The tide has turned! A top Wall Street investment bank's late-night report: AI warfare has entered the "meat grinder" stage, only companies that survive will be qualified to talk about $BTC

Market analysis indicates that the artificial intelligence foundational model industry is shifting from expectation-driven to demand-driven. A recent report systematically addresses the top ten concerns of investors, suggesting that model quality has become the primary variable determining the market landscape, and industry differentiation will accelerate.

The report suggests that China’s AI market is at a clear turning point. The demand for coding and agent scenarios is growing rapidly. Domestic model capabilities have reached or even surpassed the levels of leading models in the U.S. from a year ago, while local pricing is more aligned with economic benefits, both of which improve the return on investment. The year 2026 is a key year for whether Chinese enterprises can replicate the U.S. growth curve of 2025.

Taking Anthropic as a reference, its annual recurring revenue increased from $10 billion in December 2024 to $190 billion in March 2026, a growth of about 19 times in just 15 months. The Chinese market has the conditions to follow a similar path, especially in the coding field, where internet giants like Tencent, Alibaba, and ByteDance have integrated related tools into their existing ecosystems, shifting demand from individual demonstrations to full deployment.

Question 1: Is AI demand linear growth, or is it an inflection point explosion? Demand is inflection point-driven. As long as model quality is good enough to unlock real application scenarios, usage will switch from linear growth to an “upward curve” explosion. The U.S. market provides strong evidence. China currently has the foundational conditions for a similar explosion. On the agent side, OpenClaw has become an important catalyst, pushing usage scenarios from single-turn interactions to multi-step task execution.

Question 2: Will API pricing rise, fall, or differentiate? Pricing will not move in one direction; differentiation will be the main theme. Strong models create pricing power. As hardware and algorithm efficiency continue to improve, the unit cost of inference will continue to decrease. The end result is a differentiated pricing structure: models that maintain cutting-edge capabilities can achieve both volume and price increases simultaneously; models that fail to iterate will face price drops.

Question 3: If pricing is not the main battlefield, where is the focus of competition? The main battlefield has shifted from token price to model capability. This is a key change compared to last year. In the fastest-growing coding and agent scenarios, quality is far more important than unit price. In multi-step workflows, the essence of what customers purchase is “successful task completion.”

The report provides an intuitive mathematical example: if the single-step success rate rises from 85% to 98%, the final completion rate of a 20-step task will jump from 4% to 67%. Under this logic, the model with the lowest price per token may have the highest actual comprehensive cost for completing each task. The report also points out that companies with strong cutting-edge models can easily extend into the low-end market, but companies that solely rely on low prices will struggle to enter the high-end market.

Question 4: Why is the foundational large model still a “life-and-death struggle” industry? Small technological gaps, endless iteration cycles, and converging monetization models create a highly brutal industry. The capability gap between various large model companies in China is often smaller than investors expect. In this industry, “standing still” is not a neutral outcome but implies a loss of status.

The convergence of business models has intensified the pressure for elimination. Revenue growth and profit margins primarily depend on product strength, and switching costs remain low, which means companies that lose technological momentum will quickly lose defensive capabilities in business and finance, and the number of truly reliable companies in the industry will gradually decrease.

Question 5: What are the determinants of profitability? The core issue is whether gross profit growth can consistently exceed R&D spending growth. The basic economic model of the token business is clear. As model efficiency and inference chip efficiency continue to improve, the gross margins of cutting-edge models should gradually rise. However, the outlook for operating profit is more complex.

Anthropic serves as a cautionary tale: even when monthly revenue levels reached $140 billion in February 2026, the company simultaneously announced a new round of financing of $300 billion, emphasizing continued cutting-edge development—high revenue does not mean normalized training intensity. The benchmark scenario is that both Zhipu and MiniMax are expected to turn a profit starting in 2029. The report emphasizes that more important tracking indicators than specific profit years are: the continuous growth trend in usage and the ongoing improvement in unit economic benefits.

Question 6: How should investors track model strength? It is necessary to combine three dimensions: token price, usage, and third-party assessments. Token price is the most important indicator as it is the real-time expression of a company’s market positioning for its products. The price gap with the best models is becoming a good proxy for the actual competitiveness of models.

Token usage reflects the real choices of users and developers. Third-party API aggregators like OpenRouter can serve as references, particularly for the growth of agent-type workloads. In terms of third-party assessments, Artificial Analysis provides structured evaluations, while LMArena reflects the blind preference of real users, and both complement each other.

Question 7: With internet giants aggressively entering the B-end, what is the future for independent model companies? The competitive boundaries are converging, ultimately returning to a contest of model capabilities. Alibaba has clearly identified cloud and AI as strategic priorities. Tencent’s agent products have covered all scenarios for individuals, developers, and enterprises. OpenAI is also shifting its commercialization focus toward enterprise products and coding deployment.

The leading companies share a consistent direction: AI is evolving from “consumer-end functionality” to “a tool that directly generates enterprise revenue.” In this context, independent model companies can no longer rely solely on a “cloud-neutral” label to create a moat, and internet giants cannot fully cover the deficiencies in model capabilities merely by relying on ecosystem traffic advantages. When deploying AI, enterprise customers still prioritize model quality.

Question 8: What factors determine a company’s survival? Talent comes first, computing power second, and organization third—none of the three can be missing. Top research talent remains core. The technical judgment of senior management itself is a competitive factor. In terms of computing power and capital, cutting-edge training is expensive, and the economic viability of inference depends on the quality of infrastructure. Organizational execution is almost as important as the model itself in a rapidly iterating market.

Question 9: If everyone is progressing, will models eventually converge? Overall strength may converge, but they will not become identical; the market will not form a winner-takes-all pattern. Different companies have differences in architecture choice, training data, product emphasis, and technical paths, and these differences will continue to generate varying capability advantages.

The report suggests that in a market that is still rapidly expanding, multiple companies can grow simultaneously, even with some overlapping capabilities—the significance of the overall market expansion at this stage far outweighs premature concerns about commodification. In the long run, a more realistic market outcome is not “one company dominates, the rest are eliminated,” but rather a few truly capable companies, each with its own areas of advantage. As AI expands from productivity tools to consumer-end scenarios, differences in personal taste, style, and preferences will further reinforce this diversified pattern.

Question 10: How to unify the understanding of open-source/closed-source, model iteration, and global expansion risks? Iteration is a necessity; open-source/closed-source is a strategic choice, and the core risks of global expansion lie in computing power and compliance. In terms of model iteration, the expected rhythm is to launch one flagship model per year, accompanied by small upgrades driven by reinforcement learning.

Regarding open-source/closed-source, the report suggests that the answer is not either/or. Closed-source models have stronger commercial defenses; open-source helps build ecosystems, enhance adoption rates, and accelerate technical feedback. Therefore, most Chinese model companies will ultimately adopt a hybrid strategy: closed-source for the latest and strongest models, open-source for some other versions.

In terms of global expansion, the biggest risk remains in acquiring computing power. Both training and inference are highly dependent on high-performance chips, and tightening export controls will simultaneously weaken the speed of model advancement and cost competitiveness. Additionally, there are data and security compliance risks: if model deployment, user services, and data storage can achieve localization abroad, cross-border data transmission issues are relatively controllable; however, local privacy regulations and the determination of data access rights for Chinese-related entities remain sources of uncertainty.

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