HUGE GROWTH: I'm Investing In The REAL Winner of AI Robots
TLDRThe robotics industry is on the cusp of a significant shift, with humanoid robots expected to grow exponentially by 2030. Companies like Nvidia are leading the charge with innovative computing platforms like Jetson Thor, designed specifically for humanoid robots. The industry's 'Chat GPT moment' involves training robots through large action models, enabling them to learn from human-like data. This transformation is not limited to physical robots but also involves AI training in digital environments, potentially revolutionizing how robots are developed and deployed across various sectors.
Takeaways
- 🤖 The robotics industry is experiencing a significant 'chat GPT moment', suggesting a major shift in AI capabilities for robots.
- 🚀 Humanoid robots are anticipated to be a multi-trillion-dollar opportunity due to their versatility and adaptability in various environments and tasks.
- 📈 The global industrial robotics market is expected to triple in size over the next 9 years, with a CAGR of over 11%.
- 🌐 However, the humanoid robot market is projected to grow over 30 times by 2030, with a growth rate of over 60% per year.
- 🧠 The key to humanoid robots' success lies in their ability to perform a vast array of tasks, much like humans, making them a universal solution for many challenges.
- 💻 Companies like Nvidia are focusing on developing the computing power needed for robots, such as the Jetson Thor platform, which is crucial for AI inference and real-time decision-making.
- 🚗 Autonomous vehicles are essentially robots on wheels, and companies like Tesla and Nvidia are at the forefront of this technology.
- 🔍 AI training for robots is undergoing a significant shift towards imitation learning, enabled by generative AI and large action models.
- 🎥 Training data for robots can include vast amounts of video tutorials and instruction manuals, as well as virtual reality environments for reinforcement learning.
- 🌟 Nvidia's Project Groot represents a foundational model for humanoid robots, aiming to advance the field of general-purpose robotics.
- 🌐 The robotics tech stack is seeing innovations from various high-profile companies, and the field is still wide open for new players and breakthroughs.
Q & A
What is the expected growth rate of the global industrial robotics market over the next 9 years?
-The global industrial robotics market is expected to almost triple in size over the next 9 years, which is a compound annual growth rate of over 11%.
How does the growth rate of the humanoid robot market compare to that of the industrial robotics market?
-The global humanoid robot market is expected to grow more than 30 times in size by 2030, which works out to a massive growth rate of over 60% per year over the next 6 years.
What are some of the challenges associated with non-humanoid robots designed for specific tasks and environments?
-Challenges include finding the right expert to program a specific kind of robot and only being able to repair or replace it with a similar model.
How do humanoid robots address the challenges faced by non-humanoid robots?
-Humanoid robots provide a universal solution to many of these challenges as they can perform a large number of tasks in almost any environment, just like humans do today.
What are some key features of Nvidia's Blackwell GPUs that make them suitable for humanoid robots?
-Blackwell GPUs perform AI inference 30 times faster than previous generations, have a built-in engine for reliability, availability, and serviceability, and can benefit from new updates or optimizations released by Nvidia.
What is the significance of the 'chat GPT moment' for robotics?
-The 'chat GPT moment' for robotics refers to the development of large action models where robots can learn by imitation, just like humans do, through video or text prompts as inputs and commands and controls as outputs.
How does Nvidia's Isaac reinforcement learning gym contribute to the training of humanoid robots?
-Isaac allows humanoid robots to learn how to adapt to the physical world through reinforcement learning, using training data from real or synthesized examples of how expert humans and other robots perform tasks.
What is the potential advantage of training robots in digital environments before deploying them physically?
-Training in digital environments allows for the use of millions or even billions of examples, including rare edge cases, and enables robots to be tested and refined until they can perform tasks expertly, safely, and reliably before being uploaded to the physical robots.
Which companies are mentioned as major players in the development of foundational models for AI training in robotics?
-Companies like OpenAI, Coaar, Google, Meta Platforms, and Nvidia are mentioned as building foundational models for AI training in robotics.
What does the speaker suggest is the best investment one can make?
-The speaker suggests that the best investment one can make is in oneself.
How does the speaker describe the current state of the robotics industry in relation to generative AI?
-The speaker describes the robotics industry as being at the very start of the generative AI era, with many high-profile companies and startups working on embedded chips for robots and self-driving cars, and foundational models, training services, and AI data centers being built to support them.
Outlines
🤖 The Rise of Humanoid Robots and AI Innovations
This paragraph discusses the significant advancements in the robotics industry, particularly focusing on humanoid robots. It highlights the potential for these robots to enter the market much sooner than anticipated and emphasizes that the true winners in this industry might be companies that never directly sell robots. The speaker intends to delve into the advantages of humanoid robots, the recent 'chat,GPT moment' for robots, and the major players in the field. The global industrial robotics market is growing, but the humanoid robot market is expected to grow at an even faster pace, presenting a multi-trillion-dollar opportunity. The speaker argues that the focus should be on the technology stack and the computing power required for these robots, rather than just the physical form of the robots themselves.
🚗 Nvidia's Role in the Robotics Revolution
This paragraph explores Nvidia's pivotal role in the robotics industry, comparing their position in the market to that of Tesla in the automotive sector. The discussion centers around the computing power needed for robots, highlighting that smaller, lightweight computers capable of processing vast amounts of data in real-time are crucial. Nvidia's new Blackwell GPUs are mentioned, which are designed for data centers, autonomous vehicles, and humanoid robots. The Drive Thor platform整合了汽车中的多种功能, giving cars significant computing power for AI processing. The paragraph also touches on the importance of Nvidia's technology in enabling Mercedes-Benz's Drive Pilot to achieve level three autonomy, surpassing Tesla's level two at the time of recording.
🌐 Protecting Your Data in the Age of Online Fraud
This paragraph takes a brief detour from robotics to discuss the issue of online fraud and identity theft. The speaker introduces Delete Me, a subscription service that removes personal information from online data brokers. The service provides regular scans and a privacy report detailing the removed information. The speaker emphasizes the importance of data privacy and offers a discount for viewers interested in using Delete Me's services.
🧠 AI Training and the Future of Robotics
The final paragraph returns to the theme of robotics, focusing on AI training and the 'chat,GPT moment' for robots. It discusses the development of large action models that allow robots to learn from video or text prompts, enabling them to learn by imitation, similar to humans. The speaker mentions Nvidia's Project Groot, a foundation model for humanoid robots, and Isaac reinforcement learning gym, which provides a virtual environment for training. The paragraph also highlights the potential for on-site, real-time training of robots through demonstration and reinforcement learning. The speaker concludes by noting that the robotics industry is still wide open, with many companies contributing to its growth, and encourages viewers to understand the science behind the stocks in this emerging field.
Mindmap
Keywords
💡Humanoid Robots
💡AI Innovations
💡Chat GPT Moment
💡Autonomous Vehicles
💡Nvidia
💡AI Training
💡Generative AI
💡Isaac Sim
💡Digital Twins
💡Robotics Tech Stack
Highlights
The robotics industry is experiencing a major innovation and humanoid robots could arrive sooner than anticipated.
The focus should be on the technology stack rather than just the physical robot chassis.
Industrial robotics market is expected to triple in size over the next 9 years.
The global humanoid robot market is expected to grow over 30 times by 2030.
Humanoid robots offer a universal solution to challenges faced by specialized robots.
Investors should look beyond companies like Boston Dynamics and Tesla to find the next big winner in robotics.
The computers inside robots need to be small, lightweight, and low power while processing large amounts of data.
Nvidia's Blackwell GPUs are designed for data centers, autonomous vehicles, and humanoid robots.
Nvidia's Drive Thor platform integrates multiple functions for autonomous vehicles, providing 1000 teraflops of compute power.
Mercedes-Benz's Drive Pilot, powered by Nvidia's platform, has achieved level three autonomy.
The Jetson Thor platform is designed specifically for humanoid robots, offering high performance and reliability.
Nvidia's Blackwell chips have built-in engines for reliability, availability, and serviceability.
AI training for robots is transitioning to imitation learning, similar to how humans learn.
Nvidia's Project Groot is a general-purpose foundation model for humanoid robots.
Isaac Sim is Nvidia's physics simulation environment for training AI models through GPU-based reinforcement learning.
AI models for robots can be trained on real or synthesized examples and tested in digital environments.
The robotics tech stack includes chips for AI inference, data centers for AI training, and generative AI for imitation learning.
Major companies like Google, Meta Platforms, and Amazon are building foundational models and training services for robotics.
We are at the beginning of the generative AI era for robotics, making it a crucial time to understand the science behind the stocks.