4E Labs | Computing Power and Governance: AI + Web3 New Civilization

In recent years, AI (Artificial Intelligence) and Web3 (Decentralized Internet) have emerged as two core directions of technological evolution. AI focuses on efficiency, reconstructing productivity through automation and intelligence; Web3, on the other hand, is oriented towards fairness, reshaping production relationships through blockchain and decentralization. The synergistic development of the two may define the technological paradigm of future society --- --- AI-driven efficiency revolution, Web3 ensures equitable distribution. This article will explore the logic and path of this dual-track revolution from three dimensions: technological essence, social impact, and integration prospects.

I. AI: The Engine of the Efficiency Revolution

Technological Breakthroughs and Industrial Transformation of AI

• Emergence of Large Language Models (LLMs): AI models like ChatGPT and Claude have evolved from "tools" to "collaborative partners," capable of handling complex tasks (such as code generation, legal analysis, and medical diagnosis), significantly lowering the threshold for acquiring professional knowledge.
• Automation and Intelligent Production: AI + robotics (such as Tesla Optimus and Figure 01) are transforming the manufacturing industry, making "unmanned factories" possible. AI-driven supply chain optimization (like Amazon's logistics forecasting) can reduce operational costs by 20%-30%.
• Leap in Individual Productivity: AI Agents (like Devin and AutoGPT) can autonomously complete tasks, and in the future, "one-person companies" will become mainstream, challenging the traditional organizational models of enterprises. The rise of AI Agents is rewriting the organizational forms of companies. According to our market research, emerging AI Agent startup teams generally maintain a size of 15-50 people, and surprisingly, most of these teams indicate no plans for large-scale expansion, as AI has already taken on the majority of development work. Currently, human involvement is mainly needed in operations, PR, and HR, but this situation may change rapidly --- --- the Agentization of marketing placements, effect analysis, and recruitment is already showing early signs.

AI Agents have achieved a qualitative transformation from auxiliary tools to "digital employees,"

demonstrating three core capabilities:

1. On the cognitive level, they can deeply understand complex instructions and intelligently break down task logic;
2. On the execution level, they can autonomously call APIs to complete specific operations;
3. More importantly, their evolutionary capability allows them to continuously optimize decision quality through ongoing reflection and scenario rehearsals.

Practical data confirms the commercial value of this transformation: after a company introduced AI Agents, customer service response time was reduced from 5 days to 5 seconds, achieving a 100-fold increase in efficiency; the team size was reduced from 10 to 2 people plus 1 Agent, lowering human resource needs by 80% while the business error rate decreased by 50%.

This transformation is reshaping the work paradigms of all functional departments --- --- the efficiency of resume screening in HR has improved from hours to minutes; the marketing team's advertising creative output capability has exploded from 10 pieces per day to 50 pieces per minute; the role of corporate management has fundamentally changed, with CEOs no longer acting as commanders but needing to coordinate the collaboration between human employees and AI Agents like a symphony conductor. This reconstruction of productivity relationships marks the arrival of a new era of human-machine collaboration.

The Economic Impact of AI: Growth and Imbalance

AI technology is profoundly reshaping the fundamentals of the global economy. According to McKinsey research, by 2030, AI is expected to contribute $13 trillion to the global economy through optimized resource allocation, significantly enhancing total factor productivity (TFP). This productivity revolution is triggering structural changes in the job market:

1. On one hand, repetitive jobs such as customer service and basic programming face the risk of being replaced, with Goldman Sachs predicting that around 300 million jobs worldwide will be affected, creating a stark efficiency gap between "old car" traditional enterprises and "Transformers" AI companies;
2. On the other hand, emerging professions such as AI trainers and human-machine collaboration managers are emerging, but skill mismatches during the transition period may cause short-term employment pains. More concerning is that due to the heavy reliance of AI development on computing power and data, tech giants like OpenAI, Google, and Meta may form an "AI monopoly," further exacerbating wealth inequality.

This asymmetric development of productivity and production relationships is creating a new digital economy ecosystem where efficiency and fairness need to be rebalanced.

Institutional Challenges: New Governance Issues Arising from Deep AI Applications

The rapid development of AI technology is bringing unprecedented social governance challenges, primarily reflected in three key dimensions:

1. First, there is the regulatory dilemma of algorithm governance. Taking the Amazon recruitment AI discrimination case as an example, it exposes systemic issues such as training data bias, model black boxes, and the lack of evaluation standards. Currently, the EU AI Act and technical de-biasing methods are exploring solutions.
2. Second, there is a structural shift in decision-making power. When AI dominates 80% of high-frequency trading and participates in policy-making, new control frameworks such as "human-in-the-loop" mechanisms and algorithmic circuit breakers become crucial.
3. Finally, there is the legal reconstruction of human-machine relationships. From the Saudi robot citizenship controversy to the copyright issues of AIGC, the traditional legal system faces fundamental challenges, necessitating the establishment of new institutional arrangements such as algorithmic liability insurance. The resolution of these complex issues urgently requires interdisciplinary research in "algorithm governance" and may even necessitate the establishment of dedicated digital legislative bodies to effectively address the profound changes in social relationships brought about by AI.

II. Web3: The Cornerstone of the Fairness Revolution

Web3 is redefining the value paradigm of the digital age, with its core being the construction of a truly user-led internet architecture. Through the decentralized characteristics of blockchain technology, Web3 fundamentally changes the ownership of data control --- --- public chains like Bitcoin and Ethereum ensure that no single entity can monopolize network power, effectively preventing traditional platforms from arbitrarily banning accounts or freezing assets. This technological architecture has given rise to a new model of "user sovereignty economy":

1. On the data level, NFTs and decentralized identity (DID) systems enable users to truly own their digital assets and identity information. Innovations like the Brave browser rewarding users' attention with BAT tokens are reshaping the distribution mechanism of data value;
2. On the collaboration level, DAO organizations achieve decentralized collaboration globally through smart contracts. Platforms like GitCoin have proven that blockchain-based contribution verification mechanisms can more fairly measure and reward participants' work value. These innovations collectively form a new digital economy system that is resistant to censorship, empowers users, and distributes rewards based on contribution, akin to the leap from feudal land ownership to modern property rights systems.

How does Web3 respond to the challenges of the AI era?

Web3 technology provides an innovative solution framework for key issues in AI development.

1. In terms of data markets, decentralized data platforms like Ocean Protocol are reconstructing the circulation model of data elements, allowing individuals to securely sell data usage rights through encryption technology, rather than being collected for free by tech companies. Meanwhile, the combination of federated learning and blockchain creates a privacy-preserving collaborative training paradigm that meets the data needs of AI models while safeguarding user privacy rights.
2. In the dimension of AI governance, smart contracts are encoded as the "digital constitution" of algorithms, automatically executing ethical norms such as "AIGC content traceability labeling," while permanent storage protocols like Arweave provide immutable audit trails for AI decision-making processes.
3. More revolutionary is Web3's transformation of wealth distribution mechanisms: by automatically taxing AI company profits through on-chain smart contracts to implement UBI plans, it constructs a socialized distribution pipeline for algorithmic dividends. In the future, it is likely to develop an AI contribution proof (PoAC) mechanism, making AI systems that provide computing power legitimate participants in the on-chain economy, receiving corresponding token incentives. This recursive design of "technology governance technology" is forming a new paradigm for the self-evolution of digital civilization.

Bottlenecks and Breakthrough Directions of Web3

Currently, Web3 technology still faces three core challenges for large-scale adoption:

1. First, the user experience barrier is too high. Ordinary users face complex wallet management, fluctuating gas fees, and easily lost mnemonic phrases, necessitating the industry to quickly promote user-friendly solutions like account abstraction.
2. Second, there is uncertainty in the regulatory environment. Regulatory actions, represented by the SEC's lawsuit against Coinbase, are casting a shadow over innovation, necessitating the establishment of a clear compliance framework.
3. Finally, there are efficiency bottlenecks in the technology itself. The processing capacity of mainstream public chains like Ethereum (15-30 TPS) is far from meeting the data throughput needs of the AI era, requiring optimization through Layer 2 scaling solutions like Optimism and zkSync, or modular blockchain architectures like Celestia, to enhance performance while maintaining decentralization. The extent to which these challenges are resolved will directly determine whether Web3 can transform from a geek experiment into the infrastructure supporting digital civilization.

III. AI + Web3: Synergistic Evolution and Social Reconstruction

The current technological integration of AI and Web3 is advancing along three key directions:

1. First, decentralized AI (DeAI) is reconstructing the artificial intelligence infrastructure through distributed architecture. Projects like Bittensor are building open training networks that break the computing power monopoly of traditional tech giants, with the innovation lying in adopting DAO governance models that allow the community to collectively decide the development path of AI models through on-chain voting, including open-source strategies and training directions.
2. Second, AI technology is deeply empowering the Web3 application ecosystem, not only automatically detecting smart contract vulnerabilities and optimizing gas fees but also giving rise to new "AI Agent" roles. These digital agents, equipped with autonomous decision-making capabilities, can represent users in complex operations such as DeFi liquidity mining and NFT trading, achieving more efficient on-chain asset management.
3. Finally, blockchain technology provides a transparent solution for AI governance by submitting key AI decisions (such as ethical algorithms for autonomous driving) to DAO community voting and permanently hashing training data on-chain for record-keeping, constructing an auditable and traceable AI governance framework, which is particularly important in highly sensitive fields like medical AI. The synergistic evolution of these three directions is shaping a new paradigm of digital civilization that maintains technological innovation vitality while possessing institutional constraints.

The future social form will present two distinctly possible scenarios due to the different development paths of AI and Web3:

In an optimistic scenario, the collaboration between AI and Web3 will build an inclusive society of "human-machine co-governance" --- --- AI systems, as efficient productivity engines, will be responsible for value creation, while Web3's smart contracts and DAO mechanisms will ensure fair and transparent value distribution, forming a new economic paradigm of "machine production, on-chain distribution." In this system, humanity will be liberated from repetitive labor, shifting towards more creative work, with universal basic income (UBI) automatically distributed through cryptocurrencies to meet basic living needs, while cross-border DAO organizations gradually take over the coordination functions of global issues such as climate governance and public health.

However, if development becomes imbalanced, it may fall into a "technological oligarchic monopoly" cyber feudalism: a few tech giants mastering core AI technologies may form digital hegemony through computing power monopolies, while the Web3 concept becomes marginalized, and society splits into "algorithmic aristocrats" who control AI production resources and the "useless class" replaced by automation, with the Gini coefficient breaking historical extremes. Meanwhile, authoritarian governments may leverage AI surveillance and social credit systems to construct digital totalitarianism, forming an "algorithmic Leviathan." The divergence of these two possible futures fundamentally depends on our current efforts to promote technological democratization and institutional innovation.

To move towards the ideal future of human-machine co-governance, collaborative efforts from multiple stakeholders are needed:

At the policy level, countries should quickly establish interdisciplinary AI ethics committees to promote the implementation of global governance frameworks similar to the EU AI Act, while providing clear institutional space for Web3 innovation, including compliance pathways for cryptocurrencies and legal recognition of DAOs. The business sector should practice technology for good, with tech giants opening core AI resources to promote ecosystem development, similar to Meta's open-sourcing of the Llama model;

Web3 projects should focus on real-world application scenarios like SocialFi and DePIN, breaking through the current limitations of financial speculation. For individuals, mastering cross-disciplinary skills in Prompt engineering and smart contract development related to AI and Web3 will become essential in the new economic era, and they should actively participate in DAO governance to collaboratively shape technological ethics through decentralized collaboration mechanisms.

Only through the synergy of policy guidance, corporate practices, and citizen participation can we ensure that the technological revolution truly serves the overall well-being of humanity rather than exacerbating social division. This multi-layered collaborative governance is the key path to avoiding technological risks and achieving inclusive development.

Conclusion: The Ultimate Goal of the Dual-Track Revolution

The success of this revolution relies not only on technological innovation but also on how humanity shapes institutions and culture. History tells us that every leap in productivity (from agriculture to industry to digital) is accompanied by social reconstruction, and this time, we have the opportunity to actively design a fairer future. History shows that technological revolutions must balance efficiency and fairness. The industrial revolution gave rise to corporate systems and labor laws, and the symbiosis of AI and Web3 may shape a new paradigm:

• Short-term: AI prioritizes solving productivity bottlenecks, while Web3 explores small-scale use cases (such as the creator economy).
• Long-term: Web3 provides a governance framework for AI to avoid "technological tyranny"; AI feeds back into Web3 scalability, promoting large-scale implementation.

Ultimately, the integration of the two is not just a technological combination but an upgrade of civilization --- --- building an efficient and inclusive digital society between machine intelligence and human values.