Momenta reports narrowed losses, Tencent Hy4 faces compute queues, and BoE warns of AI market risks.
H1 2026 revenue reached RMB 1.602 billion, a year-on-year increase of 75.9%, with a gross margin of 73.2%. After excluding non-operating factors such as fair value changes in preferred shares and share-based payments, the adjusted net loss narrowed to RMB 14.097 million, approaching break-even. More important than the growth itself is the signal: an autonomous driving company long labeled with "high R&D investment, high delivery costs" has proven for the first time with auditable financial data that its mass-production model can turn positive. A 73.2% gross margin is close to software company levels, directly chipping away at the market consensus that "the Robotaxi story can only survive via financing." In Momenta's revenue structure, licensing fees and mass-production services each account for half, and economies of scale are beginning to cover R&D expenses. This serves as a template for algorithm companies moving toward a software P&L statement. This creates valuation benchmarking pressure for already-listed smart driving supply chain companies like Horizon Robotics and RoboSense. The market is beginning to reprice autonomous driving companies based on unit economics rather than the number of design wins. High-R&D targets that are not yet profitable and rely on hardware deliveries to stack revenue will be scrutinized quarter by quarter under a magnifying glass. What Momenta secured is not just a break-even figure, but the pricing standard for the entire sector.
Released late at night on August 28, the Hy4 preview (770 billion total parameters, 49 billion active, 1 million token context, fully open-sourced under Apache 2.0) saw its WorkBuddy inference cluster overwhelmed by requests within just 3 days. Officials urgently expanded capacity and promised continuous dynamic resource allocation, but still did not rule out continued queuing during peak hours. This release without a press conference made headlines thanks to the queues. Queues upon release are an extreme signal of the authenticity of demand for domestic open-source models. Free and open-source spending zero marketing dollars yet instantly maxing out high-end compute indicates that enterprise-level API call demand is far from satisfied. The other side of the coin is Tencent's insufficient total high-end compute and concentrated peak concurrency. Looking at this alongside Zhipu's announcement of the "Large Foundation" route on the same day, the conclusion is clear: the domestic model competition has officially entered the second half, where compute reserves are key. Only those holding GPUs dare to open-source and lower prices. Direct beneficiaries are the domestic compute chain on the inference side and cloud service providers with their own compute pools. For small and medium-sized model companies relying on third-party compute, this is a warning bell on the cost side—in the era of free open-source, inference bills will only get more expensive. When raising funds, it is best to include GPU budgets in the business plan upfront.
Bailey publicly stated that threats posed by frontier AI models are deepening and could cause a disorderly correction in financial markets. Almost simultaneously, Reuters reported that central bank governors are collectively discussing the impact of AI agents on financial markets. AI enters the central bank agenda for the first time as a source of financial stability risk, rather than as a strategic industry. This is a key turning point in regulatory narrative. Previously, AI was a supported entity in policy discussions; now it is being treated as a systemic risk exposure. Once "AI bubble + AI stability risk" becomes a joint statement by central banks, allocation discipline for sovereign funds regarding the AI sector will tighten. Volatility amplifiers like agent trading may also attract targeted regulatory rules. Valuation-wise, this directly points to the risk premium of high-valuation AI leaders like NVIDIA and Palantir, which have seen massive YTD gains. On the flip side, AI safety, model auditing, and trade regulation tech companies may become hedge beneficiaries of policy. Government procurement budgets in this niche are worth tracking.
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