AI Moats: Future-Proof Your Passive Investing
#35 A Young Professionals Weekly Investing Insights
The AI revolution is reshaping the business landscape.
And it’s also redefining the way company moats work - and the way passive investors should look at them.
Why?
Because this AI revolution is also going to impact the long-term performance of your ETF investments.
Here’s why:
As AI transforms industries, traditional competitive advantages are being rewritten. The market leaders of yesterday may not be tomorrow's winners.
Within your diversified ETF portfolio, some companies are likely to harness AI's potential, skyrocketing ahead. Others, slow to adapt, risk falling behind or becoming obsolete.
Without a clear understanding of AI's impact, you may be unknowingly exposed to greater risk or missing out on significant growth opportunities.
The solution?
To develop a strategic understanding of AI's influence on business models and competitive dynamics.
The knowledge in this issue will empower you to:
Critically evaluate the AI-readiness of companies within your ETF holdings.
Identify potential winners and losers in the AI-driven economy.
Make data-driven decisions about rebalancing your portfolio to align with the evolving market landscape.
By embracing this AI-informed approach, you'll be better positioned to:
Optimise your ETF allocations for long-term growth in the age of AI.
Mitigate potential risks from AI disruption in your portfolio.
Capitalise better on emerging opportunities created by the AI revolution.
Gain confidence in your investment strategy - knowing it's aligned with the transformative power of AI.
Ok - so let's dive into into the key factors reshaping competitive advantages in the AI era:
Data Moats in the AI Age 💾
Imagine you have a big box of special Lego blocks that no one else has. That's what data is like for companies using AI.
AI is like a robot brain that learns from information. The more good information it gets, the smarter it becomes.
Some companies have lots of special information that other companies don't have. This is called a "data moat".
Why is this important?
AI needs lots of information to learn and get smart.
Companies with special information can teach their AI to be really good at things.
Other companies can't easily copy this information - so they can't make their AI as smart.
This means companies with good data moats can do things better than other companies - a bit like having a secret recipe that makes your cookies taste amazing, and no one else knows how to make them taste as good.
Examples:
-Google (GOOGL): They own Waymo, which has information from billions of pretend car trips. This helps their self-driving cars learn to be super smart drivers.
-Appen (APX on Australian stock exchange): They make special information that teaches AI to understand what people say and what things in pictures are.
-Nvidia (NVDA): They make special computer chips that help AI think faster and learn from lots of information quickly.
ETF example: Global X Robotics & Artificial Intelligence ETF (BOTZ) includes companies that use and create lots of AI data.
Network Effects Made Stronger by AI 🕸️
Picture a playground where every new kid makes the games exponentially more fun. That's network effects on AI.
The virtuous cycle:
More users = More data
More data = Smarter AI
Smarter AI = Better user experience
Better experience = Even more users
Repeat!
Why it's a big deal:
Creates a "rich get richer" effect for established players
Makes it super tough for newcomers to break in
AI network effect all-stars:
-Facebook (META): Every like, share, and comment makes its algorithms scarier-smart
-LinkedIn (MSFT): The ultimate job-matchmaking AI, powered by millions of career histories
-Etsy (ETSY): AI that learns from every handmade purchase, connecting makers and buyers like never before
ETF example: Invesco NASDAQ Internet ETF (PNQI) has many companies that benefit from network effects.
Scale Advantages in AI 🏋️♂️
Now let’s imagine we’re building a huge sandcastle.
The bigger your bucket and shovel, the faster you can build it - makes sense.
Well that's like scale advantages in AI.
Big companies have more money and better tools to work on AI - meaning:
They can buy more powerful computers to train AI.
They can hire more smart people to work on AI.
They have more information to teach their AI.
And it’s important because it means big companies might be able to make better AI faster than smaller companies. But sometimes - clever small companies find smart ways to compete too.
Examples:
-Amazon (AMZN): They have lots of money to spend on making their AI better for things like Alexa and product recommendations.
-Google (GOOGL): They can afford to hire many smart AI experts and buy powerful computers.
-Microsoft (MSFT): They partner with OpenAI and have lots of resources to work on advanced AI.
ETF example: Technology Select Sector SPDR Fund (XLK) includes big tech companies with scale advantages.
AI-Enhanced Switching Costs 🔒
When you use a product with AI for a long time:
The AI learns what you like and how you do things.
It becomes really good at helping you.
If you try to switch to a different product, you'd lose all that personalized help.
Why is this important? Well it makes customers want to stay with the same company for a long time.
This in turn helps companies keep their customers and earn more money.
Examples:
-Salesforce (CRM): Their AI learns about each company's customers, making it hard to switch to another system.
-Adobe (ADBE): Their creative software uses AI to learn how you like to edit photos and videos.
-Spotify (SPOT): The more you listen, the better it gets at recommending music you'll love.
ETF example: iShares Expanded Tech-Software Sector ETF (IGV) includes software companies with high switching costs.
Brand Trust in the Age of AI 🤝
Imagine you have two ice cream shops.
One you know makes yummy ice cream, and one you've never tried.
Which would you choose? That's like brand trust with AI.
AI can be confusing and hard to understand. So:
People trust AI more when it comes from companies they already know and like.
This gives famous companies an advantage when they make new AI products.
Well-known companies might find it easier to succeed with new AI products - and people might be more willing to try their AI even if it's not perfect at first.
Examples of Brand Trust Kings:
Apple (AAPL): People trust their AI features because they're known for making good products.
IBM (IBM): Their Watson AI is trusted because IBM has been around for a long time and is known for being smart with computers.
Tesla (TSLA): People trust their self-driving AI because Tesla is famous for making cool electric cars.
ETF example: Vanguard Mega Cap Growth ETF (MGK) includes many well-known brands working on AI.
AI Flywheel Effects 🎡
Think of a big wheel that spins faster and faster as it goes.
That's like AI flywheel effects.
Some AI systems get better the more they're used:
As more people use it, the AI learns and improves.
Because it's better, more people want to use it.
This makes the AI learn even more, and so on!
Companies that manage create these self-improving AI systems have the potential to grow really fast - and it can be hard for other companies to catch up once the wheel starts spinning fast.
Examples:
-Netflix (NFLX): The more you watch, the better it gets at suggesting shows you'll like.
-Pinterest (PINS): As you use it more, it gets better at showing you pictures and ideas you'll love.
-TikTok (privately owned): The more videos you watch, the better it gets at showing you fun videos you'll enjoy.
ETF example: First Trust Dow Jones Internet Index Fund (FDN) includes companies benefiting from AI flywheel effects.
The AI Talent Race 🏃♂️
Imagine if only a few people in the world knew how to make the best toys.
Everyone would want them to work for their toy company.
That's like the AI talent race.
There aren't many people who are really good at making advanced AI:
These experts can create amazing new AI technologies.
Companies that have these experts can make better AI faster.
Meaning companies that can get and keep the best AI experts are set to do better in the long run, while it can be hard for other companies to catch up if they don't have these experts.
Examples:
-DeepMind (owned by Google, GOOGL): They hired many of the world's top AI researchers.
-OpenAI (privately owned, but partnered with Microsoft, MSFT): Started by smart people who want to make really advanced AI.
-NVIDIA (NVDA): They attract top AI talent to make their AI chips even better.
ETF example: Global X Artificial Intelligence & Technology ETF (AIQ) focuses on companies leading in AI development.
AI Deployment Capabilities 🚀
Having a brilliant AI idea is great, but turning it into a real product that people love? That's the true superpower.
Why it's critical:
The fastest company to deploy wins the market
Real-world feedback helps improve AI faster
Happy customers = $$$
Examples:
Amazon (AMZN): They're good at using AI in things like Alexa and their online store.
UiPath (PATH): They help other companies use AI to make office work easier and faster.
Lemonade (LMND): They use AI to make getting insurance and handling claims really quick and easy.
ETF example: ARK Innovation ETF (ARKK) includes companies that are good at turning AI ideas into real products.
AI as a Moat Eroder 🏰💥
Think of AI as a magical bridge that lets people cross moats easily.
Sometimes, AI can break down the advantages that protected some companies.
AI can make it easier to do things that used to be hard:
This means some companies might lose the special skills or knowledge that made them special.
New companies using AI might be able to do things just as well as older, established companies.
The risk is then that some companies that used to be strong will face new challenges.
On the flip side, it becomes easier for new companies with clever AI to compete with big, old companies.
Examples:
-Upstart (UPST): Their AI is challenging how banks decide who gets loans.
-Zillow (Z): Their AI estimates house prices, which used to require local real estate experts.
ETF example: ARK Fintech Innovation ETF (ARKF) includes companies using AI to challenge traditional businesses.
The Power of AI Ecosystems 🌐
Imagine a group of friends who all have different superpowers and work together.
A bit like these guys:
That's like an AI ecosystem.
Some companies team up to share their AI strengths:
They might share information, powerful computers, or ways to reach customers.
Together, they can do more than they could alone.
And just like we see it with the Avengers, companies in strong AI teams just do better than companies working alone.
Examples:
-Microsoft (MSFT) and OpenAI: They work together to make really smart AI using Microsoft's resources and OpenAI's ideas.
-Google Cloud (GOOGL) and C3.ai (ticker: AI): They team up to help other companies use AI.
-Amazon Web Services (AMZN) and Hugging Face: They work together to make it easier for people to use advanced AI models.
ETF example: First Trust Cloud Computing ETF (SKYY) includes companies forming powerful AI partnerships.
Quantum Computing on the Horizon 🔮
Think of quantum computing like a super-fast, magical calculator that might change how AI works in the future.
Quantum computers could make AI much, much faster:
This might lead to AI that can do amazing new things.
Companies working on this now might get a big head start.
Examples:
IBM (IBM): They're working hard on making quantum computers that could supercharge AI.
Google (GOOGL): They're also trying to build quantum computers for future AI breakthroughs.
IonQ (IONQ): They're a smaller company focused just on making quantum computers work.
ETF example: Defiance Quantum ETF (QTUM) includes companies working on quantum computing and other future technologies.
Why is this relevant for you again?
Well because we are living in a true revolutionary period.
And understanding how to navigate these times is really important.
These AI driven changes in how companies compete will help you understand:
Which types of companies might do really well in the future because of AI.
Which companies might face new challenges because of AI.
How to choose ETFs that are ready for an AI-powered future.
Thanks for reading.
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This newsletter is for informational purposes only and is not intended as financial advice. The insights provided are illustrative and should not be the sole basis for investment decisions. Readers should conduct their own research and consult professional advisors before investing. The authors and publishers are not liable for any financial losses resulting from actions taken based on this content. Investing in the stock market involves risk, including potential loss of capital.