Introduction: 2026, The "Cooling-Off Period" and "New Inflection Point" of AI Commercialization
As of August 2026, the global tech industry stands at a delicate turning point. If 2023 was the "explosive first year" of generative AI, and 2024-2025 the "computing power arms race period," then 2026 has undoubtedly become the "cooling-off period" and "deep waters" of AI commercialization. With diminishing marginal returns on large model capabilities and increasingly stringent scrutiny of AI ROI by enterprise clients, the once-popular "API call fees" and "Copilot subscription models" are facing unprecedented growth bottlenecks.
Against this backdrop, a new wave of change is quietly brewing—a shift from "selling software" to "selling results." In the AI SaaS 2.0 era, the core business model is evolving from traditional subscriptions (SaaS) to value-based "Pay-for-Performance" (PaaS - Performance as a Service). This paradigm shift will not only reshape the global software industry landscape but also bring profound valuation reshaping opportunities to the enterprise service sectors of the Hong Kong and A-share markets.
The Crisis of Subscription: When "Copilot" Becomes a Decoration
Over the past two years, enterprise software vendors have successively launched AI assistants by integrating large model capabilities, attempting to monetize by raising subscription fees. However, market feedback has poured cold water on these enthusiastic vendors. Recent industry survey data shows that although over 80% of enterprises have tried purchasing AI-enhanced software, the actual active usage rate (DAU/MAU) is generally below 30%.
The core of the problem lies in "value mismatch." Traditional SaaS models charge per head or per month, regardless of whether users actually create value from it. But in the AI era, business owners are no longer willing to pay for tools that "might create value"; they prefer to pay for "actual cost reduction and efficiency improvement results." When a company spends millions deploying an AI customer service system only to find that problem resolution rates haven't significantly improved, or that email conversion rates generated by AI sales assistants are mediocre, a decline in subscription renewal rates becomes inevitable.
This "subscription fatigue" forces the capital market to re-evaluate the valuation logic of SaaS companies. The story of relying solely on user growth no longer holds water; investors are starting to ask: How much money does your AI actually make for clients?
AI SaaS 2.0: The "Performance Economy" of Shared Risk
Facing the dilemma of subscriptions, leading AI vendors in 2026 began exploring a more disruptive business model—"Pay-for-Performance." The core of this model lies in deeply aligning the interests of AI vendors with their clients' businesses, no longer selling tools but directly selling business outcomes.
- Sales and Marketing: Emerging AI sales agents no longer charge monthly but take commissions based on the order value of successful conversions. For example, an AI marketing platform promises to pay only when the ad creatives it generates bring actual sales leads. This "betting" model greatly lowers the barrier to entry for clients while also pressuring AI vendors to continuously improve model accuracy.
- Customer Service: The billing standard for AI customer service systems is shifting from "number of seats" to "tickets resolved" or "labor costs saved." Vendors deploy AI bots directly, and clients pay a fee for every customer inquiry successfully intercepted and resolved. This effectively turns AI vendors into "partners" in the business process.
- Supply Chain and Finance: In complex supply chain optimization or quantitative investment research scenarios, AI system fees are directly linked to the logistics costs saved or excess returns generated. Under this model, AI technology is no longer a cost center but directly transforms into a profit center.
This shift towards the "performance economy" marks the complete transition of the AI industry from technology-driven to business value-driven. It requires AI vendors to not only understand algorithms but also possess industry know-how, capable of diving deep into business processes to solve the most thorny practical problems.
Reconstruction of Valuation Logic for HK Enterprise Service Sector
For the long-undervalued HK enterprise service sector, this trend is undoubtedly significant positive news. For a long time, the Hong Kong market valued SaaS companies mainly based on PS (Price-to-Sales) multiples with a large discount, due to market concerns about the high churn rates and low moats of mainland SaaS enterprises.
However, with the popularization of the "Pay-for-Performance" model, valuation logic is undergoing fundamental changes:
First, improved revenue quality. Performance-based revenue often has higher margins and stronger stickiness. Once the AI system becomes an engine for client revenue generation, the willingness to switch vendors will be extremely low. This will significantly improve the Net Dollar Retention (NDR) of enterprise service companies, thereby gaining a higher valuation premium.
Second, shifting from PS to PE. When AI vendors can directly share in the dividends of client business growth, the nature of their revenue will be closer to "commissions" or "service fees," possessing higher profit certainty. The market will gradually shift focus from revenue growth rate to net profit and cash flow. This will benefit leading companies with solid industry implementation capabilities, bringing their valuation systems closer to those of mature traditional software companies or even consulting firms.
Specifically, vendors listed in Hong Kong with deep data accumulation and customer bases in e-commerce SaaS, fintech, and enterprise communications are best positioned to complete this transition first. They can leverage existing customer relationships to quickly enter performance-based payment scenarios, thereby achieving a "Davis Double Play" in performance.
New Mainline of "AI Implementation" in A-Share Market
Compared to Hong Kong stocks, the AI investment logic in the A-share market often focuses more on "import substitution" and "vertical industry applications." At the 2026 time node, with the rise of the "Pay-for-Performance" model, the speculative hotspots in A-shares will further sink from pure computing hardware and large model algorithms to the "AI application layer" with industry implementation capabilities.
Investors should focus on targets that possess monopolistic data in vertical fields and can deeply integrate AI technology with business processes. For example, in the field of healthcare informatics, enterprises that can effectively help hospitals improve turnover rates through AI-assisted diagnosis; in the field of industrial software, enterprises that can significantly reduce factory energy consumption through AI scheduling. These companies are no longer simple software vendors but "operators" of industry digital transformation, and their performance elasticity will far exceed market expectations.
Challenges and Risks: The Gap Between Ideal and Reality
Although "Pay-for-Performance" paints a beautiful blueprint, it still faces many challenges during actual implementation.
First is the problem of performance attribution. In a complex business environment, closing an order is often the result of multiple factors working together. How to precisely define the AI system's contribution ratio? This requires establishing a set of fair, transparent, and mutually recognized quantitative standards; otherwise, commercial disputes are easily triggered.
Second is data privacy and security. To achieve optimal results, AI models often need deep access to core business data. Today, when data sovereignty is increasingly valued, how to ensure data is not abused or leaked is a prerequisite for establishing trust. Private deployment and federated learning technologies will become key to solving this problem.
Finally is cash flow pressure. For AI vendors, performance-based payment means a longer collection cycle. Large costs need to be invested in the early stages of model training and system deployment, while returns are gradually realized in the future. This puts extremely high demands on corporate fund chain management capabilities. Small and medium-sized AI startups may be eliminated because they cannot support this "Valley of Death," and industry concentration is expected to further increase.
Conclusion: Embracing the Era of "Pragmatists"
In Q3 2026, the AI industry is undergoing a baptism of separating the real from the fake. The squeezing out of the bubble is not a bad thing; it marks the industry moving towards maturity. The transition from "subscription" to "Pay-for-Performance" is essentially the process of AI returning from "showing off skills" to "business common sense."
For investors, the era of storytelling is over, and the moment of verifying performance has arrived. In the Hong Kong and A-share markets, those "pragmatic" AI enterprises that can truly dive into the business fabric, dare to share risks and benefits with clients, will cross the cycle and become the leaders of the new round of long-term tech bull markets. AI Gold Rush is no longer digging for a gold mine of technology, but a gold mine of commercial value. In this paradigm shift, only players with deep industry insights and strong implementation capabilities can ultimately win.
