If you had asked a carriage driver in the late 19th century what he needed most, he almost certainly would have answered: I need a faster horse. He would never have requested an internal combustion engine.
In a recent thought-provoking analysis, Tianqiao Chen, founder of Shanda Group and the Tianqiao and Chrissy Chen Institute (TCCI), uses this classic metaphor to diagnose a critical error in modern corporate strategy. He argues that today's business leaders are trapped in a "skeuomorphic pitfall," mimicking the shapes of the old world rather than leveraging AI to create something genuinely new.
According to Chen, the vast majority of enterprises are merely "AI Enabled" — adding AI buttons to legacy software or grafting AI departments onto obsolete hierarchies. While this approach offers short-term comfort, Chen warns it is ultimately a "cul-de-sac" that prolongs the life of systems deserving of obsolescence. True revolution, he posits, requires "recoding the enterprise from the genetic level."
Chen's framework outlines three distinct stages of evolution: AI Enable, AI Native, and AI Awaken.
Stage I: AI Enable — The Trap of Addition Logic
Chen describes the current "AI Enable" phase as relying on simple addition logic: Old Process + AI Plugin = New Process.
In this model, humans remain the "CPU" of the workflow — the central processors handling judgment and connection — while AI serves merely as a powerful "GPU" for faster calculation. Chen compares this to strapping an internal combustion engine onto a horse carriage; while speed increases, the chassis eventually disintegrates under the thrust.
For organizations to cross from this additive phase to true multiplication, Chen identifies three critical technical "mutations" that must occur:
- From Probabilistic Fitting to Logical Reasoning: AI moving from predicting the next word to unfolding internal chains of thought, shifting human roles from line-by-line review to "exception monitoring."
- From Text Dialogue to Tool Action: Agents evolving from chatbots that offer advice into autonomous entities that execute complex workflows across APIs and browsers.
- From Statelessness to Long-Term Memory: Systems developing an "enterprise-grade hippocampus" to retain institutional memory, transforming experience from a human asset into a system asset.
Stage II: AI Native — The Liquefaction of Business
When these mutations are completed, the business world hits a tipping point. Chen defines the AI Native stage as the moment when "AI becomes the CPU," and humans shift to managing strategy and exceptions.
He uses a vivid metaphor to describe this transition: the melting of ice. Traditional corporations are built like solid blocks of ice, with rigid departments designed to minimize high connection costs. However, Chen argues that AI acts as a massive heat source. As agents reduce information friction to near zero, the rigid corporate structure "melts" into a liquid state. Data, talent, and resources begin to flow automatically to where they are needed, without the need for a heavy administrative skeleton.
To help leaders determine if they have reached this stage, Chen proposes three litmus tests:
- The Survival Question: If AI is removed, does the business merely slow down (Enabled) or does it collapse (Native)?
- The Flow Question: Do AI agents "handshake" and pass tasks directly to one another, or are humans still connecting the nodes?
- The Memory Question: Does the system "devour experience," automatically turning human errors into new system rules?
Stage III: AI Awaken — The Final Boundary
Beyond efficiency lies what Chen calls the AI Awaken stage — a phase where the fundamental definition of work is challenged.
In this stage, AI evolves from an "Executor" to a "Discoverer," breaking into the wilderness to find laws and solutions humans have never seen. Chen poses a stark challenge: when AI begins to doubt the objective itself or rewrite the reward function, humanity faces the will of a new species.
Why allow this evolution? Chen's answer is pragmatic, "To Win." He argues that the limit of an AI-native enterprise is ultimately the limit of human cognition. To breakthrough, organizations may be forced to allow AI to define "what is better" beyond human logic.
Conclusion: A Challenge to Management
Chen's analysis concludes with a chilling question for today's managers. As we move from Enable to Native and finally touch Awaken, we are dismantling the final moats of human intelligence.
"When this silicon species is not only more diligent than me, but begins to understand 'what is right' better than me... is there still a necessity for my existence?"
For Chen, the transition is inevitable. The question is no longer about what tools to buy, but how to exist in a world where correctness is computed and decision-making is outsourced.
