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After AI Agent became popular, many people have already begun to write obituaries for SaaS. But I think it is too early for that.

Investors are indeed panicking. In early 2026, the panic of the SaaS apocalypse swept through the entire tech circle. At the end of January, Anthropic released a function update that allowed Claude to call plugins, and the market value of software stocks in the US evaporated hundreds of billions of dollars in the subsequent three weeks.

The logic behind their panic is quite simple. They believe that since AI is already capable of writing its own code, finding vulnerabilities, and even dynamically generating tools, the cost of writing code approaches zero. Once Agents can continuously create various customized tools for businesses, the rental software companies, which painstakingly built their moats, will naturally lose their advantages.

Thus, from CrowdStrike to IBM, from Salesforce to ServiceNow, regardless of how impressive their earnings reports are, they are all experiencing severe sell-offs.

At the same time, countless AI entrepreneurs are presenting their business plans to VCs, claiming they want to "become the middleware of the Agent era" and "start businesses for Agents."

They are all betting on one thing: creating tools is the sexiest business of this era.

But if we shift our gaze away from those PPTs to examine the real aspects of how businesses operate, we will find that it is actually not the case.

Software has never sold code

There is a classic and repeatedly validated theory in economics called "factor scarcity transfer." Every productivity revolution makes a type of previously scarce factor abundant while another previously overlooked factor becomes extremely scarce, leading to a concentration of wealth towards the latter.

Before the industrial revolution, labor was scarce; steam engines made mechanical labor abundant, and scarcity shifted to capital and factories, making factory owners the wealthiest people of that era.

The internet revolution made the cost of information dissemination zero, shifting scarcity to users' "attention," thus traffic became a massive business.

Today, the AI revolution is making the ability to write code and create tools extremely abundant. In the Agent era, where code is no longer scarce, where exactly has scarcity shifted to?

In fact, over the decades of development in the software industry, code itself has never truly become a moat.

Every line of code in the Linux system is free, but that does not prevent Red Hat from being acquired by IBM for a staggering $34 billion; MySQL is free, yet Oracle is able to sell expensive service contracts after acquiring it. The code of PostgreSQL can be downloaded by anyone, but AWS's Aurora database service still generates billions of dollars each year from enterprise customers.

With code being free, business continues to thrive, and in fact, it thrives quite well.

The most critical elements are actually these three things: the solidified business processes, years of accumulated customer data, and the extremely high switching costs generated from it.

When you purchase Salesforce, you are not buying the source code of that CRM system, but rather over 500 trillion records of enterprise customer data managed behind it, as well as the experience of how it seamlessly integrates sales, customer service, marketing, and other processes. This data is not just cold lines of code; it is the living time and history of the business.

A company that has been using Salesforce for ten years has all its communication records, transaction histories, and sales opportunity follow-up points in there. Migrating away is not merely an issue of changing software; it is equivalent to moving the entire memory of the company. This is why Salesforce can still report an annual revenue of $41 billion and set a target of $63 billion by 2030.

Returning to the framework of factor scarcity transfer. Since Agents can create tools themselves and the cost of writing code is now zero, what is the most scarce factor in the enterprise services scenario?

Choking the Agent's neck

What truly chokes Agents is not the lack of hands but the absence of "context" in their brains.

A super Agent equipped with all tools is like a top-performance juicer. It spins incredibly fast and has sharp blades, but if no one throws in fruits, it certainly cannot make you a glass of juice.

McKinsey pointed out in its annual report that 88% of enterprises are using AI, but only 23% have truly scaled-up Agent systems in some aspects of their businesses. What is choking them is not that the large models are not smart enough, but that the enterprise data architecture is unprepared.

SAP's President of Data & Analytics, Irfan Khan, mentioned in an interview with MIT Technology Review: "Enterprises cannot throw away their entire general ledger system for an Agent, because Agents cannot function without business context."

The "business context" referred to here includes: the financial compliance baseline of the company, the regulatory requirements of the industry, the preferences and history of this customer over the past decade, the payment terms and default records of this supplier, the performance history and promotion path of this employee... These things are neither publicly available on the internet nor retrievable through crawlers, nor can AI predict or generate them through text.

Foundation Capital partner Ashu Garg holds the same view. He stated that Agents need not just data, but a "contextual graph," a reasoning layer that captures not only what the business has done but also how the business thinks. This can only be accumulated from real business operations and cannot be manufactured out of thin air.

In this logic, scarcity has shifted from "the ability to create tools" to "having irreplaceable business context data."

Since Agents cannot produce a glass of juice themselves, then who holds those fruits?

The golden age of data landlords

The answer points to those old players who were once thought to be overtaken by AI.

On February 23, 2026, Bloomberg launched the Agentic AI interface called "ASKB." The Bloomberg Terminal is one of the most representative entities in the software industry. Although there are only 325,000 subscription users worldwide, each account charges $32,000 annually, meaning Bloomberg can generate over $10 billion in revenue each year from these 325,000 accounts, accounting for over 85% of Bloomberg LP's total revenue.

For the internet industry, where "the more users, the better," this seems counterintuitive; Bloomberg has built a solid business fortress with a very small number of paying users.

The reason it can succeed is simple: Bloomberg possesses the most complete, real-time, and deeply structured financial data globally. This data is the result of decades of continuous investment, including real-time market data, historical archives, news corpora, analyst reports, company financial data... Any institution wanting to make serious decisions in the financial domain has no choice but to use it.

For the newly launched ASKB, AI is the engine, while Bloomberg's unique data is the only fuel. Any Agent wishing to operate in the financial domain cannot fabricate this data out of thin air; it can only obediently connect to Bloomberg's interface.

WatersTechnology provided an insightful commentary: Bloomberg's Agentic layout demonstrates "how those who own data turn AI into their cash machine."

This logic holds true across all verticals. Veeva controls compliance and research data in the global pharmaceutical industry, and any pharmaceutical company’s Agent must utilize this data to handle clinical trials and regulatory filings; Epic holds electronic health records for over 250 million patients in the US, meaning every diagnostic suggestion from a healthcare Agent requires this real medical data underpinning; LexisNexis monopolizes vast legal document archives, making it impossible for legal Agents to conduct case searches and compliance analysis without it.

This data is the crystallization of decades of business operations in the real world, it is the sediment of time and an irreplaceable history. This is also the ultimate embodiment of "factor scarcity transfer": when everyone has access to top AI engines, what truly determines success is whether you can find your unique oil field.

In the past, these subscription-based data services were sold to human analysts. A large institution might need to purchase 100 Bloomberg terminal accounts. But in the future, when machines become data consumers, an institution might operate thousands of Agents that frantically call these proprietary data interfaces in milliseconds.

This represents a leap in scale. The number of queries that a human analyst can handle in a day is limited, but the call frequency of Agents can far exceed that of humans. Demand for continuous, real-time, high-value data will see exponential growth. The subscription-based business logic will not only remain unshaken but will be infinitely amplified by the insatiable appetite of machines.

While code has approached zero, data is starting to collect rents.

However, does this mean that all SaaS and data companies can sleep soundly?

Not all SaaS hold this card

If this article is understood as an indiscriminate boost for the SaaS industry, it would be a huge mistake. AI has brought about a ruthless divide within SaaS.

TechCrunch interviewed several leading VCs in early March 2026 and asked them what they least wanted to invest in now.

Silicon Valley investors have already voted with their feet. Simple workflow encapsulation, horizontal tools applicable to any industry, lightweight project management—these stories that used to support a round of financing are now facing the common fate of being directly passed on. The reason is simple: these Agents can do these tasks effortlessly. Software companies without exclusive data are quickly losing their qualification to enter the capital’s vision.

This judgment has split the SaaS world in two.

One half consists of those merely offering thinly wrapped tool products, placing public data under an attractive interface, or simply optimizing a certain single-point operational process. The moat for these products is essentially user habits and interface stickiness.

However, as Jake Saper from Emergence Capital said, "In the past, getting humans to form habits in your software was a strong moat. But if Agents are doing this work, who cares about human workflows?"

This type of SaaS truly faces a significant threat. The GTM tool stack is a typical case. Gainsight, Zendesk, Outreach, Clari, Gong—all occupy adjacent functions like customer success, customer service, sales outreach, revenue forecasting, and call analysis, each requiring separate budgets, operations, and integrations. AI-native companies can now use one Agent to bridge all these processes, thus significantly diminishing the existence value of these point tools.

The other half of SaaS deeply integrates into the core business processes of enterprises, holding irreplaceable proprietary data. Such companies will not only not be replaced by Agents but will also become more valuable due to the existence of Agents.

Taking Salesforce as an example, in February 2026, Salesforce's earnings report revealed that the annual recurring revenue for Agentforce rose to $800 million, a year-on-year increase of 169%; they had delivered a cumulative total of 2.4 billion "Agentic work units," processing nearly 20 trillion tokens; they had signed over 29,000 Agentforce clients, with a quarter-on-quarter growth of 50%. More importantly, the combined ARR of Agentforce and Data 360 exceeded $2.9 billion, a year-on-year increase of over 200%.

Marc Benioff stated on the earnings call, "We have rebuilt Salesforce into the operating system of the Agentic Enterprise. The more AI can replace work, the more valuable Salesforce becomes."

Salesforce has not been replaced by Agents; instead, it has become the soil in which Agents operate. Its value precisely comes from the business data and process context that Agents cannot bypass.

ServiceNow's CEO Bill McDermott publicly announced in February 2026, "We are not a SaaS company."

He is not denying himself; he is actively cutting apart. His logic is that SaaS is a concept about "software delivery methods," and what ServiceNow aims to become is the orchestration and execution layer of enterprise AI Agents. AI can discover problems and provide suggestions, but the actual actions executed within enterprise systems still need platforms like ServiceNow that are deeply embedded in workflows.

Workday released "Sana" on March 17, 2026, a conversational AI suite that deeply integrates HR and financial data. The core logic of this product is not to replace Workday with AI but to feed AI with Workday's data.

Workday holds compensation, performance, organizational structure, and financial budgeting data for thousands of enterprises. The depth and uniqueness of this data cannot be replicated by any AI-native startup in the short term.

Thus, the true moat is not whether you have data, but whether the data in your hands is inaccessible, unpurchaseable, and incapable of being created by others.

In the next decade, who will collect rents

In every technological revolution, the biggest profits are often taken not by the individuals who invent the groundbreaking new technology but by those who quietly grasp the scarce factors necessary for the new technology's survival. In this rapidly developing AI era, the capabilities of large models will become stronger, and Agents' ability to write code and create tools will become increasingly widespread.

When these capabilities, once seen as black technology, become infrastructure, the logic of "factor scarcity transfer" leads to only one conclusion: the group desperately creating tools for Agents is unlikely to be the ultimate winner of this era.

Foundation Capital stated in its February 2026 analysis that the overall market value of the software industry will expand to ten times its current size in the next decade. However, this tenfold growth will not be evenly distributed among all software companies; it will highly concentrate on those players who can truly navigate the Agent era.

The true winners will be those who hold data assets that Agents cannot bypass.

For today's entrepreneurs and investors, there are only two destinies for entrepreneurs in this era: one is to frantically create shovels for Agents, and the other is to first grab that piece of land. You should have a clear idea of what you are doing right now.

Don't focus on the Agent's hands; focus on choking the Agent's neck.

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