AI and Web3: Can They Redefine Productivity and Production Relations?

Over the next two decades, business models and employment forms may shift dramatically. Reviewing the past 20 years of internet evolution — from portal giants (NetEase, Sohu, Sina) to individual site owners, then to self-media, and from state-owned enterprises to private firms and gig platforms like Didi and food delivery — reveals an irreversible trend: macro-level platformization and micro-level individualization of work.

Three layers drive this trend. Socially, human production has continuously moved from collective action toward individual specialization, reducing governance complexity. At the enterprise level, platforms such as Meituan, Didi, and ByteDance achieve scale with only thousands of formal employees while coordinating millions of practitioners through efficient platform ecosystems that close the value-production-and-monetization loop. Individually, people seek autonomy when basic security and infrastructure allow; the rise of social insurance and digital tools makes freelance work viable.

The arrival of generative AI (AI 2.0) provides a massive productivity boost. A children's picture book that once required a multi-role team over days can now be produced by one person using ChatGPT and MidJourney. Just as mobile internet, GIS, and algorithms enabled Didi and Meituan to match supply and demand at low cost, AI infrastructure lets individuals or small teams deliver production-grade output.

Consequently, large platforms must consider converting employees into platform practitioners with stable income streams. Smaller firms and individuals need to restructure workflows using AI tools, turning rigid collaboration into loose alliances. Societally, the enterprise-centric social security system must be rebuilt to support increasingly atomized work — mirroring how state-owned enterprises once pushed logistics socialization.

A core tension emerges: centralized platforms clash with decentralized employment. Platforms control digital assets, unilaterally adjust revenue splits, distribute content arbitrarily, and can promote or ban creators at will. This asymmetry constrains further productivity gains.

The web evolved from Web 1.0 (one-way publishing) to Web 2.0 (two-way co-creation), yet contributors do not own the virtual assets they generate. Physical property rights rest on objective existence and neutral state enforcement. Virtual assets lack such infrastructure: networks are human-operated, centralized, costly, and biased.

Web 3 attempts to solve this by building a public, decentralized virtual world where users own data as assets, secured by blockchain immutability, with DAOs enabling collaboration and tokens redesigning revenue distribution. However, current token issuance and trading entities remain non-physical, non-neutral, and lack credible backing — a root cause of crypto bubbles.

In summary, AI is the likeliest catalyst for a productivity revolution, while Web 3 is the likeliest technical carrier for matching decentralized production relations (though still immature). The future may see free individuals and loose alliances working under AI and Web 3 infrastructure with robust operational tooling. This logic also hints at why OpenAI's Sam Altman pursues both AGI (high-value creation) and Worldcoin (value distribution). Timing is critical: premature or delayed alignment with natural economic laws backfires.