刘飞

AI’s main battleground in 2025 may shift from model races to application delivery, with product definition, operating speed, and organizational coordination becoming scarcer assets as model capabilities converge. ByteDance, Alibaba, and Tencent are all separating or restructuring their model and application teams, reflecting a shared view that “models are models, applications are applications.” OpenAI’s jump from GPT models to ChatGPT was enormous, but the market now wants products to go “another ten or twenty steps” beyond that.

Plunging inference costs are expanding demand while potentially continuing to compress the revenue pool for the model layer and cloud API market. 刘飞 relayed 修寒’s rough estimate that leading third-party applications could each generate daily traffic on the order of tens of billions of tokens, with perhaps hundreds of such applications, implying a market of only several billion yuan to RMB10B. Per-token costs fell roughly 100x in 2024; if total costs fall another two orders of magnitude in 2025, maintaining the existing market size would require a 100x increase in usage. 刘飞’s social business currently consumes several million tokens a day, and he doubts such usage KPIs are realistic for most applications.

“Applications” and “Agents” are not separate tracks: most AI products classified today as scenario applications can be understood as small Agents with controlled boundaries. Perplexity breaks down questions, calls information sources, and recombines the answers; AI coding, AI PPT, and file summarization similarly package general-purpose models into defined workflows. The key is not whether a product carries the Agent label, but whether it can find the “key” that fits a specific user need and keep refining every tooth.

The advantage of products such as 豆包 and 夸克 comes not only from their underlying models, but from the organizational ability to rapidly replicate validated use cases and turn them into low-friction experiences. 庄明浩 chooses products based on whether the team can quickly follow new breakout use cases; “this is completely not a technical reason, but an organizational and operational one.” 夸克 has evolved from a browser and “Swiss Army knife” toolkit into “an AI all-purpose assistant for 200 million people,” while 豆包 positions itself as a “super assistant.” Both are competing for the next unified entry point.

The AI Native label is losing decision-making value: users do not care whether a feature is powered by a foundation model, rules, or human judgment; they care only whether it solves the problem. 刘飞’s summary is that “AI is not a scenario at all,” just as no one calls themselves a C or Java product manager. A knowledge-base founder once removed language such as “AI food analysis” from the product description, suggesting that technology narratives aimed at investors are giving way to real usage and retention.

AI glasses are one of the strongest areas of hardware consensus in China and the US, but the opportunity comes with supply-chain, inventory, and form-factor risks. The market could split between products using Qualcomm chips priced at roughly RMB1,500–2,000 and domestic-chip products priced below RMB1,000; glasses can also capture first-person, personalized data, creating new inputs for generation and Agents. But compute, battery life, weight, thermal management, and whether to use a split-device design remain unresolved. “All those pitfalls are still right there.”

Near-term automation value will appear first in tasks with verifiable outputs, while subjective content production remains clearly behind coding. The path from Cursor to Devin has made “hand-writing code becoming performance art” imaginable, but AI PPT and content summarization still miss what humans consider important. The most practical way to participate is not to “learn AI” in the abstract, but to use products such as 豆包 and 夸克 against everyday tasks and search for product opportunities in real-world friction.