Key Takeaways: CITIC Securities attributes the tech stock correction to three unresolved AI forward-pricing variables — commercialization pace, compute-to-pricing-power conversion, and model distillation risk — not just high long-end Treasury yields.
The tech stock correction stems from three unresolved AI forward-pricing questions — commercialization pace, compute-to-pricing-power conversion, and model distillation risk — not simply high long-end Treasury yields, CITIC Securities said in an Aug. 20 research note.
"The long-end rate rise reflects AI investment crowding out social capital — it is the result of AI investment still being strong," the CITIC Securities research team wrote. "Long-end rate increases mostly reflect real-rate rises; using them to explain tech stock declines is not reasonable."
Tech giants' debt financing expanded from $87.49 billion in 2025 to $219.22 billion year-to-date, up 150.6 percent. OpenAI's API spend share among more than 70,000 US enterprises tracked by Ramp rose from 28.5 percent in May to 36.0 percent in July, while Anthropic's fell from 71.2 percent to 63.4 percent. GPT-5.6 Sol accounted for 14.9 percent of API spending by July, up from zero in May.
The three variables determine whether AI hardware remains priced as scarce infrastructure or reverts to commodity utility pricing — a distinction worth hundreds of billions in market capitalization across Nvidia, TSMC, and the broader AI supply chain.
Commercialization Pace Splits OpenAI and Anthropic
Investor concern about AI monetization has centered on Anthropic's annualized recurring revenue, which grew about 18 percent monthly from May through July. Including OpenAI narrows the slowdown: CFO Sarah Friar told an internal meeting in August that quarter-to-date ARR growth was about 35 percent, per CNBC. A newer narrative holds that a large share of frontier-model token consumption flows through cloud service providers' Token-as-a-Service channels — Bedrock, Vertex, Foundry — rather than directly into model-maker ARR, meaning direct revenue figures systematically understate end-market growth. That TaaS narrative has supported hardware valuations, but it has yet to attract new capital unless agent-based commercial use cases beyond coding emerge, CITIC said.
By paid enterprise count, Anthropic still leads at about 44 percent versus OpenAI's 40 percent, but OpenAI's momentum is clearly recovering. The share shift is almost entirely attributable to GPT-5.6 Sol, which went from zero API spend share in May to 14.9 percent in July. If compute capacity enables faster model releases and higher inference throughput, "grabbing more compute to gain higher application market share" becomes a self-reinforcing rationale for model makers to keep investing in infrastructure.
Compute Advantage vs. Distillation Risk
But static share does not equal pricing power. OpenRouter data shows Anthropic's weekly token usage declined after mid-July while OpenAI, DeepSeek, and MiniMax all rose. If frontier model capabilities converge and switching costs stay low, compute advantages yield temporary share rather than sustainable excess margins. The total addressable market matters more than share distribution.
The bigger question is whether training-side compute advantages translate into durable model gaps. A mid-August paper from MATS Research and ELLIS Tübingen — "Stealing Reasoning Traces from Proprietary LLM APIs" — detailed how reasoning chains can be extracted from current API architectures, suggesting expensive training moats face low-cost catch-up risk. If distillation persists, compute "pick-and-shovel" pricing reverts to public-infrastructure utility models. If frontier labs solve anti-distillation by year-end and ship materially stronger models, the compute race intensifies and AI hardware stays priced as scarce.
Treasury Buybacks and Positioning Shape the Backdrop
The US Treasury's Aug. 19 announcement to double long-end buybacks from $2 billion to $4 billion per operation — targeting 10- to 30-year bonds from Sept. 9 through Nov. 4 — carries limited direct market impact against $32 trillion in publicly held debt. Its real function is to anchor expectations that the Treasury will intervene if long-end rates rise disorderly, compressing term-premium tail risk. The risk: deepening market distrust of fiscal discipline and accelerating Treasury selling. JPMorgan strategists warned the buyback blitz may push yields higher, the opposite of the Treasury's intent.
The macro shift weakens near-term Fed rate-hike expectations, which could narrow the K-shaped divergence between AI and non-AI sectors. But the structural forces pushing long-end rates higher — AI hardware investment crowding out bond demand, sticky energy prices, and Hormuz risk — remain unchanged.
In A-shares, CITIC channel data shows active private funds raised equity exposure from 71.7 percent to 79.0 percent in the first week of August, the largest weekly increase since 2017. Large private funds' stock position index hit 88.56 percent by Aug. 14, a 2026 high, with 77.11 percent at full position. The most aggressive capital has already driven this rebound, meaning optimistic expectations are largely priced in.
CITIC recommends rotating from AI price-surge plays toward core assets with volume certainty — gas turbines, wafer fabrication platforms, and semiconductor equipment — while adding energy, nonferrous metals, innovative drugs, and export-capable brokers. In a range-bound market, investors should temper expectations and avoid macro narratives, the report said.
This article is for informational purposes only and does not constitute investment advice.
