Overview of AI Value Chain
In my previous journal, I talked about changing my investment strategy to focus on companies that will benefit from secular trends. One of the biggest trend today is Artificial Intelligence (AI). As such, this research post marks the beginning of my new investment strategy – exploring the AI value chain.
🤖What is Artificial Intelligence (AI)?
According to Google, they defined AI as follows:
“It is a field of science concerned with building computers and machines that can reason, learn, and act in such a way that would normally require human intelligence or that involves data whose scale exceeds what humans can analyze.”
AI is not a singular technology but several subsets, each with its own scope and applications. But the foundation to all AI subsets are Machine Learning and Deep Learning.
📊Machine Learning (ML): Involves teaching computers to learn from data & then making predictions or decisions without being explicitly programmed. There are many types of ML such as supervised, unsupervised or semi-supervised learning as well as reinforcement learning
🧠Deep Learning (DL): This is a subset of ML. It’s inspired by the way human brain works, using neural networks to analyze large amounts of data. It can analyze more complex patterns
Because of ML and DL, we have applications or other AI subsets such as generative AI, robotics, computer vision, natural language processing, etc.
🌊The Three AI Waves
Spear Invest – a fund management firm, has identified that there are 3 AI waves:
The first was about “Prediction” where AI models were trained to recognize patterns in data and predict the outcome. For example, recognizing images, understanding speech, recommending video or item to buy, etc.
The second wave is about “Generative”. This is what we are currently experiencing with all the generative AI models from openAI, Google, Microsoft, Deepseek, etc. These models can learn patterns and generate new content such as images, videos, etc.
The next wave would be “Physical”. This is where AI interacts with physical world such as robotics and autonomous driving. While we have seen these already in action, it is still not perfect.
For example, there are five levels of autonomous driving technology:
Level 1: The lowest level of automation, providing basic driver assistance like adaptive cruise control.
Level 2: Advanced driver assistance where the vehicle controls steering and speed, such as Tesla’s Autopilot.
Level 3: Conditional driving automation where vehicles can make decisions based on the environment, but human intervention is needed if the system fails.
Level 4: High driving automation, such as driverless taxis operating independently within approved areas and specific conditions.
Level 5: Full driving automation, where vehicles are entirely self-driving and require no human input, though this remains a goal yet to be achieved.
Most modern cars today are still in level 2 where human attention is still required. In this sense, we are still at the early stage of physical AI.
⚛️The AI Value Chain
Developing an AI application begins with choosing an appropriate ML or DL algorithms. The algorithm is then undergo training using prepared “data” and continuously tuning it to improve accuracy. Thereafter, it is deployed.
A powerful AI application depends on 3 key factors:
📊Quality, diversity and quantity of data used for training
🛠️Methodologies and algorithms employed during training
💾Computational resources and infrastructure supporting the training process
The third factor represents a critical bottleneck with substantial investments from major tech companies worldwide for its development.
This is because data is the lifeblood of all AI applications, and robust data infrastructure is essential for collecting, storing, processing, and managing the vast amounts of data required for ML and DL.
From an investor’s standpoint, this area presents promising opportunities to identify high-growth stocks. It includes businesses that provide services like cloud storage or create special computer chips designed for AI.
These areas are getting a lot of attention because they play a key role in advancing AI technology, making them promising options for long-term investments.
Below is an overview of the AI value chain. It is categorized into 3 broad areas; hardware, data infrastructure and applications:
In the coming weeks, I will be sharing each of this area in-depth starting with:
AI Data Infrastructure
AI Applications
Disclaimer
The information provided in this blog post is for educational purposes only and should NOT be construed as financial advice. Investing in stocks and ETFs involves risk, and there is no guarantee of profits. Past performance is not indicative of future results. It is important to conduct thorough research or consult with a qualified financial advisor before making any investment decisions. The author is NOT a financial advisor and is sharing his personal experiences and opinions only.