Why Does the Term 'Year of AI Computing Power Realization' Have Pitfalls? —Understanding the Four Hurdles from Policy Signals to Actual Orders in One Article
This article critiques the phrase "The First Year of AI Computing Power Cashing In," arguing it oversimplifies a complex, multi-stage process. It proposes a "Four Gates" framework to assess the true commercialization of domestic AI computing power (like Huawei's Ascend chips):
1. **Policy Procurement:** Widely open in 2026. Significant government funding and large bulk orders from tech giants like Alibaba and Tencent exist. However, purchasing hardware is not the same as deploying it for real use.
2. **Real Deployment:** A crack has opened. The key evidence is DeepSeek V4, a top-tier AI model fully migrating from NVIDIA's CUDA to domestic computing platforms. This proves the capability for real, high-level tasks, but widespread adoption beyond leading tech firms is still nascent.
3. **Mature Software Ecosystem:** A narrow crack has opened. While frameworks like Huawei's CANN are progressing, they lag far behind NVIDIA's vast, established CUDA ecosystem in terms of supported models and developer ease-of-use. Building this middle-to-downstream developer environment is estimated to need 1-2 more years.
4. **Scalable Replication:** Essentially closed. This final gate, where thousands of mid-sized enterprises across various industries can easily adopt the technology without major migration costs, is not expected before 2027-2028.
The core risk is conflating these stages. While 2026 marks a real turning point in policy-driven procurement and proving technical viability (Gates 1 & 2), the phrase "cashing in" is premature for the full industry. True, large-scale value realization depends on the later, slower-to-open gates of software maturity and scalable replication to the broader market. DeepSeek V4's shift is identified as the most critical 2026 signal, changing the narrative from "can it work?" to "when will supply meet demand?"
marsbit05/08 11:34
