The transformative effects of the new technological revolution, particularly driven by neural network agents and automation, are raising pressing questions about the future of work. Will traditional labor be eradicated? To understand this, we must first define labor itself. As Marx described, labor is fundamentally the process of interaction between humans and nature, wherein individuals use their own activities to mediate and regulate the exchange of materials between themselves and the environment. The tools used for labor, whether they are shovels, tractors, or robotic factories, are secondary to the fundamental human need that drives this process. While labor itself is not at risk, the structure of wage employment certainly is.
Humanity has already undergone two significant revolutions that fundamentally altered the organization of labor. The first, the Neolithic revolution, marked the shift from foraging to production, linking humans with natural cycles and resources. The Industrial revolution further reduced humanity to mere suppliers of labor, emphasizing physical and cognitive capabilities and paving the way for commodity production. Today's quest to expand human intellectual capacity has ushered in a third revolution that seeks to formalize cognitive skills into marketable entities.
Different forms of labor necessitate distinct relationships among people. Commodity production implies a society of producers who are essentially isolated suppliers of labor. As competencies are freed from being commodities, society will inevitably reorganize itself. The neural network revolution is already extending beyond its initial applications in media content generation, prompting concern among publishers over the protection of intellectual property.
Furthermore, the advancement of neural network tools has made continued scientific endeavors increasingly reliant on these technologies. In fields where knowledge is expanding at an unprecedented rate, no individual can master the breadth of information across multiple disciplines necessary for significant breakthroughs that are often interdisciplinary. The complexity of certain areas, such as meta-material generation and protein folding, presents challenges that only neural networks can effectively tackle, enabling a scaling of scientific productivity previously unimaginable.
The development of these information processing tools marks a departure from linear processes constrained by human capacity. The emergence of self-improving neural networks, which utilize their own agents for development, creates a feedback loop that drives rapid advancements. Robotics, long hampered by algorithmic limitations, is now entering an era where embodied intelligence trained in simulated environments can adapt and excel in the real world.
Despite the excitement surrounding artificial intelligence, some observers caution against viewing the industry as a bubble. This caution stems not from the lack of profitability of major AI companies, as the economics of inference are rapidly improving, nor from the usual initial overinvestment characteristic of emerging sectors. Rather, the crux of the concern lies in the notion that the development of AI does not expand commodity markets but rather dismantles them.
Neural networks and their applications democratize skills that were once privately owned by specialists and corporations. As these competencies become accessible for direct use, individuals can accomplish tasks themselves that they previously had to pay for. This shift is causing turmoil in sectors like SaaS, insurance, law, and accounting, as exclusive capabilities become widely available.
The expectation that neural networks will act as multipliers for commodity production, similar to previous technologies, is misguided. Direct consumption will not lead to market expansion but instead contraction, impacting GDP growth.
The market will undoubtedly feel these effects. However, the bubble surrounding AI will not burst anytime soon, as this technology has the potential to offer significant advancements in speed and efficiency. For countries like the United States, which faces rising competition from China, leveraging these advancements is crucial to maintaining global leadership. As a response, the U.S. is actively working to slow China's progress by restricting access to technology and resources while investing heavily in AI as part of a strategic initiative.
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