Investing in Generative AI Stocks

Stewart Noyce -

Executive Summary

Generative AI technology transforms how professionals work, helping them achieve better results while enabling companies to cut costs and boost performance. With spending in this sector expected to reach $1.3 trillion in 2032, how can you invest in this fast-growing market today?

This article offers a clear framework for picking stocks with the potential to survive and thrive. Four key criteria guide analysis of both publicly traded companies like Google, Microsoft and Amazon and private companies like OpenAI, Anthropic and xAI.

Now is the time to act. Use this insight to find promising companies and invest in them at a great price. To invest in privately traded stocks, get connected with a broker or participate in a syndicate with other investors, and watch your portfolio grow.

As a disclaimer, I am a private investor and not a financial professional. My posts and articles are not financial advice, but insights that might be educational for a person of similar mindset and life position. Please share your comments to increase the wealth for the next person.

Introduction

Since the 1950s, artificial intelligence (AI) has steadily evolved, with major breakthroughs in areas like machine learning and natural language processing. Over time, the tech industry has embraced AI, integrating its features into many popular products.

Generative AI, a specific type of AI, gained massive attention with the release of ChatGPT in late 2022. Its ability to power conversational tools unlocked new ways for people to improve productivity. Users can interact naturally with these tools, which can remember and adapt to the context of conversations.

Businesses quickly followed, using generative AI to improve their products and create new solutions. For example, companies use AI to add features like type-ahead suggestions in coding tools or enhanced customer support. These innovations are fueling explosive growth, with Bloomberg Intelligence projecting the industry to reach $1.3 trillion by 2032.

For investors, this is a critical moment to assess which companies might dominate this transformative industry. Are today’s tech leaders or emerging startups better positioned to shape the future? This article explores how to identify companies worth investing in today for long-term gains.

Public vs Private Stocks

The most obvious opportunity is to invest in one or more of the many large publicly traded companies that have an AI strategy. For example, you might consider one of the cloud computing hyperscalers (Google, Amazon, Microsoft, IBM or Oracle), an AI-driven tech giant (Apple, Meta, Tesla) or one of the leading hardware chip providers (Nvidia, Intel, AMD).

But if you want to capture significant gains, equivalent to the 30-40% year over year growth in industry revenue, then you will want to consider a pure play generative AI company. And in particular, this article argues for companies that either build or modify foundation models to meet the needs of millions of people.

Such opportunities exist today, both for companies like OpenAI, Anthropic, and Mistral that are developing foundation models, and those pursuing focus strategies in enterprise or content such as Perplexity, Cohere, and Stability AI.

Despite the daunting upfront costs of building and training advanced models, the majority of companies in this space are poised to achieve margins comparable to enterprise SaaS companies, leveraging the foundational work of a select few model-building competitors. Early investment in the winners will be well rewarded. Let’s consider now how investors might find the next “multi-bagger” investment.

Investment Criteria

When making any investment it’s imperative to buy good companies at a great price. While this is an adage with many variations, the key to your success is ultimately to buy a company that will still be in business and making profits ten years from now.

This article focuses entirely on those factors that contribute to accurate identification of the companies that will both survive and thrive in the coming generative AI industry expansion. You do the work to make a short list of favorites. Then you can pull the trigger to buy when you like the price.

To imagine who will lead our AI future, let’s look to the near past. How were Microsoft, Amazon, Google and Facebook so successful in recent decades? These winners demonstrated a clear pattern: they each created enormous value for users and customers while building a protective moat through network effects.

Winners are easy to name in hindsight, but harder to predict before the race starts. How can we handicap today’s competitors to predict their potential success? Here are four criteria commonly used for stock picking fleshed out with details specific to generative AI.

Value Proposition

Start by understanding the unique value propositions offered by each player, which exposes their relative positioning. Delivery of value to a buyer who expresses critical unmet needs is the first battleground for market share. Companies today fall into one of three categories:

  1. Horizontal Products: These tools are designed to serve a wide range of users across different industries. For example, OpenAI’s ChatGPT and Anthropic's Claude are used for tasks like writing, brainstorming, and answering questions, making them useful to many types of users.
  2. Vertical Solutions: These focus on solving problems specific to an industry, like AI-powered drug discovery tools in healthcare or fraud detection in financial services.
  3. Role-Focused Tools: These are tailored to specific professions, like GitHub Copilot for software developers or customer support bots for service teams.

OpenAI and others made initial foundation model investments from technological ambition and scientific curiosity. Scientists and engineers designed and built those first models. Early on, those pioneers gave developers widespread free access to their foundation models. They hoped to encourage developer experimentation, which would lead to new use cases, developed ecosystems, and market feedback.

Only after widespread adoption of OpenAI ChatGPT in public beta did developers and knowledge workers both pick up on the value of generative AI. Immediate adoption with paid subscriptions came from individuals who gained access to the latest models to try out.

Personal productivity usage quickly gave evidence to critical unmet need. People found the tools valuable for creative thinking and streamlining daily tasks. Typical usage includes:

  1. enhanced communication
  2. time and task organization
  3. learning and knowledge acquisition
  4. creativity and content generation
  5. problem solving and decision support
  6. entertainment and engagement
  7. accessibility and inclusion
  8. personalization and customization

Following quickly, companies started adding AI-driven improvements to existing products, some of which would be role based and some targeted at vertical industries. Examples include:

  1. Software development and integrated development environments (IDE features)
  2. Customer support and service automation (chatbots and virtual agents)
  3. Content creation and management (video and audio editing suites)
  4. Customer relationship management (CRM system features)
  5. Healthcare and diagnostics (drug discovery, diagnostic tools, health assistants)
  6. Retail and e-commerce (shopping recommendations, try-ons)
  7. Education and learning platforms (personalized learning, automated grading)
  8. Financial services (fraud detection, credit scoring, personalized financial planning)
  9. Logistics and supply chain (predict demand, manage inventory, optimize routing)
  10. Human resources and talent management (resume screening, employee engagement)

Sustainable Advantage

In this early period of market hype, explosive growth in usage, and massive investments, we see companies laying the foundation for their relative strategic advantage. You may certainly think of it as the “moat” they are digging to protect their businesses.  Here are three factors that one might consider when comparing companies in generative AI.

Superpowers -  What does the founding team bring to the party, and what unique capabilities has the company accumulated over time?  Individuals might bring technical chops, extensive research and domain knowledge, and growth company experience. Groups in the company may have become expert in processes that deliver high quality technology and/or products faster.

Strategic Control - Describe that viable market position (the strategic control point) where a company can maintain and grow its profitability despite competing industry forces. To achieve true sustainable competitive advantage, companies should look beyond generic capabilities and focus on deep specialization, ecosystem-building, and operational efficiency.

Flywheel - Find those systemic processes by which a specific company will grow organically and efficiently. In generative AI, one quickly sees that access to user data (their experiences, their interests, specific goals) leads to better training of models. Look for companies that deliver immediate tangible value, focus on proprietary data collection, amplify user feedback and engagement, build a developer ecosystem, leverage network effects (like virality), iterate quickly based on metrics, and scale efficiently.

Business Model

In the long term, a successful company that makes money for its investors has positive cash flow and increasing profits. It doesn’t go to the banks or capital markets to raise money for the next year. Here are specific attributes of a company’s business model to examine.

Business Design - The company’s business model reliably generates profits. It distills the strategy into products sold for more than the cost of goods and the overhead of the company.  Taken a step further, business model design expresses the “moat” or sustainable competitive advantage defined by the company’s strategy. Implementation options that companies are looking at today include horizontal value delivery vs vertical, open source models vs closed, subscription vs transaction vs consumption pricing, and sourcing options / partnerships.

Contribution - The cash flow generated by the company from its contribution margin (the operating margin of the business before paying for overhead) measures the power of the underlying business design, and all of the other factors that go into it. Current structures in generative AI include: consumer subscriptions with premium tiers, direct to developer API access with consumption based pricing, and enterprise licensing with custom contracts.

Investment - Until the company has free cash flow to fuel its growth, it must raise capital from investors. How much money does the company really need to raise to build out its strategy?  In generative AI, the foundation model companies are raising enormous rounds of financing, most of which is going straight to the tech giants for the hyperscale compute services and the hardware infrastructure those services require. Focus strategies need much less investment.

Ability To Execute

Companies with clear and compelling strategy may underperform and even fail because they lack an ability to execute. Look closely at past achievements, current objectives and resource levels to refresh your assessment. Think of execution as “evidence” of progress against strategy.  Look for companies that consistently and transparently deliver on promises while improving relative to competition.

Achievements - What has the company done so far? Do these proof points support its stated strategy. For example, has a foundation model company used the money it raised to build a high quality model and distribution that shows users are responding positively to it? Does the company with a focus strategy have strong partnerships and internal expertise that delivers clear value to its intended audience?

Objectives - Next, does the company clearly and transparently say what it is going to accomplish in the next 6-12 months? Top performers tend towards strong management that introduces value to customers on a steady drumbeat. They signal what’s company and deliver.

Resources - Finally, does the company have both the funding and the people to accomplish its long term goals and near term objectives?  Large equity raises may certainly look impressive but they need to be adequate or your investment will be diluted later. Also, people are key. The company needs leaders and workers who fit the strategy.

Review

The companies that achieve sustainable profitability while growing their value proposition for both customers and creators will dominate the industry in a decade. These are the firms to invest in now. Let’s review a list of the top competitors today.

Today’s participants in generative AI fall into two broad categories: emerging pure-play companies and established tech giants. The tech giants are public companies whose stock may be bought on a public stock exchange today. Stock of the emerging players is privately traded, but there are ways to accumulate these shares as well.

This article does not attempt to review these industry combatants in detail. Rather, it sets the stage for discussion of their relative potential over time and their actual achieved success.

Emerging Players

  • OpenAI (ChatGPT, etc.): Pioneering conversational AI for consumer and enterprise use, this company with first mover advantage moved quickly to capture market share with a highly conversational approach to chat. Its integration with Microsoft’s ecosystem accelerates its reach while focusing on enterprise partnerships.
  • Anthropic (Claude): Backed by Amazon and Google, and led by founders of OpenAI, Anthropic prioritizes safety and alignment, positioning itself as a responsible alternative.
  • Mistral: Backed by Amazon, Nvidia and a large contingent of enterprise companies and investors, Mistral focuses on open-weight models that cater to developers and enterprises seeking transparency and customization.
  • Cohere: Specializeing in natural language processing tools aimed at developers, Cohere offers fine-tuned models for specific use cases.
  • xAI: Spun out as a separate company, this startup represents Elon Musk’s response to the need for safe and open foundation model. It will likely see immediate use in X (Twitter).
  • Perplexity: Quickly gaining traction as a chat bot alternative that links to sources, Perplexity also features an open repository and collaboration hub, facilitating access to state-of-the-art models for researchers and businesses alike.

Public Tech Giants

  • Google (DeepMind, Bard): Leveraging its vast data repositories and AI research leadership, Google integrate generative AI across its search, cloud, and enterprise services.
  • Microsoft: Partnering with OpenAI, Mistral and Llama, Microsoft embeds generative AI across its Office Suite, GitHub Copilot and Azure cloud platform offers.
  • Meta (LLaMA): Driven by a desire to maintain control of the best possible generative AI models across its product portfolio, now and in the future, Meta drives development and improvement of the Llama open source model to make it an industry standard like Linux.
  • Amazon: Embeds AI deeply in AWS and consumer devices, using its scale to cater to a diverse set of customers. Partners with all major foundation models.
  • Apple: Delivers AI within its walled ecosystem, prioritizing privacy and user-centric design.
  • Nvidia: Sells semiconductor chips and systems to the companies that build hyper scaled cloud computing centers for AI model development and use.

Each company’s approach influences its potential stock returns. Established giants already demonstrate profitability and scalability, but their AI initiatives represent incremental growth. Emerging players, though riskier, offer the possibility of outsized returns by capturing a more direct share of the generative AI market.

Call to Action

The opportunity to capitalize on generative AI’s transformative potential is now. Investors can readily access the growth of companies like NVIDIA, Meta, Amazon, Google, Microsoft, and Apple, which are driving innovation and capturing significant market share. These stocks are already showing upward momentum, offering immediate opportunities to participate in this historic trend.

For those seeking the pure-play upside potential of OpenAI, Anthropic, Mistral, or Cohere, secondary shares are the key. These investments grant direct exposure to companies shaping the next wave of AI. If you’re ready to explore this extraordinary space, contact me for access to targeted syndicates and exclusive opportunities.

This is the decade of generative AI. Don’t miss your chance to invest in the future.

Disclaimer

No one can actually see the future, let alone predict it. This article does not make that claim. It states a thesis, enumerates points for critical thinking, and highlights some companies that are presently active as a suggestion for deep analysis. Please remember, none of this is explicit investment advice. It is for educational purposes only.