Token Consumption Surges Tenfold in Six Months, VC Partner Warns AI Startups to Steer Clear of Tech Giants' Core Battlefield

Liu Yi'ang personally experienced OpenClaw's complete journey from viral sensation to retreat: he started using it in February and switched to Hermes in May. This individual open-source project surpassed 300,000 GitHub stars within four months, with a peak of 17,000 new stars in a single day—an epic breakthrough in the open-source world. It even sold out the Mac mini—a product line Apple had nearly discontinued, which was revitalized as a result.

More importantly, OpenClaw gave a boost to Chinese-made large models. In the first week after Chinese New Year this year, Kimi's revenue exceeded its total revenue for all of 2025. The shift from conversation to agents is a topic worth revisiting repeatedly this year and over the next five years.

But OpenClaw quickly retreated. Liu Yi'ang believes security concerns may have been only one factor. As a user, he experienced two core pain points: first, version updates once occurred daily, and after each update all plugins stopped working and required reconfiguration—a painful process; second, token consumption was a black box—users had no idea what the AI was doing, and an infinite loop could blow through five hours' worth of token quota in five minutes.

He concluded that this is the fate of independent open-source software. OpenClaw's rise and fall warrants self-reflection, but for the industry as a whole it serves as an alarm—this industry is changing.

After OpenClaw's retreat, the agent industry as a whole continues to grow. Anthropic is now the international leader, with ARR reaching $45 billion, of which Claude Code—a software product—accounts for over $4 billion in ARR. Note that this is not model revenue but software product revenue, achieved in just 14 months since launch. Codex is also updating frequently and catching up—a compelling two-horse race.

Liu Yi'ang made a core judgment: the future AI industry ecosystem will be built on a token distribution business model. Whoever controls the most critical nodes in token distribution will capture the largest rewards in this industry chain. This is the same logic as internet-era giants controlling traffic distribution nodes.

But token distribution differs structurally from traffic distribution. First, the switching cost for large models is extremely low—one command and you've switched, essentially seamless and imperceptible. Large models themselves have almost no network effects—unlike WeChat, where users simply cannot switch away. Second, token production has a clear cost; economies of scale exist but are not as strong as in traditional internet industries.

Today's industry chain architecture is changing: model providers → token routers/factories (Alibaba Cloud's Bailian, ByteDance's Volcano Engine, SiliconFlow) → agent layer (Work Buddy, Codex, Claude Code, open-source projects) → end applications. End applications are also distributing tokens; many applications are essentially "wrapping a shell" around token resale. Whether this model can hold remains to be seen.

In the Chinese market, Liu Yi'ang cautioned that while everyone is discussing Zhipu and MiniMax, the real players to watch are Alibaba Cloud and ByteDance—these are the largest ecosystem players in China today. DeepSeek represents a different approach: models only, ultra-high cost-performance, concentrated force for single-point breakthroughs—a strategy inseparable from the capabilities and architecture of Huawei's Ascend chips in China.

Note: The above directions are compiled from Liu Yi'ang's presentation and represent his personal assessment of AI startup opportunities.

On the edge device layer, Liu Yi'ang specifically noted that the Qwen 35B model can already solve a great many problems. This year's Mac mini sellout demonstrates that demand for on-device model inference computing power is exploding. Model capability improvements, on-device hardware improvements, and KV cache optimization—advancing on both fronts simultaneously—will create enormous opportunities in this direction.

In video generation, Kling (spun off from Kuaishou) launched in June 2025 and has already surpassed $700 million in ARR. Chinese companies now hold a near-monopolistic position globally. This is a mid-stream, high-certainty track outside of foundation models and big tech competition.

In AI for Science, AI-driven drug discovery has developed rapidly over the past two years. Some Chinese companies, combining AI efficiency with China's execution efficiency, now have pipelines approaching those of top overseas pharmaceutical companies. However, he cautioned that using AI merely for tools and software is too thin—entrepreneurs must genuinely penetrate industries to replace low-efficiency links.

Finally, Liu Yi'ang issued a clear warning to entrepreneurs: coding has become a must-win battleground for tech giants. If entrepreneurs discover interesting opportunities, he welcomes discussion—but they must be mindful of competition from the giants.