Two Growth Stocks for 2026: Assessing Scalability and TAM in the AI Era

The stage is set for a growth-driven market in 2026. Goldman Sachs forecasts sturdy global economic expansion of 2.8%, with the US economy expected to outperform. This backdrop of above-trend growth, coupled with easing policy, creates fertile ground for companies that can scale efficiently. The central investment theme is clear: artificial intelligence is no longer speculative but a high-conviction driver of productivity and earnings. According to The Motley Fool's 2026 AI Investor Outlook Report, nine out of 10 AI investors plan to hold or buy additional AI stocks in the coming year, signaling deep market conviction.

This sets up a powerful tailwind for companies deeply embedded in the AI value chain. The opportunity lies in identifying firms that are not just riding a short-term wave but are structurally positioned to capture the long-term expansion of the AI economy. For investors, the challenge is to navigate this concentrated theme without overexposure. The market requires a portfolio approach, using new tools to build diversification beyond pure AI speculation. While AI catalyzes a capital-intensive expansion, improving fundamentals elsewhere-such as in non-AI portions of the S&P 500-offer avenues to balance a portfolio and manage concentration risk.

The bottom line is that 2026 favors selective risk-taking. The macroeconomic setup supports growth, and the AI theme provides a clear secular trend. Yet, with "hot valuations" and a weak labor market, the path will likely involve volatility. The winning strategy will be to target companies with scalable business models and clear paths to capture market share in the AI era, while maintaining a disciplined, diversified approach to the broader portfolio.

Scalability and TAM: The Growth Investor's Framework

For the growth investor, the 2026 landscape demands a new playbook. Traditional metrics, like static Total Addressable Market (TAM) models, are relics of a pre-AI world. The old math-projecting growth based on fixed geographic or customer segments-no longer applies. Artificial intelligence has fundamentally compressed sales cycles, eliminated geographic boundaries, and created entirely new customer segments while rendering others irrelevant. Companies relying on pre-AI TAM calculations risk catastrophic strategic errors, either overestimating commoditized markets or underestimating explosive new opportunities.

The new framework starts with a redefined TAM. In the AI era, market size is not a fixed number but a dynamic frontier. AI compresses the time to value, allowing solutions to reach mid-market and small businesses at scale. This can expand a company's addressable market by orders of magnitude overnight. Conversely, AI also commoditizes routine tasks, shrinking the TAM for legacy, human-intensive services. The growth investor's task is to identify firms operating in the expanding, AI-driven segments, not the contracting ones.

Scalability is the engine that turns a large TAM into outsized returns. It is defined simply: the ability to grow revenue without a corresponding rise in costs. This is the hallmark of a durable growth story. A scalable model leverages technology and automation so that serving an additional customer incurs minimal incremental expense. This contrasts sharply with non-scalable businesses, like custom consulting, where growth is capped by the need to hire more high-cost labor. The most powerful scalable models are digital-software, platforms, and network effects-where the marginal cost of adding a user approaches zero.

This leads to the critical signal: gross margins above 75%. Such high margins are a powerful indicator of strong pricing power, a differentiated product, and an efficient, automated business model. They reflect a company that can command premium prices for its value while maintaining low cost structures. The evidence shows this is not a theoretical ideal but a real benchmark. A recent analysis highlighted several leading companies with trailing gross margins above 75%, including Salesforce at 77.6% and Adobe at 89.1%. These aren't just high numbers; they represent the financial flexibility to reinvest heavily in growth, fend off competition, and sustain high returns on capital.

The bottom line for 2026 is that scalability and a redefined TAM are inseparable. The growth investor must look beyond today's revenue to the structural advantages that allow a company to capture a disproportionate share of a rapidly expanding market. It's about finding firms where AI is not just a product feature but a force that expands their TAM and amplifies their scalable economics.

Micron Technology (MU): Capturing the AI Hardware Trough

Micron Technology is positioned at the very heart of the AI hardware stack, making it a prime candidate for capturing the massive expansion of the AI economy. The company's revenue grew 62% year-over-year last quarter, a figure that underscores its direct exposure to the insatiable demand for compute power. This isn't just cyclical recovery; it's structural growth driven by the AI infrastructure build-out. For a growth investor, the key is to assess whether this demand is sustainable and whether Micron's business model can scale to meet it.

The Total Addressable Market for Micron's core product, high-bandwidth memory (HBM), is projected to be enormous. Industry forecasts point to the HBM market reaching $100 billion by 2028. That represents a multi-year growth trajectory that dwarfs the company's current scale. This isn't a niche opportunity but a foundational component for AI training and inference. As the AI model landscape evolves, the need for faster, more efficient memory continues to rise, expanding the TAM for companies like Micron that lead in this technology.

The scalability of Micron's business is further validated by the economics of the broader AI semiconductor value chain. While Micron manufactures the chips, the financial blueprint is set by its customers. Take Nvidia, for instance. The company maintains a gross margin above 73% on its AI chips, demonstrating the premium pricing power and high profitability available in this segment. For Micron, achieving similar gross margins would signal a highly scalable, capital-efficient model. The company's ability to produce HBM at scale, leveraging its manufacturing expertise, is the critical factor in converting the massive HBM TAM into outsized returns. The high-margin, scalable economics seen at the top of the stack provide a clear benchmark for what Micron must achieve to be a true growth leader in the AI era.

The bottom line is that Micron offers a direct play on the AI hardware trough. Its revenue growth trajectory aligns with the AI build-out, its TAM is projected to be massive, and the financial model of the sector points to high-margin scalability. For investors, the watchpoint is execution: can Micron consistently capture market share in HBM and maintain the operational discipline to turn that share into robust, scalable profits?

Alphabet (GOOG/GOOGL): Monetizing the AI Ecosystem at Scale

For the growth investor, Alphabet represents a unique dual-play on the AI expansion. It is not just a cloud provider; it is the operator of the world's largest digital ecosystem, now being re-architected to monetize AI at scale. The investment case hinges on two massive, interconnected TAMs: a visible, multi-year revenue backlog in its cloud infrastructure and a vast, sticky user base primed for AI-driven engagement.

The first pillar is the cloud backlog, a clear indicator of near-term TAM. Alphabet exited the third quarter with a Google Cloud backlog of $155 billion. This is more than a future order book; it is a multiyear revenue visibility that provides a predictable foundation for growth. With over 70% of its cloud customers already using AI products, this backlog is increasingly powered by AI workloads. The company's custom AI-optimized TPUs further enhance the economics, delivering better performance and energy efficiency. This combination of scale, sticky customer commitments, and cost advantages creates a formidable moat, allowing Alphabet to capture a disproportionate share of the cloud infrastructure build-out.

The second, and arguably more powerful, pillar is its user ecosystem. Alphabet commands over 3 billion monthly active Android devices and over 2.7 billion monthly active YouTube users globally. This isn't just a large audience; it's a massive, sticky TAM for AI monetization. The company is leveraging AI-powered targeting and automation to improve ad conversion efficiency across Search and YouTube, directly boosting the revenue per user. More importantly, this ecosystem generates the valuable, first-party data that fuels its AI models, creating a powerful feedback loop. As AI capabilities deepen, they can be deployed across this entire user base to drive engagement and new monetization avenues, from premium content to smarter shopping.

Valuation provides the final piece of the puzzle. Trading at a forward P/E of 27.9x, Alphabet is not cheap, but it is reasonable relative to its long-term AI-powered growth prospects. The stock has seen significant gains in 2025, yet it still trades at a multiple that reflects its scale and competitive advantages. In the context of the AI era, where companies are being valued on future market capture, this multiple suggests the market is pricing in a durable, high-growth trajectory rather than a speculative peak.

The bottom line is that Alphabet's scalability is baked into its model. It can monetize its cloud TAM through high-margin infrastructure services and its user TAM through AI-driven advertising and new digital products. The $155 billion backlog provides a visible runway, while its 5.7 billion-strong ecosystem offers an expansive frontier. For a growth investor, this is the blueprint: a company using AI not just to improve existing products, but to fundamentally expand and capture two of the largest digital markets in the world.

Catalysts, Risks, and What to Watch for 2026

The growth thesis for both Micron and Alphabet is clear, but the path to realizing it will be shaped by specific catalysts, risks, and measurable milestones. For investors, the focus must shift from the broad AI tailwind to the quarterly execution and market dynamics that will determine which company captures the most value.

The primary catalyst for Micron is the sustained, multi-year build-out of AI data centers. The company's revenue growth is directly tied to this spending cycle. With the HBM market projected to expand from $35 billion in 2025 to $100 billion by 2028, the catalyst is a massive, visible TAM. Micron's current 21% market share and committed 2026 output provide near-term visibility. The key watchpoint is whether this demand remains robust through 2026, as any slowdown in corporate AI spending could pressure its high-margin memory sales.

For Alphabet, the catalyst is the deep integration of AI into its core monetization engines. The company is already leveraging AI-powered targeting to improve ad conversion efficiency across Search and YouTube, directly boosting revenue per user. More broadly, the Google Cloud backlog of $155 billion provides multiyear revenue visibility, and with over 70% of those customers using AI products, the catalyst is the acceleration of AI-driven upsells and new service adoption. The watchpoint is the pace at which AI enhances the unit economics of its massive user base.

Both companies face significant risks. Micron is vulnerable to the cyclical nature of semiconductor demand. While AI is a powerful structural driver, the broader memory market can still experience inventory corrections or spending pullbacks. The company's high gross margin of 45.56% provides some buffer, but a sharp downturn would test its scalability. Alphabet's risk profile is more regulatory. As a tech giant with dominance in search and cloud, it faces increasing scrutiny over data practices and market power. Any significant regulatory action could constrain its ability to leverage its ecosystem for AI monetization.

The metrics to watch are straightforward but critical. For both companies, the primary indicators are quarterly revenue growth and gross margin trends. For Micron, sustained double-digit revenue growth and margin stability will signal continued pricing power in HBM. For Alphabet, accelerating cloud revenue growth and expanding operating margins will confirm the AI integration is driving profitability. A secondary watchpoint is market share. Investors should monitor for shifts in AI hardware (like Micron's HBM share) and cloud services (like Alphabet's cloud market share versus AWS and Azure) to gauge competitive positioning.

The bottom line is that 2026 will be a year of execution validation. The massive TAMs are accepted; the question is whether these companies can scale profitably within them. For Micron, the catalyst is AI data center spending; for Alphabet, it's AI monetization. The risks are cyclical demand and regulatory pressure. The watchpoints are revenue growth, margins, and market share. Success will be measured in the quarterly numbers that prove these scalable models can compound earnings in the real world.