The Robot Almanac

Adam Jonas: Robots have been around for a while. General Motors first introduced industrial robots in a body shop ina New Jersey factory back in 1961. Yes, once upon a time cars were made in New Jersey. In the first century AD, Hero of Alexandria described robotics in great detail in his book, “Automata,” around the same time he invented the steam engine.

Pre-AI, robots executed highly predictable and repeatable tasks with limited use cases, a lack of awareness and adaptability. Post-AI, robots are on a path to execute more diverse and open-ended tasks, enabling these electromechanical wonders to escape from the factory and proliferate throughout the physical world, becoming part of our daily lives.

In Volume 2 of the Morgan Stanley Robot Almanac, we go deeper into AI-enabled robots, showing you the difference between a Foundational Model and a World Model, and the exquisite interrelationships between the two. We show you the difference between Large Language Models and Vision Language Action Models to help understand the value of historic data, or “priors,” versus the value of arising data, or “context,” continuously improving the predictive decision-making of your robot.

The multiple ways that robots learn through teleoperation, simulation, and two- or three-dimensional videos. And how video game engines play a potentially critical role in narrowing the sim-to-real gap for robotics.

Nvidia enables robots through three critical compute domains spanning data center, simulation, and inference at the edge. Tesla, a robot company, is building a distributed inference cloud that interconnects its cars and humanoids into a giant mobile, swarming, intelligent data center. An extraordinary wide range of firms are trying to capture real-world data to train robot models across thousands of use cases.

In the near future, your face and upper body will be increasingly adorned with a range of sensors. We expect the number of Meta Glasses in use will double the number of Teslas on the road in just a few years, using millions of exaflops of edge compute by 2050.

Is it any wonder that robotics are attracting an enormous acceleration of venture funding over the past two years? Our Robot Almanac tracks hundreds of public and private companies driving the birth of this new Industrial Revolution, including deeper dives into key foundational model players such as Google DeepMind, Meta, Apple, Physical Intelligence, and Skilled AI.

Competition for AI supremacy between China and the United States is a powerful force vector of innovation. And while the U.S. may have an apparent lead in AI compute and AI models, China is in a dead sprint on the physical side, rapidly converting conventional manufacturing processes to make AI-enabled robots at high volumes, installing more industrial robots than all other countries combined.

China will soon lead the world in robot density, if they haven’t already. China is prioritizing scientific education over its chief economic rivals, continuing a pattern where the United States invents a novel technology only for China to scale it to broad adoption and economic dominance. In 2024, China installed 1.5 times the solar capacity of the entire existing installed base of the United States. Let that sink in.

But while the United States needs China for rare earth magnets, China needs the United States for its insatiable consumer and bleeding-edge AI technologies, keeping the rivalry in a rather delicate, if at times precarious, balance.

Rare earths. Some of the names of the metals may be hard to pronounce, but they’re even harder to live without. The use cases? Let’s start with every physical sector: autos, A&D, robots, boats, C.E., energy, healthcare. Basically, everything that moves.

And guess who controls around 60% of the world’s rare earth mining? And that’s just the mining. Who refines over 90% of the world’s rare earth metals? And you better get used to it because it takes about two decades to spool up a new rare earth mine. Our Global Robot Model, or GRoM, estimates the rare earth magnet content across all key categories of robots, projecting 1.7 megatonnes of magnets by 2050.

In 1995, astronomer and planetary scientist Carl Sagan made a disturbing prophecy in his book, “The Demon-Haunted World,” about a future United States where nearly all of its manufacturing industries slipped away to other countries, leading to adverse geopolitical and societal outcomes.

Well, we may not be there quite yet, but judging by the last 80 years of U.S. manufacturing slipping from 28% of GDP to less than 10% of GDP today suggests we’re well on our way. The intersection of AI and the physical economy offers the chance to disprove Sagan’s prophecy, and we’re all part of the story, however it ends up.

The robots are coming. Stay human.

Adam Jonas: In 1732, Benjamin Franklin first published “Poor Richard’s Almanac,” a colorful collection of weather forecasts, common-sense tips, and lessons for both young and old on industriousness, temperance, and frugality.

In 1792, the “Old Farmer’s Almanac” continued Franklin’s tradition, blending meteorological predictions, gardening tips, folklore, and advice ranging from the highly eccentric to the absurdly practical.

Paying homage to Ben Franklin’s scientific legacy, the Morgan Stanley Embodied AI Team proudly introduces “The Robot Almanac.”

In this inaugural edition, we include our most comprehensive collection of data, concepts, unifiers, and forecasts to help our clients navigate market trends and investment expressions as AI moves from the digital world to the physical world.

The Almanac is comprised of a number of physical AI verticals, including autonomous cars, humanoid robots, drones, VTOLs, autonomous weapons systems, industrial robots, space, and brain-computer interfaces.

Talking about the world before robots is like talking about the world before electricity. History books will be written about this time and the years to follow. Investment decisions made today will build the critical technology and infrastructure that reshape our world for generations to come.

The Almanac also introduces the Morgan Stanley Global Robot Model, or GRoM, a detailed bottom-up forecast of annual unit volume spanning a wide range of robot modalities across geographies through 2050.

How big’s the TAM?

Well, you can start with a global labor market of 4 billion people at $10,000 a worker, or a $40 trillion dollar labor market—more than one-third of global GDP.

We forecast the AI robotics market to reach $25 trillion dollars of annual product revenues by 2050, potentially generating over two times this amount of recurring service revenue.

Our Global Robot Model explodes the robot bill of materials, sizing key content areas such as cameras, lidar, radar, e-motors, bearings, reducers, analog and AI semiconductors, rare-earth magnets, and batteries.

The implied growth rates may surprise you. For example, by 2050, we forecast the robot bearing market to multiply by 200x, rare-earth magnets by 480x, batteries by 1,500x, and AI compute capacity by 40,000x.

Our model underpins the Global Robot Team’s curated stock expressions across sectors and geographies.

Robotics are a critical pillar of Morgan Stanley’s research efforts in private-company research and engagement.

We look forward to sharing our research insights and our network of company and institutional relationships across public and private markets as we partner with our valued clients at the dawn of a New Industrial Revolution.

The robots are coming.

Stay human.