AI and Blockchain: Navigating the Paradox of Autonomy

We live now in an age defined by two transformative technologies: AI and blockchain. Both promise to redefine industries, economies, and our societies, although the latter more as a side-effect than a goal.

AI, with its ability to automate complex tasks and low-end creativity offers prospects for real productivity gains, increased efficiency, and breakthroughs in numerous field, especially for those well-placed to take advantage of them.

Simultaneously, blockchain technology, with its emphasis on decentralization, security and self-executing smart contracts, is reshaping transactions and information sharing, supposedly promoting transparency, trust, and disintermediation..

The common factor of both AI and blockchain is autonomy from the human.

Both AI and blockchain are celebrated in the tech world for their autonomy. AI is seen as progressing toward full automation, potentially replacing human labor in numerous tasks. On the other hand, blockchain enables trustless and decentralized systems, reducing the reliance on human-governed institutions and intermediaries.

The paradox of these contemporaneous technological movements (because they are both consciously propagated movements) is that while blockchain champions decentralization, AI’s development is becoming increasingly centralized, requiring the resources and capabilities of major corporations and governments.

The common factor in both is autonomy from the human.

The pursuit of autonomy in AI and blockchain will have significant implications for human society and labor. The increasing danger is that these technologies may devalue human labor while elevating the minority who can shape their direction.

The new revolution and its norms

The rest of us are left to sharpen up our soft skills, like empathy, creativity and dealing with ambiguity. But to what end? As we automate and decentralize, there’s a risk that we will isolate the very humanity these technologies were meant to assist.

My concern goes beyond the immediate impact on labor and employment. It touches on the deeper societal and cultural shifts that such technologies can initiate.

I think of the socio-economic changes that came with the Industrial Revolution. The transition from an agrarian society to an industrial one elevated the value of wage labor and productivity (and of course there were benefits to this), but drove a self-perpetuating cycle. Work and productivity became not only a means to an end but an end in themselves, establishing a new set of social norms and expectations. The unemployed or underemployed today are stigmatized as failures and a burden, unmotivated or dishonest and, as a result, are largely socially excluded.

Similarly, as we grow more dependent on autonomous technologies, we may begin to value tasks and roles based on how well they align with these technologies’ capabilities. and that technological alignment could become as an end in itself.

Those who need help, who rely on empathy, kindness, care, creativity, and other human-centric qualities, would find themselves excluded and stigmatized.

I am not necessarily gloomy about the future, but our approach calls for a strategic re-evaluation. Don’t expect the tech-bros of blockchain or the corporations behind large language models to do this.

In our own work, while autonomy remains important for efficiency and for enabling what I hope will be wonderful new discoveries, we must consider other principles too.

We can actively support and invest in technologies that demand collaboration and engage a broad community of stakeholders. We should ensure that these technologies are developed and used with human principles and the collective good in mind.

We can’t do this without education to ensure at least a minimum understanding and knowledge of transformative at all levels of society. It is essential for democratic participation in shaping their use and governance. With proper education, we can shape policies and regulations that promote ethical use, transparency, accountability, and fairness.

I would also suggest that we can turn these same technologies to govern themselves. The paradox of simultaneous decentralization and centralization provides us with the opportunity to do that.

Playing the tech stacks against each other

Through decentralized approaches, we can distribute decision-making power, enable transparency, and ensure that AI systems align with humanistic principles and the collective good. Decentralized governance helps prevent the concentration of power and promotes inclusivity and accountability.

One example of a decentralized approach to AI governance is the concept of AI DAOs (Decentralized Autonomous Organizations). AI DAOs are blockchain-based organizations that utilize decentralized governance mechanisms to make collective decisions regarding the development and deployment of AI technologies.

AI DAOs can also incorporate mechanisms for auditing AI algorithms and considering ethical considerations. Decentralized governance allows stakeholders to collectively address issues like bias, fairness, and transparency in AI systems for responsible and human-centric AI development.

Furthermore, AI itself can contribute to addressing challenges in decentralized blockchain systems. AI can enhance consensus mechanisms, identify and mitigate vulnerabilities, address scalability issues, and improve overall performance. For example, AI can analyze and audit smart contracts, minimizing risks associated with programming errors or malicious code. It can also analyze blockchain transactions to identify patterns indicative of fraudulent activities.

So I am not pessimistic, yet. By leveraging the strengths of both centralized and decentralized technologies, we can enable a more balanced and effective approach to governance, promoting innovation, accountability, and the alignment of technology with human values.

The alternative is that in the world of the autonomous the interdependent will become our new failures, demeaned and excluded.