In the first week of February 2026, the U.S. stock market's software sector underwent a valuation restructuring. During this week, the SaaS sector saw its market value evaporate by over $1 trillion. Industry cornerstones like Salesforce and Workday, once considered safe-haven assets, experienced staggering stock price pullbacks. The trigger for this sell-off was the release of Anthropic's Cowork, but its root cause lies in a fundamental shake-up of the market's confidence in the core business logic of SaaS. As Agentic AI is increasingly applied in the B2B sector, investors have begun to realize a fact: the traditional subscription-based growth model is built on the foundation of "humans" as operators. Once AI starts replacing the functions of junior white-collar workers on a large scale, the value of software seats attached to these positions collapses accordingly.
Therefore, Wall Street's general analysis of this downturn focuses on two points: first, seat deflation, where AI Agents enable companies to no longer need to linearly increase employees to grow their business; second, functional disintermediation, where the combination of large language models and Vibe Coding allows companies to build customized tools at extremely low costs.
While these analyses accurately identify the symptoms, they fail to touch the root cause. What AI has truly destroyed is the "artisanal premium" that the SaaS industry has long enjoyed.
The proliferation of AI Agents and the valuation restructuring of SaaS companies not only mark the end of the seat economy but also signal that B2B business is returning to its most primitive and essential form.
From "Software Delivery" to "Outcome Delivery"
B2B has never been a purely technological industry; it is a service industry centered around solving business operational problems. If this valuation restructuring could be summarized in one sentence, it is that post-AI B2B is shifting from "selling software" back to "providing services."
The sole purpose of businesses purchasing B2B products is to solve operational problems, improve efficiency, or increase revenue.
Before the advent of AI, delivering services relied on an extremely complex software development process. To obtain services, customers had to accept the "methodology + tool" delivery model dictated by SaaS companies and pay a premium for high-priced software development.
This delivery model was highly effective in the past because the business environment was relatively stable, industry divisions were clear, business processes were highly standardized, and growth paths could be abstracted into universal models.
However, the emergence of AI has completely overturned this model. When code generation becomes almost free, and complex logic can be automatically orchestrated by models, the value of software as an "artisanal product" is drastically compressed. Since tools are no longer scarce, customers are no longer willing to pay a premium for the tools themselves.
The B2B industry is being forced to shed its "high-tech software" facade and return to a pure service industry form. The future competitive core will no longer be about how elegantly code is written but about who can delve into the customer's operations, uncover genuine pain points, and directly deliver business outcomes.
Counterintuitive Deduction: From Process-First to Permission-First
China's B2B business model has long differed from that of the United States.
The success of U.S. SaaS is built on a mature business environment and a standardized professional manager system, with its core value lying in exporting "best practices." Salesforce did not invent sales management through code; it merely codified the sales funnel theory validated over decades in the U.S. into software. U.S. companies purchase SaaS services to buy this validated, standardized workflow.
In contrast, B2B software in China is often viewed as a control tool. The uniqueness of the Chinese market lies in the fact that companies are more concerned with power distribution and risk control within hierarchical structures. Therefore, the architectural focus of Chinese software has never truly been on "process optimization" but is instead deeply obsessed with "permission management." Developers expend significant effort handling complex organizational structure mapping, data visibility isolation, and approval flow node control. This results in Chinese software often appearing rigid, fragmented, and lacking the seamless collaboration feel of U.S. software.
Before AI, this model was seen as the original sin of China's SaaS lag. However, post-AI, the Chinese model may become the best practice.
The core capability of AI Agents lies in autonomous decision-making and execution. They inherently reject rigid processes. For a highly intelligent Agent, the meticulously designed wizards, forms, and mandatory step sequences in U.S. SaaS are not only redundant but also obstacles to task execution. AI needs a goal, not a locked-down path. Therefore, the process moats that U.S. SaaS prides itself on will rapidly depreciate into technical debt in the face of AI.
When market changes occur at an extremely high frequency and business needs frequent restructuring, overly rigid processes become an organizational burden. Every adjustment requires waiting for software version upgrades, system configuration changes, and cross-departmental approvals. In an organizational structure centered on permissions, decision-making paths are shorter, and the system's role is closer to recording and assisting rather than prescribing actions.
The strict, hierarchy-based permission systems in Chinese software naturally align with the management needs of AI Agents. Because Chinese software was designed from day one to guard against "human" overreach, it now only needs to seamlessly transfer this defensive logic to Agents. In an environment with strictly defined data boundaries and operational permissions, companies can let AI run wild.
This might be the most counterintuitive deduction of the AI era: the Chinese market, lacking "SaaS genes," may adapt more quickly to the large-scale deployment of Agents because it possesses the most complex "permission genes."
The Outcome-Oriented B2B Era
If SaaS represents the era of "tool scalability," then B2B post-2026 is closer to "outcome scalability."
A typical example is Palantir.
Palantir's growth logic is not built on seat expansion. Its core competitiveness comes from deeply embedded delivery. Through AIP Bootcamps, they deploy engineers directly to client sites to work alongside business teams, dissect problems, and design solutions around specific operational metrics.
What they deliver is never a list of features but business outcomes.
When models can handle underlying data cleaning, modeling, and process orchestration, human experts can focus on understanding complex scenarios and decision logic. This allows technology to become a lever for efficiency, amplifying service capabilities. What clients will pay for is a promise of operational metrics.
Therefore, the value logic of B2B enterprises will also shift accordingly. Clients will place greater emphasis on the service provider's ability to understand business scenarios and deconstruct complex problems. Companies that can continuously create value will be those brave enough to immerse themselves in the frontline operations of their clients.
In the process of AI implementation, the only thing to guard against is path dependency—whether it's reliance on old processes or reverence for authority.