On June 30th, Bairong-W (ASX: 06608) announced the formal appointment of Mr. Hong Hao, a leading domestic macro strategy expert and the Managing Partner & Chief Investment Officer of Lotus Asset Management, as a Non-Executive Director of the company. Leveraging his extensive market experience, Mr. Hong will support the company in the field of capital market value management, aiding in the optimization of its value communication system to the market.

Valuing AI companies has always been a complex challenge for capital markets. The technology sector evaluates AI firms based on model parameters, implementation scenarios, and client case studies, often seeing vast potential. Conversely, the investment community assesses them through the lenses of business models, cash flow, and valuation multiples, frequently finding the picture unclear. There is always a timing gap between industry progress and market understanding.

Whoever can minimize this gap stands to gain a valuation premium. There is invariably a need for a skilled "translator" to bridge the gap between a sound business and a strong stock. Bairong-W needs Hong Hao. As a leading player in the enterprise-level AI Agent sector, Bairong's business model deserves more serious consideration from the market.

A natural chasm exists between industrial value and capital market value: if the market does not comprehend the business model or believe in the company's growth logic, it will not assign a reasonable valuation multiple. This divide is particularly pronounced in the AI sector.

AI is a high-complexity industry with diverse business models. Some focus on foundational large models, burning through capital; others specialize in computing hardware, benefiting from capital expenditure cycles but facing extreme volatility; and some develop AI applications with a wide array of business models. Ordinary investors struggle to discern the fundamental differences between these companies.

Consequently, the market often prices all related companies under a vague "AI concept" umbrella—rising together in bullish markets and falling together in bearish ones. This "one-size-fits-all" pricing approach harms the value of companies with genuine industrial implementation capabilities and clear business models.

So why hasn't the market fully grasped Bairong-W's business model? Bairong does not focus on large models or computing hardware. It specializes in the deployment of enterprise-level AI Agents—using its Results Cloud platform to mass-produce functional "silicon-based employees" that help businesses solve specific operational problems.

The key term for this model is Results-as-a-Service (RaaS). Bairong-W genuinely creates value for clients through a pay-for-results business model, meaning the Agents—the silicon-based employees—create value for enterprises.

China's commercial environment is pragmatic. While C-end users may be averse to AI services due to perceived lack of personal respect, B-end institutions urgently need AI to boost revenue and profits. Therefore, a successful AI product must be intuitive and human-centric to meet the needs of both C-end and B-end users, thereby creating value. The silicon-based employees created by Bairong have been consistently achieving this.

However, this business model is not easily understood by capital markets. It lacks the clear Monthly Recurring Revenue (MRR) disclosures typical of traditional SaaS, nor does it generate the high-profile financing news associated with large model companies. The market needs someone to translate the "Results-as-a-Service" business logic into a compelling valuation narrative—the more clients rely on it, the higher the renewal rates, leading to more stable and predictable revenue.

This is essentially a platform company valuation logic, yet the market currently attempts to understand it through a traditional SaaS framework. A significant perception gap exists here, and this gap represents an opportunity.

Mr. Hong Hao brings nearly three decades of global macro investment research experience, having worked across institutions like Morgan Stanley, Citigroup, CICC, and BOCOM International. In 2022, he transitioned from a top sell-side role to the buy-side, managing a hedge fund. On the sell-side, his research influenced the allocation decisions of countless institutional investors; on the buy-side, he became the decision-maker himself.

This unique "referee-and-player" perspective is exceedingly rare in the A-share and Hong Kong markets. For Bairong-W, Hong Hao's role is multifaceted. Firstly, he can help the market re-examine the pricing discrepancies within the AI application layer. The current market pricing for AI exhibits a clear structural issue—upstream computing power is highly sought after, while downstream applications are undervalued.

The market awards high valuation multiples to companies like Nvidia and TSMC, yet remains cautious towards AI application-layer companies. This pricing bias stems from a lack of market consensus on "how much real value AI can actually create." His involvement is expected to help the market reassess this question.

Secondly, he can assist the company in navigating capital market cycles with precision. Mr. Hong recently posited that the next three months are a critical period for observing the potential bursting of the tech bubble, with global tech and semiconductor sectors facing a significant correction. Such insights have direct implications for Bairong-W's capital market strategy, and having a top macro strategist's perspective significantly enhances strategic accuracy.

Furthermore, he will help optimize the company's capital market value communication system, enabling the market to more accurately understand Bairong's strategic value and growth logic. This is not merely symbolic "brand endorsement" but a substantive enhancement of capabilities.

Within traditional valuation frameworks, the market habitually uses Price-to-Sales (PS) ratios to price AI application companies. But is this method universally applicable? Bairong-W's RaaS model implies its revenue quality is fundamentally different from that of traditional software companies.

Revenue for traditional software firms comes from licenses; whether the client uses the product effectively or not is not directly tied to the company's income. Bairong-W's revenue stems from the tangible results clients achieve using its AI—if there is no effect, clients simply will not renew. What does this signify?

It signifies higher-quality revenue, stronger client stickiness, and a deepening competitive moat as client data accumulates and models iterate. The continual thickening of EPS would correspondingly push the PE-band higher. The market has not yet fully priced in the valuation premium inherent to this business model.

After the AI wave recedes, companies with solid business models, positive cash flow, and clear client value will undergo a revaluation. Bairong-W may be one of those companies re-examined post-consolidation. Individuals like Hong Hao, who possess deep understanding of both global macro cycles and capital market pricing logic, are uncommon in the current AI landscape.

When an AI company combines the hard power of technological implementation with top-tier expertise in articulating its capital market value, this dual-drive of "industry + capital" naturally attracts greater investor attention. This appointment sends a clear signal—the company is not only focused on industrial implementation but is also actively refining its capital market narrative.

When market sentiment is at a low, having someone who understands that cyclical forces will ultimately reassert themselves is crucial. Amidst the noise of an AI bubble, having a voice that cautions about the inevitability of parabolic declines following parabolic rises is invaluable. Such capability is particularly precious within the AI sector.