By William Guo, CEO of WhaleOps & Snowflake Ambassador

I would like to thank Snowflake for inviting me to attend Snowflake Summit as a Snowflake Ambassador.

This Summit had a much greater impact on me than I had expected.

As many of you know, I have spent my career in the data industry. I started at Teradata, then moved to IBM. Later, I was responsible for big data initiatives at enterprises such as Lenovo, CICC, and Wanda Group. After that, I became a Member of the Apache Software Foundation, and today I am the CEO of WhaleOps Open Source. Because of this background, I have always paid close attention to developments across the data industry.

Before coming to the Summit, I originally thought Snowflake would launch a number of enterprise AI products or add AI-related capabilities on top of its existing data warehouse and data platform offerings.

For many years, people's understanding of Snowflake has been quite clear: it is a cloud data warehouse company and a representative of the Data Cloud era. Its core strengths revolve around data storage, compute, performance, security, governance, sharing, and elastic scalability.

However, after spending two days at the Summit, my impression changed completely.

The biggest takeaway I had from this year's Snowflake Summit was not that it had released some new data platform features. Rather, it is aggressively reconstructing its product positioning.

In my view, Snowflake is no longer satisfied with being defined as a Data Warehouse company. Nor does it simply want to become an AI Data Cloud. Instead, it aims to transform itself into an enterprise AI + Data platform, and perhaps even the foundation of the Agentic Enterprise, putting itself on a path that increasingly overlaps with companies like Anthropic.

If I had to summarize my personal impression of this Snowflake Summit in one sentence, it would be:

Goodbye Data, Hello AI.

Of course, "Goodbye Data" does not mean data is becoming less important.

On the contrary, data has become even more important.

What has changed is the way data platforms are expressed and understood.

In the past, when we talked about data platforms, we talked about how data should be stored, processed, shared, governed, and optimized for cost efficiency.

Today, Snowflake is talking about how AI can understand enterprise data, how Agents can use enterprise data, how business users can gain insights directly through natural language, and how enterprises can enable AI to execute tasks within secure and governed boundaries.

Snowflake Product VP Christian Kleinerman made a statement during the Platform Keynote that perfectly captures this shift:

Your AI-native enterprise starts here.

If this sentence had appeared at a typical AI conference, it might have sounded like a standard marketing slogan.

But in the context of Snowflake Summit, it carries a very different meaning.

Because Snowflake is not an AI-native company. Historically, it has been a data infrastructure company. When a company with that background begins reorganizing its entire product portfolio around AI, it signals that AI is no longer an add-on feature—it is becoming a force that reshapes the enterprise itself.

1 Snowflake's Transformation: From Data Warehouse to AI Platform

In the past, when I thought about Snowflake, the first things that came to mind were data warehousing, cloud-native architecture, elastic computing, the separation of storage and compute, data sharing, and unified governance.

What Snowflake solved were several long-standing problems in traditional data platforms: fragmented data, limited scalability, complex performance tuning, inconsistent governance, and high collaboration costs.

The central narrative of this year's Summit was clearly different.

Snowflake still talks about All Data, All Workloads, and All Users. It still talks about structured, semi-structured, and unstructured data. It still talks about Iceberg, OpenFlow, Streaming, Zero Copy, and Horizon Catalog.

However, these capabilities are no longer being positioned simply as components of a better data platform. Instead, they are being framed as the foundation for a new goal: enabling enterprise AI and Agents to operate on a unified data platform.

Christian Kleinerman also made another highly important statement during the Platform Keynote:

"We need a unified architecture, both AI and data."

This statement can almost be regarded as the strategic core of this year's Snowflake Summit. It is not saying, "We support AI too." Rather, it is saying that enterprises should not build a separate AI platform outside of their data platform.

Why?

Because if the AI platform and the data platform are separated, many of the same problems we experienced during the data era will reappear: new silos, new permission systems, new governance gaps, new cost black holes, and new security risks.

We spent more than a decade eliminating data silos. If we build AI on an entirely separate stack today, we are essentially creating AI silos all over again.

So Snowflake's answer is clear: AI and Data must be unified. Data, compute, semantics, governance, security, applications, and Agents should all form a closed loop within a single platform.

Viewed from this perspective, Snowflake's Summit slogan, Make AI Real for Business, is fundamentally about turning Data into the context, fuel, and execution foundation for AI.

In the past, data platforms were built for people. People wrote SQL, viewed dashboards, configured jobs, and performed analyses.

In the future, data platforms will increasingly be built for Agents. Agents will understand business questions, invoke data capabilities, generate analytical workflows, propose actions, and even participate directly in business processes.

This is what truly struck me at this Summit.

Snowflake is not simply adding an AI assistant on top of an existing Data Warehouse. It is using data to rebuild a new AI-native foundation for the Agentic Enterprise, which is also where OpenAI and Anthropic will ultimately compete.

That is why I believe Snowflake's transformation is far more aggressive than I originally imagined.

2 CoCo, CoWork, and Desktop: Snowflake Is "Paying Tribute" to Anthropic—And Revealing a Bigger Ambition

If the first layer of change is strategic positioning, then the second layer is the product portfolio itself.

At this year's Snowflake Summit, what impressed me most was not a traditional database feature or a performance improvement metric. Instead, it was the launch of an entire collection of AI Agent-centric products and components:

• CoCo
• CoWork
• Desktop
• Skill Catalog
• VS Code Extension
• Excel Add-in
• MCP
• ACP
• Cloud Agents
• Agent Teams
• Automated Agents

When viewed together, they send a very clear signal:

Snowflake is reorganizing its product strategy in the same way an AI-native company would.

In fact, I would even say it is "paying tribute" to Anthropic.

Why do I say that?

Because AI-native companies such as Anthropic are no longer just building chatbots. They are building complete AI work systems, including Claude, Claude Code, Desktop, MCP, Artifacts, Skills, Computer Use, enterprise context, and security boundaries.

What they truly want to own is not merely a conversational interface, but the primary interface through which humans collaborate with software in the future.

The CoCo, CoWork, Desktop, Skill Catalog, and MCP/ACP announcements from Snowflake have remarkably strong parallels.

CoCo feels like Claude Code for the enterprise.

CoWork resembles an AI workspace for business users.

CoCo Desktop extends Snowflake's AI capabilities beyond the web console and into users' everyday work environments.

Skill Catalog packages Snowflake platform capabilities into discoverable, composable, and reusable skills that Agents can invoke.

So when I heard these announcements at the event, my first reaction was not:

"Snowflake has released a few more AI features."

Instead, it was:

Snowflake wants to repackage the data platform as a complete Enterprise AI Agent Operating System and enter the same strategic battleground occupied by OpenAI Enterprise and Anthropic Enterprise.

Snowflake officially announced that Cortex Code would no longer be called Cortex Code. It has been renamed to Snowflake CoCo:

"From here on, no more Cortex Code. It is officially Snowflake CoCo."

This statement is worth paying attention to.

The name Cortex Code still carried the feeling of being a coding assistant.

CoCo, on the other hand, feels much more like a standalone AI product.

Behind this rebranding is a larger ambition.

Snowflake does not want CoCo to be merely an assistant that helps users write SQL, generate code, or explain syntax. It wants CoCo to become the AI operating interface for the entire Snowflake platform.

Christian also mentioned during the keynote that over the past several months, CoCo has evolved beyond CLI and SnowSight experiences and expanded into MCP, ACP, SDKs, Agent Teams, Cloud Agents, automation capabilities, and Skill Catalog.

Among these, Skill Catalog is especially important. It enables users to share, discover, and reuse Skills. In essence, it is modularizing Snowflake platform capabilities and turning them into reusable tools for Agents.

This is extremely important.

Snowflake also explicitly announced upcoming Excel add-ins, VS Code extensions, and Marketplace partner integrations for CoCo.

During discussions at the event, many of us felt the Excel integration was particularly powerful because Excel remains the most familiar data workspace for business users.

VS Code, meanwhile, remains the most familiar workspace for developers.

Rather than forcing everyone into SnowSight, Snowflake is bringing CoCo directly into the environments where people already work.

This is also one of the most important principles behind AI-native products:

Do not force users to move into your interface. Bring your Agent into the user's workflow.

Therefore, the significance of CoCo is not that Snowflake now has its own Copilot.

The significance is that Snowflake is moving away from a traditional platform UI and toward an Agent Everywhere strategy.

Beyond CoCo, Snowflake also placed a major spotlight on CoWork at this Summit.

To be honest, when I first heard about CoWork, I was a bit puzzled. If Anthropic were launching CoWork, I could easily understand it, because Agents naturally require enterprise-grade collaboration. But from the perspective of a traditional data platform, CoWork did not seem like the kind of product Snowflake would be expected to release. CoCo helping data engineers write SQL, fix pipelines, and build applications makes perfect sense. OpenFlow, Streaming, Iceberg, and Horizon Catalog are also clear enhancements to the data platform. But what does CoWork have to do with a data warehouse?

After listening to the presentation, I gradually understood it. CoWork reveals Snowflake’s ambitions even more clearly. It is designed for business users, with the vision of enabling CEOs, sales teams, operations teams, marketers, and other business professionals to interact directly with enterprise data and gain insights as if they had their own personal Jarvis. Samsung shared a use case illustrating this idea: CoCo serves as the AI operating interface for data engineers and developers, while CoWork serves as the AI workspace for business users. Snowflake is not just trying to serve data teams; it wants to become part of the daily workflow of every business user across the enterprise.

At that point, I finally understood CoWork’s role. CoCo is reshaping back-end data engineering, while CoWork is reshaping front-end business decision-making. Together, they enable Snowflake to evolve from a data platform into an enterprise AI work platform. CoWork may seem far removed from the traditional Snowflake, but in reality, it is perhaps the closest thing to Snowflake’s future.

Building Agentic Enterprise Infrastructure—this is Snowflake’s true ambition. It also explains why I no longer see Snowflake as a traditional data company.

When traditional data companies launch products, they typically talk about performance improvements, cost reductions, additional connectors, or stronger governance capabilities.

Snowflake’s announcements this time felt much more like those of an AI company. It talked about Agents, Skills, Desktop, CoWork, natural language, business users, context, and security boundaries. In other words, Snowflake is repositioning itself from a Data Warehouse company into an Enterprise AI Platform company.

This should serve as a wake-up call for every data software company.

If even Snowflake has realized that the entry point to future data platforms will shift from SQL, BI, notebooks, and pipelines toward Agents, Skills, Context, and Workflows, then companies like ours that focus on ETL, DataOps, Data Ingestion, and Orchestration must also rethink what our products should look like.

This is not about a single product. It is a blueprint for how Snowflake is reorganizing its entire product portfolio for the AI era. CoCo, CoWork, Desktop, Skill Catalog, and MCP/ACP together reveal Snowflake’s new ambition: not just to manage data, but to become the entry point for enterprise AI.

3 AI Is Bringing Every Software Company Back to the Same Starting Line

The second major impression I took away from this Summit is that we are all part of the same ecosystem, and AI is bringing every software company back to the same starting line.

There was one moment that left a particularly deep impression on me.

Snowflake announced the Agentic Control Plane, or ACP. At that moment, I was genuinely shocked, because just last month we launched our own ACP product. Wait a minute—isn’t that a direct collision? If a giant like Snowflake is entering this space, am I finished!?

As I listened more carefully, I realized the two products are not exactly the same. Snowflake’s ACP focuses more on Snowflake-native data modeling, Text-to-SQL, semantic layers, and enabling Agents to understand and interact with Snowflake data. What we focus on is ETL, orchestration, pipelines, data synchronization, job scheduling, and the execution and governance of heterogeneous data systems. In fact, I quickly added the words “Data Engineering” in front of our product name before calling it an Agent Control Plane.

But the key point is not whether the two products are identical. The important thing is what this coincidence reveals: everyone is moving toward the same destination.

That destination is:

Future software systems must become systems that Agents can understand, invoke, orchestrate, and govern.

In the past, the differences between software companies came from many factors: brand recognition, customer base, sales channels, engineering scale, ecosystem strength, delivery capability, and product maturity. Large enterprises had their advantages, while startups faced their own challenges. But with the arrival of AI, something fascinating has happened: every software product now needs to be rebuilt for AI.

In the past, software interaction looked like this: people opened interfaces, clicked buttons, filled out forms, wrote SQL, reviewed logs, and handled exceptions.

In the future, software interaction may look like this: people define objectives, Agents understand context, invoke tools, generate plans, execute tasks, and return results. Humans increasingly take on the roles of validation, supervision, judgment, decision-making, and correction.

One of my strongest impressions from the Summit was this: when it comes to AI, every software company has been brought back to a new starting line.

Because in the AI era, every software product must be rebuilt from the ground up.

That is why Snowflake and we ended up launching similar categories of products at almost the same time. In the past, such a thing would have been difficult to imagine. Startups rarely released the same kind of products in parallel with large enterprises because the major players usually leveraged their vast resources to build everything first.

Today, however, this creates an enormous opportunity for startups.

In the past, competing directly with large enterprises on resources, branding, or customer scale was extremely difficult. But when AI begins to redefine software, large enterprises also carry historical baggage: legacy systems, legacy customers, legacy architectures, and legacy organizational processes. Startups, if they move quickly enough, can design products from day one with an Agent-native mindset.

That is what encouraged me most about attending Snowflake Summit. The kinds of products Snowflake announced this month are remarkably similar to some of the directions we ourselves explored last month. The scale, scenarios, and depth may differ, but it suggests that our understanding of where the industry is heading is remarkably aligned.

In the AI era, opportunities do not belong only to large enterprises. They also belong to entrepreneurs who can quickly recognize change and are willing to reinvent their products.

4 Looking Back at Ourselves Through Snowflake: How Do I Goodbye Data, Hello AI?

The biggest question Snowflake Summit left me with was not what Snowflake will become, but what we ourselves should become.

If Snowflake is already embracing Make AI Real for Business, then how should we, in turn, embrace Goodbye Data, Hello AI?

For years, we have worked in DataOps, ETL, Data Ingestion, Orchestration, and Pipelines. At their core, these disciplines are about managing the flow of data. We help customers move data from one system to another, schedule tasks based on dependencies, monitor failed jobs, stabilize data pipelines, and connect heterogeneous systems.

All of those things remain important. But once the AI era arrives, software itself is no longer the end goal.

In the past, we dealt with structured data, semi-structured data, files, logs, tables, fields, tasks, and workflows. In the future, we will also need to handle Knowledge, Context, Semantics, Business Rules, Lineage, Execution Memory, and Agent Actions.

When data is no longer just rows and columns inside tables, and no longer merely moving from a source to a destination, it becomes something far more important. Data becomes the context through which AI understands a business. It becomes the foundation upon which Agents take action. It becomes the fuel that powers enterprise automation.

Snowflake’s answer is to evolve from a Data Warehouse into an AI Data Platform.

Our answer is to evolve from a DataOps tool into a Data Engineering Harness for the AI era.

When people use Claude Code or Codex today, they are primarily working in Java or Python development environments. But the environment of a Data Engineer is fundamentally different. The business semantics are more complex, and the workflows are more complicated.

Snowflake’s CoCo is essentially a Data Warehouse Agent. But orchestration and data ingestion are not Snowflake’s core strengths. What data engineers truly need is a Data Engineering Harness that spans systems, databases, schedulers, and environments. In practice, this manifests as an Agentic Data Control Plane designed specifically for data engineers.

That may very well be the opportunity for WhaleOps.

There was a statement from Thomson Reuters at Snowflake Summit that left a deep impression on me:

“They can’t be wrong.”

The quote referred to professionals in legal, tax, and audit industries, where errors are simply unacceptable. The same principle applies to data engineering. That is why a Data Engineering Harness is inherently more complex than harnesses in many other domains.

Enterprise AI is not a toy.

The data tasks generated by Agents cannot merely look correct—they must actually be correct.

The analyses produced by Agents cannot simply sound convincing—they must be grounded in trustworthy data.

The data workflows executed by Agents cannot merely be automated—they must be governable, auditable, and reversible.

That is why I believe this represents our opportunity in the AI era—not simply to become smarter, but to become more trustworthy.

5 My Final Prediction: Snowflake Is Competing for the AI Entry Point, and If It Succeeds, Its Stock Could Rise Far Beyond 2×

Looking back on this Snowflake Summit, my biggest takeaway was not any single product announcement, but a much larger signal about the software industry:

AI is reshaping the entry points, forms, and value propositions of all software.

Snowflake is competing for the AI entry point. That is why it has set its sights on Anthropic as a competitor and is evolving from a Data Warehouse into an AI Data Platform.

In the future world of Data + AI, who will control the entry point—the owners of data, or the owners of AI?

My own view is that data is difficult to move, while AI platforms are relatively easy to switch.

As many people know, Snowflake’s stock price has roughly doubled over the past month. Personally, I believe that if Snowflake’s AI-entry-point strategy succeeds, its future upside will be far greater than the 2× gain we have seen over the past month.

Rethink what is possible,

The future of Snowflake AI looks incredibly promising.

(The views shared above are solely my personal opinions, do not represent any official position, and should not be considered investment advice.)