2026 To B Survival Record: The Disappearing Groups and Mutated Organizations

In 2026, the To B market is showing an extreme dumbbell-shaped structure.

On one end are the tech giants that possess massive computing power and ecosystems, while on the other end are extremely small but highly elite startup teams. Those medium-sized SaaS companies with hundreds of employees that rely on the traditional "manpower stacking" logic are in an awkward position.

Previously, developing similar products or services required a team of dozens of people and continuous financing to support. Now, two people using AI can do the work that used to require twenty people. This generational difference in labor efficiency is rewriting the competition rules of the market.

The growth equation has shifted from the linear "adding people equals adding revenue" to the exponential "leveraging AI." This fundamental change has made it difficult to balance the accounts of the old business models.

It is in this silent but profound efficiency revolution that a group of special entrepreneurs have stepped into the spotlight.

01 Those who understand the old system best are tearing it down with their own hands

In this AI-driven efficiency transformation, there is a group of "rebels" who have emerged from the core of the old system. They were once middle-level backbones in large tech companies or leading SaaS enterprises, having witnessed the glory and limitations of the traditional model. Their entrepreneurial choices are technical breakthroughs based on a deep understanding of the pain points.

Many entrepreneurs mentioned in this article, such as Lu Yang (PureBlue AI), Zhai Xingji (Yunucleus Technology), Zhao Yan (ShiCheng Marketing), and Pang Dawei (ChatExcel), have all undergone in-depth interviews with Neuters. Their experiences and thoughts have opened a window for us to observe this transformation.

1. Resume Labels

Before founding PureBlue AI, Lu Yang served as the Marketing General Manager of ByteDance's Volcengine and the Marketing Director of the Doubao large model. This experience placed him at the forefront of the AI revolution, allowing him to witness firsthand how large models are reconstructing the underlying logic of traffic distribution.

He keenly realized that the traditional SEO model, which relies on manual experience and content stacking in marketing, has become ineffective in the face of the ever-evolving and opaque AI black box. This drove him to abandon the stable path in a large company and instead explore using AI models to understand and adapt to the cognitive rules of another AI model, namely GEO (Generative Engine Optimization).

His team quickly evolved from being experience-driven to data-driven, and finally established a model-driven technical route, self-developing an environment self-perceiving data model evolution engine to replace inefficient manual guessing.

Zhai Xingji's entrepreneurial starting point also stems from his reflection on the old model.

As a former product manager at FineBI, he was deeply involved in the entire process of BI tool sales and implementation. He observed that the essence of many enterprise customers' software purchases is not a need for a more complex tool, but a need for an "employee" that can directly provide the correct answers.

Traditional SaaS delivers the possibility of process digitization, while customers often expect definite business results. This fundamental gap prompted him to found Yunucleus Technology, positioning the company as a "digital employee recruitment platform."

They no longer sell software seats but focus on creating AI digital employees with an accuracy rate of over 90% in specific positions such as pre-sales and supply chain in the manufacturing industry, which can directly deliver business results.

Zhao Yan has over fifteen years of experience in B2B digital marketing and has worked in institutions such as Zhiqu Baichuan. His long-term in-depth involvement in the industry has made him instinctively cherish the accumulation of brand and trust.

When GEO became a hot topic, many service providers resorted to mass-producing junk content and rapidly polluting information sources to compete for AI search rankings for their customers. He felt strong vigilance and resistance. In his view, this kind of "technical poisoning" may be effective in the short term, but it will inevitably damage the brand reputation in the long run and be counterattacked by smarter AI systems.

Therefore, ShiCheng Marketing, founded by him, adheres to the long-termism of taking time to do things well. It only serves customers with the same values, sets the starting point of the annual service fee at 150,000 yuan, and has verified the feasibility of its "righteous path to success" route with a 100% customer renewal rate.

Pang Dawei's trajectory spans two entrepreneurial cycles of SaaS and AI. As a serial entrepreneur in the SaaS era, he has experienced the entire process of financing, expansion, and exit.

When he chose to start a new business with ChatExcel, he deliberately abandoned the previous generation's model of "stacking people, projects, and sales." The team focused its core energy on technological breakthroughs, successfully compressing and deploying the AI model to terminal devices, achieving true edge-side intelligence, and addressing the security concerns of enterprises when handling sensitive data.

This team of less than ten people without sales staff has achieved user self-growth and a service scale in the millions through the value of the product itself, verifying the possibility of product-driven growth in the AI era.

2. Generational Gap

The common backgrounds of these entrepreneurs outline the core differences between the old and new generations of To B entrepreneurs.

The previous generation of SaaS entrepreneurs were mostly system builders.

Their core competitiveness lies in abstracting and standardizing complex offline business processes and encapsulating them into software. Their growth logic relies on building large sales and implementation teams to persuade customers to accept new management processes and tools. Their thinking is process-oriented, with the goal of optimizing efficiency, but often staying at the level of "helping customers use tools better."

The new generation of AI entrepreneurs are essentially technology breakers.

They no longer try to solidify or improve old processes with software but directly use AI technology to penetrate processes and aim for the final business results. Their thinking is result-oriented. Customers don't need to understand how AI works but only need to confirm whether this "digital employee" or "intelligent service" can accurately complete tasks and bring quantifiable revenue growth or cost savings.

The growth logic has also changed accordingly, shifting from relying on the sales promotion of the human sea tactic to relying on the product-driven approach of technological barriers and self-proven effects.

This generational change reflects the fundamental change in the technological leverage.

When AI can directly replace human labor in specific links, the focus of entrepreneurship shifts from how to organize and manage human resources to how to train and harness AI.

This also explains why these entrepreneurs from the core of the old system have become the most powerful challengers to the old model, because they know most clearly where the "wall" is and are the first to get the new tools to break through it.

02 Organizations are shrinking, and capabilities are expanding

As the business model shifts from selling tools to delivering results, the form of organizations has also evolved synchronously.

The new generation of AI startups generally present a minimalist structure, a highly elite talent composition, and the abandonment of traditional growth paths. This is an inevitable choice under the evolution of the business model.

1. The Disappearing Sales Team

In these companies, a large sales team is no longer a standard feature and may even disappear completely. The shift from relying on channel relationships and sales pitches to relying on the verifiable value created by the product itself reflects a fundamental change in the growth logic.

PureBlue AI does not have a traditional sales team, and almost all of its customers come from active inquiries.

Founder Lu Yang believes that their GEO service essentially provides algorithm-determined optimization effects, such as increasing and stabilizing the brand's recommendation rate on the AI platform from less than 30% to 100%. This effect itself is highly persuasive, far surpassing any sales demonstration.

When the technology itself forms a high enough barrier and can directly deliver obvious results, the intermediary value of channels and sales is greatly weakened.

ChatExcel takes the product-driven growth model to an even more extreme level. Its team has less than ten people, no sales staff, and no market investment. Growth entirely depends on the word-of-mouth spread and spontaneous use of the product among users, and it naturally penetrates into enterprise scenarios.

Founder Pang Dawei pointed out that in the AI era, the technology iteration cycle is calculated in months, and the core value of the product must be sharp enough to make users unable to do without it. If a sales team needs to be established to explain and promote the product with difficulty, it often means that the product does not solve real pain points or its value is not direct enough. PLG is a touchstone for whether their product can stand in the market.

This "sales-free" model can be established, relying on a key change, that is, AI technology makes the delivery of complex services or products standardized and automated enough, and its value can be directly perceived and verified by users like consumer goods, thus greatly reducing the high communication and trust costs in traditional To B transactions.

2. Talent View: Judgment and Responsibility Become Scarce

Different from the talent view that advocated youthfulness and combat effectiveness during the heyday of the Internet, these entrepreneurs show an obvious "anti-youthfulness" tendency in their talent selection. They value the ability of in-depth thinking and professional responsibility more.

Zhao Yan, the CEO of ShiCheng Marketing, clearly focused on recruiting people in the age group of "post-85s to pre-95s" when building his team. After a large number of interviews, he found that many younger applicants had difficulty calming down to engage in the in-depth and long-cycle service work required for B2B marketing.

B2B business, especially high-value services, highly depends on the industry insights, strategic judgment, and full-process responsibility for results of service providers. These qualities often require time accumulation and the tempering of complex projects.

He believes that a 35-year-old professional with family responsibilities, a stable mindset, and years of professional accumulation is far more reliable and valuable than a newcomer with only execution ability in the current environment.

This choice reflects some changes in the organizational form. In the past, many To B companies had a typical pyramid structure, where a large number of young employees were needed at the bottom for repetitive execution work, such as content production, customer follow-up, and data entry. Now, these basic execution tasks are being rapidly replaced by AI tools.

The organizational form is thus evolving into a "reverse T-shape", that is, at the top are a few elites with both industry knowledge and technological understanding, responsible for defining problems, harnessing AI, and ensuring the final delivery quality; at the bottom is a highly automated AI tool chain, undertaking most of the standardized and programmed tasks. The middle layer, which used to require a large amount of manpower for execution, is being rapidly compressed.

Therefore, the core competitiveness of the new generation of organizations no longer depends on the number and physical strength of employees, but on the multiple by which the core team can amplify their professional abilities with AI.

They recruit brains that can command AI arrays. This elite and leveraged organizational model is the root of their efficiency in challenging traditional companies with a small team.

03 Enterprises Start to Pay for the Value Generated by AI

The transformation of the business model is the most disruptive part of this AI entrepreneurship wave.

The traditional software subscription model (SaaS) is being challenged by a more direct and result-oriented model. The new generation of entrepreneurs no longer just sell the possibility of efficiency improvement; they directly sell the measurable labor value created by AI.

1. Pricing Logic: Pay for the Created Value

The change in the pricing method intuitively reflects the change in the business logic.

In the past, enterprise software was usually billed according to the number of users, functional modules, or data volume, and customers purchased the right to use the tool. Now, the core anchor point of pricing has become the human cost replaced by AI or the business increment created.

Zhai Xingji, the CEO of Yunucleus Technology, compares their charging model to "employment system." For example, the pre-sales digital employee they created for manufacturing customers can compress the complex quotation process that originally took senior engineers four days to complete into within 20 minutes, with a very high accuracy rate.

The fee paid by customers is not based on how many APIs are accessed or how much computing power is occupied, but on the human cost of engineers and the value of the time window "saved" by this digital employee.

This is a typical payment based on labor results, just like paying a salary to a professional employee who never gets tired and has super-high efficiency.

PureBlue AI adopts a more thorough pay-for-performance model. They promise specific optimization goals to customers, such as the brand's recommendation rate, ranking rate, or description accuracy on a specific AI platform. If the promised results are not achieved, no fee will be charged.

Founder Lu Yang believes that their service essentially helps brands gain traffic and trust in the AI cognitive world, which is similar to the logic of advertising placement. Customers only pay for the final exposure and mind share they obtain, not for the algorithm optimization process behind it.

This model highly binds the interests of service providers and customers and forces service providers to polish their technology to a level that can stably deliver results.

2. Delivery Logic: Quantifiable Business Increment is the Endpoint

The evolution of the business model has also profoundly changed the endpoint of delivery.

The endpoint of traditional software delivery is the successful launch of the system and the smooth use by users, and there is a certain uncertainty about whether it can generate business value. While the delivery of the new generation of AI services must point to clear and quantifiable business increments.

Catfish Technology delves into the offline chain store scenario, and its core value is not to provide a dialogue analysis tool but to directly increase the store's turnover.

Take one of their well-known catering customers as an example. For common customer requests such as "don't put chili in the big plate chicken," a general AI assistant may simply suggest changing the dish, which may lead to a lower customer unit price.

The exclusive AI trained by Catfish Technology combining the brand knowledge base and the experience of top salespeople can generate more flexible dialogue solutions, such as explaining the characteristics of chili and providing a compromise solution, thus increasing the probability of closing the deal.

By analyzing the single-store dialogue data, they can calculate the potential gross profit improvement space after optimization. For example, a store identified a potential increase of over 500 yuan in one day. Customers pay for this perceptible and calculable revenue increment.

Similarly, Youyi Technology, when launching its GEO intelligent agent, not only provides content optimization and dissemination services but also builds a detailed data monitoring system to present indicators such as the brand's citation rate, voice share, and sentiment index in AI answers to customers. This makes the originally vague brand influence measurable and traceable.

Enterprises can not only see whether they are mentioned by AI but also see in what mood and on what scale they are mentioned, as well as the relative position change compared with competitors. The deliverable has changed from a series of optimized manuscripts to an incremental report on the brand's trust assets in the AI world.

This transformation from delivering tools to delivering increments redefines the relationship between customers and service providers. It requires service providers to have a deeper understanding of the essence of customers' business and closely link their technical capabilities with customers' final performance.

This also means that only those AI applications that can truly create measurable value can obtain sustainable returns under this new business model.

04 Trust is Becoming a Hard Asset in the AI Era

In the context of the rapid development of AI technology and the high enthusiasm in the market, maintaining restraint and adhering to the bottom line has become a rare competitiveness.

The new generation of AI entrepreneurs do not blindly pursue short-term interests; they value long-term brand value more. This choice is not only out of moral considerations but also an important strategy to build real competitive barriers and win long-term trust from customers in the AI era.

1. Active Rejection of "Bad Profits"

In the early stage of entrepreneurship, maintaining strategic determination when facing attractive orders that can solve the survival problem often requires more courage than expansion.

Zhai Xingji, the founder of Yunucleus Technology, clearly rejected a customized project worth 3 million to 5 million yuan not long after the company was founded.

His reason is that although such large-scale customized projects can bring considerable short-term income, a large number of non-core and personalized development requirements will seriously consume the limited R & D resources that should be used for product standardization and core scenario refinement.

Accepting it means that the company will slide towards the path of project-based outsourcing, which is contrary to their long-term goal of building a standardized and replicable AI digital employee product.

He chose to stick to the positioning of a product-based company, rather than bear short-term cash flow pressure, to ensure that organizational resources are focused on the iteration of core capabilities.

Zhao Yan, the CEO of ShiCheng Marketing, moved the value screening to the customer selection stage.

Facing the market demand from many customers who hope to gain a short-term advantage in AI search through means such as "fast ranking" and "poisoning" (polluting information sources with junk content), he rejected them all. He regards such behaviors of exploiting technological loopholes and creating information garbage as an "overdraft" and "poisoning" of brand trust.