[Sagan’s Prophecy and AI]
In 1995, astronomer and planetary scientist Carl Sagan made a disturbing prophecy in his book the Demon Haunted World about the future of the United States where nearly all of its manufacturing industries had slipped away to other countries leading to adverse geopolitical and societal outcomes.
AI is all around us. AI listens to you. AI sees your face and body. AI knows where you are right now. Training the next chatbot. AI can read. AI can write. AI can talk. AI can make a picture of cats playing poker. But AI rarely ever actually moves. In nature, 'motility' is an organism's ability to move independently under its own power. According to fossil records, the earliest evidence of motility on earth traces back to bacterial flagella in the Precambrian era. The lines between mobile device and robot are starting to blur. A new Cambrian Explosion of organisms is dawning. AI’s about to get physical.
[Basic Principles]
As software becomes 'agentic', robots become agents. Electric machines are the corporeal 'sockets' for the AI brain. Any machine that can be automated will be automated (maybe even including you). If you solve autonomy for cars, you solve autonomy for everything. A Humanoid robot is just one of thousands of form factors of embodied AI. A broader definition? Any machine that collects photons, perceives the world around it, learns, navigates or manipulates 3-dimensional space. Embodied AI and national security are inextricably linked through ‘dual purpose’. Embodied AI lends itself to natural monopolies and utility networks across deca-trillion-$ TAMs or total addressable markets.
[A Sense of History]
It’s December 31st 1879. Thomas Edison makes the first public demonstration of his incandescent light bulb. The incredulous crowd laughs and asks: how could people buy light bulbs if they can’t afford electricity? Edison responds: ”We will make electricity so cheap that only the rich will burn candles.”
This is 5th Avenue New York City Easter Sunday in 1900. Spot the car. Here. This is the same street in 1913. Spot the horse. Here.
Between 1485 and 1490 an Italian polymath of the high renaissance made some sketches in his Codex Atlanticus of some fantastical machines. Over 400 years later in 1903, two brothers from Dayton Ohio achieved first flight over the sand dunes of Kittyhawk N. Carolina. By 1914 we have the first commercial airline flights between St. Petersburg and Tampa. Fast forward to 1967, the Boeing 737 is introduced into service. Nearly 60 years later we have a plane so similar in design they didn’t even bother changing the name. From this to this is innovation. Da Vinci would be impressed. But from this… to this… I think Leo and the Wright Brothers would be super disappointed.
[Atoms & Photons]
Let’s draw a simple 2-axis chart together. In the Y-axis we have the knowledge economy - the economy of bits & bytes. In the X-axis we have the physical economy - the economy of atoms and photons. AI is well on its way to consuming the knowledge economy - moving rapidly up the Y axis… disrupting occupations such as writing, accounting, tax, legal, CRM… and equity research analysts. But what happens when all the digital data is captured and trained? When everyone has the same compute… how will the LLMs differentiate themselves? We’ll need to move to the right… into the physical economy. But even with unlimited quantum compute you can’t train a Vision/Language/Actuation model without vision data. The race for photons has begun. But first – let’s talk about fat tuna.
[Fat Tuna]
Imagine you're on a remote island looking out to sea. 3 miles off shore is a plump 600lb blue fin tuna hunting for squid. You have no boat and no fishing tackle. How much is that un-catchable tuna worth to you? Zero. Now imagine you have a boat, fishing tackle and the latest generation fish finder. What’s that tuna worth now? In 2019 a 612lb blue fin tuna fetched $3.1mm in a Tokyo auction. Now let's turn to vision data. What is the world's visual data worth if you have no way of collecting it? Zero. Now imagine you have the ability to collect and process yottaflops and yottaflops of data. What’s that data worth now? More than zero?
[Insect Eyes]
Biology is hyper efficient. The world is full of creatures doing the greatest amount of work using the least amount of energy subject to its environmental constraints. Take the example of the humble drosophila...Just look at that punim. Now these creatures are really, really good at navigating and orienting in flight. Are fruit flies super intelligent? Well, the fruit fly’s brain is the size of a poppy seed. So compared to a sponge (which has no brain), the fruit fly is highly intelligent. Now I’m no insectologist but… Poppy seed brain beats no brain. Every. Time. Scientists believe the secret to the drosophila’s aeronautic abilities has something to do with these things. 2 enormous compound eyes that are bigger than its entire head. Each featuring around 400 hexagonal ommatidia - these insect lenses act as tiny computers - light comes into the lens which does a calculation/pre-processing the data before entering its brain. Again folks - we’re talking poppy seed, not sesame seed. Lenses are amazing computers that don’t use any of the fly’s energy and they don’t make mistakes. Hardware like this is the compounding product of Darwinian forces of biological survival, mutation & procreation over hundreds of millions of years.
But Tesla and Google don’t have hundreds of millions of years to solve autonomy - they need tools that can simulate billions or septillions of years in just a few days to get those Darwinian forces moving a little bit faster.
[Living in a Simulation]
An NBA point guard steps up to the free throw line for a foul shot. He takes 3 dribbles and closes his eyes, imagining the perfect path of the ball right into the hoop - swish. That shot never happened. It’s all in his mind. When a US Open champion imagines a perfect serve: didn’t happen. A Premiere League star, imagining a penalty kick. It didn’t physically happen. But when a robot dreams in simulation - it does so in a hyper-realistic digital twin complete with physics engines to replicate the laws of motion, thermal-dynamics, fluid dynamics, the behavior of light. As if it actually happened. As the robots collect more data, the sim-to-real gap continually narrows. When you are driving a Tesla, you’re not just driving in physical space… you’re also playing a video game feeding data into the simulated world to train Tesla’s latest FSD model. When you wear META glasses, you are teaching the robot model how to play piano, knit a sweater, pour coffee or take out the garbage.
[Historic data vs. Arising Data]
Think of a timeline of the history of the modern internet from 1995 to 2025. Over this 30-year span, what’s the most valuable contiguous 5 minutes of data if you’re training an LLM? Well… the last 5 minutes of data… only surpassed by the next 5 minutes of data. The power of recency is critical for the predictive capabilities of inference and reinforcement learning. Take this rather simple, if not esoteric, example from my office at Morgan Stanley’s headquarters in midtown Manhattan. If I throw this pink highlighter in the direction of this Ferrari 458 Italia on the coffee table and freeze time just after the highlighter leaves my hand. You will have a very good sense of the trajectory and speed of the highlighter’s flight… and the sound it makes as it enters the car. You didn’t have to travel through time to know that. Your prediction benefits from your Experience (you’ve seen it before) and the Context (you’re seeing it now).
Historic data is important, but those who have the best arising/real-time data have a major advantage.
[The Mag 7]
Industrial companies are TAM rich and tech poor. Tech companies are tech rich and TAM poor. If you’re a $4Tn market cap company, you’re not going to become a $10tn company by going after a $50bn TAM… you gotta target that $5tn TAM. That $50tn TAM. That’s not gonna happen by getting people spend one more hour of the day on a smart device. You need to address markets like transportation, manufacturing, energy, healthcare - you need to go after the physical markets and grind out those atoms. If there was ever a TAM opportunity that could exceed the size of the global economy - embodied AI is the one.
[Google’s Alpha Bet]
Have any of you ridden in a Waymo? They’re all over SF, Los Angeles, Phoenix, they just launched in Austin and will soon to be running around Atlanta, Miami and Tokyo. Our internet team forecasts Waymo’s fleet to grow from just over 1,500 units today to 23k by 2030. Now why would the world’s largest search engine company want to get into the autonomous car business? Because the tech is really good, is getting better all the time, and maybe because they want to spread their bets beyond the core search business. You may soon look back in astonishment that you ever got into a ride share vehicle with a human driver.
[Your face is a Battleground]
META is building some serious capability in AI foundation models, simulation and metaverse. But it’s Meta’s efforts in reality labs that could open up entirely new TAMs and transform the company. What if you had 2 ultra-high-definition cameras embedded in a device that you could wear on your face. Imagine those cameras capturing precious real-world data of all the things you do with your hands. Now imagine 20 million of these things in operation within 2 years - nearly 2x the number of Tesla vehicles on the road. Every META glasses user is training a humanoid avatar iterated in simulation across billions of scenarios in a digital omniverse. The glasses may be stylish. But your face…. Your face is a battleground.
[Amazon Flexes its ABS]
It’s no secret Amazon is a major force in AI. But its vertically integrated physical infrastructure uniquely positions the company for pushing the boundaries of robotics. In 2017, Amazon had 5 human workers per robot. In 2024, we estimate Amazon had around 2 human workers per robot. Amazon’s highly automated fulfillment center in Shreveport, LA is being transformed through robotics. Our internet team sees the potential for $10bn of annual savings from robotics/automation. At that scale, Amazon can turn robotics into its own business. AWS started out as an internal efficiency measure before it became nearly 60% of Amazon’s Operating Income. Could we see Amazon Bot Services in the next few years?
[Let Tim Cook]
Apple may have paused their autonomous car project but once CarPlay gets access to the video data from inside and outside the vehicle then things could get very interesting in Cupertino. The skills transferability for Apple into embodied AI is pretty obvious but we’ll list them anyway: software, hardware, compute, battery, sensor, infrastructure, supply chain. The car of the future is essentially a giant iPhone wrapped around your body like an immersive IMAX-screen covered in carbon fiber reinforced plastic attached to an electronic skateboard driven by a supercomputer. Apple doesn’t want to make a car. They want to turn your car into a mobile Apple Store.
[Tesla’s DREAMS]
We’re often asked what is Tesla’s secret sauce? What’s their moat? Well - it’s not really 1 thing but the combination of 6 attributes that set Tesla apart from its peers. Let’s look at each of them…
· Data: 7 million cars on the road today. Over 100mm by 2040.
· Robotics: In-house electric motors & actuators. Just the hands of Optimus have 22 degrees of freedom.
· Energy: Leading battery storage solutions at scale.
· AI: A world class AI team developing FSD, Dojo and custom silicon.
· Manufacturing: The most vertically integrated, US-local sourced auto company in the world.
· Space: Redundant, resilient, cyber secure comms. SpaceX is the data transport layer - the connective tissue of the AI ecosystem.
So… out of Tesla’s DREAMS… what does Elon Musk think is the single most critical component of the company’s moat? Without any doubt… Manufacturing. You need to make the probes, to collect the data to improve the probes to collect more data to improve the probes… you get the idea here. Data defines the software, software defines the hardware, hardware defines the manufacturing. Elon Musk has used the car industry as a laboratory to develop competency in other areas… The car is to Tesla what the book was to Amazon.
[Autonomous cars]
There are 1.2bn cars on earth, traveling around 12tn miles/year. That’s 2 light years annually. Roughly equal to the distance between the earth and the sun… 130k times. With an average occupancy of 1.5 passengers per car at 25mph - that’s 720bn hours of passenger time… equal to 82mm years. Humans spend 82mm years of time inside cars… every year. 82mm years ago was the late Cretaceous period at the end of the Mesozoic era. The height of the dinosaurs. The earth looked like this. Bees were pollinating the first flowering plants at the end of the Mesozoic era. Bees. Now what’s the value of an hour of your time? Well that depends who you ask but 720bn hours times anything is a very large number…. Then there’s safety… with traffic fatalities still on the rise… it seems we’re getting dumber faster than the cars are getting smarter. For those of you with a manual transmission, steering wheel-having hooptie in the garage. Don’t sell it. These baby’s will be cherished by collectors as artisanal classics from an era when humans made the machines and operated the machines. And we still get asked why Ferrari trades at 50x PE.
[Flying cars]
The next time you are flying in airplane on a clear day, take a look down at the earth. Notice all the roads, all the parking lots. Notice how much of our planet’s surface area is devoted to vehicle transport. If AI can drive thousands of times better than humans in 2 dimensions in the pedestrian filled chaos of our cities… navigating in 3-dimensions is a walk in the park. Now consider what AI can do for our antiquated air traffic control systems. We estimate the low altitude economy can eventually surpass the size of the global car market. On our calculations, 1 eVTOL can generate as much revenue as 10-15 Ubers. Advances in e-motors, energy storage, material science, communications and compute will make flying cars ubiquitous and take some stress off the surface of our planet.
[AWS]
No, not that AWS… This one [Autonomous Weapons Systems]
Elon Musk recently posted that China makes more drones in a day than the United States makes in a year. And that all future wars will be fought with drones. Let that sink in. With conventional technology it takes 5 people to operate one $30mm dollar drone. With AI, 1 person can operate 100 drones. Redundant, resilient, if necessary, attritable… working as a team in an autonomous swarm. Asymmetric capability that may call into question traditional defense budgets around the world.
[Space]
When Elisha Otis invented the safety elevator in Yonkers New York in 1852 - the venture capitalists of his time may have struggled to imagine how elevators would change architecture forever. This is New York City before the elevator. This is New York City after the elevator. We think of the reusable rocket as an elevator to space. Have you ever seen a SpaceX Starship being caught in the arms of a mechazilla? Yeah - autonomous. SpaceX has reduced launch costs 10x while increasing payload capacity 10x, while increasing satellite bandwidth 100x. That’s a 10,000x improvement in cost per gigabyte of saleable capacity in orbit. SpaceX is creating a new type of internet that provides downlink connectivity to EVERYTHING. Every car. Every plane. Every drone. Every ship. Every home. Every business. Every phone. On our model, SpaceX grew revenue by 60% in 2024 to $14bn, reaching $66bn by 2030 on our estimates. The company’s latest tender offer valued SpaceX at approximately $350bn, according to Pitchbook, making it the most valuable private company in the world.
[Sputnik on Steroids]
On October 4th 1957 shortly after7:28pm Eastern time the US Army Signal Corps in New Jersey detected an unusual ‘beep beep’ signal at 20 and 40 MHz. The Sputnik moment meant the Soviets beat the US in putting a satellite into orbit, creating great anxiety in the Pentagon. In May of 1961, President John F Kennedy announced the goal of putting a man on the moon and bringing him safety back to earth by the end of the decade. And in that moment, countless 8 year olds in the US wanted to become astronauts and US math and science proficiency skyrocketed. National security is a powerful innovation catalyst. Competition with China is reawakening the Apollo spirit and catalyzing policy and public support for the next era of innovation in the fields of AI, cyber, space, robotics, and quantum. The United States didn’t put men on the moon just to collect rocks and do donuts in a lunar rover. They did it because if we didn’t do it… someone else would have.
[Humanoids]
A tiger cub learns by watching its mother hunt. A human shaped robot learns by watching you drink coffee. Flip a hamburger. Hit a forehand. Or miss a 3 foot putt. Why humanoids? Because the world was made for humans by humans. And there are >8bn of us to watch and imitate. First adoption? Starting now - with the most boring, repetitive, dangerous tasks in environments where you can control for temperature, weather, light, humidity & where humans are in predicable areas. A manufacturing line, fulfillment center, kitchen, a lithium mine. The TAM? There are nearly 4bn people in the global workforce. With an average annual wage of $10k… that’s a global labor market of $40tn. One humanoid leased at $5/hour can replace 2 human workers making $25/hour – supporting an NPV of approximately $200k per humanoid. The US labor market has 160mm people. Every 1% substitution by humanoid is worth ~$300bn, or around $100 per Tesla share.
[The Humanoid 100]
So how can our clients express a view on the embodied AI theme? Morgan Stanley presents the "Humanoid 100" — a global mapping of equities across a range of sectors and regions that may have an important role in bringing robots from the lab to your living room. We divide the robot into the brain, body and integrators. Semis, sensors, batteries, actuators, encoders, harmonic reducers, screws, bearings, connectors, magnets, rare-earths… and many more…
[Sagan’s Prophecy Revisited]
This is manufacturing as a % of US GDP since WWII. Just look at this chart. It’s not just the magnitude of the decline from nearly 30% to 10%. But the persistent, uninterrupted linearity of the decline… decade after decade for 80 years… how much further was this going to continue before we realized that it may have gone… a little too far?
Morgan Stanley is making a strategic commitment to telling the story of embodied AI - leveraging our platform and relationships to help our clients identify the next crop of multi-generational compounders transforming industries and creating new markets we believe can exceed the size of today’s global GDP. The intersection of AI and the physical economy offers the chance to disprove Carl Sagan's 1995 prophecy. History books will be written about this time and the next 5 or 10 years. The implications across markets and geopolitics are likely to be disruptive and far reaching. The Morgan Stanley research team is here for you as we navigate these consequential times. We are grateful for your partnership and thankful for your business. Stay human.
