if you can get milk for free, why buy a cow?

DeepSeek's current situation perfectly illustrates the saying, "Why buy a cow when you can get milk for free?" They have developed SOTA (State-of-the-Art) performance models at remarkably low costs, achieving superior performance with exceptional cost efficiency through distillation based on top-tier models.

This phenomenon adds credibility to Professor Andrew Ng's prediction that AI will become basic infrastructure like electricity. As inference costs continue to decrease at a rate far exceeding Moore's Law, the moats of model service providers are gradually eroding. AI development, which once required hundreds of millions of dollars in initial investment and tens of thousands of GPU clusters, can now be accomplished much more efficiently through the distillation of existing models.

This shift is expected to lead to the decentralization of inference. As AI computation moves from centralized clouds to local edge devices, we will witness a major hardware upgrade cycle. The fact that models at the level of DeepSeek R1 can now run on a Mac Studio will bring a wave of innovation to the PC and smartphone markets, posing challenges to current AI infrastructure leaders.

Consequently, true competitiveness in the AI industry is shifting from foundation model performance to the application layer. While current AI applications focus on generating insights and data visualization through data embedding and vector search, creating true value requires a process-centric approach, as Palantir recently mentioned in "The Cybernetic Enterprise." Every critical decision-making process includes interconnected stages, handoff points, and feedback-based learning, which is particularly important in complex operational environments such as hospital staff scheduling, airline route planning, and manufacturing line optimization.

Data-centric software can support information aggregation, dashboard creation, and occasional insight generation, but this is merely "management technology" in Silicon Valley terms. This approach is designed for offline analysis and scoreboard observation, not for driving actual inventory rebalancing, fleet deployment, or critical field operations.

The expected value in the AI era isn't about incremental improvements but fundamental transformation. While data-centric architecture enabling 'RAG' workflows improves information retrieval, it remains confined to one-way visualization and insight generation. Automating core workflows requires a process-first approach and an operational architecture that can continuously test, adjust, and scale AI-driven approaches based on human feedback.

We stand at the threshold of a new era of AI democratization. This represents not just technological advancement but a complete restructuring of industry. As Real-world AI breaks through its singularity, we will witness an entirely new era of value creation.