AI Beyond the Hype: Identifying Secondary and Tertiary Winners in the US Market

The narrative of the Artificial Intelligence (AI) gold rush has, for the past few years, been dominated by a clear and compelling cast of characters. We know the “picks and shovels” vendors—the semiconductor giants like Nvidia, whose GPUs are the indispensable engines of AI computation. We watch the cloud hyperscalers—Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP)—who control the digital mines where AI models are trained and run. And we are captivated by the model makers, like OpenAI, Anthropic, and their peers, who are pushing the boundaries of what’s possible with large language models (LLMs) and generative AI.

Investing in these primary winners seems like a straightforward, if not already crowded, strategy. However, as with every major technological revolution—from the personal computer to the internet to the smartphone—the greatest and most sustainable wealth is often not created by selling the core components, but by the vast, cascading ecosystem that emerges to leverage them.

This article moves beyond the immediate hype to identify the secondary and tertiary winners in the US market. These are the companies that may not be building the AI infrastructure itself but are poised to be its most sophisticated and profitable users. They are the enterprises that will embed AI so deeply into their products, operations, and business models that they will achieve unassailable competitive advantages, fundamentally reshaping their respective industries.

Part 1: Deconstructing the AI Value Chain – Where Does the Real Value Accumulate?

To identify the next wave of winners, we must first understand the structure of the AI economy. We can visualize it as a layered stack:

The Foundation Layer (The Primary Winners): The providers of computational power, hardware, and base models. This includes:
- Semiconductor Companies (e.g., Nvidia, AMD, TSMC): The clear, initial beneficiaries.
- Cloud Hyperscalers (e.g., Microsoft, Amazon, Google): The “landlords” of the AI economy.
- Core Model Developers (e.g., OpenAI, Anthropic, Meta): The creators of foundational LLMs and AI models.
The Application & Integration Layer (The Secondary Winners): Companies that build specialized software, tools, and platforms on top of the foundation layer to solve specific business problems. They are the crucial bridge between raw AI power and practical utility.
The End-User & Enabler Layer (The Tertiary Winners): This is the broadest and potentially most lucrative category. It comprises:
- Enterprise Adopters: Non-tech companies across all sectors that successfully integrate AI to achieve massive operational efficiencies, product innovation, and market dominance.
- Specialized Enablers: Firms that provide the essential “last-mile” services—like consulting, implementation, security, and data management—required for widespread enterprise adoption.

The investment thesis for the next decade is that while the Foundation Layer will remain critically important and profitable, the economic value will increasingly flow up the stack to the Secondary and Tertiary winners, as AI becomes a ubiquitous, commoditized utility.

Part 2: The Secondary Winners – The Architects of the AI-Powered Enterprise

Secondary winners are primarily B2B software and platform companies. Their advantage lies in their deep, vertical-specific data, established customer relationships, and the ability to seamlessly embed AI into essential workflows. They don’t need to train a foundational model from scratch; they can fine-tune existing models on their proprietary data to create uniquely powerful applications.

Category 1: The Productivity & Software Development Arsenal

This category includes companies that are using AI to supercharge human productivity, particularly among knowledge workers and developers.

Microsoft (MSFT): While a primary winner through Azure, Microsoft is arguably the world’s most potent secondary winner through its integration of Copilot across its entire software ecosystem. Microsoft 365 Copilot is not just a feature; it’s a fundamental re-imagining of how work gets done in Word, Excel, PowerPoint, Outlook, and Teams. By embedding AI directly into the operating system (Windows Copilot) and the world’s most ubiquitous productivity suite, Microsoft is creating massive switching costs and value-adds that will drive subscription revenue and deepen its enterprise moat.
Salesforce (CRM): In the CRM space, Einstein GPT is doing for sales, service, marketing, and commerce what Copilot is doing for productivity. It can auto-generate personalized emails, generate code, create targeted marketing content, and provide AI-driven insights to close deals faster. Salesforce’s vast reservoirs of customer relationship data make its AI uniquely powerful and context-aware for its millions of users.
ServiceNow (NOW): For enterprise IT and workflow management, ServiceNow is embedding AI across its platform to automate complex service management processes, predict IT incidents before they happen, and streamline employee onboarding. Their Now Platform is becoming an intelligent automation engine for large corporations.
Developer Tooling (e.g., GitHub (MSFT), GitLab (GTLB)): GitHub Copilot has already revolutionized software development by acting as an AI pair programmer. It suggests entire lines of code and functions, dramatically increasing developer productivity and lowering the barrier to entry for new coders. This accelerates digital transformation across all industries, making the tools themselves incredibly valuable.

Category 2: The Data Unification & Analytics Experts

AI is only as good as the data it’s trained on. Most enterprises have their data locked away in siloed, messy, and inaccessible systems. Companies that can solve this data problem are critical secondary winners.

Snowflake (SNOW) & Databricks (Private): These are the central data platforms of the modern enterprise. Snowflake’s Data Cloud and Databricks’ Lakehouse Platform are the foundational repositories where companies consolidate their data for AI analysis. They are rapidly integrating AI and LLM capabilities, allowing users to query data using natural language and build AI applications directly on their platforms. Their role as the single source of truth makes them indispensable.
Palantir (PLTR): Palantir’s Artificial Intelligence Platform (AIP) is a controversial but powerful example. They specialize in connecting to an organization’s most disparate and complex data systems (especially in government and defense) and providing a unified operating picture with AI-powered decision-making tools. Their boots-on-the-ground implementation for high-consequence industries gives them a deep and defensible moat.

Category 3: The Vertical-Specific AI Pioneers

These companies are building AI deeply into the fabric of specific industries.

Adobe (ADBE): Adobe Firefly, integrated directly into Creative Cloud and Experience Cloud, is a prime example. It’s not a standalone chatbot; it’s a generative AI model specifically trained on Adobe’s stock imagery and licensed content, making it safe for commercial use. It empowers creators to work at the speed of imagination, from generating images to altering videos with text prompts, solidifying Adobe’s dominance in creative software.
Veeva Systems (VEEV): In the life sciences industry, Veeva is embedding AI into its cloud-based CRM and content management solutions for pharma companies. This can help automate regulatory compliance, personalize marketing to healthcare professionals, and accelerate clinical trials. Their industry-specific focus makes their AI solutions more valuable than generic alternatives.

Part 3: The Tertiary Winners – The Silent Giants of AI Adoption

The tertiary winners are often not pure-play tech companies. They are the established enterprises in “old economy” sectors that leverage AI to achieve a level of operational excellence and competitive advantage that their peers cannot match. Their gains will be reflected in expanded profit margins, increased market share, and resilient moats.

Category 1: Logistics, Supply Chain & Industrial Giants

These companies operate on thin margins where efficiency is everything. AI optimization is a game-changer.

United Parcel Service (UPS) & FedEx (FDX): These giants are using AI for hyper-efficient route optimization, predictive maintenance on their massive vehicle fleets, and dynamic pricing. AI can predict shipping volume surges, optimize load balancing in planes and trucks, and reduce fuel consumption, directly impacting the bottom line.
Deere & Company (DE): A quintessential tertiary winner. John Deere is no longer just a tractor company; it’s a technology and data company. Through its See & Spray technology and other precision agriculture solutions, Deere uses computer vision and AI to identify weeds and apply herbicide only where needed, reducing chemical use by over 90% in some cases. This creates immense value for farmers and locks them into the Deere ecosystem.
Caterpillar (CAT): Similarly, Caterpillar uses AI for predictive maintenance on its massive mining and construction equipment. By analyzing sensor data, they can predict part failures before they happen, minimizing costly downtime for their clients and creating a powerful service-based revenue stream.

Category 2: The Financial Services & Insurance Revolution

Finance is a data business, and AI is the ultimate data analysis tool.

JPMorgan Chase (JPM) & Goldman Sachs (GS): These leading banks are investing billions in AI. Applications are vast: AI-powered algorithmic trading, fraud detection in real-time, automating credit risk assessment, and personalized wealth management advice through chatbots and robo-advisors. JPMorgan’s IndexGPT even seeks to use AI to select investments for customers. Their scale and data access allow them to build AI solutions that smaller competitors cannot.
BlackRock (BLK) & Bloomberg: In asset management and financial data, AI is used to analyze alternative data (satellite imagery, social media sentiment) for investment insights, automate portfolio management through Aladdin, and provide natural language querying of complex financial databases.
Progressive (PGR) & Lemonade (LMND): In insurance, AI is revolutionizing underwriting and claims processing. Lemonade’s AI-powered chatbot can handle and pay claims in seconds. Progressive’s Snapshot program uses telematics and AI to personalize auto insurance rates based on individual driving behavior.

Category 3: Healthcare & Biotech Innovators

The potential for AI in healthcare is staggering, moving beyond administration into the core of drug discovery and diagnostics.

Eli Lilly (LLY) & Pfizer (PFE): Major pharmaceutical companies are using AI to dramatically accelerate and reduce the cost of drug discovery. AI can analyze vast databases of molecular structures to predict how compounds will interact with biological targets, identifying promising drug candidates in months instead of years. This improves R&D productivity, one of the industry’s biggest challenges.
Medtronic (MDT) & Intuitive Surgical (ISRG): In medical devices, AI is being integrated directly into hardware. Intuitive Surgical’s robots use AI to provide surgeons with enhanced visualization and guidance. Medtronic’s GI Genius system uses AI to help detect polyps during colonoscopies. These AI-enhanced devices command premium prices and improve patient outcomes.
Diagnostic Companies (e.g., in Radiology/Pathology): Companies developing AI software that can read MRIs, CT scans, and pathology slides with greater speed and accuracy than the human eye are poised for massive growth. They act as a force multiplier for skilled specialists, alleviating workload and reducing diagnostic errors.

Part 4: The Essential Enablers – The Arms Dealers for the AI Revolution

No discussion of tertiary winners is complete without mentioning the consulting and professional services firms that are essential for implementation.

Accenture (ACN), Deloitte, IBM Consulting (IBM): These firms are the bridge between AI technology and business transformation. They provide the strategy, systems integration, change management, and custom development required to deploy AI at scale within large, complex organizations. Their massive, trained workforces and industry-specific expertise make them indispensable partners for the tertiary winners mentioned above. As AI spending grows, so does their services revenue.

Part 5: A Framework for Identifying Future Winners

For investors and strategists, identifying the next wave of AI winners requires a disciplined framework. Look for companies that exhibit several of the following characteristics:

Proprietary, “Un-Duplicatable” Data: Does the company have access to a unique, large, and hard-to-replicate dataset? This is their “AI moat.” (e.g., Deere’s field data, Palantir’s government data).
Strong Economic Moats: Does the company have an existing competitive advantage (brand, network effects, switching costs) that AI will strengthen? (e.g., Microsoft’s enterprise lock-in, JPMorgan’s scale).
A Clear Path to ROI: Is the AI application directly tied to reducing costs (e.g., logistics), increasing revenue (e.g., sales and marketing AI), or mitigating risk (e.g., fraud detection)? Avoid companies with vague “AI initiatives.”
Proven Execution Capability: Does the management team have a track record of successful technology integration? A great AI strategy is useless without the operational excellence to implement it.
Strategic Acquisitions & Partnerships: Is the company actively acquiring AI startups or forming strategic partnerships with primary winners (e.g., cloud providers) to accelerate its capabilities?

Conclusion: The Long Game

The initial frenzy around AI is understandably focused on the foundational players who are enabling this new world. However, the history of technological disruption teaches us that the most enduring and transformative value is captured not by those who sell the tools, but by those who master their use.

The secondary winners—the software companies embedding AI into their core products—are creating the new standard for how work is done. The tertiary winners—the enterprises using AI to reinvent their industries—are building the durable competitive advantages of the 21st century. They are the “silent giants” whose stories may be less glamorous than the model-makers but whose financial performance over the long term may be far more impressive.

Looking beyond the hype to this broader ecosystem is not just a prudent investment strategy; it is a lens through which to understand the fundamental restructuring of the entire US economy. The age of AI is not coming; it is here. And its ultimate victors will be those who apply it, not just those who create it.

FAQ Section

Q1: Aren’t the primary winners (like Nvidia) still the safest bet?
They are a core holding for exposure to the AI theme, but their future is not without risk. Their valuations are often high, reflecting massive expectations. They also face the constant threat of technological disruption (e.g., new AI chipsets, a shift to more efficient architectures) and customer concentration risk (e.g., if cloud providers design their own chips). Secondary and tertiary winners often have more diversified revenue streams and their AI success is a direct driver of their core business profitability.

Q2: How can I, as an individual investor, research a company’s true AI capabilities beyond their marketing?
Look for concrete evidence:

Earnings Call Transcripts: Analyze what the CEO and CFO are saying. Are they specific about use cases and ROI? Or are they just using buzzwords?
Patent Filings: Search for patents related to AI and machine learning in the company’s name.
Job Postings: Are they aggressively hiring AI engineers, data scientists, and machine learning specialists?
Product Demos & Launches: The proof is in the product. Can you see and use the AI features they are promoting?

Q3: What are the biggest risks for secondary and tertiary AI winners?

Implementation Risk: The failure to integrate AI technology effectively into existing workflows and culture.
Regulatory Risk: Increasing scrutiny around data privacy, algorithmic bias, and the potential for new regulations that could limit AI use.
Cybersecurity Risk: AI systems can be new vectors for attack, and data breaches involving AI models could be catastrophic.
Model Obsolescence: Relying on an AI model that is quickly superseded by a more advanced one from a competitor or primary winner.

Q4: Are there any ETFs that focus on secondary and tertiary AI winners?
While there are AI-focused ETFs like BOTZ (Global X Robotics & Artificial Intelligence ETF) and AIQ (Global X Artificial Intelligence & Technology ETF), they often have significant exposure to primary winners. A more targeted approach may involve investing in thematic ETFs focused on specific tertiary sectors being transformed by AI, such as cloud computing (WCLD), cybersecurity (CIBR), or digital transformation, and then analyzing the individual holdings for their AI adoption strategy.

Q5: Is it too late to invest in this theme?
We are still in the early innings of enterprise AI adoption. While the primary winners have seen significant runs, the application and integration phase for secondary and tertiary winners is just beginning. Many companies are still in the pilot or early rollout stages. The full productivity and profit gains are likely to materialize over the next 3-10 years, creating a long runway for growth for the companies that execute well.

Q6: How does the concept of “Moats” apply specifically to AI?
In the AI context, a moat is what prevents a competitor from easily copying your AI advantage. The strongest AI moats are:

Data Moat: Unique, proprietary, and continuously refreshed data that trains a better, more specific AI model.
Workflow Moat: AI that is so deeply embedded into a critical business workflow that it becomes invisible and indispensable, creating high switching costs.
Talent Moat: Access to the best AI researchers and engineers.
Scale Moat: The computational resources and capital to train and run large, sophisticated models.

Capitalbytes

Capitalbytes