Is AI merely the latest echo in the chamber of technological hype, or is it the harbinger of a profoundly different future? History teaches us that the most transformative innovations are those that seamlessly integrate into the fabric of society, becoming so ingrained in our daily routines life without them is difficult to imagine. Recall the advent of the Internet—met with skepticism by many, it has now become the very scaffold upon which modern society is built.
Entertaining the debate on AI's legitimacy may provide intellectual sport for economists and theorists, those who observe the fray from a safe distance, wagering nothing but opinions. However, for those of us in the trenches of investment and innovation, the perspective is decidedly different.
Our objective is clear: to identify and leverage the opportunities presented by this wave of technological advancement. This isn't about joining the chorus of voices questioning AI's value or predicting its potential to reshape the landscape of industry and society. It's about understanding how to strategically position ourselves to benefit from the inevitable changes it will bring. In the end, our mission is to generate wealth by riding the crest of this wave, not to ponder its existence.
As I've laid out before, the long waves of innovation we're targeting are segmented into three distinct yet interconnected phases, marking the progression through the sixth and seventh waves of innovation. These phases—Human Augmentation, Robotic Autonomy, and the Machine Economy—each represent a leap forward in how technology integrates with and enhances our world. The overarching phase of Infrastructure investment and renewal continues across the entire long wave.
Understanding these phases is useful not just for conceptualizing where innovation is headed but for identifying where the tangible, investable opportunities lie. As these waves of innovation unfold, our focus remains on how to strategically position ourselves to capitalize on the transformative changes they bring. It's about actively investing in and profiting from the future it's creating.
50 sub-themes in AI for 2024
I am currently tracking the following 50 themes across the long wave accessible to investors in both private and public companies. Some of these themes are best approached by emerging companies who can innovate without limitation at the end of the value chain, while others are best approached by existing companies who are in control of long chains.
Phase I: Human Augmentation
In this phase, the focus is on enhancing human capabilities through technology. This could include developments in areas such as augmented reality, biotechnology, neural interfaces, and advanced prosthetics. Technologies like optoelectronic contact lenses, exoskeletons for increased physical strength, and brain-computer interfaces might become prevalent. The aim is to augment human senses, cognition, and physical abilities, thereby expanding the boundaries of human experience and productivity.
Investment Themes:
Generative AI
AI models capable of generating synthetic data, media content, code, designs, and other original artifacts. Includes technologies like GANs, diffusion models, autonomous coding engines, AI synthetic media, and neural architecture search.
Augmented Reality
Use of AR and VR technologies to augment or modify human perception and interaction with the physical and digital world, for education, training, entertainment, or therapeutic purposes.
Cognitive Enhancement
Technologies aimed at enhancing mental capabilities, such as memory, focus, or decision-making. This can involve nootropic drugs, brain-computer interfaces, and neural implants.
Sensory Augmentation
Enhancements that expand or improve human sensory perceptions, like vision, hearing, or touch. This includes technologies like bionic eyes, hearing implants, or sensory augmentation devices.
Cybernetic Interfaces
Integration of cybernetic devices with the human body to restore lost functions or enhance capabilities, ranging from simple wearable devices to complex implants interfacing with the nervous system
Exoskeletons & Wearables
Wearable technologies that enhance physical strength, endurance, or mobility. Exoskeletons for industrial, military, or medical use are prime examples.
Life Extension
Research and technologies focused on extending the human lifespan and combating the effects of aging. This can include cellular therapies, anti-aging drugs, and lifestyle modification technologies.
Neurotechnology
Development in technologies that interact directly with the human brain, such as neurostimulation devices for therapeutic purposes or to enhance cognitive abilities.
Genetic Modification
Advanced techniques in genetic engineering, such as CRISPR, to prevent genetic diseases or enhance certain human traits, raising significant ethical and societal questions.
Collaborative Robots
The development of collaborative robots (cobots) designed to work alongside humans in various settings, enhancing human capabilities while ensuring safety and efficiency.
Advanced Prosthetics
Advanced prosthetics are highly sophisticated artificial limbs that not only replace lost body parts but also restore functionality, controlled by the user's muscle signals or neural impulses, offering increased mobility and dexterity, and in some cases, sensory feedback, closely mimicking the natural movement and functionality of real limbs.
Healthcare
Beyond just service robots, the deployment of AI and autonomous systems for diagnosis, treatment recommendations, elderly/patient care, and hospital workflow optimization.
Decision Making
Tools and systems that enhance human decision-making capabilities, not just through cognitive enhancement but by integrating AI and machine learning insights directly into business, healthcare, and personal decision processes.
Emotional Intelligence
Incorporating emotional intelligence into AI systems to better understand and predict human emotional responses, enabling machines to make decisions that consider human psychological and emotional states, thereby creating more human-centric services and interactions.
Phase II: Robotic Autonomy
This phase marks a shift towards independent robotic systems. Here, robots and AI systems advance to a point where they can operate autonomously, without direct human oversight. This phase is characterized by widespread adoption of autonomous vehicles, fully automated manufacturing and service industries, and AI systems capable of complex decision-making. While this phase brings efficiency and innovation, it also poses challenges like the displacement of human labor and the need for societal adaptation to a new economic landscape.
Artificial General Intelligence
The development of sophisticated artificial intelligence and machine learning algorithms that enable robots to learn, adapt, and make decisions autonomously. This includes advancements in neural networks, deep learning, and reinforcement learning.
Autonomous Vehicles
The evolution and proliferation of autonomous vehicles, including self-driving cars, drones, and unmanned aerial vehicles (UAVs). This sub-theme extends to autonomous shipping, logistics, agriculture, and public transportation systems.
Automated Manufacturing
The use of autonomous robots in manufacturing processes, known as Industry 4.0, where robots can operate independently, communicate with other machines, and optimize production workflows.
Hazard Robots
The use of autonomous robots in dangerous or inaccessible environments, such as deep-sea exploration, space missions, disaster response, and hazardous waste handling.
Service Robots
The deployment of robots in service industries, such as healthcare, hospitality, and retail. This includes autonomous surgical robots, service robots in hotels and restaurants, and customer service bots.
Swarm Robots
The study and deployment of swarm robotics, where groups of robots operate collectively, coordinating their actions to achieve complex tasks.
Robotic Cybersecurity
Ensuring the security and integrity of autonomous robotic systems, particularly in critical applications, to prevent hacking, data breaches, and other cyber threats.
Energy Harvesting Robots
Robots equipped with the capability to harvest energy from their environment, such as solar, kinetic, or thermal energy, reducing or eliminating their need for external power sources and enhancing their operational endurance.
Robo-ethics
The incorporation of ethical and moral decision-making capabilities in autonomous robots, particularly those involved in sensitive areas such as healthcare, eldercare, and autonomous vehicles, to ensure decisions are made considering human values and ethics.
Cross-Domain Robots
Robots that are not just confined to one environment but can operate across multiple domains, such as aerial drones that can dive into water, becoming submersible drones, or terrestrial robots that can transition to aerial or aquatic modes, enhancing versatility and utility.
Bio Hybrid Robots
The development of biohybrid robots that incorporate biological components, such as muscle tissues or neural cells, into robotic systems. This could lead to robots with more natural movements and the ability to self-heal from minor damages.
Phase III: Machine Economy
In this phase, the focus shifts towards a self-sustaining ecosystem of machines and AI systems that operate, communicate, and make decisions independently of human intervention. This phase represents a profound evolution in autonomous technology, where systems are not only independent of human oversight but are also geared towards servicing, maintaining, and enhancing each other.
Interoperable Systems
The development of standards and protocols for interoperability among different machines and systems, allowing seamless data exchange and collaboration across various platforms and industries, enhancing the efficiency and scalability of autonomous operations.
Tokenization
The conversion of real-world assets into digital tokens on a blockchain, enabling machines and AI systems to own, buy, sell, or invest in physical assets through secure, transparent transactions, potentially revolutionizing asset management and investment strategies.
Blockchain and DAOs
Blockchain-enabled organizations that operate without centralized control, governed by smart contracts and consensus mechanisms among members, could redefine business operations, ownership, and governance in the Machine Economy.
Self-Regulating Systems
Machines and AI networks capable of self-regulation, self-repair, and autonomous adaptation to changing conditions without human input.
Autonomous Build and Repair
Robots that can autonomously construct, repair, or even replicate themselves or other structures without human intervention. This includes the use of 3D printing technologies in remote or hazardous environments, potentially transforming construction, maintenance, and manufacturing industries.
M2M Communications
Advanced forms of machine-to-machine communication allowing machines to coordinate complex tasks, share resources, and optimize efficiency across various industries and sectors.
Autonomous Supply Chain
Supply chains where production, logistics, and distribution are fully managed by interconnected autonomous systems, optimizing the flow of goods with minimal human involvement.
AI-Driven Innovation
AI systems that not only operate existing technologies but also innovate and develop new technologies, potentially leading to rapid advancements without direct human creativity or engineering.
Energy Management and Distribution
Autonomous systems managing the generation, distribution, and optimization of energy resources, including smart grids and renewable energy systems.
Environmental Management
Machines autonomously monitoring environmental parameters and managing ecosystems, potentially aiding in conservation efforts and disaster response.
Overarching Phase: Infrastructure
The foundational technology layers that support the development, training, and deployment of artificial intelligence systems. This includes data storage solutions, processing units, networking capabilities, and the software frameworks necessary for AI research and applications.
Closed Source Models
Proprietary AI models developed and maintained by organizations without publicly releasing the source code. These models are often commercial products or services, where the underlying algorithms and data are kept confidential to maintain competitive advantage or for security reasons.
Open Source Models
AI models and algorithms that are freely available for anyone to use, modify, and distribute. Open source models encourage collaboration and innovation in the AI community by allowing researchers and developers access to cutting-edge technology without the barriers of cost or restricted access.
Model Hubs
Online repositories or platforms where pre-trained AI models are shared and can be accessed by developers. Model hubs facilitate the reuse of AI models across different applications and industries, speeding up the development process and fostering collaboration among AI practitioners.
Computing Hardware
The physical components necessary for the operation of computers and other devices capable of conducting AI and machine learning tasks. This includes CPUs (Central Processing Units), GPUs (Graphics Processing Units), TPUs (Tensor Processing Units), and other specialized hardware designed to accelerate AI computations.
Networking
The technology and processes involved in connecting computers and other devices together to support the communication and exchange of data. In the context of AI infrastructure, networking is crucial for distributing AI tasks across multiple machines, enabling cloud-based AI services, and facilitating the transfer of large datasets.
Data Platforms
Integrated technology solutions that allow for the efficient management, storage, processing, and analysis of large datasets. In AI, data platforms are essential for handling the vast amounts of data required for training machine learning models and for providing the infrastructure to deploy these models at scale.
Quantum Computing
The development of quantum computers could revolutionize computing power, enabling breakthroughs in simulations, optimizations, and cryptography, which could significantly impact AI, cybersecurity, and data analysis.
Neuromorphic Computing
An advanced computing concept inspired by the structure and function of the human brain. Neuromorphic computing aims to mimic neural architectures to create more efficient and powerful computing systems, particularly for tasks related to pattern recognition, sensory processing, and decision making in AI.
Infrastructure Monitoring
The process of continuously overseeing the performance and health of the computing infrastructure supporting AI systems. This includes monitoring hardware (servers, GPUs, etc.), software (operating systems, AI applications), and networks to ensure optimal performance, detect issues early, and maintain system security and reliability.
MLops
Collaborative practice and system that integrates machine learning, software development, and operations to automate and streamline the deployment, monitoring, and management of machine learning models. MLOps aims to enhance the efficiency, quality, and lifecycle of AI projects by promoting continuous integration, delivery, and deployment, ensuring models remain relevant and performant in production environments.
New Energy
Advanced energy solutions, including but not limited to batteries and hydrogen fuel cells. It focuses on sustainable, efficient technologies critical for powering autonomous systems and ensuring their reliability and environmental sustainability.
Global Connectivity
Advancements in satellite technology and global internet coverage, aiming to provide high-speed connectivity everywhere, which is crucial for real-time data exchange in autonomous systems and M2M communications.
Intelligent Infrastructure
The integration of IoT devices and smart technologies into infrastructure, such as buildings, roads, and utilities, to make them more adaptive, efficient, and resilient.
Edge Computing
As autonomous systems generate vast amounts of data, edge computing's role in processing data closer to where it is generated for quicker decision-making and reduced latency becomes critical.
Sustainable Materials
Innovations in materials science for building more sustainable, efficient, and adaptable technologies, from biodegradable electronics to advanced composites for robotics, could be a pivotal trend.
Conclusion
The 50 investable AI technology themes detailed here provide a blueprint of the innovation landscape primed to unfold over the coming decades. As the momentum of autonomous systems and AI continues to accelerate across industries, these themes highlight the frontiers where new value creation and disruption will likely occur.
By allocating capital along these emerging technology frontiers, we can ride the long technological waves shaping the future. In upcoming articles, we'll take a deep dive into each of these themes and the companies innovating at the frontiers to identify potential opportunities and estimate their value.
