Seizing Investment Opportunities in the Era of AI
Part 2 of 2 part series
Part 2: Seizing Investment Opportunity in the Era of AI
In this two part series I provide investors with a playbook for succeeding in the AI era.
In Part I of the guide, explore the AI landscape and gain the context you need to thrive, while learning strategies to avoid the pitfalls that ensnared many during the dot-com bubble. In Part II, discover my investment approach, criteria for identifying AI winners, and some stock ideas to consider for the AI era.
AI Investing approach: Waves of value creation
In 1879, a breakthrough set the stage for a global revolution. After years of experimentation, Thomas Edison—blending inventive genius with business savvy—perfected the incandescent light bulb. This seemingly simple invention wasn’t the first of its kind, but it was the first practical and affordable enough to light every home, street, and factory. Edison’s innovation didn’t just introduce a new way to light the night—it catalyzed the widespread adoption of electricity, sparking cascading effects across industries, cities, and societies.
Edison understood that to truly bring light to the world, he needed more than just a brilliant idea; he needed a way to spread that light far and wide. So, he embarked on an ambitious mission, not just to create a bulb, but to build an entire electrical grid that could power it and other applications.
In 1882, Edison flipped the switch on the Pearl Street Station in New York City, the world’s first commercial power plant. From this modest building, a network of transmission lines spread out, linking homes, businesses, and factories to a revolutionary new energy source. This marked the first order effects of Edison’s invention—laying the infrastructure, brick by brick, wire by wire, to illuminate the future with electricity. As a result, an entire ecosystem of electric motors, machine tools, conveyors, and industrial equipment emerged, making electricity widely accessible and highly practical.
The story didn’t end there. With the infrastructure and tools in place, industries quickly adapted electricity to their operations. Factories buzzed with electric motors, replacing the clatter of steam engines and driving new efficiency in mass production. Textile mills, steel plants, and others harnessed this energy, fueling an era of unprecedented growth.
Electricity also sparked the creation of new sectors—consumer electronics, household appliances, and more. Businesses that embraced this power found themselves at the forefront of a rapidly evolving economy.
The third wave of electricity’s influence rippled through society itself, transforming the way people lived and worked. With electric lighting and transportation, cities became more vibrant and productive, leading to massive levels of urbanization. Electricity extended working hours and changed lifestyles, enabling evening activities and the growth of entertainment industries (e.g., radio, cinema). Homes became sanctuaries of comfort, equipped with electric heating, cooking, refrigeration and TVs. Quality of life improved with better public health (e.g., sanitation systems) and education (e.g., electric lighting in schools). Electricity didn’t just power machines; it powered progress.
Just as the light bulb sparked a revolution almost 150 years ago by unlocking the potential of electricity, a chat bot introduced to the world on November 30th, 2022 prompted the next great technological transformation - artificial intelligence (AI). As with electricity, this revolution will unfold in waves, each building on the last to create profound changes across industries and society.
How can investors capitalize on this predictable pattern to capture the economic value that will arise at each stage of AI's unfolding impact?
Investors can draw on patterns from past technological revolutions to pinpoint critical inflection periods where certain waves will peak, guiding them in reallocating capital to the next wave.
An expansive ecosystem of AI infrastructure, platforms, and applications is rapidly being built. As this AI ecosystem evolves, it will ripple outward, revolutionizing entire industries, and ultimately transforming society.
No market hypothesis is bulletproof, and there are many ways my thesis could go sideways. It's possible that bullish investors like myself are overestimating the speed and impact of AI adoption. Predicting the precise timing of transitions between waves is impossible. However, in a world governed by probabilities and with the acceptance that cycles inevitably reverse, I believe that mindful and adaptable investors with the right strategy can emerge successful from the AI boom ahead of us.
To harness the extraordinary growth potential ahead, while also adapting to the inevitable volatility, I have developed the Wealthy Owl's AI Investing Playbook:
Navigation: Riding and adapting to the waves of AI value creation
Diversification: Diversifying across the layers of the AI ecosystem
Balance: Guarding against risk with an eye on opportunity
Discipline: Focusing on sustainable valuations and fundamentals
By grounding our strategy in these pillars, investors can effectively capitalize on what could be the biggest driver of value creation the world has ever seen, while managing its inherent risks.
1. Navigation: Riding and Adapting to the Waves of AI Value Creation
The pattern is familiar: disruptive technology emerges, and the growth curve follows a predictable path — initial investments in the foundational layers pave the way for more widespread adoption and transformation in enterprise and consumer markets. The AI revolution is no different. Investors who understand this wave-based progression can position themselves to ride each crest, capitalizing on growth as it migrates from the AI ecosystem to the industries and societies they reshape.
The AI boom is already in full swing, still in the early innings, led by infrastructure and hardware companies. Much like Cisco was the key enabler of value creation during the early internet boom, Nvidia has become the powerhouse behind the training of generative AI models like OpenAI's ChatGPT. Nvidia has seen substantial market cap gains as revenues soared over the last 12-18 months. However, I believe the majority of value creation at this foundational layer has peaked. With valuations now at lofty levels, investors would be wise to shift their focus—except in a few cases (more on this later) - to the emerging layers of the AI ecosystem.
Early winners are emerging in the next wave - platforms and tools that enable the use of GenAI to power the next gen applications and extend AI's reach across diverse industries. Companies that provide AI development environments, tools, and frameworks (e.g., Microsoft, Amazon, Google, Palantir) have already started to capture the attention of more investors as they become critical enablers of AI across various industries.
Once platforms are in place, the value shifts further to enterprise and consumer applications that leverage AI to transform business operations or enhance consumer experiences. This includes AI-driven enterprise solutions (e.g., Microsoft Copilot Studio, Salesforce Agents) and consumer products (e.g., devices, AI-powered apps and services). I have prioritized enterprise apps initially as the consumer space can be more precarious to pick the eventual winners. Remember when everyone thought MySpace and Friendster were the long term winners of the emerging social media sector? I do see some existing consumer businesses that are already starting to benefit from applying Generative AI to their business, but the question remains - what will be the AI-era equivalents of new, mobile-first platforms like Instagram, Airbnb, and Uber?
Just as electricity adoption had second and third-order effects on productivity, safety, manufacturing, domestic life, economic growth, urbanization and new forms of entertainment, GenAI will also have massive ripple effects. GenAI's impact will extend far beyond the tech industry, it is poised to reshape industry, the economy and labor markets, and society by analyzing vast datasets, predicting outcomes, and generating not just content but also actions.
The staggered investment approach to the AI revolution
From a tactical investment perspective, riding the waves of AI value creation means staggering your entry and exit from each wave, actively monitoring market dynamics, and regularly adjusting your portfolio to optimize returns as new opportunities arise and mature throughout the AI revolution. This approach can help you mitigate some of its inherent risks, such as the impossibility of getting the timing right, dealing with market sentiment and technology uncertainty.
It can be visualized through a staggered investment strategy, where allocations shift across various AI sectors over time, from infrastructure to industry transformation, while maintaining a reserve for emerging opportunities. Adjusting these allocations as each wave emerges and matures helps investors capitalize on growth and manage risk throughout the AI investment horizon.
Here's how you can implement this strategy:
Identify Targets and Set Allocation Goals: Choose key sectors and companies within the AI opportunity space and establish clear allocation goals based on your strategy. We are now 12 to 18 months into the cycle, and I have ~15% of my AI portfolio in the infrastructure and hardware layer, 50% in the platform layer, 20% in enterprise apps, 10% in consumer, and 5% in industry transformation.
Stagger Entries and Exits: Strategically time your entry and exit across these selected targets, aligning with the different stages of AI development. Start with smaller investments and gradually increase the amount as you gain more confidence in the sector or as the wave progresses. For instance, you might start with a 10% allocation to consumer applications and increase this to 20% over the next year as adoption accelerates. Just as you stagger your entry, plan for a staggered exit from investments. Gradually reduce your exposure to sectors or companies that have matured or reached your target valuation, while reinvesting in the next emerging wave. If you’ve invested in AI hardware and infrastructure early on, you might start selling portions of those holdings as the wave matures and use the proceeds to invest in AI platforms or applications. Exit stocks when things get out of hand valuation wise or when you see you've made a mistake in trying to choose a winner. Don't wait, react quickly and move on. We don't want our mistakes to become bigger than they need to be. The benefits to this approach make it worth it:
Risk Mitigation: Reduces the impact of market volatility and timing risks
Flexibility: Allows for adjustments based on new information, market conditions, and technological developments
Cost Averaging: Benefits from dollar-cost averaging, potentially lowering the overall cost of investments
Opportunistic investing: Capitalize on market corrections or dips to buy at lower prices.
3. Monitor and Adapt: Continuously monitor the performance of your investments and the broader market environment. Adjust your allocations as needed based on how each wave is developing. Sell positions in maturing and/or crowded layers and reallocate investments to capitalize on emerging opportunities and maximize growth as the AI market evolves.
2. Diversification: Diversifying Across the Layers of the AI Ecosystem and beyond
Anyone who says they know how the AI investing story will unfold in the near term is lying to you. While AI will undoubtedly drive significant economic value, predicting exactly where it will emerge and sustain is impossible. While long-term value often shifts to the application layer in these sort of platform shifts, the industry is still in the early stages of building out the AI infrastructure and platforms. There is growth ahead of us, but lots of uncertainty and volatility as well.
To mitigate the risks of timing and concentration, it's essential to diversify your investments across the entire AI ecosystem and into industry transformation, spanning multiple sectors and stages, from early to late - ensuring you're well-positioned for whatever the future holds.
Layers and players
Over the last twelve months my focus has been on learning about the layers in the AI ecosystem and beyond, to identify opportunities for investment and the individual players that standout in each layer. While I own the companies outlined in the frame label below, these are not stock recommendations. Read on to learn more about the opportunities across each layer and stay tuned for my favorite AI stocks later in the article.
Hardware
The hardware layer in the AI ecosystem includes companies like NVIDIA, AMD, and TSMC that supply the semiconductors and specialized processors needed for AI training and inference. It also encompasses suppliers to these companies, such as Applied Materials, which provide essential equipment for semiconductor manufacturing. As the AI leaders aspire to build more complex models, the demand for cutting-edge hardware is critical, particularly in the early-to-mid stages of the AI boom. While I believe much of this sector has reached or is near peak levels I plan on holding my current positions for a while longer as AMD gains against Nvidia, Edge compute and AI PC demand picks up for Qualcomm, and TSMC and Applied Materials benefit from the overall semiconductor market growth.
Energy
The rise of artificial intelligence is creating a tremendous opportunity for the energy sector. As AI continues to evolve, the power consumption needed to train large language models and run AI applications is skyrocketing. Tech giants like Microsoft, Amazon, and Meta have made significant investments in energy infrastructure to fuel their AI ambitions, from signing power purchase agreements with nuclear startups to acquiring nuclear-powered data centers. This AI-driven surge in energy demand is set to dramatically reshape the utility sector. According to Goldman Sachs, data-center power demand will increase by 160% by 2030, turning data-center expansion into a generational growth opportunity for utilities. At the same time, the rapid growth of AI infrastructure is forcing utilities to invest heavily in grid expansion and energy storage to meet the rising demand. Companies across the energy value chain—from utilities to midstream firms and electrical equipment providers -are well-positioned to capitalize on this boom.
Data Center
The AI boom is creating an investment opportunity in the data center space, as the need for compute-intensive AI workloads is driving a surge in demand for facilities optimized for AI and related workloads. The sector is expected to see significant growth in the coming years, with industry forecasts calling for global data storage capacity to grow from 10.1 zettabytes (ZB) in 2023 to 21.0 ZB in 2027, reflecting compound annual growth of 18.5% [Source: IDC, Revelations in the Global StorageSphere, July 2023]. There is a lot of opportunity across the data center value chain for retail investors to capitalize on this growth: sustainable energy, cooling technologies (e.g., Vertiv, Schneider Electronics), data center construction (e.g., Eaton), edge computing and data center operators (e.g., Equinix and Digital Realty).
Cloud Service Providers
Cloud service providers (CSPs) are perfectly positioned to capitalize on the AI boom, offering the critical infrastructure that allows researchers and companies to train, fine-tune, serve up and manage generative AI models. Cloud giants like Amazon, Microsoft, and Google are well-positioned to capture the growing demand for AI workloads by offering not just raw computational power but also specialized services like AI-as-a-Service (AIaaS), enabling businesses and developers to utilize AI models in their solutions and applications without needing to build or manage the underlying infrastructure.
The opportunity is vast. According to Nvidia’s CEO Jensen Huang, for every dollar CSPs spend on Nvidia hardware, they generate five dollars in revenue from renting out their AI infrastructure to customers around the world. The market for AIaaS alone is projected to reach $55 billion by 2028.
Major players like Microsoft and Amazon have already seen real traction in this space. Microsoft’s Azure OpenAI service added over 6,500 new customers last quarter (Q2 2024), with AI contributing two percentage points to Azure's 25% year-over-year growth. Meanwhile, Amazon is rapidly evolving its AI offerings through its Amazon Bedrock platform, which simplifies the process of building and scaling generative AI applications
The scale of this opportunity is so vast that even companies like Oracle - traditionally seen as lagging in the cloud space - have found new momentum. Oracle has pounced on the AI demand, striking deals with Microsoft and OpenAI to provide its cloud infrastructure as supplementary capacity for large-scale AI model training. Their recent investment to build one of the world’s largest AI data centers, equipped with acres of Nvidia GPU clusters, opens up more revenue upside for Oracle. Oracle’s ability to capitalize on this trend underscores just how much demand exists, even beyond the market leaders.
With AI quickly becoming an integral part of every software solution, the cloud sector represents a reliable and scalable way to profit from the AI boom, without being overly reliant on any one specific AI model or application.
Model Builders
The opportunity for retail investors to buy shares of companies that are building and training AI models is pretty limited today. The only two public companies I'm aware of that have built and deployed their own foundational, large language models (LLMs) are Meta (the open source LLaMA) and Google (Gemini). As I wrote in part I, these "God-like" foundational models are becoming commoditized. This doesn't necessarily mean there will be no value to the "God-Like" models and the companies that build them. AI will become one of the most important commodities to build and operate a successful business or government. It's more likely, as we’ve seen in the oil market, this commoditization will give rise to one or two extremely valuable companies selling these “commodities. And, the one or two eventual winners haven't been crowned yet. While OpenAI appears to be the leader, they are not a public company yet, and we all now how choosing the long term winner early in the cycle is fraught with uncertainty.
Beyond the battle for the foundational LLMs amongst the AI giants, a long tail of smaller and more specialized models has emerged. Tech titans Microsoft (Turing-NLG) and Amazon (Titan), and others are building smaller, specialized models that offer variety across modality, performance, latency, cost, and security. There is immense value in creating smaller, specialized models optimized for specific tasks such as on-device AI processing, medial imaging analysis or 3D graphics creation.
I'm investing in companies that serve as AI model purveyors, like Palantir, Microsoft, and Amazon, because it's still unclear which foundational model builders will dominate long-term. These companies are model-agnostic, offering their customers the best tools for the job, positioning them for sustained demand.
Data Platforms
Data platform providers like Palantir, Snowflake, and Confluent are playing a critical role in enabling AI to be applied effectively across enterprises by providing the necessary infrastructure and tools to manage, analyze, and operationalize vast amounts of data. Their solutions enable organizations to bring together diverse data sources as a foundation for training, deploying, fine-tuning and managing AI models. Without data platforms to integrate proprietary data, organizations are left with AI models running on generic fuel from public data, limiting performance in their unique environments. Data platforms act as the pipeline that delivers proprietary data, serving as specially formulated fuel that optimizes models for the company’s unique needs and challenges.
Developer Tools
The biggest productivity gain of GenAI at this early stage of adoption has been realized in areas of coding and DevOps. AI-powered code completion tools like Microsoft's GitHub Copilot and GitLab are being rapidly adopted by developers.
With demonstrable results showing that developers who use GitHub Copilot code 55% faster than their counterparts who did not use Copilot, it's no wonder AI-powered developer tools are gaining widespread traction.
Enterprise
Today's modern commercial and government organization is undergoing a massive transformation that will deliver productivity and scalability advantages not seen in any previous disruptive technology transition period. To help organizations realize the full benefits of AI across all of their functions, in a safe and secure way, emerging and legacy providers are readying their wares and salivating at the growing addressable market.
One of the biggest mistakes investors can make in the enterprise AI space is underestimating the addressable market. Are you old enough to remember the so-called industry experts in the 1980s who dismissed personal computers as a toy for hobbyists? AI hardware and software providers to the enterprise are NOT just going after IT budgets. Let's not fool ourselves. The long term promise of AI is to replace labor that can be supplanted by sophisticated AI models, while creating new types of jobs we can't anticipate today.
The long-term addressable market for the winning AI enterprise solution providers includes labor budgets, a massive expense line item in income statements.
The AI enterprise solution beneficiaries will be security and observability companies, software companies and automation providers.
As I argued in part I, AI is making developers more productive and coding more accessible, leading to more software in the world. More software, means more data will get produced that needs to be observed, analyzed and optimized. This boosts demand for observability software companies such as Datadog and Elastic. More software getting created also means more security software will be required to protect the software, setting the stage for a massive tailwind for security leaders such as Crowdstrike. As a secondary revenue driver for security software companies, the rise of large language models (LLMs), strengthens bad actors. As threats become more sophisticated, the need for enhanced security grows.
Leading enterprise software companies such as Microsoft, ServiceNow, and Salesforce have made big moves over the last 12+ months integrating LLMs into their enterprise applications. These AI fueled features are making customers stickier, upselling premium capabilities and growing share. I expect AI to continue to accelerate revenue growth for the enterprise software providers that are smartly applying it to their apps (i.e., AI is NOT a feature) and evolving their business models (e.g., moving from seat based licenses to consumption billing). There will be winners and losers.
Two emerging categories of AI enterprise solutions that I'm monitoring are AI Agents and Vertical AI.
AI agents are advancing towards fully autonomous handling of complex, multi-step tasks. While they aren’t yet reliable enough for all scenarios, the rapid progress in agentic workflows is unveiling significant potential for future applications.
The rise of large language models (LLMs) is driving a new wave of Vertical AI, that targets functions and industries previously out of reach. Vertical AI focuses on automating high-cost, repetitive tasks across sectors like Business and Professional Services, which alone accounts for 13% of U.S. GDP. Vertical AI is already starting to replace human labor in sectors such as low level legal and accounting services.
Robotics and Autonomation
Thanks to cutting-edge generative AI techniques, robotics and automation products can now master intricate tasks by observing human demonstrations. This groundbreaking advancement enables generalizable robotics by empowering robots to acquire fresh skills with minimal examples, all without altering their fundamental code. The concept of diffusion policy, forged through collaboration with respected institutions like Columbia and MIT, has facilitated the teaching of 60 distinct skills to robots.
These advancements will impact manufacturing, logistics, and domestic robots, making them more efficient and capable of handling a wider variety of tasks. In the broader scope of automation, it could lead to the creation of systems that can predict maintenance needs, optimize efficiencies, and adapt to changing conditions without human intervention.
"The total addressable market for humanoid robots is projected to reach $38 billion by 2035, up more than sixfold from a previous projection of $6 billion, Goldman Sachs Research analyst Jacqueline Du, head of China Industrial Technology research, writes in the report. Their estimate for robot shipments increased fourfold, to 1.4 million units, over the same time frame.
I see a big investing opportunity in robotics in the future. In the near term, the best retail investment opportunities may be in the factory automation space through players such a ABB, Rockwell and Fanuc Corporation. Intuitive Surgical, the maker of the da Vinci surgical system, is a leader in robotic-assisted, minimally invasive surgery.
The longer term opportunity goes beyond factories and surgery rooms. The future will include humanoid robots in our homes and offices. Tesla is the best known name for betting on a humanoid robot future, with long standing rumors that Apple is investing in building it owns robot.
Consumer
The future impact of Generative AI on consumers is still uncertain, as the integration of AI into consumer hardware products like phones, smart home devices, and wearables is in its early stages. This uncertainty could lead to significant shifts among market leaders such as Apple, Amazon, Google, Meta, and Samsung. For instance, Meta's integration of its LLM Llama into Ray-Ban smart glasses shows promise, yet it remains unclear when such innovations will scale to billions of users.
As we witness the emergence of LLM-native applications like Perplexity for search, Character.ai for companionship, and Midjourney for image generation, it's clear that some early-stage companies are beginning to attract and retain dedicated audiences, potentially displacing legacy incumbents. However, the consumer AI market is still in its infancy, with few categories showing real depth and value. The rise of multimodal capabilities and new AI-driven interactive experiences suggests that significant innovation lies ahead, transforming activities like socializing, gaming, entertainment, and shopping.
Investors will need to be patient and skeptical as consumer AI companies evolve, addressing challenges such as privacy, ethics, user interfaces, and the utility of general-purpose AI assistants. Since I can’t predict the future my plan is to focus on the consumer based companies whose AI strategy and investments I have confidence in, such as Meta (and its open source AI model Llama) and Roblox (using AI to make experience creation accessible to all). Over the long term, I will monitor the AI consumer space for emerging winners that will change all of our lives with novel form factors and new services.
AI Driven Industry Transformation
Predicting the specifics of AI-driven transformations at this early stage is fraught with uncertainty, making it premature for investors to make bets on sweeping changes that are still far off. However, investors should be on the look out for companies making the right moves and investments in AI to transform their industries.
Biotech companies are already using Generative AI to design new molecules and accelerate drug discovery. In the financial services sector, companies are beginning to use GenAI to automate risk assessment, enhance fraud detection, and offer more personalized financial planning. Meanwhile, the media industry is on the brink of significant shifts as GenAI starts to automate content creation, paving the way for an explosion of personalized content tailored to individual preferences. Ridesharing leader Uber is set for another disruption in the mobility industry by partnering with Autonomous Vehicle (AV) providers like Waymo to bring AVs to its network.
Instead of speculating on distant and unpredictable societal shifts, investors should focus on companies leveraging AI to drive revenue growth and cost reduction, as well as system integrators like Accenture and Cognizant that are facilitating this transformation. These early adopters are setting the stage for future leadership, and by recognizing them now, investors can position themselves to benefit from the ongoing AI-driven industrial transformation.
3. Balance: Guarding Against Risk with an Eye on Opportunity
As the world grappled with the onset of the COVID-19 pandemic in March 2020, markets plunged into chaos, and uncertainty gripped investors. Yet, amidst the turmoil, a new
narrative emerged - one promising to reshape how we live and work. With government stimulus bolstering the economy and digital technologies filling the gaps left by lockdowns, companies like Zoom, Shopify, and Teladoc found themselves at the forefront of this transformation.
These stocks soared as investors quickly recognized the potential for remote work, e-commerce, and digital healthcare to become the new normal. Early investors were richly rewarded, riding the wave of what seemed to be a permanent shift in daily life.
But as the world began to reopen, reality set in. Not all pandemic-induced changes were here to stay. While some behaviors endured, many reverted to pre-pandemic norms, causing these once high-flying stocks to falter.
This period serves as a powerful reminder for us investors who are participating in the current AI boom era. It’s easy to get caught up in the excitement of technological breakthroughs and the promise of a transformed future. But just as the pandemic’s digital darlings peaked and then stumbled, so too can today’s AI innovators.
The lesson is clear: while it’s crucial to recognize and invest in groundbreaking opportunities, it’s equally important to stay grounded, wary of hype, and prepared for the possibility that transformational technology shifts can take time to evolve into sustained habits and operating structure.
Navigating the promise and uncertainty of the AI era requires a strategic balance of evolving risk management, discerning opportunity recognition, and mindful investing.
Evolving Risk Management & Dynamic Portfolio Strategies
The AI sector investor must be emotionally resilient, able to withstand periods of market downturns without panicking or making impulsive decisions. Traditional risk management strategies such as diversification and valuation discipline are essential but not enough. Modern investors must also adapt to new risks like technological obsolescence, regulatory changes, and volatile market sentiment. A dynamic approach to portfolio management is key to navigating these challenges effectively.
Its crucial investors stayed informed about emerging trends and potential regulations to protect their investments.
Market sentiment in AI can shift rapidly, driven by news and public opinion. Regularly reassessing your portfolio and making tactical adjustments keep investors risk and reward quotient in balance. Investors should keep cash on hand for quick action on new opportunities.
Recognizing Opportunities Beyond the Hype
In a booming sector like AI, seizing opportunities requires more than just following trends. It demands a deep understanding of what drives long-term value and sustainability. Not every company riding the AI wave is a true innovator.
Take Palantir and C3.ai as examples. Palantir, a true AI innovator with unique, mission-critical platforms and differentiated technology, contrasts sharply with C3.ai, which often spends more effort marketing itself as an AI leader than on developing truly differentiated technology - underscoring the need to discern real innovation from trend-chasing in the AI sector.
Focus on those with a proven track record of setting trends, not just following them. Assessing a company’s ability to adapt, scale, and integrate into larger systems is key to determining its long-term viability. Understanding a company’s role within the broader ecosystem is also crucial. Is its technology foundational, like cloud computing for AI, or could it be replaced by future developments? This approach helps identify opportunities with staying power, and avoiding those at risk to be the next group of companies made obsolete by ChatGPT's next model update.
Balancing Emotion with Foresight
Investing in AI can be emotionally charged, driven by excitement and the fear of missing out. The mindful investor balances emotional intelligence with strategic foresight to navigate these challenges effectively. Recognizing biases like overconfidence and recency bias leads to more rational investment decisions.
Sometimes, the best action is inaction. Waiting for the right opportunity often yields better results than rushing in. Scenario planning, considering best and worst-case outcomes, prepares investors for various market conditions and positions them ahead of potential trends.
4. Discipline: Focusing on Sustainable Valuations and Fundamentals
Despite what will likely be some tempting AI related IPOs ahead of us, and overpromises from CEOs looking to align their company to the AI theme, we must maintain a disciplined approach that prioritizes sustainable valuations and strong fundamentals.
Valuing technology stocks is both an art and a science, requiring investors to be part futurist and part financial analyst. How do you quantify the potential of a groundbreaking AI startup that's yet to turn a profit but could revolutionize industries? Traditional metrics often fall short when dealing with disruptive technologies, network effects, and exponential growth.
Instead of seeking precise intrinsic values, tech investors should aim to be in the valuation ballpark. Obsessing over exact figures can lead to wasted time and missed opportunities. Set aside the Discounted Cash Flow (DCF) spreadsheets and embrace the uncertainty that accompanies today's breakthrough technologies.
Tech stocks typically trade at higher valuations than other sectors. For example, the Nasdaq's forward Price to Earnings (P/E) ratio averaged 20x between 2000 and 2020, compared to the S&P 500's 15x. If technology investing were as simple as comparing P/E multiples, you could easily spot undervalued stocks with a spreadsheet, but it's not that straightforward.
Mark Mahaney, in his book Nothing but Net, highlights the importance of evaluating whether a stock trades at a premium or discount relative to its growth rate. High growth rates can justify seemingly high P/E multiples, as the future earnings stream of a high-growth company is worth more than that of a low-growth company. Mahaney's rule of thumb suggests that a P/E multiple aligned with or modestly above a company’s forward EPS growth is "ballpark reasonable." For example, a 20% EPS growth outlook might justify a P/E of 20x to 40x in some cases. The key is to assess whether the growth is sustainable and whether the company possesses attributes that could make it a premium revenue grower.
Look for quality tech stocks trading at a discount to their growth rates, but don’t hesitate to pay a fair price when the potential for substantial long-term growth is evident.
Not all tech stocks fit neatly into traditional valuation models. Companies like Amazon and Netflix defied expectations with minimal earnings and sky-high P/E multiples but rewarded patient investors as their business models matured. These examples illustrate the importance of identifying future profit machines, even when current earnings are low or nonexistent.
For companies with no earnings, P/E ratios are useless, but a creative use of Price to Sales (P/S) ratios, combined with thoughtful comparisons, can offer insights. Mahaney suggests asking whether comparable public companies are profitable, whether specific segments within the business are profitable, and whether scale or management actions could drive future profitability.
As I wrote in Part I, today's AI frenzy stands apart from past bubbles, particularly the dot-com era, because many of the leading public companies in AI, such as Palantir, Google, Microsoft, and ServiceNow, have strong fundamentals and are profitable. Additionally, the confidence displayed by AI leaders like Meta and Amazon, who have initiated dividend payments despite massive CAPEX, underscores the belief that their cash flows will continue to grow over the next decade. This means that investors in this AI era are not forced to choose between relentless growth and sound fundamentals. Instead, the focus should be on identifying companies with strong financial health, credible and sustainable AI monetization strategies, and the potential to establish a formidable competitive moat.
How to identify AI winners
When it comes to investing in the AI space, distinguishing the true winners from the hype can be challenging. While many companies claim to be at the forefront of AI innovation, only a select few will ultimately create lasting value. Identifying these future AI winners means finding companies with financial strength, difficult to replicate AI assets (e.g., access to proprietary data, advanced AI model development), a deep bench of AI talent, or control over key elements of the AI value chain. This section will explore the key factors to consider when evaluating AI companies, helping you navigate through the noise and focus on those that have the potential to drive sustained growth and profitability. While no company will have all of these attributes, focus on those that demonstrate as many as possible, and never compromise on strong leadership.
1. Corned AI resource
Borrowing a concept from Hamilton Helmer's 7 Powers framework, we arrive at the first AI-era success factor: the cornered resource. Helmer defines this as preferential access, under attractive terms, to a coveted asset that can independently enhance value. In the AI sector, companies with preferential access to coveted AI assets hold a significant advantage. These unique AI assets, such as proprietary data, specialized AI hardware, or a pool of top researchers and product developers, provide companies with a powerful competitive edge when kept under exclusive control.
By "cornering" these resources, companies create a protective moat around their business, making it incredibly difficult for competitors to encroach on their territory. This kind of advantage isn't just a short-term win; it's a long-term strategy for dominating markets and leading AI innovation.
Meta, with the billions of users active on its family of apps every day, has access to a always growing and sufficient treasure trove of data to train it open source Llama LLM on. Companies like NVIDIA or Google, with their custom-built GPUs and TPUs, have cornered the hardware market by designing chips that are optimized specifically for AI workloads. These specialized chips make AI computations faster and more efficient, giving them a leg up in the race to power the next generation of AI applications. Another example is OpenAI, whose proprietary GPT model is the crown jewel in the AI world. Its models have been built by the world's leading AI researchers and overseen by a brain trust of unique founders (that has now seen a major exodus).
Competitors may try, but the barriers to entry - whether it's the scale of data, specialized hardware, or the unique talent needed to develop AI models; are so high that catching up becomes nearly impossible.
The value of cornered AI resources lies in their ability to keep competitors at bay. These resources enable companies to operate at a level others simply can't. Whether that means delivering AI solutions faster, more efficiently, or at a lower cost. Cornered AI resources are game-changers. It's the unique, hard-to-replicate asset that allows a company to rise above the competition and build a sustainable advantage in the AI-driven economy. As AI continues to reshape industries, those who hold these cornered resources will have the upper hand in defining the future of innovation.
2. AI Driven Differentiation
To find investable AI companies capable of delivering the premium revenue growth expected from their lofty valuations and substantial CAPEX investments, we must focus on those applying AI in truly differentiated ways.
AI driven differentiation is about how a company leverages AI to set itself apart from the competition by delivering unique, high-impact value. It's not good enough to automate routine tasks, generate marketing copy or write a poem on demand. These are all things any company can deliver by integrating the latest frontier models into their product. Instead, AI-driven differentiation happens when AI is applied to solve complex problems, create new experiences, or drive innovation that’s hard for others to match.
Take Roblox, for example. They are uniquely applying AI to their content network effect by empowering anyone, even those without technical skills, to easily create on their platform. As a platform that thrives on user generated content, AI is a huge unlock for their business. As more social media influencers, artists, fashion designers, storytellers and even casual users can create and express themselves in seconds, the platform’s experiences and digital assets grow, attracting more users. An expanding user base, in turn, draws more developers and creators, creating a powerful virtuous cycle that is hard for competitors to replicate.
For investors, a key sign of AI-driven differentiation is whether a company is successfully turning its AI innovations into revenue growth. Look for proof points in financial reports: is the company making meaningful revenue from selling AI-driven products or services? While detailed breakdowns are rare, hints can be found. Even though specifics are not always clear, these numbers show how AI is starting to move the needle for large companies.
3. Adaptive AI Excellence
History shows that early adaptation during major platform shifts is crucial, but the initial stages are filled with uncertainty. In AI, key unknowns persist: Will LLM advancements hit a limit? Can synthetic data drive growth as real-world data diminishes? Companies must also navigate evolving regulations around AI bias, privacy, and heightened cybersecurity risks. Long-term success may depend on unforeseen breakthrough, or on overcoming fundamental limitations. To thrive, companies need to continuously reassess their strategies, pivot as needed, and adapt to the ever-changing AI landscape.
In such an uncertain and rapidly evolving AI landscape, success depends on adaptability and excellence. Adaptive AI Excellence is all about a company's ability to not only build cutting-edge AI systems but also remain flexible and responsive as the AI ecosystem shifts. It’s a balance between pushing the frontiers of building frontier AI technology and staying agile enough to adapt quickly to new players; limitations in AI models, UI and hardware; new business models such as consumptive billing and innovations such as OpenAI o1 that will sherlock several AI startups.
On top of the tech side, real adaptive excellence means keeping an eye on ethical AI. It’s about making sure systems are fair, transparent, secure and responsible, while balancing the need to stay agile in the short term with a solid long-term game plan
4. Strong leadership
Leadership is a crucial criterion when identifying future AI winners because strong, visionary leaders can make the difference between a company that promises AI transformation and one that advances AI and wields its power to create long-term value. Leadership in AI is not just about technical expertise; it’s about having the foresight to navigate a fast-moving landscape, the courage to make bold decisions, and the ability to inspire and align teams around a clear vision for AI’s role in the company’s future.
Visionary leadership ensures a company isn’t just following trends but is actively setting them. Leaders with a deep understanding of AI’s potential can identify the right opportunities to invest in, and they often have the ability to see how AI can be integrated into the core of a business, rather than simply as an add-on tool.
Jensen Huang, the founder and CEO of NVIDIA, is the architect of today's AI revolution thanks to his early recognition of the potential for Graphics Processing Units (GPUs) to accelerate AI workloads. Under his leadership, NVIDIA transformed from a gaming graphics company into the foundational pillar of AI infrastructure. NVIDIA GPUs are critical in training large-scale AI models like Chat GPT, and Huang's leadership continues to push the company into new AI frontiers, including the modern data center, autonomous machines, and the use of AI in healthcare and scientific research.
Second, decisiveness and adaptability are key traits in AI leadership. The most successful leaders know when to commit to big AI bets and when to pivot if a strategy isn’t working. AI is a high-stakes, high-reward field, and those who can confidently steer their company through uncertainty, while adapting to new developments, are far more likely to come out ahead.
Microsoft CEO Satya Nadella’s investment in OpenAI in 2019, long before generative AI gained mainstream attention, was a strategy masterstroke that positioned Microsoft ahead of competitors in the race to bring AI to the enterprise and consumers. Microsoft quickly integrated OpenAI’s technologies into core products like Azure, GitHub Copilot, and Microsoft 365 Copilot, making AI central to Microsoft’s business strategy. His bold move to secure an exclusive cloud partnership with OpenAI ensured that Azure would be a key platform for AI innovation.
OpenAI has been at the forefront of generative AI with breakthroughs like GPT-3, GPT-4, and more recently its o1 model. Sam Altman created and adapted a unique hybrid capped-profit structure, allowing the company to balance its research-driven, mission-focused culture with the ability to attract capital. This structure has fostered curiosity and collaboration while maintaining a strong commitment to ethical AI development. Altman’s recent push toward a product-led culture reflects another strategic pivot, aimed at ensuring OpenAI can continue pushing the boundaries of AI, while bringing its technologies to the masses.
To determine if a company has the right leader in place for success in AI, retail investors should evaluate the leader's track record in navigating technological changes and making strategic AI investments or pivots. Strong leaders often form key AI partnerships and make forward-thinking investments that drive future growth. The ability to attract and retain top AI talent is another crucial indicator, reflecting a company’s competitive edge. Additionally, leaders focused on a robust AI innovation pipeline and a successful track record of monetizing AI investments demonstrate strong execution capabilities.
5. Parallel AI Bets: Finding AI Leaders Among the Megacaps
To be used only when evaluating megacap stocks as potential future AI winners, focus on companies making parallel AI bets. Large companies investing in multiple, sometimes competing, AI projects or technologies simultaneously helps them hedge against the inherent uncertainty of the fast-moving AI ecosystem by spreading their resources across a range of innovations. In a world of volatility and fluidity, its a way for companies that can afford it to increase their probability of success. Companies can place multiple bets on different AI approaches, decide to develop proprietary AI models while partnering externally, or target both enterprise and consumer markets. This is the strategy megacap companies like Microsoft, Google, and Amazon are taking today, spreading their bets across the AI model world.
In the late '80s Microsoft made an important parallel bet. The company pursued multiple operating system projects, MS-DOS, Windows, OS/2 (with IBM), and Unix, recognizing that not all would succeed. While OS/2 failed, the lessons learned strengthened the successful bet on Windows, which eventually dominated the market. Today’s AI landscape presents similar opportunities for megacap companies to diversify across different technologies and sectors, positioning them to seize emerging opportunities while minimizing risk.
While these large cap companies have the financial strength and operational bandwidth to manage multiple high-risk bets at once, smaller companies face high risk with this approach. Emerging companies limited resources would lead to a dilution of focus, making it harder to fully execute any one initiative effectively.
AI stock ideas
While I've emphasized the importance of diversification throughout part I and II of this investor guide to profiting from the AI era, I wanted to share a few AI stock ideas that may fly under the radar at times. I've invested in several companies that are well-positioned for the AI era, such as Meta, Microsoft, ServiceNow, Amazon, Google, and TSM. The three stock ideas profiled below may still be in the early stages of their AI journey, with significant runway for market cap growth over the next 5 to 10 years.
Roblox (NYSE: RBLX)
Roblox is one of those rare founder led technology companies with an awe inspiring mission; innovation and transactional platform capabilities; and the most advanced ecosystem in a massive thematic that will change the nature of the economy, business and society. They check a lot of the "potential generational company" boxes and they have quietly positioned themselves as an AI leader for this next era.
Roblox is making parallel AI bets by applying AI across multiple facets of its platform. The experience platform company has over 250 AI models live on its platform today. One AI model analyzes voice chat in real time and screens for bad language, helping Roblox live up to its promise of civility. At its most recent developer conference, Roblox announced its most ambitious AI model ever. What has already started as a new incubation project, the company is developing a Roblox-created 3D foundational model to power generative AI creation on the platform.
Roblox is in a unique position to build AI models for the 3D and immersive web. It has a cornered resource in its vast, user-generated ecosystem of 3D content and real-time interactions between users and immersive worlds. This unique dataset gives Roblox a significant advantage in training AI models for content generation, personalized experiences, and game mechanics, fueling AI-driven innovation on its platform.
Read the Wealthy Owl Roblox deep dive.
Palantir (NYSE: PLTR)
Palantir stands out as an investment idea for the AI era, driven by its proprietary data integration capabilities and its cutting-edge Artificial Intelligence Platform (AIP). While its Foundry and Gotham products have established the company’s ability to unify complex datasets from diverse sources, AIP takes this expertise a step further. AIP allows organizations to turn large language models (LLMs) into agents and automations within their applications, unlocking new levels of AI-driven decision-making. This integration is crucial, as the ability to securely and reliably operationalize AI hinges not only on the power of models like LLMs but also on the quality and structure of the data they process. Palantir's deep experience in managing complex data environments, its semantic data layer (referred to as an ontology) combined with its model-agnostic approach, enables it to provide the infrastructure organizations need to make the most of AI.
With its strong commitment to ethical AI, scalable infrastructure, and high barriers to entry, Palantir is positioned as a key enabler of AI at scale, ensuring its long-term relevance and success in a rapidly evolving market.
Read the Wealthy Owl Palantir deep dive.
Uber (NYSE: UBER)
Uber has become synonymous with modern urban transportation - a true "verb" for mobility. Over the last 10 years, they have disrupted the global transportation industry.
AI has been a key part of establishing Uber as the urban mobility leader. They use AI to optimize routes and improve safety through features like crash detection and fraud prevention. Its AI-driven approach to dynamic pricing and route optimization has set it apart from competitors, offering faster, cheaper, and more efficient services while adapting in real time to supply and demand fluctuations.
In the next chapter of urban mobility, driven by autonomous vehicles, Uber is just getting started. Uber has placed several parallel bets with autonomous vehicle companies, such as Waymo, Aurora, Motional, and Nuro, to offer autonomous rides and deliveries on its mobility platform. By converting more of its driver supply side network to autonomous vehicles Uber will be able to dramatically lower rider fees to make the service accessible to more people and therefore grow its addressable market. I don’t think the full impact of autonomous driving on its business is totally baked into Uber’s current stock price and therefore may offer investors some upside.
Uber has a massive and super valuable dataset that includes real-time geolocation info, rider behavior, driver performance, traffic patterns, and logistics data from cities all over the world. This treasure trove of data helps Uber fine-tune its algorithms for ride-hailing, Uber Eats, and freight logistics, making it a cornered resource for boosting efficiency, predicting demand, and improving overall operations with AI.
Read the Wealthy Owl Uber deep dive.
As we move deeper into the AI era, it's hard to imagine a future where consumers, businesses, and governments aren't impacted in a major way by AI, or aren't actively investing in it to drive transformation.
With new models constantly emerging, like those from OpenAI, it’s clear that we’re only scratching the surface of AI’s potential. Now is the time to think long-term, project a few years ahead, and identify the companies poised for outsized returns.
Don’t limit your thinking to just the immediate, first-order effects. Consider the second and third waves of AI-driven value creation as well. Focus on sectors and categories that will both drive AI innovation and leverage its capabilities for transformation. It's important to maintain diversification across sectors and invest in the best companies in each space. Conviction in multiple winners is key to managing risk while capturing the full potential of AI’s transformative impact.
Before making investment decisions please do your own research and/or seek the advice of a professional adviser. This is not a stock recommendation. I'm just sharing my approach and companies I'm investing in.