issued by banks which actually charge the interest and take the lending risk. 

Startups doing card issuance include:

Stripe

Payments infrastructure for the internet

https://stripe.com/issuing

Galileo

The API Standard for Card Issuing and Digital Banking

https://www.galileo-ft.com/

ModernBanc

Launch banking products fast. (accounts, cards, and loan products)

https://www.modernbanc.com/

Synapse

The Launchpad for Financial Innovation

https://synapsefi.com/

Lastbit

Pay or Get Paid in Bitcoin or Euros

https://lastbit.io/

Letter

Beautifully designed banking made for high net-worth individuals.

https://letter.co/

Starship HSA

MONEY FOR HEALTH.

https://www.starshiphsa.com/

Checking and Savings accounts (Personal)

Synapse

The Launchpad for Financial Innovation

https://synapsefi.com/

Galileo

The API Standard for Card Issuing and Digital Banking

https://www.galileo-ft.com/

ModernBanc

Launch banking products fast. (accounts, cards, and loan products)

https://www.modernbanc.com/

LVL

Bitcoin custodian and FDIC insured checking

https://lvl.co/

Checking and Savings accounts (Business)

Mercury

Banking built for startups

https://mercury.com/

Bank Novo

Powerfully simple business banking

https://banknovo.com/

Arival

The first fintech bank

https://arival.com/

Synapse

The Launchpad for Financial Innovation

https://synapsefi.com/

Modern Banc

Launch banking products fast. (accounts, cards, and loan products)

https://www.modernbanc.com/

Home Loans

Better

https://better.com/

Blend

https://blend.com/

Point

https://point.com/

Salt Lending

https://saltlending.com/

Auto Loans

Carvana

https://www.carvana.com/

Upstart

https://www.upstart.com/

Upgrade

https://www.upgrade.com/

Commercial Loans

nCino

The Worldwide Leader in Cloud Banking

https://www.ncino.com/

Credit Scores

Credit Karma

https://www.creditkarma.com/

Intuit

https://turbo.intuit.com/free-credit-score-report/

Merchant Services (Point of Sale, Payments)

Square

https://squareup.com

PayPal

https://www.paypal.com/us/home

Stripe

https://stripe.com/

Adyen

https://www.adyen.com/

Clover (Fiserv)

https://www.clover.com/

Business Lending

Kabbage

https://www.kabbage.com/

Crowdz

https://www.crowdz.io

Investing Services

Consumer Facing

Robinhood

https://robinhood.com

Public

https://public.com/

Trade Republic

https://traderepublic.com/

Equilibrium Ventures

https://www.equilibriumventures.com/

Vinovest

https://www.vinovest.co

Otis

https://www.withotis.com/

Starship HSA

https://www.starshiphsa.com

Farm Together

https://farmtogether.com/

Tuned

https://www.tuned.com/

Tribevest

https://www.tribevest.com/

Retirement Plans

Wealthfront

https://www.wealthfront.com/

Betterment

https://www.betterment.com/

Sofi

https://www.sofi.com/

Acorns

https://www.acorns.com/

API Driven Banking

Modern Banc

Launch banking products fast. (accounts, cards, and loan products)

https://www.modernbanc.com/

API Driven Investing

DriveWealth

https://drivewealth.com/

Alpaca

https://alpaca.markets/

Gallileo

https://www.galileo-ft.com/

*I have invested in one or more of these companies.

Disclaimer

In no event will Prdctnomics or any of the Prdctnomics parties be liable to you, whether in contract or tort, for any direct, special, indirect, consequential, or incidental damages or any other damages of any kind even if Prdctnomics or any other such party has been advised of the possibility thereof. 

The writer’s opinions are their own and do not constitute financial advice in any way whatsoever. Nothing published by Prdctnomics constitutes an investment recommendation, nor should any data or Content published by Prdctnomics be relied upon for any investment activities.

Prdctnomics strongly recommends that you perform your own independent research and/or speak with a qualified investment professional before making any financial decisions.

Google Trends for Sentiment in 1 minute

Google trends can be used to look at relative search volumes over different time frames and within different regions. Since Google Trends can show when interest is increasing in a given topic we can use it as a tool to examine mimetic behavior. Drill downs into related search terms are simple to perform and Google gives the options of pivoting on top related terms as well as most recent rising terms. Here we will use Google Trends to look at the amount of interest and where the interest is originating geographically for stocks, cryptocurrency, bonds, decentralized finance, non-fungible tokens, and startups. Data from Google Trends will be presented to the extent possible without interpretation. 

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The Data: Stocks and Cryptocurrency

Interest in search terms over time (5 Years)

Google searches globally for “buy stocks” and related terms surged in March of last year and again at the end of January 2021. Bitcoin interest had surged in December 2017.

Aggregate view (high volume regions only) (12 Months)

Aggregate view (including low volume regions) (12 Months)

Top regions and cities for searches for “buy stocks” and related queries (12 Months)

Top regions and cities for searches for “buy bitcoin” and related queries (12 Months)

Related to “Buy Stocks” - Rising last 7 days

The Data: Gold

United States (5 Years)

Worldwide (5 Years)

The Data: Bonds

The Data: Defi, NFT

The Data: Startups

Another tool in the kit

Google Trends is an interesting and under utilized tool for discovering where and how people are imitating each other which has implications for businesses and investing. The above has shown a couple ways to use it to ask questions and derive insights. What trends do you find useful? Leave a comment below.

Until next time,

Prdctnomics

Disclaimer

In no event will Prdctnomics or any of the Prdctnomics parties be liable to you, whether in contract or tort, for any direct, special, indirect, consequential, or incidental damages or any other damages of any kind even if Prdctnomics or any other such party has been advised of the possibility thereof. 

The writer’s opinions are their own and do not constitute financial advice in any way whatsoever. Nothing published by Prdctnomics constitutes an investment recommendation, nor should any data or Content published by Prdctnomics be relied upon for any investment activities.

Prdctnomics strongly recommends that you perform your own independent research and/or speak with a qualified investment professional before making any financial decisions.

I will try to stay away from predictions and focus on the products as they are but I am curious how relevant banks will remain in the future as Fintech both commoditizes and disrupts them. If I did product management for a bank I would be focusing on how we can reduce user frictions, provide tremendous value with little to no fees, and do so wrapped in gorgeous user experiences simply as a matter of survival.

All of these companies have the potential to aggregate demand and become the front end to what are currently our banking, shopping, spending, dining, and transportation services. If anything they are not ambitious enough and should take inspiration for what Tencent has accomplished with WeChat.

All imagery is from the respective company’s marketing materials or investor presentations.

PayPal

PayPal is going to be targeting 3 areas of consumer finance. 

Payments with checkout, p2p, charitable giving, and QR code based payments.

Integrating financial services like around spending, saving, credit, and cryptocurrency. 

Shopping tools and portals directly in the PayPal experience. 

Square

Square’s digital wallet is the cash app, one of the most exciting and complete financial products outside of China. Cash app provides direct deposit, bitcoin investing, stock investing, an alternative to payday loans, and a debit card. Anecdotally many people I know in their teens through their 20s use Cash App instead of both banks and brokerages. 

Facebook

Facebook has been late to this arena but by virtue of their market position and one of the largest user bases of any company in human history I suspect they have a strong shot at becoming the west’s super app. Payments and e-commerce are being thoughtfully built into every Facebook experience, all of which will also benefit from existing network effects. There is also the free optionality of being the potential payment layer for virtual reality community and games. Finally Facebook has shown tremendous ambition with the plans to launch their own cryptocurrencies / stable coins. 

Apple

Apple’s product focus, hardware install base, and ownership of iMessage give it natural distribution points for future digital wallet innovations. I look forward to seeing continued innovation here. 

Google

As with Apple, Google has a huge install base in the Android OS, and potential for deeper payment integration with the Chrome web browser. Google’s product portfolio would lend itself well to the “super app” concept. 

Uber

Finally an honorable mention for Uber. Uber is evolving into a logistics focused super app, and the potential to aggregate increasing percentages of demand for moving people and things is obvious to me. 

Other

What is missing from the above? DoorDash, Lyft, and more single vertical focused players could become hybrid logistics and fintech offerings. API driven finance is in its infancy and just as Wealthfront has commoditized banking with their savings products brokerages will likely just be the back end custodians of your chosen super app. The TAM for all of these products is absolutely massive individually, combined they could be the growth engine for these or new products for many years to come. The other wild card here is if any of these companies are able to disrupt the current payment rails of VISA and MasterCard. 

It is going to be an interesting decade. 

Disclaimer

In no event will Prdctnomics or any of the Prdctnomics parties be liable to you, whether in contract or tort, for any direct, special, indirect, consequential, or incidental damages or any other damages of any kind even if Prdctnomics or any other such party has been advised of the possibility thereof. 

The writer’s opinions are their own and do not constitute financial advice in any way whatsoever. Nothing published by Prdctnomics constitutes an investment recommendation, nor should any data or Content published by Prdctnomics be relied upon for any investment activities.

Prdctnomics strongly recommends that you perform your own independent research and/or speak with a qualified investment professional before making any financial decisions.

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Looking back at 2020 it was truly the year of the technology platform. Although a huge amount of demand was no doubt pulled forward due to the continuing pandemic various technology platforms shifted from being luxuries to being necessities for staying in touch, delivering food, shopping, investing, and staying entertained.

Rather than remove traditional gatekeepers, platforms have replaced them. We have seen reintermediation rather than disintermediation. - Sparkline Capital

What is a platform

I’d like to quickly define what a platform is as described here. A platform is any business or technology where it’s users benefit from network effects. Specifically this means that users of the product derive more value from the product the more users it has. Telephone networks were the original example of this hence “network effects”.

Network effects also known as demand-side economies of scale should not be confused with traditional supply side economies of scale which typically is shown as decreased costs of production as production scales.

Network effects can be direct such as with telephone users where additional users are the value, or indirect where interdependent users of a complimentary product drive demand of a different product. Examples of indirect network effects could be hardware becoming more valuable as a result of new software use cases. GPUs and non-graphic computing use cases come to mind such as cryptocurrency mining or machine learning workloads.

Network effects can easily become positive feedback loops where more users and content makes a network more valuable to join which causes more people to join resulting in more users and content in a virtuous cycle.

The Platform Economy

Sparkline Capital has done an amazing job quantifying the impact of platform companies on the investment universe. Having read all of their research over the last couple weeks I’ve been consistently impressed and am excited to share some of their findings.

They split platforms into two categories, transaction platforms and innovation platforms. Transaction platforms are intermediaries for direct exchanges or transactions (Facebook, AirBNB, VISA, YouTube, Zoom being examples). Innovation platforms are platforms that provide a tech foundation that other firms develop complementary innovations on. Examples include operating systems, semiconductors, cloud computing, gaming, and web browsers. Admittedly I could see some companies like Amazon falling into both categories.

The value of any platform to its users will be “a nonlinear function of the size of their networks”. Digital technology and the internet is making scale never before possible in human history increasingly common and thus platforms are becoming an ever greater part of the economy and our lives.

Platforms are an ever increasing percentage of the US economy

Additionally platforms have outgrown the general stock market by an increasing amount overtime.

If this is because platforms were previously undervalued or not remains to be seen but obviously price to sales expansion is quite rampant in popular companies today.

Sparkline goes on to examine the under what conditions platforms create value, why platforms tend towards natural monopolies, and investment specific information regarding platforms.

When considering both private and public investments the potential of that investment to mature into a thriving platform is top of mind for me.

So far platforms have primarily been successful in capital light communication and information technology sectors but last mile logistics, real estate, and many untapped sectors which may become digitized as IoT mainstreams are current and future opportunities for platforms respectively.

You can read the entire Sparkline Capital article on platforms here.

E-Commerce in 2020

The Marketplaces Year In Review is worth a read in it’s entirety but a couple highlights below.

“2020 was the best year for e-commerce marketplaces in over a decade. E-commerce growth had a step change, and marketplaces captured most of it. In aggregate, it was the most successful year for sellers and brands that transact through them, too. The Year in Review looks at the state of marketplaces and describes the most important trends.” - Marketplace Pulse

Source: marketplacepulse.com

Etsy

Etsy is a standout winner in the US e-commerce arena nearly doubling it’s gross merchandise volume and attracting massive demand and subsequently new sellers to the platform.

“On April 3rd, the same day the White House announced guidelines that Americans should wear masks outside of the home, Etsy sent a push notification to every seller: “Calling all sellers. Start making face masks.” Hundreds of thousands of existing and new sellers started selling face masks.

“I woke up to discover it was suddenly like Cyber Monday,” Etsy’s CEO Josh Silverman told the Financial Times. “But everyone in the world wanted only one product.” That product was face masks.” - Marketplace Pulse

Source: marketplacepulse.com

Source: marketplacepulse.com

Etsy’s total catalog size increased from ~60M to 80M products in 2020.

Amazon

In 2020 Amazon 3P sales increased 47% while 1P sales increased 35%, resulting in 62% of AMZN’s global merchandise sales being fulfilled the third party marketplace.

Over this time over 1 billion dollars was deployed by firms buying up 3P sellers and their brands on Amazon.

Source: marketplacepulse.com

It is very interesting to see dollars move from attempting to compete with Amazon’s ecosystem to just buying companies that are part of it. This is part of a broader micro private equity trend that I expect to continue in a big way across most platform businesses.

To read the rest of the deep dive on marketplaces once again check out Marketplaces Year In Review for 2020.

Whats next for 2021

I suspect platforms will continue to become increasingly impactful on the lives of the average internet user, and due to their concentration of power will also be increasingly scrutinized by regulators and politicians for real and imagined harms. As an early adopter and investor I will be keeping an eye out for clever problem solving by platforms in their infancy as well as continue to observe emerging and international platforms which have already hit their growth stride.

Are you building or investing in a platform you’re excited about? Please share it in the comments below 👇

Disclaimer

In no event will Prdctnomics or any of the Prdctnomics parties be liable to you, whether in contract or tort, for any direct, special, indirect, consequential, or incidental damages or any other damages of any kind even if Prdctnomics or any other such party has been advised of the possibility thereof. 

The writer’s opinions are their own and do not constitute financial advice in any way whatsoever. Nothing published by Prdctnomics constitutes an investment recommendation, nor should any data or Content published by Prdctnomics be relied upon for any investment activities.

Prdctnomics strongly recommends that you perform your own independent research and/or speak with a qualified investment professional before making any financial decisions.

Part 1: Competition is for Losers with Peter Thiel

Part 2: The Little Book that Builds Wealth with Pat Dorsey

Part 3: The Philosophy of Fundsmith

Disclaimer

In no event will Prdctnomics or any of the Prdctnomics parties be liable to you, whether in contract or tort, for any direct, special, indirect, consequential, or incidental damages or any other damages of any kind even if Prdctnomics or any other such party has been advised of the possibility thereof. 

The writer’s opinions are their own and do not constitute financial advice in any way whatsoever. Nothing published by Prdctnomics constitutes an investment recommendation, nor should any data or Content published by Prdctnomics be relied upon for any investment activities.

Prdctnomics strongly recommends that you perform your own independent research and/or speak with a qualified investment professional before making any financial decisions.

Today we will review legendary investor Terry Smith’s investor philosophies. Fundsmith was founded in 2010 by Terry Smith and he has grown AUM to £26bn as of 2019. He believes in rigorous in house, bottom up analysis of individual companies when investing and their portfolio size is under 30 stocks. Their strategy is relatively straightforward and has been embraced by many Growth At Reasonable Price investors (GARP) as well as investors looking for compounders. 

Investment Strategy

1. Invest in good companies

2. Don’t overpay

3. Do nothing

What is a good company?

A good company is one that has:

1. High returns on operating capital employed in cash

2. Growth driven from reinvestment of their cash flows at high rates of return

3. Make money from a large number of everyday, small ticket, repeat, predictable transactions

4. Able to protect returns against competition

5. Resilient to change, particularly to technological innovation

6. Operating in sectors with intangible advantages: brands, distribution, installed base, franchisers

High returns on operating capital

If a company makes more than its cost of capital over time it grows capital, if it makes a rate lower than its cost of capital it loses money. Figuring out cost of capital is a challenging estimate but if you’re investing exclusively in companies with very high returns on invested capital it’s exact value becomes less important. 

Most investors, investor literature, and investing newsletters focus on gains in earnings per share and not the earnings rate of equity on capital employed. 

“This is a machine for losing money”. 

As discussed in previous articles Airlines are a poor business that everyone enjoys beating up on. This is because airlines consistently have lower returns on invested capital than their cost of capital. 

On average Airlines make -5% per annum in capital deployed, they are a really bad industry generally speaking. 

One might wonder why airlines don’t just go bankrupt? Well they do go bankrupt all the time, recapitalize, and then continue operating without much impact to their customers or employees. Shareholders however lose. 

There are a number of cyclical factors that can drive airline company share prices higher which people betting on the industry cite as reasons. These include low relative valuations, consolidation, energy price changes, investing during a recession, etc. 

However investing in an industry that perpetually doesn’t make returns on capital deployed is like investing in a melting ice cube while you wait for good things to happen, assuming they ever do. 

Contrast this with a “good” company. 

Unilever is a “good company”, it grows in value steadily year by year because its returns on capital are at such an excess compared to its costs of capital. Even if you time it the investment wrong (investing in 2008) the high returns on capital could “dig you out” over time. This contrasts structurally bad industries where timing really is everything. 

Growth in earnings can be an illusion in the absence of looking at returns on invested capital and many high growth companies do fail if their revenue growth doesn’t generate durable advantages.

Fundsmith publishes their “look through” metrics and compares that to the return on capital employed of the FTSE100 or S&P500 as a show of quality in their fund. 

Growth driven from reinvestment of their cash flows at high rates of return

It’s no good having high returns without a source of growth. Without a source of growth a company will not be able to retain their earnings and reinvest them at high rates of return. They would instead be forced to pay them out in dividends. You should never invest for income, but instead invest in companies that can retain their income and reinvest it in sources of growth. Specifically the desire is for secular growth, not cyclical growth. 

Reinvestment requires secular growth

Fundsmith looks at several areas for continued secular growth. One is the consumerization of the developing world. As the global middle class grows they will consume more products that improve their quality of lives. 

Another source of growth is the premiumization of the developing world. For example we might not drink as much as we used to, but we drink more expensive drinks than we used to. Examples would be craft drinks vs mass market drinks. 

Another is aging populations. Demographically the world is becoming older on average and companies that best serve the growing population of elderly will do better.

White space, white space is a marketing term for an area you can grow into over time. The majority of people who need vision correction globally have not gotten it yet, but once they can afford to they likely will.

Another theme with tremendous white space that Fundsmith likes is Payments. That is partially because their big competitors are actually not each other but cash which still makes up most of the world’s transactions. Explaining to your children how cash is created, managed, circulated, collected, then distributed again will seem insane compared to going to Starbucks and buying a coffee with their app. 

Pets are a fabulous growth area in the developed world and increasingly in the developing world. We are prepared to spend seemingly unlimited amounts of money on our pets. There are a tremendous number of specialty pet foods for every imaginable goal you have for your pet. However, the pinnacle of pet businesses is veterinary care. One type of company they invest in is veterinary diagnostics because pets cannot communicate how they feel. 

Testing and diagnostics outside of the veterinary business are also interesting to them. Testing is a growth area because of the growth of the nanny state, and trends to safety. Think of testing meat for contaminants or increased medical screening. 

Make money from a large number of everyday, small ticket, repeat, predictable transactions

Major construction projects and movie studios are two examples of businesses that make money in large chunks where a lot can go wrong. 

Recurring everyday, predictable events are the things Fundsmith likes to invest in. 

Reservation systems, payments, grooming, eating, and drinking are all themes Fundsmith invests in.  

Able to protect returns against competition

There is a law of economics called mean reversion. When companies make high returns on capital it attracts competition and competition lowers those returns. 

What do you have that can protect against competition?

Usually it is one or more of the above. We will pay more for branded goods than unbranded goods, and we pay more for primary brands than secondary brands. Brands, unlike patents can last forever.

Distribution is another factor, if we brainstorm a new vodka many distributors will already have exclusive agreements with incumbents. Getting it in front of consumers at scale will be challenging if possible at all. Building supply chains from scratch is difficult (e.g. Milk products). If you enter the milk market you must build that supply chain and distribution network from scratch.

Sometimes it is an install base of equipment or software that protects against competitors. Examples are elevators and escalators. Legally they must be serviced, and that is where most of the business comes from: maintenance not installs. Another is software that once installed is difficult to replace. 

Patents are their least liked form of moat because patents expire. 

We like companies that are resilient in the face of technological change. It is difficult to predict the winners in companies subject to massive technological change. 

Of the top 10 software companies in 1984 only 1 exists anymore, they believe this makes picking long term winners in tech difficult. 

Another industry with rapid change and shifting incumbents is biotech and pharma. 

Most countries have a 4 part approval process for drug approval. The odds of the company getting through all the stages of potential failure is actually very low.

Fundsmith believes this is a really bad industry from an investment standpoint as a result of the difficulty of predicting winners and losers in advance. 

Otis’s safety elevators have not substantially changed in over 100 years but the problem they solve has persisted and so has their business. 

Intangible Advantages

We don’t like tangible advantages (like buildings) because you can always replicate buildings with debt. Intangible businesses to contrast can not easily be replicated. 

Companies Fundsmith believes has intangible advantages:

Terry likes to invest in companies that have already won, not try to pick who is going to win. Brown-Forman the distiller of Jack Daniels survived not only 2 world wars and the great depression but also prohibition of alcohol. Fundsmith also likes companies that can leverage other people’s money such as franchises. 

Don’t Overpay

Is it cheap is an important question, but a secondary one to if a company is truly a good business. 

Overtime valuation will be less important than the fundamental returns of the business if those returns are sufficiently high. 

This example is helpful for understanding the compounding of two businesses with differing growths, initial valuations, and ending valuations: 

Over the long term the higher returns on capital of company A overwhelmed both the valuation compression of company A and the multiple expansion from a lower starting valuation of company B. 

The vast majority of index returns (80%) come from EPS growth not multiple growth. 

Looking at a “good company” like Unilever this is shown to be even more true:

90% of the returns over the period can be attributed to EPS growth, and only 10% from P/E growth. Most of the returns come from what the company does not what you as an investor do when investing in good businesses.

Fundsmith does however compare valuations (FCF yields) within their portfolio among each other, their investable universe, the broader market, and bonds. They also project their FCF out 4 to 5 years into the future to compare their companies growth rates and to balance current and future projected FCF yields when making decisions. 

Do Nothing

“Almost every time I sell a good company it is a mistake.” 

Miscellaneous, Q&A

Fundsmith has extremely low transaction and voluntary dealing costs relative to the industry because they almost never make transactions. 

Fundsmith looks for big private companies that are similar to the public companies they invest in. Examples include Mars (Candy, Pet Foods), Liquor businesses, Franchise businesses (other people’s money to grow), household cleaning products, repeat use medical products, and medical devices.  

Fundsmith owns 20-30 stocks and invests alongside their investors. They believe this gives them a sufficient degree of diversification and aligns incentives. 

Fundsmith won’t own resource companies, utilities, airlines, banks, insurance, or other cyclical companies. 

They don’t attempt to make macroeconomic predictions. They believe the best hedges against inflation are companies with pricing power as shown by high gross margins. 

If you liked this and would like to see more similar content please subscribe, share, and like. 

Part 1: Competition is for Losers with Peter Thiel

Part 2: The Little Book that Builds Wealth with Pat Dorsey

Part 3: The philosophy of Fundsmith

Photo source. 

Disclaimer

In no event will Prdctnomics or any of the Prdctnomics parties be liable to you, whether in contract or tort, for any direct, special, indirect, consequential or incidental damages or any other damages of any kind even if Prdctnomics or any other such party has been advised of the possibility thereof. 

The writer’s opinions are their own and do not constitute financial advice in any way whatsoever. Nothing published by Prdctnomics constitutes an investment recommendation, nor should any data or Content published by Prdctnomics be relied upon for any investment activities.

Prdctnomics strongly recommends that you perform your own independent research and/or speak with a qualified investment professional before making any financial decisions.

Here we will take a deep dive into the concept of competitive advantage or an economic “moat” from Pat Dorsey. 

Pat Dorsey on Moats

Put simply capitalism works: capital seeks the highest returns possible. 

If a company is making a lot of money other companies will immediately seek to compete with it

High profits attract competition as surely as night follows day. Competition eventually destroys profits. 

If you look at the highest cohort of companies by profitability today and check again in 10 years most will have lower returns on capital due to competition. There are a minority of businesses where that is not the case. These purported structural advantages Pat Dorsey calls moats. 

This allows businesses to make large profits over a longer period of time than academic theory would suggest.

What are the types of moats? 

A moat is not a hot product. At one point Heelys was an 800 million dollar market cap company.  

When companies cease to raise prices on a regular basis it is usually a sign they are losing pricing power. This could imply there is a viable competitor, or there is some event going on causing them to lose their market position. These are usually the first signs their moat is eroding. 

Pat looked for companies that made well in excess their costs of capital for long periods of time. 

Tiffany will charge 20% more than competitors for the exact same diamond, but the perception of the Tiffany product makes the decision worth it for people. Example: “will probably get a bigger smile from the recipient if it is in that Tiffany box, vs not in that Tiffany box, so they can charge.”

Low cost brands that lower search costs by being well known let people make quick choices to buy especially when the absolute price difference isn’t that big (name brand Ketchup, Coca-Cola). It is however not enough for a brand to lower search costs if it does not give the brand pricing power. 

Patents are a legal monopoly on a product but they expire, can be challenged, or ignored. They are much more valuable as a portfolio of many patents vs an individual blockbuster patent. If a company has only one valuable patent the incentive to challenge is high, but if a company has many patents applying to their business the incentive to challenge declines. 

Licenses and approvals can be a moat. It is hard to start a new casino, people don’t like living next to landfills or gravel pits, etc. FAA approvals for airline parts are hard to get, most aftermarket parts are single source so they often get 40% margins. 

Brands are valuable when they deliver a consistent or aspirational experience. This means if the product is selling well you don’t change the damn product! 

Schlitz used to be the second highest volume selling beer in the US. They changed the flavor of Schlitz and they never recovered their market share. If people are buying why change it?

Tiffany gets over 40% of their revenue from items that sell for under $200. One of the ways they maintain the high end nature of their brand is by putting the most expensive items in the front of the store, it maintains an aura of exclusivity and scarcity. Upon walking to the back of the store the prices are lower and your mind has anchored to the higher prices. 

Aspirations differ by culture and area, so you want to find companies that can adapt. For example Jack Daniels emphasizes an old school cowboy branding in Russia, but has developed a more urban / youthful persona in China.

Switching costs can provide a moat if the company deeply integrates with their customers. 

Service contract attachments can also be a moat, for example in the elevator business. Rolls Royce sells their jet engines via “Power by the hour”. 

Another switching cost advantage is having a very high benefit to cost ratio. For example a high quality lubricant that can increase the uptime of a giant mining machine, even if that lubricant costs 20% more than the next highest cost lubricant it is such a low cost input relative to the labor, machinery, and cost of downtime that the customer will happily pay a premium. 

Network effects are fairly famous in Silicon Valley. A product with network effects is any product where the value of using the product increases with additional users. Examples include a social network, telephone service, a payment processing network, or a messaging app. Another example is connecting fragmented buyers and sellers. Henry Schein aggregates fragmented demand and fragmented supply and this allows them to charge more in the dental business. 

Beware of radial vs interactive networks. Radial networks are much less valuable. Example of a radial network vs interactive network is Western Union vs VISA. Western union is a series of spokes or channels and doesn’t become more valuable with more users in a non-linear way. 

Some examples of process advantages were companies that “near shored” their fashion product manufacturing to respond faster to changes in consumer tastes. The issue with process improvements to a moat is they tend to be short lived, competitors will learn of them and replicate them as best practices. 

Scale tends to be a much more robust advantage. DHL lost over 1 billion trying to compete with UPS and FedEx in the USA despite being a very well run business because they never could reach the established scale of the existing package delivery firms in the US. 

Niches can also lead to enormous profits. Many niches are not large enough to support many players and also have limits to growth that make them unattractive to new entrants, but as an incumbent they can be enormously profitable subject to those limitations. 

Does management make a big difference? 

Usually not, Pat gives the example of a great Jockey on a goat, vs him on a horse. He will probably beat the goat. 

The quality of management needed to succeed is inversely related to how good of a business is. Moats can protect management from their own mistakes. Airlines will never have lower costs than the day they open for business, no matter how great their management they tend to destroy capital. Planes wear down, employees gain seniority with higher pay, etc. 

Good managers can still make great businesses better. Amazon is always looking for ways to improve the customer experience which has no cap on how good it can become. Costco is always looking for ways to scale and offer lower prices or higher quality products to their customers. Those cost savings lead to more customers which leads to more scale which leads to lower prices in a virtuous cycle. 

Bad managers enter businesses they don’t understand lowering returns on invested capital. One example is Cisco entering the consumer business. Garmin had great franchises in avionics, as well as vehicle GPS then tried to enter the handset market for cell phones out of fear of GPS becoming a feature of phones, which it did. 

A key point to note here is if companies are diversifying because because their core businesses are dying they are more likely to destroy capital making irrational and desperate choices along the way. Alternatively if they are diversifying out of strength where their core businesses are already thriving they are more likely to be making rational choices on what businesses to enter next. 

There are rare examples of operators who start in poor businesses and still manage to become extremely successful. In Q&A Deloitte did a study on the “3 rules” of what successful businesses did where returns do not reverse to mean. You can read about that here. The gist of the study is rule 1: don’t compete on price, compete on value. Rule 2: revenue before costs. Don’t cut costs while there are still opportunities to obtain higher prices or higher volumes. 

During the Q&A traditional value investing was also discussed. Time is the friend of a good business, but not that of a bad business. 

Local factors can create moats for businesses that wouldn’t exist elsewhere. For example car washing businesses in Germany are uniquely advantaged because there are regulations on where car owners can wash their cars in Germany. There are also more opportunities for minimum efficient scale (Thai language media). 

Flavors for candy generally are not popular outside of their home country so local candy firms often have moats, while the same is not true for beer which does travel. 

Why do moats matter?

These slides go together well, a moat without the ability to reinvest adds to certainty (possible outcomes are limited) but should be valued lower than a business with a moat and reinvestment opportunities. The example he uses is McCormick, consumption of spices just isn’t going to grow that much. He states it is a business you wouldn’t want to pay 30 times earnings for. It currently trades for 32.5 at the time of this writing. He also lists Microsoft and Oracle as companies without many reinvestment opportunities which may have been true in 2015. 

Moaty businesses are not limited to the “Warren Buffet” type companies. Even if slow growth stable companies can pay out a lot of their free cash flow there are more desirable businesses that can reinvest those profits at a high sustained rate and those are “totally awesome”. 

Real world example: Motorola. Motorola from the time it released the RAZR to its peak had 22% market share, but there was no lock in. People over estimated the moat Motorola had and lost substantially if they were investing as if the success of the RAZR was going to continue. 

As an illustration of the opposite problem Buffet mentions he was buying into Wal-Mart stock but it started to move up in price and they decided to wait for better prices. The opportunity cost of this ended up being 8 billion dollars to Berkshire. 

Most investors spend time on margin of safety and not on opportunity cost. 

Isn’t the moat priced in?

Sometimes it is priced in, Cisco trading at over 100+ P/E in 1999 being one example, but most investors have a very short time frame. The average turnover of a US mutual fund is 100%, and most investors focus on short term changes in price and not long term changes in moat. 

“All of the information is in the past, but all of the value is in the future”. 

Pat Dorsey believes that quantitative data is efficiently priced into securities and picked over by funds, but qualitative data where an investor must work to understand the factors that lead to moats is less examined. 

When looking to model high growth companies it is helpful to think in fixed vs variable costs, and consider what marginal costs to generate marginal revenue a company will have as it scales. 

If you liked this and would like to see more similar content please subscribe, share, and like. 

Disclaimer

In no event will Prdctnomics or any of the Prdctnomics parties be liable to you, whether in contract or tort, for any direct, special, indirect, consequential or incidental damages or any other damages of any kind even if Prdctnomics or any other such party has been advised of the possibility thereof. 

The writer’s opinions are their own and do not constitute financial advice in any way whatsoever. Nothing published by Prdctnomics constitutes an investment recommendation, nor should any data or Content published by Prdctnomics be relied upon for any investment activities.

Prdctnomics strongly recommends that you perform your own independent research and/or speak with a qualified investment professional before making any financial decisions.

How I approach investing

Nothing has been more harmful to my investing performance than the combination of the efficient market hypothesis and the concept of investing based on price to earnings or price to book multiples. 

It is pretty easy to see why. It seems very different business models actually deserve very different valuations. While I didn’t do poorly by being in index funds and ETFs I missed out on some obvious trends because it didn’t fit into my mental models. One of the most destructive was the tendency to avoid companies due to price to earnings multiples. These valuation mental models are heavily skewed towards a time when public businesses were largely homogeneous and lacked competitive advantages. When factories adopted new processes and technologies others quickly did the same. Excess profits were quickly competed away so the best way to ensure profits when investing as I understand it was to buy into companies as far below their net asset value as possible. That isn’t to say classic value concepts are broken (although they are certainly out of style right now), but these new models of thinking appear to be working much better for me right now. Could it be short lived? Could we be in a bubble in quality and growth? Certainly. It wouldn’t be the first time either. However I seem to have both more resolve and success with these lines of thinking than my attempts at value and factor investing.

Over time I find myself revisiting these concepts and applying these mental models again and again. With these reality filters once you see it, you can’t unsee it. 

What are my new mental models?

Investing in value and struggling businesses is a bit like buying a melting ice cube for less than its currently worth, while investing in quality and growth assets is like investing in a snowball rolling down a hill. Both approaches have merit, unique and differing risks, and likely require different temperaments to be successful.

When looking at the above before even thinking about valuation one must think about what makes a good company. What are the best companies? How does one distinguish between mediocre and great? It turns out very few meet the bar. Many snow balls and hills may turn out to smaller than we expected, or worse end up being melting ice cubes.

When looking for the biggest compounders of capital one must first eliminate most companies which don’t compound their capital at rates higher than their cost of capital. To understand the prerequisite circumstances for when this happens I’ve learned from several people who have had immense success as entrepreneurs and investors in these types of businesses and somehow found the time to share their knowledge with us plebeians.

To share these mental models I will first walk you through 3 videos that are more valuable than my entire university economics education was regarding equity investing. While I also cover, summarize, and elaborate on the contents of the videos I do recommend watching each. 

We will start with Peter Thiel, entrepreneur, investor, and author of Zero to One.

Competition is for Losers with Peter Thiel (How to Start a Startup 2014: 5)

Riches in Niches

It is much better to own and then grow with a niche than to try to enter a large existing market. It is always better to be the biggest fish in a small pond.

Start small and monopolize, it is easier to dominate a small market than a large one. If you think your initial market is too big, it is. 

Almost all monopolists in Silicon Valley started with a small market and then expanded into adjacent markets. 

Amazon started with books and moved up the value chain to general e-commerce, and now every imaginable physical and digital good, cloud services, etc. 

Ebay started with collectible goods like beanie babies and expanded to all goods. 

PayPal started with just eBay as their target market. 

Facebook’s initial market was Harvard University. 

Business schools typically discount the relevant initial markets for startups which appear so small that they have no value, but startups can build from these markets outward into ever larger markets. 

If a company is starting with a massive market (all of energy for clean tech) presenting their potential market as hundreds of billions or trillions is a red flag. You want to be a one of a kind company in a small ecosystem, not the 10th thin solar panel company, or the 100th restaurant in Palo Alto. 

As a business it's good to go after markets that seem so small they don’t make sense then scale from domination in that small market outward. 

The history of technology is such that each great opportunity happens only once, the next Bill Gates won’t start an operating system company, the next Mark Zuckerberg won’t start a social media company. 

All unhappy companies are alike: they failed to escape the plague of competition. 

If iterating on an existing concept your product must be an order of magnitude better than the existing product or it will be hard to convince people to adopt it. 

This is not the monopoly you’re looking for. 

Monopolists pretend to have competitors, while non-monopolies pretend not to. 

Non-monopolies will try to emphasize their competitive advantage often when none exists, for example presenting their restaurant as the only British restaurant in San Francisco, which isn’t actually a monopoly as any restaurant is a substitute.

True monopolies will emphasize their revenue streams are a small total percentage of many combined markets, like all of advertising not just search or digital advertising. Amazon has broken out its market share in the US as a percentage of commerce, not a percentage of e-commerce when discussing with regulators. 

This example with Google is particularly interesting as America re-examines antitrust actions against big tech. Google owns almost the entire search market and has purchased many digital advertising giants outside of search over time, so the below is very out dated. 

However Google presents itself as an advertising company, or technology company, not a search company with the goal of showing themselves as a small part of a significantly larger market. This creates the narrative Google is extremely small relative to the total market and therefore not a monopoly. Alphabets “other bets” may be an attempt to further obscure this monopoly power.

High gross margins and free cash flow, as well as cash build up on the balance sheet are indicative of potentially being a monopoly as they demonstrate pricing power. 

What are attributes of a lasting monopoly? 

While hard to get started a monopoly can form due to network effects (Facebook being the classic example). Network effects are exceedingly difficult to establish (why be the first person on a social network?), but even harder to disrupt.

Any business that has very high fixed costs and very low marginal costs tends towards monopolies. ISPs / Cable, Cell Carriers, etc.

While not examined in depth here monopolies based on brands alone are possible.

Software by virtue of having no or very low marginal costs can easily scale with demand should it arise.

These companies, if they can continue their growth, have most of their potential value far in the future. However Silicon Valley has a tendency to over focus on immediate growth, and under focus on durability. 

Even proprietary technology is tricky, and it alone is insufficient to be a durable competitive advantage. There are many cases where there was a tremendous breakthrough in technology which was then leapfrogged. This innovation is excellent for society but not for your business. By definition it is ideal to be the last mover not the first.

An example industry that underwent this was the disk drive industry where many companies had the best disk drive technology for a short duration of time. Clayton Christensen explores this more in depth in The Innovators Dilemma. These incremental innovations in disk drives never quite reached the escape velocity needed to avoid future competition.

The question must be asked which companies are likely to still be the leader 10+ years later in their niche.

History of innovation

Thiel goes on to describe great innovations that created tremendous value for society but the inventing party didn’t capture any value from their invention. Businesses often create significantly more value than they capture, and its actually rare that inventors capture more than ~0% of the value created from their invention.

Examples of this are early textile factories. The competition structure prevented both the capitalists and the laborers from capturing the value of their innovations. The revolution in textile manufacturing and steam mills did not really add new names to the land owing aristocracy in Great Britain, Albert Einstein did not get rich from his breakthroughs in physics, and the Wright Brothers while ending up well off never reached the same level of wealth as later monopolists.

Vertically integrated monopolies when successful are immensely powerful, but take a long time to build and are often complex. Henry Ford and Ford Motors is one such example, Standard Oil another, and SpaceX has the potential of becoming a modern example. 

Software itself seems to lend itself to monopoly more often than the world of atoms. This might be due to low marginal costs, economies of scale, and fast adoption which allows the company to scale quick enough to evade robust competition.

The microeconomics and structure of industries matters a tremendous amount. Markets with bad microeconomics are often later rationalized away by the parties in such industries. For example the idea that a good scientist doesn’t want to make money is one such rationalization.

Psychology of Competition

Thiel suggests we often think of “losers” as those who can’t compete, but in reality we should revalue this as competition itself being the issue. The draw to competition could partially be a psychological attraction to the struggle of competing, imitative or herdlike thinking, and / or as a form of validation. When one’s peers are working up the same ladder it is hard not to desire the same.

20k people a year move to L.A. to become movie stars, maybe 20 make it. Intensity of competition increases in elite schools, and managerial class jobs. The difficulty in differentiating yourself from others leads to greater and greater competition over smaller and smaller stakes. 

Thiel discusses leaving an elite law firm where everyone outside wanted to get in, and everyone inside wanted to get out.  

Competition of course makes you better at whatever it is you are competing in, but it often comes at the price of asking bigger questions about what is truly valuable. 

Lessons

“Don't always go through the tiny little doors that everyone tries to rush through,  maybe go around the corner, and go through the vast gate no one is taking.”

The above has a lot of implications not just for investing but also choosing where to work, what to work on, and avoiding group think. Peter Thiel is famous not only for his intellect but also for when he chooses to be a contrarian. No doubt walking away from the prestigious and high income legal career track he was on was challenging and must have seemed insane to most. It was also absolutely essential to getting to where he is today.

People and businesses that do something completely new have a luxury: no one else is doing what they do so they have no competition. If entering a crowded field to stand out one must be significantly better, an order of magnitude better. Truly great businesses have the magic combination of few likely competitors due to their competitive advantages, and a long runway of growth ahead of them. This means most of the companies’ potential value is in the distant future.

Short term thinking, recency bias, and anchoring have historically made me reluctant to bet on winners. After all they were cheaper not long ago, and their price to free cash flow might be unjustifiable with this and even next quarter’s earnings. However assuming I am right about a company’s market position and growth potential then my time horizon and the ability to hold long term is a competitive advantage.

Part 2

In part two I will walk though the ideas of Pat Dorsey.

This brings us to the important concept of disintermediation.

disintermediation

 dis·​in·​ter·​me·​di·​a·​tion | \ ˌdis-ˌin-tər-ˌmē-dē-ˈā-shən  \

Definition of disintermediation

1: the elimination of an intermediary in a transaction between two parties

Source: Merriam Webster

Source: Wikipedia Commons

Social media is disintermediating traditional media, debundling individual creators from organizations, and capturing the new value this provides more efficiently than legacy media was ever capable of doing.

In the case of content creation there has been a continuous growth in supply of content from users of the social media platforms themselves. This content “crowds out” legacy media, and the creators accessible on social are also usually the primary sources in the legacy media. Increasingly the legacy media organizations are just another account competing for views against every other account on platforms they do not control. This combined with how most news sites have been monetized likely represents a permanent cap on the success of their businesses without fundamental change. Furthermore the accounts they are competing for attention with are often more authoritative original sources than themselves, and thus get frequently captioned in legacy media content.

A fresh example is during the COVID-19 crisis. I became aware in early January from individuals who were either subject matter experts or contrarians able to see trends in advance that COVID-19 was a significant issue. Individuals such as Trevor Bedford, Balaji S. Srinivasan, and Jon Stokes. Whilst all very different individuals they bravely sounded the alarm while the media consensus was still that fear of COVID-19 was worse than the disease. Despite the legacy media pressuring people not to wear masks and running articles suggesting fear was worse than the disease, the above individuals never wavered in their message. To illustrate how contrarian this really was at the time here are some headlines:

Washington Post: How our brains make coronavirus seem scarier than it is

Washington Post: Get a grippe, America. The flu is a much bigger threat than coronavirus, for now.

“Clearly, the flu poses the bigger and more pressing peril; a handful of cases of the new respiratory illness have been reported in the United States, none of them fatal or apparently even life-threatening.”

- Lenny Bernstein, Washington Post health and medicine reporter

New York Times: In Europe, Fear Spreads Faster Than the Coronavirus Itself

We’ve learned a lot since these articles were created but it is important to highlight how each of the people mentioned above went against popular opinion at a time when it was incredibly difficult to do so and sounded the alarm, again and again, for months. They also did so with little fanfare or even opposition from legacy media at first. While each of the individuals above could have their own article on sounding the alarm on COVID19 I will focus on Trevor.

Trevor along with his colleagues at The Seattle Flu Study sounded the alarm on community spread in the United States with proof via testing no doubt saving countless lives. To those following the right people it has been obvious since February that COVID-19 is a respiratory illness, an airborne virus capable of human to human transmission, and that masks for effective for greatly slowing or halting its spread depending on the type of mask and how it is worn.

One of the interesting things the above illustrates is the legacy media’s subject matter experts used for sources are the same people on social media posting in great detail all of this overlapping information for free. With this crisis you would have been much better served avoiding the traditional gate keepers of information and going directly to the source, specifically with Trevor a Harvard educated biology PhD.

Assuming you are able to identify good primary sources there is little value in having additional intermediaries. One potential value add is discovery of new content, however social media has also handled that exceedingly well with different platforms coming up with different often addicting ways to see more of what you engage with.

More broadly people seem to be using social media as their sole gateway to 1st party sources of information. In this world legacy media companies are one of many high follower count accounts people choose to consume content from. Even if people did not consciously choose to switch their information consumption gatekeeper it would appear two things are true:

1. A sufficiently large enough group of people have adopted these platforms as their primary information sources that public opinion is shaped by them and ad dollars have moved to them.

2. Individuals are increasingly following specific writers whom they trust, as opposed to following an entire media institution.

For years now social media companies have been capturing not only most of the value the legacy news corporations previously did, but also growing new markets for attention. Since there is overlap with content creators on social and legacy media most of the financial value of the readership will shift to where attention is spent.

It doesn’t hurt that ad spend also seems to be significantly more targeted and higher ROI on social media platforms vs alternatives due to the volume of information they’ve accumulated on their users.

The value capture and shift in attention can clearly be seen by looking at the fundamental revenue and net income growth (or lack thereof) of legacy media companies vs social media companies.

Revenue of legacy media companies:

Source: Macrotrends.net

Net income of legacy media companies:

Source: Macrotrends.net

Below is a breakdown of new media platforms: Facebook, Twitter, Snap Chat, and Google (YouTube) with their corresponding revenues:

Source: Macrotrends.net

Net Income of new / social media companies:

Source: Macrotrends.net

Here is the profit margins and recent sales growth of each company:

Data source: Finviz.com

Finally let us look at revenue and net income between the large social medias and large legacy medias over time with a shared axis:

Source: Macrotrends.net

Source: Macrotrends.net

While legacy media has declined or stagnated social technology has absolutely thrived. This is not a zero sum game for attention and trust but it does appear that these trends will continue.

The above is an interesting lens to what has already happened, but what is more interesting is what happens next. The implications of these shifts, if new media moats are defensible, and how best to profit in the new emerging direct to consumer order will be covered in a future article.

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What patterns does technological disruption usually follow? Disruption is a heavily misused and overloaded term today. Typically true technology disruption starts on the low end of the market, follows an S curve of utility as sustaining improvements are made to the initial innovation, and those improvements allow the disruptor to move up market thus disrupting incumbents.

Source: Wikipedia commons

The innovator's dilemma is thus: When managing a business do you invest in cost reductions / process improvements (efficiency innovations), improvements to your current products as driven by data and customer asks (sustaining innovations), or do you invest in new technology that while inferior today has the potential to eventually displace your current products? The final one has the most risk, longest payoff time, and is likely to cannibalize your current highly profitable products in the process.

The final one is called disruptive innovation. Disruptive products are usually significantly less expensive than alternatives or create a new market that has no alternatives. The initial disruptive tech may seem impractical, niche, or a “toy” use case compared to existing tech, but successfully addresses a use case that is not attractive to incumbents.

At the start of the process the innovation may seem lacking significant utility, but as it improves and finds product market fit, more impactful improvements can be identified. Due to the economies of starting on the low end of the market, existing adjacent markets often look attractive to enter to the disruptive firm, whilst it will be rational to the incumbent firm’s management to retreat to the remaining high margin business areas they have available to them. This allows the disrupting firm to move “up market” into areas owned by incumbent firms often with less resistance than one would expect.

One example in the book is Honda Motorcycles. Honda wanted to expand outside of Japan and compete with Harley Davidson and BMW in the USA. Honda’s smaller simpler bikes were not competitive on features, or quality vs the US road bike market which was substantially different than the urban bike market of Japan.

The USA Honda team stumbled upon the use case of off-roading with the bikes themselves which the cheaper and smaller motorcycles excelled at. The Honda dealer network rejected the small Honda Super Cub precisely because they were listening to customers who wanted bigger, more feature-rich road bikes. Listening to customers is essential to producing sustaining innovations, but interviewing existing prospective customers outside of the right niche for disruptive innovations is likely to lead a product team astray.

Honda pivoted to a different significantly smaller distribution channel: Sporting Good Stores. As is typical in disruptive innovations, established producers do not see value in the innovative technology as they don’t actually know how the tech will be used or which features of it will be valued, and their customer base often has no use case for the innovation. Success hinges entirely on the innovating firm finding a market that values the innovation. This market often differs from the market the firm set out to enter and typically has a much smaller TAM.

As the newly created off-road motorbike market took off Honda and other Japanese bike firms captured most of the market share, whilst incumbent firms (in this case Harley Davidson) were not incentivized to enter the niche market. Initial use cases for the disruptive tech are often of no interest to market leader firms’ existing customers, are lower margin businesses, and as a market for an inferior good present a brand risk to incumbent firms. 

The success of marketing a disruptive technology to a new market provides funds for sustaining innovations making the disruptive tech better each subsequent iteration. Eventually the disruptive technology becomes competitive with incumbent firms’ products, which usually have the added benefit to the disruptor of being higher margin products. This allows the disruptor to cross the moats of existing market leaders forcing them to retreat further up market themselves driven by their own customers asks and perceived needs.

If this pattern continues unchecked, the incumbent firm risks being replaced by the disruptor. Some firms will incubate disruptive technology themselves to fend off lower market disruption. Intel famously went down market with the Celeron processor to fend off low-end rivals like AMD after Andy Grove, then CEO of Intel, read Christensen’s book.

Disruption is a perpetual risk and many business moats are short lived as a result, even businesses with excellent management teams. One such example of a company that successfully self-disrupted multiple times before failing to do so was Kodak. Kodak successfully shifted their business multiple times self-disrupting (for example from clear high quality black and white film, to at the time lower quality color film) before failing to see the threat of digital photography.

Driven by the demands of an existing customer base, a company will be incentivized to chase sustaining innovation after sustaining innovation which pleases customers and drives profits in the near term vs investing in a potential new markets that might not become as profitable for years if ever.  Business risk aversion and taking the seemingly prudent data driven path can eventually prove fatal when competing with a true disruptor. Quarterly earnings reports also encourage short term thinking and efficiency innovations over long term capex in potentially disruptive innovations. Many public companies are locked into both cycles of pleasing investors and existing customers making them vulnerable to bottom up disruption.

This creates an interesting opportunity: by having a longer time horizon a small insightful group of people can compete with less agile giants. First by growing in niches that are rationally ignored by incumbents, and then by moving up market. This applies to both startups and intrapreneurs.

Is your employer investing in disruptive innovations? Are your investments vulnerable?

An understanding of disruption can help you make better choices in how you build, and understand how well run companies get blindsided by disruptors.

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