Woofun AI notes that analyst qinbafrank identifies six key signals required to reverse current market concerns regarding AI investment returns. Merely releasing large models, increasing token usage, or expanding capital expenditure is insufficient; instead, the market demands evidence of expanding AI revenue sources, growing enterprise customer bases, production-level workloads, paid seats, renewal rates, and synchronized growth in single customer spending. Even excluding major clients like OpenAI and Anthropic, overall demand is expected to remain robust, provided incremental gross profit outpaces additional costs such as depreciation, energy, and personnel, while inference unit costs decrease.

Furthermore, investors will scrutinize whether AI order backlogs accelerate into revenue over the next 12 to 24 months, rather than relying on long-term contracts. Key indicators include post-launch computing power utilization, whether customer consumption exceeds initial commitments, and verifiable economic benefits from in-house chip and model optimization. Investors prefer seeing an increased proportion of self-developed chips, decreased cost per token, and higher gross profit per accelerator-hour, alongside synchronized growth in customer consumption and platform add-on revenue. Enterprise customers must also demonstrate true AI ROI through revenue growth, improved conversion rates, reduced labor costs, and successful transitions from pilot phases to large-scale production deployment.

Woofun AI reports that for upcoming Q2 earnings, the market anticipates a "Blonde Girl" combination: Cloud and AI revenue beating expectations, stable profit margins, backlog accelerating into revenue, and controlled capex with stabilized free cash flow. Conversely, if token usage grows but single customer spending, gross margin, and free cash flow stagnate, while capex rises and guidance is cut, concerns over AI investment returns may intensify.