Summary of Key Points
This article argues that the era of AI is witnessing a shift in economic paradigms, moving from past mass-standardized production to a "deep economy" characterized by on-site adaptation. The core idea is that AI enables supply systems to dynamically match the constantly changing needs of individuals, much like reconfigurable chips (FPGAs) that can be programmed for various purposes. The focus of this "deep economy" is not merely on improving efficiency but on creating "exclusive value" and satisfying consumers' desire for novelty by transforming customized experiences into reusable modules through a concept called "stacking capabilities." The article also discusses the technical prerequisites, shifts in value logic, business operations, and potential risks associated with this new economic model.
Detailed Analysis
1. The Deep Economy: A Revolution from One-size-Fits-All to Tailored Solutions
The traditional economy was based on mass standardization—factories producing identical products and supermarkets selling a uniform range of goods, with the goal of reducing costs and reaching a wide audience. However, with AI, demand has evolved from fixed preferences among a group of people to dynamic needs in different contexts for individual consumers. The article uses FPGA chips as an analogy: while traditional ASIC chips have fixed functions, like standardized products, FPGA chips can be programmed on-site to adapt to various scenarios, similar to how supply systems in the deep economy can adjust to user-specific situations.
Examples:
- Palantir (a data company): Instead of selling generic software, it sends engineers to clients' business environments (such as governments or enterprises) to integrate algorithms into their processes (e.g., for counterterrorism or supply chain management), providing customized solutions and offering ongoing services.
- Shein (fast fashion): It does not produce mass-market hits but responds quickly to small orders, launching new styles based on users' real-time preferences, fulfilling the desire for constant novelty.
In essence, the deep economy offers tailored products that can evolve with the user's needs.
2. Overcoming Barriers with the Deep Economy's Technical Toolbox
The deep economy relies on a combination of technologies to overcome the challenges of customization:
- Ecosystem platforms (e.g., Taobao, TikTok): These platforms consolidate user data and preferences, allowing companies to understand dynamic consumer behavior rather than relying on static demographic labels.
- Generative AI (e.g., ChatGPT, Midjourney): They can quickly generate multiple design options for a low cost when users request, such as designing a hoodie with a starry pattern.
- 3D printing: It eliminates the need for expensive molds by directly manufacturing products, significantly reducing costs (e.g., customizing shoes without the need for new molds).
- Digital twins: They simulate product performance in virtual environments before production, avoiding costly physical trials.
- Platform-based forecasting: They predict which products will be popular based on user data, enabling companies to prepare materials in advance.
- Cryptographic trust mechanisms (e.g., blockchain): They ensure secure data sharing across companies without exposing sensitive information.
These technologies together make customization both affordable and efficient.
3. A Shift in Value Logic: From Material Scarcity to Dual Scarcities
In the past, the scarcity was of physical resources; now, with AI, materials are more abundant, but new types of scarcity have emerged:
- Exclusive fit: Products must perfectly suit the user's current context, considering factors like lifestyle and support systems (e.g., diabetes treatment plans that account for work habits and dietary preferences).
- Desire for novelty: Humans naturally seek freshness and variety (e.g., Shein launching thousands of new styles weekly or personalized learning paths in education).
The article calls this a "dual scarcity economy," where the focus is on creating a sense of exclusivity and novelty.
4. How Companies Can Participate: The Power of Stacking Abilities
In the deep economy, companies cannot afford to customize everything from scratch; instead, they need to develop "stacking capabilities"—the ability to turn each customization into a reusable module for future use. There are four key strategies:
- Immersive understanding: Engage deeply with customer contexts by sending engineers to experience their problems firsthand.
- Practical documentation: Document successful customization experiences as templates for similar issues.
- Modular solutions: Combine different modules to create customized services (e.g., educational platforms offering personalized courses in mathematics, programming, and art).
- Risk monitoring: Implement systems to ensure that AI-generated content is accurate and reliable.
5. Risks and the Future of the Deep Economy
While the deep economy offers many benefits, it also comes with challenges:
- Risks:
- High dependency: Strong ties between companies and customers may lead to exploitation (e.g., difficulty for Palantir in switching suppliers or price increases).
- Data privacy: Companies collecting large amounts of user data can be misused.
- Informational silos: Customization may create an echo chamber, limiting exposure to diverse perspectives.
- Future trends: There will be less waste as products are produced on demand, and the focus will shift from ownership to experiences and innovation (e.g., renting clothes instead of owning them).
In summary, the deep economy is a trend that, if well-managed, can meet consumer needs while addressing these risks.
In One Sentence
AI is transforming the economy from mass production to tailored solutions that evolve with the user. The key lies in using technology to overcome customization barriers and fulfill the desire for exclusivity and novelty, while being mindful of potential pitfalls.