The Market Has Shifted from Paying for Imagination to Paying for Execution

The market does not care about your narrative. It cares about your numbers. The recent correction in tech stocks is not a macro event. It is a repricing of unverified promises. A major Chinese brokerage report has reframed the entire AI trade, shifting the attribution of the sell-off from external factors like Treasury yields to internal industry variables. The conclusion is stark: AI stocks have entered a verification phase. The market is no longer paying for potential. It is paying for proof. For years, the AI trade was simple. Buy the narrative. GPT-4 drops, the stock goes up. Multimodal progress, the stock goes up. The valuation anchor was technical breakthrough expectations. That era is over. The new anchor is commercialization. The report identifies three verifiable pricing variables: the pace of commercialization, the efficiency of compute conversion, and the evolution of the model gap. The biggest potential variable, however, is something called anti-distillation. This is not just a technical footnote. It is a potential structural shift in the entire competitive landscape. Let me break down the core logic. The report argues that the transmission chain of compute advantage to market share to model gap is the central competitive dynamic. Compute is the moat. The moat is pricing power. This is reshaping value distribution across the AI supply chain. Compute infrastructure providers, GPU manufacturers, and cloud service providers are gaining bargaining power. The model layer and application layer are getting squeezed from both sides. The introduction of anti-distillation suggests the competition is moving from a model capability race to a data and knowledge asset protection phase. This will fundamentally alter the diffusion path of AI technology. I have seen this pattern before. In 2017, I manually audited 45 ICO whitepapers. I rejected 90% of them for lacking viable utility. The same principle applies here. The market is now rejecting projects that lack viable commercialization. The report's core insight is that the market's patience window is narrowing. If the top players cannot deliver better-than-expected commercialization data in the next two to three quarters, the valuation system may shift from a price-to-sales multiple to a price-to-earnings logic. That would trigger a systemic de-rating. Let me be precise about the commercialization problem. The current revenue growth of top AI companies still relies on acquiring new customers, not deep monetization of existing ones. OpenAI's annualized revenue has surpassed $4 billion, but inference costs remain high. Anthropic's revenue is growing fast, but gross margins are under pressure. The industry is still in a phase of trading revenue for market share. The unit economics are not yet validated. The pricing model is still cost-plus, based on tokens or seats. There is no mature value-based pricing. This means AI companies have not yet established pricing power directly linked to customer value creation. The report highlights a critical disconnect. The technology investment curve is steep and rising. The revenue realization curve has not yet shown an exponential inflection point. The capital markets are repricing this temporal mismatch. The market's expectations have shifted from technical leadership equals commercial success to verifiable customer retention and willingness to pay. The penetration rate controversies around Microsoft Copilot and the actual adoption rates of Salesforce Einstein GPT show that enterprise AI budgets are growing, but deployment is slower than early optimistic expectations. Now, let me address the elephant in the room: anti-distillation. The report calls it the biggest potential variable. If top model vendors use technical means, such as output watermarking or API usage restrictions, to prevent competitors from training new models on their outputs, the catch-up path for smaller AI companies will be cut off. The industry could accelerate from a flourishing ecosystem to an oligopoly. This is a deeper concern. If the model gap solidifies due to anti-distillation, the speed of AI innovation diffusion will slow significantly. This is particularly impactful for the Chinese AI industry, which relies on the open-source and distillation path to catch up. From my perspective as a battle trader, this is a critical risk to monitor. The report's framing of the compute gap question is telling. The question is not whether the compute gap exists. It does. The question is whether it will translate into an irreversible model capability gap. This depends on two factors: the duration of the compute gap and whether non-compute factors, such as algorithmic innovation and data quality, can partially offset the compute disadvantage. Let me talk about the competitive landscape. The report implies that AI competition has shifted from a single-dimensional model capability race to a multi-dimensional competition involving compute, models, commercialization, and ecosystems. In this framework, pure technical leadership is no longer sufficient. Compute reserves, commercialization execution, and ecosystem lock-in effects collectively form the competitive barrier. The current landscape is one superpower and multiple strong players. OpenAI still leads in model capability and ecosystem maturity, but Anthropic, Google, and Meta are catching up faster than the market expects. The competition focus has shifted from whose model is stronger to whose combination of model, compute, and ecosystem is better. OpenAI's deep binding with Microsoft, Anthropic's compute cooperation with Amazon, and Google's full-stack self-developed advantages all reflect this multi-dimensional competition. The compute reserves of top AI companies have formed a generational advantage. The H100 cluster scale of OpenAI and Microsoft, Google's TPU v5p deployment, and Anthropic's compute lock-in agreements make it difficult for new entrants to compete under equal compute conditions. Here is the contrarian angle. The report's discussion of the model gap implicitly concerns the Chinese AI industry. Under compute controls, will the US-China AI model gap widen? The positioning of anti-distillation as the biggest potential variable suggests deep concern about this risk. But there is another layer. The report's mention of K-shaped divergence convergence implies a trading strategy signal. Dollar weakness and reduced rate hike expectations may trigger a rebalancing of funds from US AI leaders to other markets, including A-shares. However, the sustainability of this rebalancing depends on whether the AI industry fundamentals support valuation convergence. I have to be skeptical here. The report's advice to avoid excessive grand narratives is a warning about AI narrative bubble. The market's expectations for AI already contain a large amount of grand narrative components, such as AGI approaching and productivity revolution. Once these narratives cannot be realized as concrete business results, the risk of valuation correction will significantly amplify. This is where my 2022 Terra/Luna defense experience comes in. I triggered a pre-defined emergency protocol to liquidate 100% of my stablecoin holdings into cold storage, avoiding a 90% portfolio drawdown. The same principle applies here. You need clear, non-negotiable rules for capital preservation. The report's core contribution is shifting the attribution framework for AI stock pricing from macro interest rate dominance to industry fundamental dominance. It proposes three verifiable pricing variables and identifies anti-distillation as the biggest potential variable. The deep meaning is that the AI industry has entered a verification period. The market will shift from paying for imagination to paying for execution. In this phase, valuation divergence among AI companies will intensify. Only those that can deliver on all three dimensions, commercialization, compute efficiency, and model capability, will maintain valuation premiums. Let me give you the actionable framework. The top three risks are clear. First, AI commercialization continues to miss expectations, triggering a systemic de-rating as the valuation system shifts from PS to PE. Second, anti-distillation causes the industry structure to solidify, cutting off the catch-up path for smaller AI companies. Third, compute supply chain risks, such as GPU supply shortages or export controls, delay training plans and cause cost overruns. The top three opportunities are equally clear. First, AI commercialization verification targets with clear commercialization paths and verifiable revenue growth will gain premiums in valuation divergence. Second, beneficiaries of compute efficiency improvements through algorithmic optimization or hardware innovation will gain competitive advantages in a compute-scarce environment. Third, trading opportunities from K-shaped divergence convergence, as dollar weakness and reduced rate hike expectations may trigger fund rebalancing. Arbitrage is the immune system of the protocol. Trust is a variable; verification is a constant. The market is now verifying. The question is whether you are positioned for the verification phase or still stuck in the imagination phase. The signals to track are clear. In the short term, watch the quarterly reports of top AI companies for revenue growth, gross margins, and customer retention. In the medium term, watch for anti-distillation technical measures and the performance gap between open-source and closed-source models. In the long term, watch for killer applications or standardized deployment inflection points. The market has shifted from paying for imagination to paying for execution. The question is not whether AI will change the world. It will. The question is which companies will survive the verification phase. The answer will determine the next decade of technology investing. Are you ready for that trade?