Has SNOW stock increased over 40% in two weeks? Is the AI investment logic shifting from chips to data platforms?

By the end of May 2026, Snowflake (SNOW) became one of the most watched AI concept stocks in the U.S. stock market. After the earnings report was released, the company's stock price quickly broke through $250, up more than 40% in two weeks, not only hitting a new high for the period but also prompting the market to revisit a previously overlooked question: once the AI industry enters the commercialization phase, are the biggest beneficiaries still just chip companies?

Over the past two years, AI investments have almost followed the same main theme—data center expansion, growing GPU demand, and cloud computing infrastructure upgrades. Large amounts of capital have continued flowing into the semiconductor and hardware supply chains, driving rapid increases in the market value of related companies. However, as more companies begin applying generative AI to customer service, marketing, office automation, data analysis, and enterprise management processes, the market has started to realize that the true value creation in AI depends not just on models and computing power, but on data.

To some extent, Snowflake’s recent rise is not just a financial report rally but a re-pricing by the capital markets of the next phase of opportunities in the AI supply chain. As capital begins seeking new beneficiaries beyond chips, data platforms, enterprise software, and AI application infrastructure are re-entering investors’ view.

Snowflake’s stock hits new highs after earnings report

The most direct catalyst for Snowflake’s recent surge came from its latest quarterly earnings. The company’s results not only exceeded market expectations but also raised its future growth guidance, providing a strong boost for investors who had been concerned about slowing growth in the enterprise software sector.

Compared to the revenue figures themselves, the market pays more attention to the signals from management. During the earnings call, repeated mentions of demand for AI-related services from enterprise clients were made, with more customers beginning to build new business processes around generative AI, incorporating data analysis, data management, and automation capabilities into their future budget plans.

In recent years, many companies’ AI investments remained mostly experimental, focusing on testing model capabilities and exploring application scenarios. But as large models mature, companies are entering the deployment phase. In this process, the importance of data quality, data governance, and data sharing capabilities has rapidly increased—areas in which Snowflake has long been deeply involved.

Capital markets tend to anticipate industry changes. When investors see AI commercialization accelerating, they begin to reassess the long-term value of data platform companies, which is one of the key reasons why SNOW’s stock price has surged sharply in a short period.

Expansion of AI capital expenditure is flowing into the data layer

From 2024 to 2026, one of the biggest keywords in the global tech industry is AI capital expenditure. Whether it’s cloud service giants or large internet companies, they are continuously increasing investments in data center construction, driving demand for GPUs, servers, network equipment, and storage systems.

The core logic at this stage is very clear: build computing power first, then develop applications.

However, as model training capabilities continue to improve, a new problem has emerged. Even with advanced models and ample computing power, companies may not be able to quickly generate commercial value, because the real impact on AI effectiveness often depends not on model parameters but on the company’s own data resources.

Many companies’ internal data are long dispersed across different departments, databases, and business systems. Sales teams hold customer data, operations teams have behavioral data, finance departments manage operational data, yet these data sources often lack unified management mechanisms. For AI systems, such data silos can severely impair analysis and decision-making.

Therefore, as infrastructure for computing power matures, AI capital expenditure begins flowing into the data layer. Companies need to invest more in building unified data platforms, improving data governance, and enabling AI to access and utilize internal information.

This shift means that the beneficiaries of the AI supply chain are expanding, with data platform companies beginning to share growth dividends previously mainly enjoyed by chip companies.

Why enterprise deployment of AI is increasingly dependent on unified data platforms, and data management capabilities are becoming a new competitive barrier

If the past few years’ focus in enterprise digital transformation was on moving to the cloud, then the next few years’ focus for AI transformation is likely to be on data integration.

The greatest value of generative AI lies in helping enterprises improve decision-making efficiency and automation, but all of this depends on high-quality data. Companies want AI to answer customer questions, generate sales reports, analyze market trends, and even execute some operational tasks—all of which require internal data support.

The problem is, most companies’ data environments are far from ideal.

Many organizations, after years of development, have accumulated large amounts of data from different sources. ERP systems, CRM platforms, marketing tools, and various internal databases are often independent, leading to ineffective data flow. When companies try to introduce AI, the first challenge is not the model itself but data integration.

This is why unified data platforms are becoming a critical infrastructure for enterprise AI development.

Meanwhile, the logic of market competition is also changing. In the past, discussions about AI focused more on model capabilities, but as open-source models advance, the gap between models is narrowing. For most enterprises, what is truly difficult to replicate is not the model but their own data assets and data management capabilities.

Who can manage, integrate, and utilize data more efficiently will be more likely to establish a long-term competitive advantage. Data management capabilities are thus becoming a new moat in the AI era.

For Snowflake, this trend reinforces its core value. The company not only provides data storage and analysis capabilities but also helps enterprises build unified data infrastructure, enabling data sharing and collaboration across different business scenarios.

Why Snowflake is a key beneficiary of the AI commercialization cycle

From an industry development perspective, the AI sector is experiencing a shift from being technology-driven to being business-driven.

In the early stages, the market focused on model performance and technological breakthroughs, making chip companies the biggest beneficiaries. As commercialization progresses, investors are paying more attention to customer growth, real-world application scenarios, and revenue realization, increasing the importance of software and data platforms.

Snowflake is positioned at this critical juncture.

As enterprises begin building AI agents, automation workflows, and intelligent decision systems, data platforms become the vital bridge connecting models with business scenarios. Regardless of which model approach a company adopts, a platform capable of unified data management and access is essential.

This means Snowflake’s growth logic does not depend solely on the success of a particular model but on the development of the entire AI application ecosystem.

For capital markets, this business model has strong long-term attributes. As enterprise AI deployment scales up, demand for data platforms is also expected to grow in tandem. Compared to companies solely dependent on hardware upgrade cycles, data platforms can generate long-term benefits from ongoing data usage needs.

This is why more and more investment institutions are viewing Snowflake as a key beneficiary of the AI commercialization cycle.

Why institutional funds are flowing back into cloud computing and enterprise software sectors

SNOW’s recent strong performance is not an isolated event; it also reflects a change in market capital allocation logic.

Over the past two years, most AI-related capital was concentrated in a few leading semiconductor companies. As their valuations continued to rise, institutional investors began seeking new growth directions. Compared to chip sectors that have already benefited significantly from AI narratives, enterprise software and cloud computing sectors have historically performed more modestly, leaving room for expectations to be restored.

On the other hand, enterprise AI applications are gradually moving from concept validation to actual deployment. Corporate budgets are shifting from simply purchasing computing power to building comprehensive AI operational systems, including data management, model management, security governance, and automation workflows.

This shift has driven the market to refocus on the value of enterprise software companies.

From a capital flow perspective, investors are trying to build a more complete AI investment framework. Beyond chips, servers, and data centers, data platforms, enterprise software, automation tools, and AI agent infrastructure are becoming new research directions.

Snowflake’s rise to prominence partly reflects this trend and demonstrates the market’s expectations for the deepening expansion of the AI supply chain.

How crypto users can trade SNOW stocks on Gate TradFi

For long-term AI-focused crypto investors, SNOW’s rise offers an important window into traditional tech market dynamics.

Through Gate TradFi’s product ecosystem, users can participate in trading various global stock CFDs, including SNOW. Stock CFDs do not require direct ownership of the underlying stocks but allow trading based on price movements, enabling investors to access different markets.

The basic process for trading SNOW on Gate TradFi involves entering the TradFi trading platform, searching for SNOW products, choosing trading directions, setting position sizes and risk management parameters, and managing positions afterward. For users familiar with crypto markets, Gate’s unified account and multi-asset trading mode can reduce cross-platform operational costs and improve capital efficiency.

It’s important to note that stock CFDs are leveraged derivatives, which can amplify both gains and risks. Investors should fully understand the product mechanics and risk management rules before participating.

Can the AI software and data platform sectors catch up after Snowflake’s strength?

For the market, Snowflake’s rise is not just about a company’s earnings beating expectations; it signals a new diffusion of the AI industry’s investment logic.

If the past two years’ keywords were GPUs and computing power, then in the coming years, the focus is likely to shift gradually 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 does not mean the chip logic is over. AI infrastructure still requires substantial computing power, but the market is now looking for a second growth curve.

Companies that can continue to benefit are often those capable of participating in AI development while helping clients realize commercial value. Data platforms, enterprise software, and agent infrastructure are thus poised to attract more attention.

Snowflake’s recent sharp rise may just be the beginning. As AI moves further into the deep waters of commercialization, market recognition of data assets’ value could continue to grow.

Summary

SNOW’s stock surged over 40% in two weeks, seemingly driven by better-than-expected earnings and upward guidance, but more fundamentally, it reflects the market’s reassessment of the importance of data platforms in the AI era. As AI shifts from model training to commercial applications, enterprise demand for unified data platforms, data governance, and automation workflows is rapidly increasing.

For investors, this indicates that AI investment logic is expanding from a single chip narrative to a more comprehensive industry chain. Data platforms, enterprise software, and AI application infrastructure may become the new focus in the coming years, with Snowflake being one of the most representative cases of this trend.

FAQ

Why did SNOW’s stock rise over 40% recently?

SNOW’s recent rise was mainly driven by better-than-expected earnings, upward revisions of full-year guidance, and growing enterprise AI demand, prompting the market to reevaluate Snowflake’s long-term value in AI data infrastructure.

Is Snowflake an AI company?

Snowflake is not a large model development company, but it is an important infrastructure player in the AI industry chain, focusing on enterprise data management, analysis, and AI application support.

Why is the data platform becoming a key track in the AI era?

Data platforms help enterprises integrate internal data resources, and high-quality data is fundamental 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 benefits mainly from enterprise AI deployment and data demand growth, whereas AI chip companies benefit more from infrastructure expansion and data center capital expenditure.

Can crypto users trade SNOW stocks?

Crypto users can participate in SNOW stock CFDs via Gate TradFi, thus gaining exposure to investment opportunities in the AI supply chain within traditional financial markets.

Does Snowflake’s rise mean the AI investment logic has changed?

Snowflake’s rise does not mean the end of the chip logic, but it shows that the market is beginning to focus on data platforms, enterprise software, and AI application layers as new growth drivers.