MiniMax open-sources Code CLI, Xiaomi reveals high training costs, and Qianxun predicts humanoid robot speech understanding.
On the evening of September 18, domestic large model company MiniMax opened its programming assistant, MiniMax Code CLI v0.4.12, to global developers and fully open-sourced it under the MIT license. The MIT license is the most permissive in the open-source world, allowing anyone or any company to freely modify products and use them commercially without paying licensing fees. Official evaluation figures show a 76.7% pass rate on real-world coding tasks. Previously, the mainstream approach among domestic players was closed models charging per call. MiniMax directly open-sourced the tool layer, following a strategy to seize the developer entry point—distributing tools for free while monetizing backend model calls and compute services. MiniMax is rushing toward an HKEX listing, and developer ecosystem data will directly impact its valuation story.
Two pages recently made public by Xiaomi laid bare the bottom line of the large model business: on one side, training costs exceed 200,000 RMB per hour, with GPU burn rates hitting millions daily; on the other, external API pricing charges just a few yuan per million output tokens. A token is the smallest unit AI uses to process and generate text, so selling tokens is essentially charging by word count. Between buying and selling, the price gap spans dozens of times. This encapsulates the profitability dilemma of the entire large model industry—as model capabilities compete harder and get stronger, service prices compete harder and drop lower. Beijing's recently released "Ten Measures for the Token Economy" treats tokens as a new economic lever to cultivate, which is the right direction, but vendors across the industry are betting that cost curves will plummet once scale increases. For companies like Xiaomi with hardware and application ecosystems, losses on large models can be offset by whole-device sales; for startups relying solely on selling model capabilities, every price cut flattens the gross margin curve in their funding stories further.
Gao Yang, Assistant Professor of Robotics at Tsinghua University and Co-founder/Chief Scientist of Qianxun Intelligence, told Reuters on September 18 that humanoid robots could understand verbal instructions and complete most general tasks as early as next year, but entering homes to perform practical work still requires time. The value of this judgment lies in refining the industry consensus realization timeline into two steps: "understanding" and "working." Capital markets previously valued robot companies based on "one-step-to-perfection" pricing; a two-stage rollout implies a significant gap between demonstration periods and delivery periods. Beyond the body itself, industry infrastructure is converging ahead of technical routes: after robot bodies are deployed in batches, standardization of data collection, simulation training, and operations scheduling becomes the new bottleneck. Whoever defines this foundation gains pricing power in the next round.
On September 15, HarmonyOS Smart Mobility and AITO Auto successively issued statements regarding changes in cooperation mode: The AITO brand still belongs to HarmonyOS Smart Mobility, existing rights and subsequent services are unaffected, and Huawei continues to "empower," but leadership over product definition, design, and brand operations begins to be redrawn. In these 36 hours, frontline store narratives, old owners' insurance renewal inquiries, and used car residual value expectations showed visible divergence. AITO is the fastest-moving sample of the smart selection model; its shift from "family" to "partners" marks that the alliance form of Huawei-affiliated automakers is entering deep waters. Short-term channel friction is inevitable, but long-term, rising autonomy for each brand might actually activate the entire HarmonyOS Smart Mobility system. Three hard indicators to judge the quality of this adjustment: ownership of definition rights for future AITO new cars, the commission structure in Huawei stores, and most directly—will October deliveries drop?
According to CNN disclosures, during the US-Iran conflict this spring, a false intelligence report generated with AI circulated within the US military: claiming abnormal movements by Chinese cargo ships, leading the US military to seriously evaluate plans to intercept or even board and inspect vessels. It was later confirmed that the intel content was an AI hallucination—the model earnestly fabricating non-existent facts—and action was stopped by manual verification at the last minute. This is the first publicly disclosed dangerous near-miss event after AI entered the military decision chain, exposing the most fatal flaw of large models directly in the worst-case scenario: hallucinations are jokes in chat windows, but in battlefield situation assessment, they could trigger accidental discharges. Following the exposure, members of both parties in Congress demanded rules for military AI usage. The hundred-plus expert open letter and the Big Three's deceleration debate this week will leverage this case to intensify pressure; security audit and explainability tracks have instead gained the hardest procurement justification.
On September 18, the three companies announced the formation of an AI Energy Management Alliance, aiming to use AI to optimize data center power dispatch and grid coordination, bringing along a batch of power and data center supply chain companies. Soaring power consumption of AI data centers is already the largest incremental load on the US grid; disputes over data centers competing with residents for power have occurred in both Texas and Virginia. Top companies choosing to co-build standards equals admitting: without energy solutions, there is no next round of compute expansion. Reports of heavy buying of Meta-linked high-yield bonds for data centers and EQT betting $2 billion on small batteries this week all say the same thing—electricity is becoming the throat of the AI business. "AI Power Middle Layer" companies like grid dispatch software, energy storage, and small modular nuclear reactors gain backing from top firms, shifting valuation logic from themes to orders.
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