Friends, if you still think the AI rally is only about NVIDIA, AMD, and those compute stocks, then you may be missing the next key player that Wall Street is quietly repricing.

Just on May 27, 2026, Snowflake, after falling from $281 to around $174 over 12 straight months and being almost forgotten by the market, suddenly surged 37% in after-hours trading, briefly topping $238. This was not an ordinary earnings rebound. Snowflake is rapidly transforming from a simple data storage platform into the core control plane for the AI agent era.

The earnings report showed a surge in AI feature usage, a strategic AWS partnership worth as much as $6 billion, and a technical布局 through its acquisition entry into the Model Context Protocol, all of which led the market to reprice it.

The question is, as AI moves beyond model training into a stage where agents need to operate at scale, securely, and in a governed way, who can become the infrastructure that allows enterprises to truly put AI to work? Was Snowflake’s violent move just the beginning? Has it really seized the next big wave in AI infrastructure, or was this just an overreaction in market sentiment? In this episode, we will break down the real logic behind Snowflake’s sharp surge, its business-model shift, and its position in the broader AI supply chain.

Earnings Data and Market Reaction

First, imagine a scene like a massive cargo ship moving slowly and clumsily across the ocean, only for hidden rocket boosters to suddenly ignite underneath it and send it blasting straight through the atmosphere into outer space. It sounds absurd, but that roughly captures the real financial phenomenon Snowflake staged on Wall Street on May 27, 2026.

That day, after the U.S. market closed, Snowflake released its fiscal 2027 first-quarter earnings. The regular-session close was $174.60. Before that, the stock had been falling for more than 12 months from its 52-week high of about $281, at one point dropping below $120 and remaining heavily suppressed beneath the major bottom neckline around $180-200.

However, once the earnings were out, the market reacted violently. The stock exploded in after-hours trading, jumping about 37% in a single day and reaching as high as around $238.81. Trading volume hit 17.34 million shares, up 190% from normal levels.

According to Snowflake’s official earnings release, product revenue for the quarter reached $1.334 billion, up 34% year over year and well above Wall Street’s expectation of about $1.32 billion. Non-GAAP earnings per share came in at $0.39, also beating the market estimate of $0.32. Net revenue retention rose to 126%, showing improvement for several consecutive quarters. The company also raised its full-year product revenue guidance to $5.84 billion, implying growth of about 31%.

In addition, Snowflake announced a five-year strategic infrastructure agreement with AWS worth up to $6 billion, with a focus on AI-related cloud compute and Graviton chip architecture. Together, these figures show that real AI-driven consumption is translating into verifiable revenue growth and customer retention. Combined with a volume-confirmed technical breakout from the bottom, institutional money quickly repriced the stock, driving a powerful after-hours reversal. Wall Street analysts then raised short-term technical targets into the $250-275 range.

Two Strategic Moves Driving the Repricing

Although revenue of $1.33 billion, 34% annual growth, and EPS of $0.39 all beat expectations, and full-year guidance was also raised, these numbers alone do not seem enough to justify a 37% after-hours surge for a company with a market cap in the hundreds of billions.

What really gave institutions the confidence to step in was buried in the most core and hardest-to-fake metric in the report—net revenue retention, or NRR. Snowflake’s NRR climbed for three straight quarters from its prior low of 124%-125% to the latest 126%. Under a consumption-based billing model, an NRR of 126% means that customers who spent $100 last year not only stayed, but spent $126 this year.

This shows that the work running on the platform is producing real value that customers must keep paying for. The clearest proof is that clients are now putting real money into AI compute and generating sustained consumption.

The $6 Billion Strategic Infrastructure Agreement with AWS

What truly drove the market’s repricing behind the earnings was Snowflake’s two strategic moves disclosed this quarter, rather than the revenue growth alone.

First is the $6 billion strategic infrastructure agreement signed with AWS. The significance of this deal is not only its record size, but also Snowflake’s continued emphasis on large-scale adoption of AWS Graviton chips. In the AI inference stage, enterprises need to call models continuously and frequently for real-time analysis and decision-making, which creates persistent compute consumption far beyond traditional transactional workloads.

Because Graviton is based on ARM architecture, it can significantly reduce power consumption and cost per compute unit for high-concurrency, low-latency AI inference tasks compared with traditional x86 architecture. This gives Snowflake the confidence to raise its full-year non-GAAP operating margin guidance to 13.5% in one shot. What this reflects is not simple cost cutting, but a real effort to lower the marginal cost of AI compute through optimization of the underlying hardware architecture.

Acquiring Natoma and Entering the Model Context Protocol

Even more strategically important is Snowflake’s acquisition of Natoma and the resulting entry into the Model Context Protocol (MCP).

Before enterprises can truly deploy AI agents at scale, the biggest obstacle is never that the model is not powerful enough. It is: how can AI safely and precisely access contextual data inside the enterprise? MCP is essentially a standardized and auditable secure channel between AI models and enterprise data.

For example, suppose a retail company wants to use an AI agent to analyze regional sales data from the past three years and predict inventory risks. If the model is directly connected to the entire data lake, AI might have excessive permissions and read sensitive information such as executive compensation or supplier contracts, or even inadvertently leak core trade secrets when generating reports.

The role of MCP is to let AI read only the necessary data context in a structured way within a predefined permission scope, ensuring analytical accuracy while minimizing the risk of data leakage.

At present, more than 7,100 accounts are using Snowflake’s Cortex Code and other AI features, while another 9,100 accounts are actively calling various AI capabilities. This shows that more and more enterprises are no longer content with simply storing data; they are starting to truly use it, and to use it through AI agents.

This also explains why the market was willing to grant Snowflake a valuation premium far beyond what it had received before—because it is evolving from a traditional data warehouse into an indispensable control plane and governance hub for enterprise AI agents.

Snowflake’s Underlying Architectural Differences Versus Databricks

To evaluate Snowflake objectively, we have to break down its underlying architectural differences from its biggest rival, Databricks.

Databricks originated from Apache Spark, natively supports Python and Scala, excels at processing unstructured data, and offers a high degree of flexibility and customization. It is well suited for enterprises that want to open the hood, tune parameters, and train custom models themselves.

Snowflake, by contrast, has taken a completely different path, emphasizing out-of-the-box deployment and extreme ease of use. Its killer feature, Zero Copy Sharing, keeps data stored only in its original encrypted location. When sharing, it transmits only access permissions and compute metrics. The receiving party can run complex analysis in its own cloud environment while being completely unable to take away the raw data. This is extremely valuable in the AI era, because training models requires large amounts of clean data, but enterprises are absolutely unwilling to let core confidential data leak out.

However, once AI agents are added, the complexity of the system’s underlying layers does indeed rise exponentially. Snowflake’s brilliance lies in fully encapsulating complex governance and permission control deep inside the system, so that the end-user interface does not become more complicated; in fact, it becomes simpler. Tasks that once required SQL can now be done simply by issuing commands in natural language.

To objectively assess Snowflake’s current valuation and competitiveness, it has to be compared with Databricks, its biggest rival, at the architectural level. Databricks comes from the Apache Spark ecosystem and natively supports Python and Scala. It has clear advantages in handling unstructured data such as logs, images, and sensor data, as well as in building end-to-end machine learning workflows.

It is more like an open platform, allowing data scientists and engineering teams to directly open the underlying engine, freely adjust parameters, write custom code, and even train dedicated models from raw data. Such companies usually have strong technical teams and are willing to invest substantial development resources in the data platform in exchange for greater flexibility and customization.

Snowflake, however, has chosen a completely different route. From the beginning, it has emphasized extreme out-of-the-box usability and a low barrier to entry. One of its most differentiated features is Zero Copy Sharing. This technology keeps data in its original encrypted storage location at all times and, when sharing, only passes access rights and compute metrics. The recipient can directly run complex analyses on the data in its own cloud environment, but cannot download or copy the raw data away. This has strong practical significance in today’s AI era.

For example, a large retail company may want to analyze sales and inventory data together with external supply-chain partners to optimize replenishment strategies. Under a traditional approach, vast amounts of transaction data would need to be copied and transferred to the other party. This not only involves high data transfer and storage costs, but more importantly exposes core trade secrets such as detailed customer purchase behavior and pricing strategies.

With Snowflake’s Zero Copy Sharing, the retailer can directly grant analytical access to its partner. The partner can run models and reports in its own environment without ever touching the raw data, enabling collaboration while preserving data sovereignty.

Of course, once Snowflake begins adding AI agent features at scale, the governance complexity of the underlying system does rise exponentially. Permission management, data lineage tracking, model-call auditing, and context security controls all become far more complicated than traditional data querying.

Snowflake’s skill lies in packaging all of this complex logic into the lowest layer of the platform, while keeping the end-user experience as simple as possible. In the past, users had to master SQL in order to extract value from data. Now they can issue instructions directly in natural language, allowing AI agents to automatically complete cross-table analysis, report generation, and even forecasting tasks. This design philosophy—“hide the complexity behind the scenes and leave simplicity to the user”—is becoming an important differentiating advantage in its AI infrastructure competition.

Warnings and Potential Risks Behind the Earnings

Although market sentiment was clearly ignited by this earnings report, there is still another side to it that requires calm attention.

Even with double-digit product revenue growth, Snowflake still posted an operating loss of $326 million under strict GAAP accounting in the first quarter. A large part of this came from the dilution effect of stock-based compensation, or SBC. In simple terms, the company grants a large amount of equity incentives in order to attract and retain top talent. While this helps long-term competitiveness, it also continuously dilutes existing shareholders’ ownership. In a high-growth phase, this is not unusual for tech companies, but when the market’s expectations for growth are already very high, ongoing dilution becomes an indirect cost that cannot be ignored.

In addition, on the eve of the earnings release, insiders sold shares more heavily in the $175-176 range. Although the reasons may include personal tax planning or asset allocation adjustments, selling at such a critical juncture still raises concerns for some investors.

At the same time, after the after-hours rally, Snowflake’s forward revenue multiple has been pushed above 14.5x. That means the market has almost fully priced in several years of high AI-related growth. If there is any sign of slowing growth or weaker-than-expected results, the valuation could face rapid downward pressure.

Management also specifically warned during the earnings call that enterprise customers embracing AI may face what it called a “bill shock.” Because Snowflake uses a consumption-based pricing model, when large numbers of AI agents continuously call data, run analysis, and generate reports in the background, cloud compute usage can rise rapidly, causing bills to far exceed traditional data-query levels.

For example, a mid-sized manufacturing company may originally spend only tens of thousands of dollars per month on data analytics. But if it deploys dozens of AI agents to monitor supply chains in real time, predict demand, and automatically generate decision reports, the cost could multiply several times in a short period. This would force CFOs to scrutinize which scenarios truly require advanced AI features and which can be solved in more traditional ways, creating uncertainty for future revenue predictability.

How Should Investors Respond to This Surge?

So how should ordinary investors respond to this rally?

First: Evaluate Snowflake in the context of the long-term AI infrastructure trend, not short-term stock price swings

The current market enthusiasm for Snowflake mainly comes from its strategic shift from a traditional data warehouse to an AI agent control platform. This is not just concept-driven hype; it is supported by real data.

In the first quarter of 2026, Snowflake’s product revenue grew 34% year over year to $1.334 billion, while net revenue retention rose to 126%, showing that existing customers are continuing to increase their consumption of AI-related features. More importantly, the company’s $6 billion strategic infrastructure agreement with AWS and the fact that more than 7,100 accounts are using AI tools such as Cortex Code, with another 9,100 accounts actively calling various AI capabilities, show real traction.

These numbers suggest that enterprises are indeed shifting data from “storage” to something that is actively called and governed by AI agents. If you believe that over the next 5-10 years enterprise demand for secure and controllable AI data infrastructure will continue to grow, then Snowflake’s sector has a long-term structural opportunity.

That said, this does not mean volatility can be ignored in the short term. Over the past 12 months, Snowflake’s stock fell more than 38% from its $281 peak, showing that concerns about slower growth are very real. This 37% after-hours surge is essentially the market repricing the AI transformation story.

Investors are advised to treat Snowflake as a long-term AI infrastructure holding rather than a short-term momentum trade. One can build a core position in stages when the stock sees a clear pullback and valuation falls back into a more reasonable range, rather than going all-in at the height of market euphoria.

Second: Pay close attention to the current valuation, control position size, and set a margin of safety

After this earnings report, Snowflake’s forward revenue multiple has quickly risen to above 14.5x, which is a relatively high level for a growth tech stock. Although the company’s first-quarter non-GAAP results were impressive, it still recorded a $326 million operating loss on a GAAP basis, mainly due to share dilution from stock-based compensation. This means that behind the high growth, existing shareholders’ ownership is still being diluted.

At the same time, management explicitly mentioned the possible “bill shock” caused by AI agents, meaning that once customers use a large number of AI features, cloud costs may rise quickly and affect the stability of future revenue.

In this situation, investors should avoid putting their entire portfolio into a single stock. Even if you are bullish on its long-term transformation, you should control the position size of any one stock in your portfolio and keep some cash or defensive assets available so you have room to add if the valuation pulls back.

You should also focus on a few core metrics: whether net revenue retention can stay above 125%, whether the number of accounts using AI features continues to grow, and whether the company can gradually improve GAAP profitability over the next few quarters. Only when these data continue to improve does the high valuation have support. If growth disappoints or customers become more price-sensitive, the stock could pull back more sharply than many people expect.

Third: Build a clear tracking framework and disciplined process instead of blindly following market sentiment

For a company like Snowflake that is in transition, the most dangerous thing an investor can do is simply follow market sentiment. It is better to build a relatively objective tracking system and focus each quarter on several key data points: whether product revenue growth stays above 30% year over year, the trend in net revenue retention, the growth in active AI feature accounts, and the improvement in free cash flow.

You should also pay attention to management’s wording during the earnings call regarding “bill shock” and customer usage, since that often reflects real demand better than raw revenue numbers alone.

In practice, a staged entry and dynamic adjustment strategy can be used. For example, maintain a relatively small position near current highs, and if a meaningful pullback occurs later (for instance, if valuation falls back to 10-12x forward revenue), gradually add more. Conversely, if the stock keeps rising sharply without a matching fundamental improvement, consider trimming the position to lock in some profit.

In addition, Snowflake should be positioned as part of a broader AI supply-chain portfolio rather than viewed in isolation. It can be paired with names from different segments such as compute and software tools to diversify the risk that any single company’s transformation falls short of expectations. Ultimately, investment decisions should be based on your own judgment about long-term demand for AI infrastructure and your assessment of the valuation’s margin of safety, not simply on short-term stock performance.

Overall, Snowflake is gradually transforming from a company that sells databases into a platform that sells AI infrastructure and governance capabilities. In the AI era, that direction is indeed attractive over the long term. But the currently elevated valuation requires the company to prove in the foreseeable future that it can deliver sustained GAAP profitability.

Against the backdrop of hardware compute becoming increasingly widespread and costs continually falling, the real bottleneck in enterprise AI deployment is often no longer “whether there is enough compute,” but “whether there is a sufficiently clean, secure, controllable underlying infrastructure that lets AI directly and safely access data.” Whoever builds an irreplaceable governance capability and trust in this layer is more likely to occupy a more favorable position in the AI competition over the next decade.