The rapid development of AI agent technology is ushering in a new “golden age”, but it also brings a deep “model trap”. Entrepreneurs are faced with the challenge of how to transform AI technology into sustainable, high-value business outcomes. This article provides an in-depth analysis of three typical business models in AI Agent entrepreneurship: the traditional “Party B” model, the transitional “Party C” model, and the “Party A” model that ultimately points to value control.
The explosive development of generative artificial intelligence, especially AI Agent technology, is sweeping the world at a speed beyond imagination: from Silicon Valley to Zhongguancun, from developer communities to top industry summits to industry leaders such as OpenAI CEO Sam Altman’s clear outline of the future timeline for AI Agents, all indicate the arrival of a “golden age”.
However, a deep “model dilemma” has also emerged – the potential of AI Agents is limitless, but how to transform technology into sustainable, measurable, and high-capital value business outcomes?
I thinkIn the current wave of AI Agent entrepreneurship, three typical business models have emerged – the traditional “Party B” path, the transitional “Party C” role, and the “Party A” model that ultimately points to value control.
We might as well peel back the cocoon layer by layer to see how AI Agent jumps out of the path dependence of “migrant workers” and evolves into a “value controller”.
Struggles and bottlenecks of traditional paths: Agent’s “Party B” and “Party C” models
Under the wave of AI Agents, traditional business models are bearing the brunt and facing unprecedented scrutiny and challenges. Although those who try to simply graft AI into the old framework may achieve certain efficiency improvements in the short term, it is still difficult to get rid of the fate of meager profits and weak growth without fundamental model changes.
AI-empowered “new Party B” – trapped in the “digital profit pool” small profit trap
The so-called “new Party B” usually refers to those traditional SaaS (software as a service) providers and enterprise service providers in the AI era, or those emerging enterprises that use AI technology as their core competitiveness but still play the role of “supplier”.
Their core business logic still revolves around selling “tools” (such as software licenses, user seats, project-based development) to customers or providing AI-assisted consulting services with “human hours”. The empowerment of AI has undoubtedly brought them the possibility of improving efficiency and optimizing services, but the underlying vulnerability of their business model has been exposed under the catalysis of AI.
To achieve these three challenges, product managers will only continue to appreciate
Good product managers are very scarce, and product managers who understand users, business, and data are still in demand when they go out of the Internet. On the contrary, if you only do simple communication, inefficient execution, and shallow thinking, I am afraid that you will not be able to go through the torrent of the next 3-5 years.
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The dilemma it faces stems first from the natural limitations of profit pools. Party B mainly competes for the “digital profit pool”, which accounts for a very low proportion of the total revenue of enterprise customers, and this part of the budget usually accounts for only 1% to 3% of the total revenue of the enterprise. This is a Red Sea market that has long been crowded and highly involuted, and no matter how advanced AI technology is, if it stays in this narrow pond, the profit margin is destined to be slim.
What’s even more fatal is that the technological equalization effect of AI is rapidly depreciating “code assets”. At the same time, the traditional data model is also stretched thin in the AI era – traditional SaaS is good at recording and statistical analysis of structured data, but in the AI-driven intelligent era, enterprises need to dig deep into massive, multi-modal, and unstructured data and form real-time business closed-loop feedback. Finally, the obsolescence of standardized functions is even worse. The charm of AI Agent lies in its dynamic intelligent adaptation capabilities and personalized service potential. This makes the static, standardized functional modules on which traditional SaaS depends quickly lose its attractiveness in the eyes of users. Customers are no longer satisfied with a simple “tool”, they are eager for a “decision brain” that can assist in decision-making and even execute independently.
In the Chinese market, this dilemma is particularly prominent:
On the one hand, Chinese enterprise customers are generally not willing to pay for the SaaS subscription model, preferring one-time project-based procurement and privatization deployment, which makes it difficult for Party B to achieve sustainable revenue at scale.
On the other hand, the high R&D, sales, operation and maintenance costs brought about by the project system, coupled with the generally low customer unit price and renewal rate, have formed a “three highs and two lows” curse that is difficult to get rid of. Although AI technology can reduce costs to a certain extent, if the business model does not change, it will still treat the symptoms rather than the root cause. The pressure of accounts receivable has made many Party B companies struggle in the quagmire of cash flow, seriously affecting their development potential and the valuation of the capital market.
What is even more alarming is that many AI “new Party B” often choose to do “light AI” in order to achieve rapid monetization in the short term, avoiding those “dirty work” that can really precipitate the value of the industry, and only focusing on some scenarios that are easy to standardize. This strategy seems to be “short and fast”, but in fact it is to drink poison to quench thirst. Because it cannot accumulate industry-specific in-depth data and scene cognition, it also cuts off the channel for the continuous evolution of AI models, making it difficult to form irreplaceable core barriers. In the long run, it will surely be out of the higher-level AI competition.
Therefore, for the “new Party B” in the field of AI Agent, this is more like a transition road full of thorns than an end where you can settle down. If you want to make a breakthrough, you can only deepen your cultivation in extremely vertical segments, build industry barriers that are difficult for others to reach, or actively explore the transformation to a deeper value delivery model.
AI-driven “value chain integrators” (Party C) – surviving in the cracks is still a key hub
In the ecosystem of AI Agents, there is another type of role – “Party C”, that is, those companies that act as technology providers and work with system integrators, large industry solution providers, or traditional B2B channels to embed their core AI capabilities into larger solutions. They are like connection points in the value chain, trying to find their own ecological niche between A and B.
On the surface, this model does have its appeal. For example, it can significantly reduce the initial customer acquisition cost, and enterprises can quickly reach the market with the help of partners’ channels without building a large sales team. At the same time, some project risks and upfront investments can also be shared with partners. For some small teams with core AI technology but lack market resources, this is an effective way to quickly verify technology and accumulate industry experience.
However, an in-depth analysis of its business logic reveals that the “Party C” model faces many deep constraints and growth bottlenecks. The biggest problem is that the business growth of “Party C” is highly dependent on partners. The speed of its market penetration, the breadth of customer reach, and even the right to speak in specific projects are deeply affected by the partner’s business development capabilities, strategic preferences, and the stability of the partnership. This strong dependence makes “Party C” lack the strong momentum of independent growth and the ability to resist external risks.
In addition, the AI capabilities of “Party C” are often still regarded as a “technical module” or “functional plug-in” in the overall solution, and its “tool” fate is difficult to get rid of. This means that once better alternative technologies appear, or integrators choose to develop their own AI capabilities, the status of “Party C” is in jeopardy. Due to the lack of direct access to end customers and direct control over business results, it is difficult to form customer stickiness and core barriers based on in-depth service and continuous optimization.
Therefore, in the eyes of the capital market, the imagination space of the “Party C” model is relatively limited. Investors often prefer companies with independent brands, direct customer relationships, and scalable, predictable business models. Because of the above characteristics, “Party C” is often undervalued and is more regarded as a technology supplier rather than a creator of core values.
It can be seen that the “Party C” model may play a “small but beautiful” way of survival for some AI Agent startups at a specific stage, or as a springboard to enter the market. However, if it is regarded as the ultimate goal of long-term development, it is likely to fall into continuous “component” competition, and it will be difficult to achieve a real value leap in the magnificent wave of AI commercialization.
The beginning of the paradigm revolution: AI Agent “does Party A” and pays according to the results
There are problems with the model of Party B and Party C, so will there be a “Party A” and “Party A” model?
I think this is the ultimate direction of AI Agent entrepreneurship, that is, Result-as-a-Service, or even Success-as-a-Service, which is also called “Outcome-based Model” or “Performance-based Model” in foreign countries.
The essence of “AI as Party A” is that it is not simply to sell a set of software, an algorithm, or provide on-time consulting services to customers, but that AI companies rely on their strong technical and engineering capabilities to directly “end” the operation responsibility of a customer’s core business, and be responsible for the final business results – not only to provide advanced AI tools and technologies, but also to be responsible for “contracting labor, materials, and results” to provide customers with end-to-end, full-chain solutions. Here, AI is no longer just an enabling tool, but also the core driving force for comprehensive reshaping and deep control of customer business processes, operation systems, and even asset allocation.
As Sequoia Capital insights, “the next round of AI sells not tools, but income.” The intervention of AI Agents has made this “pay-for-results” model, which was once difficult to promote on a large scale due to technical limitations, from a beautiful vision to a business reality.
From a commercial point of view, “AI doing Party A” is actually directly jumping into the geometrically amplified profit pool, from 1-3% of the digital profit pool to the human resources profit pool (generally 20-40%, such as AI customer service, AI sales), and even possibly into the “asset profit pool” (such as optimizing equipment operation and maintenance efficiency through AI), “supply chain profit pool” (such as optimizing logistics costs through AI scheduling), which is at least 10-20 times the scale.
At the same time, when you regard yourself as “Party A”, your core business processes, data systems and even organizational structure will be deeply integrated with the customer, forming a cooperative relationship of “you have me, I have you”. This deep binding brings extremely high conversion costs, as long as the service quality meets the standard, customers naturally tend to maintain long-term cooperation, high stickiness and high repurchase rate are natural. Just as management consulting giant Accenture was able to sign a decade-long and billion-dollar operational outsourcing contract, this is the core logic.
Therefore, at this time, the AI Agent must be heavily vertical: the large model is 10,000 meters wide and 10 meters deep, so it can directly replace a lot of work, although these still require human final review; What AI Agent needs to do is to be 1 meter wide and 10,000 meters deep, and build a five-fold moat that integrates technology, data, industry know-how, ecological resources and risk control capabilities, which is of great value.
Finally, AI Agent companies must personally enter the front line, because the real barriers are often hidden in those seemingly cumbersome, non-standard, and high-friction “dirty work”. These links precise the most real and valuable industry data and scenario cognition, and are the “fuel” for AI models to continue to iteratively optimize and approach the essence of business. Avoiding these is equivalent to cutting off the evolutionary path of AI, and in the end, it will only be sadly eliminated in the higher-level AI competition.
There is actually a case of AI doing Party A, that is, Kobold Metals:
This startup, backed by tech giants such as Bill Gates and Jeff Bezos and top capital, has completely disrupted the traditional mineral exploration industry. It no longer just sells a set of AI prospecting analysis tools to mining companies, but uses its powerful AI platform “Machine Prospector” to accurately locate mineral deposits with mining value, directly participate in investment, acquire mining rights, and become an “AI owner”.
In this way, KoBold Metals directly translates AI capabilities into control of high-value mineral resources, earning substantial returns far beyond technical service fees from the final sale of minerals and project interests, as evidenced by its large copper mines discovered in Zambia.
epilogue
Through an in-depth analysis of the three major business models of AI Agent entrepreneurship, we can clearly see an evolutionary context from tools to value, from auxiliary to dominant: although the traditional “Party B” model can improve efficiency with the blessing of AI, its inherent “selling ability” thinking and dependence on “digital profit pools” make it difficult to get rid of the dilemma of low-profit competition; The transitional “Party C” model is often difficult to grow into an independent industry giant due to its dependence on “selling integration” and limited value sharing. Only the “Party A” model with “result as a service” as the core, through deep involvement in the customer’s core business process, and the orientation of “selling results and creating value”, can we truly unleash the disruptive potential of AI Agent and open up a broad blue ocean market.
Behind this is the inevitable result of the trend of equalization of AI technology. When advanced AI capabilities are no longer the patent of a few giants, and it is difficult to build a lasting moat by relying solely on technological leadership, the value of “selling tools” will naturally continue to decrease. At the same time, whether it is corporate customers or end users, the ultimate pursuit of “actual effect” and the careful calculation of the input-output ratio are strongly promoting the implementation of the concept of “result as a service”.
The sea of stars of AI Agent has been shown in front of us, and only those companies that dare to take the pen of “results” and explore the RaaS model can finally draw their own glorious chapter in this trillion blueprint.
