【徐瑾】梁文锋的商業邏輯與矽谷敘事截然不同。科技圈有人將他比作張小龍，但他骨子裡更像一位務實的南方商人——重視成本、願意分利、看得見大局。他明確表示API定價標準是伺服器十個月回本，融資所得優先全部換成顯卡，並預判國內基座廠商最終將收斂至三至四家。這套在資源約束下的生存邏輯，強調「活下去比講故事更重要」，在國內AI圈比任何願景都更具分量。他對開源的態度同樣值得關注：最強模型完整開源，自用版與開源版保持一致，不擔心第三方分流，因為算力與成本才是真正的壁壘。用開放換生態、用成本優勢守住核心，這是商人的算盤，而非佈道者的願景。

梁文錋對AGI經濟規模的判斷具有深刻洞見。他認為AGI最終將佔整體經濟的10%，而推動這一切的核心變量是成本。這一判斷與互聯網擴張史高度吻合——從伺服器到頻寬再到雲計算，每一次成本的數量級下降都帶來一輪新的應用爆發，AI亦不會例外。他進一步指出，互聯網格局將在AI時代重演：頭部聚合平台吃掉大部分價值，長尾創作者分走剩餘，被淘汰的是中間那批不上不下的企業。國內基座廠商收斂至三至四家，說的正是同一件事——行業洗牌後，活下來的人靠的是清晰的成本與規模邏輯。

在技術路線上，梁文錋展現出罕見的克制。他明確指出持續學習是當前核心瓶頸，而多模態和世界模型僅是配套組件，不值得投入核心資源。他認為AGI是漸進過程，不存在突變奇點，模型大約每兩至三個月更新一版。這種節奏感本身帶著歷史感，是長期主義者才會有的表述。Naval Ravikant將AI定義為這個時代的槓桿工具，而槓桿的關鍵從來不在工具本身，而在於誰能以最低成本持續運轉。梁文錋比大多數人更早想清楚了這件事，儘管風險同樣真實存在。（關鍵數字： three months）

Liang Wenfeng's business logic stands in sharp contrast to Silicon Valley narratives. Some in the tech community compare him to Zhang Xiaolong, yet he is fundamentally a pragmatic southern Chinese businessman—cost-obsessed, willing to share profits, and able to see the bigger picture. He has stated explicitly that API pricing targets a server payback period of ten months, that funds raised are prioritised entirely for purchasing GPUs, and that domestic foundation-model vendors will ultimately consolidate to three or four. This survival logic under resource constraints, emphasising that "staying alive matters more than storytelling," carries more weight in China's AI sector than any grand vision. His stance on open source is equally notable: the strongest model is fully open-sourced, the internal and open-source versions remain identical, and he does not fear third-party diversion because compute power and cost constitute the real moat. Trading openness for ecosystem and defending the core through cost advantage—this is a businessman's calculus, not an evangelist's vision.

Liang Wenfeng's assessment of AGI's economic scale offers profound insight. He believes AGI will eventually account for 10% of the overall economy, with cost as the core variable driving this transformation. This judgement aligns closely with the history of internet expansion—from servers to bandwidth to cloud computing, each order-of-magnitude reduction in cost triggered a new wave of application proliferation, and AI will be no exception. He further argues that the internet's structural pattern will repeat in the AI era: dominant aggregation platforms will capture the majority of value, long-tail creators will claim the remainder, and the companies eliminated will be those stuck in the middle. The consolidation of domestic foundation-model vendors to three or four speaks to the same dynamic—after the industry shakeout, survivors will be those with clear cost and scale logic.

On technical direction, Liang Wenfeng displays a rare restraint. He identifies continual learning as the current core bottleneck, while classifying multimodal capabilities and world models as merely supporting components unworthy of core resource investment. He views AGI as a gradual process with no sudden singularity, with models updated roughly every two to three months. This sense of pacing itself carries a historical sensibility—a framing that only a long-term thinker would adopt. Naval Ravikant defines AI as the leverage tool of this era, yet the key to leverage has never been the tool itself but rather who can operate it continuously at the lowest cost. Liang Wenfeng figured this out earlier than most, though the risks remain equally real.