At the end of May 2026, Snowflake (SNOW) emerged as one of the most closely watched AI stocks in the US equity market. Following its earnings release, the company’s share price quickly surged past $250, marking a gain of over 40% in just two weeks. This not only set a new short-term high, but also reignited a previously overlooked question: As the AI industry enters the commercialization phase, will chipmakers continue to be the primary beneficiaries, or is the landscape shifting?
Over the past two years, AI investment has largely centered on a single theme—expansion of data centers, rising GPU demand, and upgrades to cloud infrastructure. Massive capital flows have consistently targeted the semiconductor and hardware supply chain, fueling rapid growth in market capitalization for related companies. However, as more businesses deploy generative AI for customer service, marketing, office automation, data analytics, and enterprise management, the market is beginning to realize that the true value driver in AI isn’t just models and computing power—it’s data.
In many ways, Snowflake’s recent rally isn’t simply a reaction to earnings; it represents a broader revaluation by capital markets of the next phase in the AI value chain. As investors look beyond chips for new beneficiaries, data platforms, enterprise software, and AI infrastructure are once again coming into focus.
Snowflake Sets New Short-Term High Following Earnings Release
The most direct catalyst for Snowflake’s latest surge was its quarterly earnings report. Not only did the company beat market expectations, it also raised guidance for future growth—a welcome sign for investors concerned about a slowdown in enterprise software.
More than the revenue figures themselves, the market focused on signals from management. During the earnings call, demand for AI-related services among enterprise customers was repeatedly highlighted. Increasingly, clients are building new business processes around generative AI, incorporating data analytics, data management, and automation into their future budget planning.
In recent years, many companies’ AI investments remained experimental, focused on testing model capabilities and exploring use cases. But as large models mature, enterprises are moving into real deployment. In this phase, the importance of data quality, data governance, and data sharing grows rapidly—areas where Snowflake has long been a leader.
Capital markets tend to anticipate industry shifts. As investors recognize the accelerating commercialization of AI, they’re reassessing the long-term value of data platform companies. This is a key reason SNOW shares have climbed so sharply in a short period.
AI Capital Expenditures Are Shifting Toward the Data Layer
From 2024 to 2026, one of the biggest themes in global tech is AI capital expenditure. Whether it’s cloud giants or major internet firms, everyone is ramping up investment in data center construction, driving increased demand for GPUs, servers, networking equipment, and storage systems.
The logic in this phase is straightforward: Build computing power first, then develop applications.
However, as model training capabilities improve, a new challenge emerges. Even with advanced models and abundant compute, enterprises may not generate business value quickly—because the real determinant of AI effectiveness isn’t model parameters, but the company’s own data resources.
Many organizations have data scattered across departments, databases, and business systems. Sales teams hold customer data, operations teams track behavioral data, finance manages operational data, and these datasets often lack unified management. For AI systems, this "data silo" severely limits analytics and decision-making.
As compute infrastructure matures, AI capital expenditures are increasingly flowing toward the data layer. Enterprises need to allocate more budget to building unified data platforms, strengthening data governance, and enabling AI to access and leverage internal information.
This shift means the AI value chain is broadening, with data platform companies now sharing in growth previously dominated by chipmakers.
Why Enterprise AI Deployment Depends on Unified Data Platforms—Data Management as a New Competitive Edge
In recent years, digital transformation for enterprises meant moving to the cloud. In the coming years, AI transformation will likely focus on data integration.
The greatest value of generative AI lies in boosting decision-making efficiency and automation—but all of this depends on high-quality data. Enterprises want AI to answer customer questions, generate sales reports, analyze market trends, and even execute some operational tasks, all of which require robust internal data support.
The problem is, most companies’ data environments are less than ideal.
After years of growth, many firms have accumulated diverse data systems. ERP, CRM, marketing platforms, and various internal databases often operate independently, preventing effective data flow. When companies try to introduce AI, the first hurdle isn’t the model—it’s data integration.
That’s why unified data platforms are becoming foundational for enterprise AI initiatives.
At the same time, market competition is evolving. Previously, the focus was on model capabilities, but as open-source models advance, differences between them are narrowing. For most businesses, the real differentiator isn’t the model, but their own data assets and management capabilities.
Those who can manage, integrate, and utilize data most efficiently are best positioned for lasting competitive advantage. Thus, data management is becoming the new moat in the AI era.
For Snowflake, this trend reinforces its core value. The company not only offers data storage and analytics, but more importantly, helps enterprises build unified data infrastructure, enabling data sharing and collaboration across business scenarios.
Why Snowflake Is a Key Beneficiary in the AI Commercialization Cycle
From an industry perspective, AI is shifting from technology-driven to business-driven growth.
In the early stages, the focus was on model performance and technical breakthroughs, making chipmakers the biggest winners. As commercialization takes hold, investors are paying more attention to customer growth, real-world applications, and revenue realization—elevating the importance of software and data platforms.
Snowflake sits at the heart of this transition.
As enterprises build AI agents, automated workflows, and intelligent decision systems, data platforms become the critical bridge between models and business applications. Regardless of which model solution a company chooses, it needs a platform for unified data management and access.
This means Snowflake’s growth isn’t tied to any single model’s success, but to the broader development of the AI application ecosystem.
For capital markets, this business model offers strong long-term potential. As enterprise AI deployment scales up, demand for data platforms is likely to grow in tandem. Unlike companies reliant solely on hardware upgrade cycles, data platforms benefit from ongoing data usage needs.
That’s why more institutional investors are viewing Snowflake as a major beneficiary in the AI commercialization cycle.
Why Institutional Funds Are Returning to Cloud Computing and Enterprise Software
SNOW’s recent strong performance isn’t an isolated event—it reflects a shift in market allocation strategies.
Over the past two years, most AI capital concentrated in a handful of leading semiconductor companies. As valuations for these firms soared, institutional investors began seeking new avenues for growth. Compared to chipmakers, which have already benefited from the AI narrative, enterprise software and cloud computing have seen more modest gains, offering greater room for upside.
Meanwhile, enterprise AI applications are moving from proof-of-concept to real deployment. Budgets are expanding from purchasing compute to building comprehensive AI operations, including data management, model management, security governance, and automated workflows.
This shift is prompting renewed attention to the value of enterprise software companies.
From a capital flow perspective, investors are constructing a more holistic AI investment framework. Beyond chips, servers, and data centers, data platforms, enterprise software, automation tools, and AI agent infrastructure are becoming new areas of focus.
Snowflake’s rally reflects this trend and signals the market’s expectation for deeper expansion within the AI value chain.
How Crypto Users Trade SNOW Stock on Gate TradFi
For crypto asset investors who have long tracked the AI sector, SNOW’s surge offers a valuable window into the traditional tech market.
Through the Gate TradFi product suite, users can trade a variety of global stock CFDs, including SNOW. Stock CFDs allow investors to participate in price movements without directly owning the underlying shares, enabling access to investment opportunities across different markets.
Trading SNOW on Gate TradFi involves entering the TradFi trading market, searching for the SNOW product, selecting a trade direction, setting position and risk management parameters, and managing ongoing positions. For users familiar with crypto markets, Gate’s unified account and multi-asset trading model reduces cross-platform operational costs and enhances capital efficiency.
It’s important to note that stock CFDs are leveraged derivatives, and market volatility can amplify both gains and losses. Investors should fully understand product mechanics and risk management rules before trading.
Can AI Software and Data Platforms Sustain the Rally After Snowflake’s Surge?
The biggest takeaway from SNOW’s rally isn’t just one company’s earnings beat—it’s the emergence of new directions in AI value chain investment logic.
If the past two years were defined by GPUs and compute, the coming years may see the focus shift toward data, software, and application ecosystems. More companies are building AI workflows, driving demand for data management, enterprise automation, and intelligent decision systems.
However, this doesn’t mean the chip narrative is over. AI infrastructure still requires substantial compute support; the market is simply seeking a second growth curve.
The companies most likely to benefit going forward are those that both contribute to AI development and help clients realize commercial value. Data platforms, enterprise software, and agent infrastructure are poised to attract greater attention.
Snowflake’s recent surge may be just the beginning. As AI moves deeper into commercialization, the market’s appreciation for data assets is likely to grow.
Conclusion
SNOW shares soared over 40% in two weeks, driven by an earnings beat and raised guidance—but the deeper reason is the market’s renewed assessment of the importance of data platforms in the AI era. As the AI industry transitions from model training to real-world applications, demand for unified data platforms, data governance, and automated workflows is rising rapidly among enterprises.
For investors, this signals a shift in AI investment logic—from a chip-centric narrative to a more comprehensive value chain. Data platforms, enterprise software, and AI infrastructure may become new areas of sustained focus in the coming years, with Snowflake standing out as a prime example of this trend.
FAQ
Why did SNOW shares rise over 40% recently?
SNOW’s recent surge was driven by a strong earnings beat, upward revisions to full-year guidance, and growing enterprise AI demand. The market reassessed Snowflake’s long-term value as a provider of AI data infrastructure.
Is Snowflake considered an AI company?
Snowflake isn’t a developer of large AI models, but it is a key infrastructure player in the AI value chain. Its core business focuses on enterprise data management, analytics, and supporting AI applications.
Why are data platforms becoming a key sector in the AI era?
Data platforms help enterprises integrate internal data resources, and high-quality data is essential for deploying AI applications. As AI commercialization advances, the importance of data platforms continues to grow.
How does SNOW’s investment logic differ from that of AI chip companies?
SNOW primarily benefits from the rollout of enterprise AI applications and rising data demand, while AI chip companies gain from expanding compute capacity and increased data center capital expenditures.
Can crypto users trade SNOW shares?
Crypto users can trade SNOW stock CFDs via Gate TradFi, gaining exposure to AI value chain investment opportunities in traditional financial markets.
Does Snowflake’s surge signal a change in AI investment logic?
Snowflake’s rally doesn’t mean the chip narrative is over. Instead, it shows the market is beginning to focus on new growth opportunities in data platforms, enterprise software, and the AI application layer.
