Author: HF

As the capabilities of models become increasingly homogenized, what exactly should AI companies sell?

▲ Value Transfer: From Delivering "Capabilities" to Delivering "Results"

In the past year, the AI entrepreneurship field has gone through an intense competition in model capabilities. Countless products have emerged, and vast amounts of capital have poured in, presenting an unprecedented prosperous scene.

However, beneath this wave, a more fundamental question is surfacing: when model capabilities become increasingly homogenized, what exactly should AI companies sell?

The importance of this question lies in the fact that it directly determines the company's competitive barriers and long-term ceilings.

The Hidden Concerns of Tool-Based Models

▲ Weak Moat: Endless Arms Race with Underlying Models

Most of the AI companies that have secured funding in the current market belong to a "tool-based" business model. Its core logic is to deliver a powerful tool to professional users, enabling them to complete their work more efficiently. Whether it is the programming assistant Cursor, the legal analysis assistant Harvey, or the design tool Midjourney, they essentially follow this paradigm.

The logic of this model is clear and easy to understand, but it also carries a widely overlooked structural risk: the core barrier of tool-based companies is essentially built on model capabilities.

What does this mean? If your model is strong enough today, your product works well; if a stronger model emerges tomorrow, users may turn and leave immediately. In an industry where model capabilities are rapidly iterating, those who sell tools are essentially engaging in an endless arms race with model providers.

More critically, as large companies begin to offer basic model capabilities for free, the survival space for tool-based companies will be further compressed.

So, if not selling tools, what should be sold instead?

Value Proposition of Result-Oriented Models

▲ Crossing the Intermediate State: Hiding the Tools and Delivering the Results

A noteworthy shift is occurring: some AI companies are starting to skip the "tool" intermediary layer and directly deliver final results to clients.

The so-called result-oriented model means that clients no longer need to learn how to use a specific software but directly delegate their work tasks to the AI system. The former sells "capabilities," while the latter sells "results."

For example, in the financial domain, traditional software companies sell feature-complete financial systems, requiring businesses to equip professional accounting operational systems. In contrast, the new generation of AI companies provides "month-end closing services" directly—clients upload raw receipts, and the AI completes the entire processes of auditing, bookkeeping, and report generation, ultimately delivering compliant financial results. The pricing model shifts from "software subscription fees" to "service commissions."

This model already has several practical cases in the domestic market:

The core difference lies in: tool-based companies optimize the "work process," while result-oriented companies deliver the "work endpoint."

Structural Advantages of Result-Oriented Models

▲ Dimensionality Reduction Strike: From Lowering Customer Acquisition Costs to Establishing Data Barriers

From a business logic perspective, the result-oriented model has three significant advantages over the tool-based model:

1. Fundamental improvement in customer acquisition efficiency. Tool-based products require extensive pre-sale investments to educate users on how to use and make the best of them. Result-oriented products only need to answer one question: can it help me complete this work? The decision-making chain is significantly shortened, and the cost of building customer trust is notably reduced.

2. Natural establishment of pricing models. Tool-based product pricing typically relies on indirect indicators such as the number of users and functional modules, making it difficult for clients to accurately assess value. Result-oriented products can be priced directly based on business outcomes—how many reimbursement items completed, how many contracts reviewed, how many data entries generated. Value assessment is clear, and willingness to pay is more stable.

3. Deep accumulation of data barriers. This is the most crucial distinction. Tool-based companies primarily accumulate user behavior data—click frequency, time spent, feature preferences. Result-oriented companies, on the other hand, accumulate business outcome data—what conditions determine compliance, what contracts entail risk, what reimbursements are abnormal. This domain knowledge data will become the core competitiveness of the next generation of models; the larger the business volume handled, the deeper the understanding of the industry, and the more challenging it becomes for latecomers to catch up.

Strategic Entry Point: AI Reconstruction of Outsourcing Services

▲ Urban Renewal: AI is eating away the profit pools of traditional outsourcing

For entrepreneurs looking to enter the result-oriented model, an efficient strategy is to start from business scenarios that have "existing outsourcing bases."

The logic is that if a certain task has been outsourced by businesses, it indicates that this task possesses three characteristics—businesses accept external completion, there is a ready budget, and clients only care about results and not the process. The essence of AI companies entering the market is "replacing outsourcing suppliers," rather than "changing business habits."

The following domestic tracks exhibit clear adaptability:

A Thought-Provoking Question

▲ The Ultimate Inquiry: In 2026, what exactly are you selling?

When assessing the investment value of AI projects, a core question is: does the company sell tools or results?

This is not an absolute binary judgment. Some tool-based companies have established strong competitive positions based on excellent user experience and user stickiness. However, from a long-term perspective, result-oriented companies have a higher ceiling, more stable barriers, especially having irreplicable advantages in data accumulation.

By 2026, competition in model capabilities will gradually cool down, and innovation in business models will become the new main battlefield. For every entrepreneur in the AI field, the fundamental question to answer is:

Do you want customers to use your tools, or delegate the work to you?

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