Summary of Key Points
This article exposes the illusion of many companies claiming to have improved efficiency through AI: In most cases, so-called AI-driven efficiency enhancements are either achieved by layoffs (reducing the “denominator” of labor productivity) or by shifting labor costs to IT/development expenses (an accounting trick), without actually leading to business growth or organizational improvements. True AI efficiency requires two essential conditions: first, the rate of business output growth must exceed the combined growth of human and machine resources; second, there must be accompanying changes in organizational structures (breaking down departmental barriers and optimizing decision-making processes). The article concludes with three practical indicators to help companies verify whether their claims of AI-driven efficiency improvements are genuine.
The Illusion of AI-Driven Efficiency: A “Digital Game” of Cost Replacement
Many companies claim that AI has increased efficiency by 30%, but in reality, they have simply replaced labor costs with AI expenses. The money saved from layoffs is often offset by the costs associated with computing power, model training, and data labeling, resulting in higher overall expenses. Tech giants like Amazon and Microsoft, for example, invest heavily in AI (with substantial capital expenditures) while simultaneously conducting large-scale layoffs, which leads to a significant decrease in free cash flow.
More “clever” companies use accounting maneuvers by categorizing AI-related costs under “IT” or “R&D” rather than “labor costs.” This makes their labor productivity reports look better on paper (output divided by labor cost), but the total company expenses remain unchanged. It’s like moving money from “daily expenses” to “education spending”; it may seem more “productive,” but your wallet doesn’t get any fatter.
The Truth About Labor Productivity: True Efficiency Comes When the Numerator Grows Faster Than the Denominator
The essence of labor productivity is the “input-output ratio”—output (revenue, profit, sales volume) divided by input (labor or total human and machine resources). Companies that improve labor productivity through layoffs are essentially changing the denominator. With fewer employees, the ratio improves, but if output doesn’t increase, it’s like losing weight without gaining strength. For instance, if a department reduces its staff by one-third and still has the same amount of work to do, with the remaining employees working overtime, AI is merely helping them complete tasks that they would have done anyway more quickly—this isn’t true efficiency improvement; it’s a form of waste.
True efficiency improvement should involve increasing the numerator (output growth) faster than the input (resource growth). For example, if 10 people used to generate 1 million in revenue and now 10 people generate 1.5 million (a 50% increase in the numerator while the denominator remains unchanged), or if 12 people generate 2 million (a 100% increase in the numerator with a 20% increase in the denominator), that’s when a company is truly becoming more competitive.
Individual Efficiency Does Not Equal Organizational Efficiency: AI Cannot Save an Unreformed Organization
While AI can make individuals work faster (e.g., completing a three-day task in half an hour), the overall organization does not become more efficient. The bottleneck lies not in individual speed but in coordination and communication issues. For example, after a plan is created, it still has to go through multiple approval layers, involve cross-departmental disputes, and wait for leadership approval, with no significant improvement in overall efficiency.
Worse yet, AI can complicate collaboration: Each department uses its own AI tools to collect data and analyze information, leading to inconsistent results and more difficult conflicts. It’s like having calculators in every department, but if the calculations differ, no one can convince anyone of the correctness of their findings. AI is just a tool; without changing organizational structures (e.g., breaking down departmental barriers and streamlining processes), it’s like replacing a leaky bucket with a larger faucet—more water flows out, but the bucket still doesn’t hold much.
Three Indicators to Verify AI-Driven Efficiency
The article provides three simple indicators to assess whether AI has truly improved efficiency:
1. Token Efficiency: Calculate as “business output divided by total human and machine input (labor costs + AI expenses). If this ratio continues to rise, it indicates genuine efficiency improvement; if not, it’s an illusion.
2. Key Decision-Cycle Time: Measure the time required for three typical decisions (budget approvals, cross-departmental resource allocation, new product launches). If AI hasn’t reduced these times, it means AI is only a tool and has not changed management practices (e.g., still requiring multiple levels of approval).
3. Efficiency Sharing Index: Has the additional business revenue generated by AI been shared with employees? If employees receive no extra benefits, it suggests that AI is just an expense without real motivation; everyone is merely going through the motions for their bosses.
For most companies, these indicators either show zero or negative values—AI-driven efficiency improvements are merely figures on a PowerPoint slide.
In Conclusion: Don’t Mistake Layoffs for Genuine Efficiency Improvements
Layoffs have immediate effects (they can be highlighted in quarterly reports), but organizational change is a long-term process that faces internal resistance. Many companies opt for the former, but eventually, the consequences will become clear: when there are no more employees to lay off and AI costs continue to rise while the organization remains unchanged, the “unpaid debt” of organizational reform will catch up with them. The most expensive aspect will be the unreformed organization.
In summary, AI-driven efficiency improvement is not about changing tools; it’s about changing how work is done. Business growth must exceed resource input, and the organization must become more flexible to leverage AI effectively. Otherwise, AI is just a “high-end version of Office software” or an expensive toy that doesn’t deliver real value.