During the first week of February 2026, the U.S. stock market's software sector underwent a valuation reset. In that single week, the SaaS sector lost over $1 trillion in market capitalization. The stock price pullbacks of industry cornerstones once considered safe-haven assets, such as Salesforce and Workday, were staggering. The immediate trigger for this sell-off was the release of Anthropic's Cowork, but its root cause lies in a fundamental market reassessment of the core business logic of SaaS. As Agentic AI is increasingly applied in the B2B domain, investors have begun to realize a fact: the traditional subscription-based growth model is built on the foundation of "humans" as operators. Once AI begins to replace the functions of junior white-collar workers on a large scale, the value of software seats attached to those positions collapses accordingly.
Therefore, Wall Street's general analysis of this downturn focuses on two points. First, seat deflation: AI Agents mean enterprises no longer need to linearly increase staff to grow their business. Second, functional disintermediation: the combination of large language models and Vibe Coding allows enterprises to build customized tools themselves at extremely low cost.
While these analyses accurately identify the symptoms, they fail to touch the root cause. What AI is truly destroying is the "artisanal premium" that the SaaS industry has long enjoyed.
The proliferation of AI Agents and the valuation reset of SaaS companies not only signal the end of the seat economy but also mark 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 enterprise operational problems. If this valuation reset could be abstracted into one sentence, it is that post-AI B2B is shifting from "selling software" back to "providing services."
The sole purpose for enterprises purchasing B2B products is to solve operational problems, improve efficiency, or increase revenue.
Before the advent of AI, delivering services necessarily relied on an extremely complex software development process. To obtain services, customers had to accept the delivery form of "methodology + tools" dictated by SaaS companies and pay a high premium for software development.
This delivery form was highly effective in the past because it existed in a relatively stable business environment with clear industry divisions of labor, highly standardized business processes, and growth paths that could be abstracted into universal models.
However, the emergence of AI has completely overturned this model. When code generation becomes nearly free, and complex logic can be automatically orchestrated by models, the value of software as an "artisanal product" is compressed to its limit. 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" veneer and return to a pure service industry form. The future core of competition will no longer be how elegantly code is written, but rather who can deeply immerse themselves in the customer's site, uncover the real pain points, and directly deliver business outcomes.
Counterintuitive Deduction: From Process-First to Permission-First
The form of B2B business in China has long differed from that in the United States.
The success of U.S. SaaS is built upon a mature business environment and a standardized professional manager system. Its core value lies in exporting "best-practice processes." Salesforce did not invent sales management through code; it merely solidified the sales funnel theory validated over decades in the U.S. into software. U.S. enterprises purchase SaaS services to buy this validated, standardized workflow.
In contrast, B2B software in China is often viewed as a control tool. The particularity of the Chinese market lies in enterprises' greater focus on power distribution and risk control within bureaucratic hierarchies. Therefore, the architectural focus of Chinese software has never truly been on "process optimization" but has been excessively obsessed with "permission management." Developers expend enormous 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 collaborative feel of U.S. software.
Before AI, this model was seen as the original sin behind the lag of Chinese SaaS. But after AI, the Chinese model may well become the best practice.
The core capability of an AI Agent lies in autonomous decision-making and execution. It naturally resists 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 even obstacles to its 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 when 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 happen to naturally fit the management needs of AI Agents. Because from day one, Chinese software has been designed to guard against human overreach; now it only needs to seamlessly transfer this defensive logic to Agents. In an environment with strictly defined data boundaries and operational permissions, enterprises can let AI run rampant.
This might be the most counterintuitive deduction of the AI era: the Chinese market, which lacks "SaaS DNA," may adapt more quickly to the large-scale deployment of Agents precisely because it possesses the most complex "permission DNA."
The Outcome-Oriented B2B Era
If SaaS represents the era of "tool scaling," then B2B post-2026 is closer to "outcome scaling."
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, deconstruct problems, and design solutions around specific operational metrics.
What they deliver is never a feature list, but business outcomes.
When models can handle the 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 will be a promise on an operational metric.
Consequently, the value logic for B2B enterprises will also migrate 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 courageous enough to deeply engage with clients' frontline operations.
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.