In the first week of February 2026, the US software sector underwent a valuation restructuring. During this week, the SaaS sector's market capitalization evaporated by over $1 trillion. Stocks of industry pillars like Salesforce and Workday, once considered safe-haven assets, saw staggering pullbacks. The trigger for this sell-off was Anthropic's Cowork launch, but its root cause lies in a fundamental shake-up of the core business logic of SaaS. As Agentic AI is increasingly applied in the B2B space, 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 to massively replace the functions of junior white-collar workers, the value of software seats tied to these positions collapses.
Thus, for this downturn, Wall Street's general analysis focuses on two points: first, seat deflation, where AI Agents mean companies 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.
These analyses accurately identify the symptoms but fail to touch the root cause. What AI truly destroys is the "artisan premium" that the SaaS industry has long enjoyed.
The proliferation of AI Agents and the valuation restructuring of SaaS companies are not just the end of the seat economy; they also mark the return of B2B business to its most primitive and essential form.
From "Software Delivery" to "Outcome Delivery"
B2B has never been a purely technical industry; it is a service industry centered around solving business problems for enterprises. If we abstract this valuation restructuring into one sentence, it is that post-AI B2B is moving from "selling software" back to "providing services."
The sole purpose for enterprises to purchase B2B products is to solve business problems, improve efficiency, or increase revenue.
Before the advent of AI, delivering services relied on extremely complex software development processes. To obtain services, customers had to accept the delivery form of "methodology + tools" set by SaaS companies and pay a high premium for software development.
This delivery form was highly effective in the past because it was a relatively stable business environment with clear industry divisions, highly standardized business processes, and growth paths that could be abstracted into general models.
But 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 "artisan product" is compressed to the limit. Since tools are no longer scarce, customers are unwilling to pay a premium for the tools themselves.
The B2B industry is being forced to shed its "high-tech software" guise and return to a pure service industry form. The core of future competition will no longer be how elegantly code is written, but who can go deep into customer sites, uncover real pain points, and directly deliver business outcomes.
A 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 US SaaS is built on a mature business environment and a standardized professional manager system, with its core value lying in outputting "best processes." Salesforce didn't invent sales management through code; it simply solidified the sales funnel theory validated over decades in the US into software. When US companies buy SaaS services, they are buying this validated, standardized workflow.
In contrast, in China, B2B software is often seen as a control tool. The particularity of the Chinese market is that companies focus more on power distribution and risk control within a hierarchical system. Therefore, the architectural focus of Chinese software has never truly been on "process optimization" but is deeply obsessed with "permission management." Developers spend a lot of effort handling complex organizational structure mapping, data visibility isolation, and approval flow node control. This results in Chinese software often feeling rigid and fragmented, lacking the smooth collaborative feel of US software.
Before AI, this model was seen as the original sin of China's SaaS lag. But after AI, the Chinese model may become the best practice.
The core capability of AI Agents lies in autonomous decision-making and execution. It naturally rejects rigid processes. For a highly intelligent Agent, the carefully designed wizards, forms, and mandatory step jumps in US SaaS are not just redundant but are obstacles to executing tasks. AI needs a goal, not a locked path. Therefore, the process moat that US SaaS prides itself on will quickly depreciate into technical debt in the face of AI.
When market changes are frequent and business needs constant restructuring, overly rigid processes become an organizational burden. Every adjustment requires waiting for software version upgrades, system configuration changes, and cross-department approvals. In a permission-centric organizational structure, decision paths are shorter, and the system's role is closer to recording and assisting rather than prescribing actions.
The strict, administrative-level-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 the Agent. In an environment with strictly defined data boundaries and operational permissions, companies can let AI run freely.
This may be the biggest counterintuitive inference of the AI era: the Chinese market, lacking a "SaaS gene," may adapt to the large-scale deployment of Agents faster because it possesses the most complex "permission gene."
An Outcome-Oriented B2B Era
If SaaS represents the era of "tool scaling," then B2B after 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 deep embedded delivery. Through AIP Bootcamps, they station engineers directly at customer sites, deconstructing problems with business teams and designing solutions around specific operational metrics.
What they deliver is never a feature list, 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 customers pay for will be a commitment to a business metric.
Therefore, the value logic of B2B companies will also shift accordingly. Customers will place more emphasis on the service provider's understanding of business scenarios and ability to deconstruct complex problems. Companies that can continuously create value will be those willing to delve deep into the front-line business of their clients.
In the process of AI implementation, the only thing to be wary of is path dependency—whether it's reliance on old processes or worship of authority.