Key Insights:

• The Web3-AI sector is finding it challenging to integrate effectively into the accelerating AI technology landscape, threatening its relevance unless key infrastructure improvements are made, according to Jesus Rodriguez from IntoTheBlock.
• This sector often focuses on speculative fads instead of establishing essential infrastructure, which hampers real advancement.
• Lacking vital resources such as data, computational power, and skilled professionals, the gap between Web3-AI and mainstream AI platforms continues to widen.

Artificial intelligence (AI) is seen as a transformative technology of this era. Although the idea of decentralized AI operating on Web3 infrastructure is appealing, Web3-AI has yet to gain substantial foothold in the larger AI landscape. As developments in AI accelerate, the window for Web3 to establish itself as a foundational technology is diminishing.

This article discusses a provocative but crucial idea: Web3 is falling behind in the AI domain. Historically, there comes a moment in every tech evolution where a significant disruption becomes impossible. If Web3-AI continues to ignore fundamental infrastructure in favor of superficial trends, the justification for constructing future AI systems on decentralized networks may vanish entirely.

The Web3 AI Narrative Fallacy

In “The Black Swan,” Nassim Nicholas Taleb described the “narrative fallacy”: the inclination to create coherent stories from disconnected events. The current condition of Web3-AI exemplifies this; the community often lauds high-performing but ultimately irrelevant trends, mistaking them for genuine progress in the AI market. Although there is some innovation, the widening gap between Web3-AI and mainstream AI solutions is no longer sustainable.

The focus on narrative innovation has led Web3 to conflate experimentation with actual progress, resulting in misallocated resources that favor novelty over foundational capability. The apparent momentum is concealing the reality that a majority of current development in Web3-AI diverges from the crucial pathways needed for actual AI breakthroughs.

AI and Tech Evolution

To comprehend Web3-AI’s precarious standing, it’s beneficial to analyze how technology evolves. Breakthroughs typically proceed in interdependent waves; for instance, mobile computing was fostered by prior advancements in cloud infrastructure and innovations in gaming hardware prompted the rise of AI chips. Technologies aiming to stay relevant in new patterns must remain anchored in their predecessors.

Web3-AI lacks this continuity and failed to engage meaningfully with the innovations that spurred the generative AI era. By missing out on the cloud computing cycles and the early stages of AI model development, it finds itself in a foundational deficit—attempting to surf a wave without a board.

Moreover, technological infrastructures generally converge around a handful of dominant players. Historical insights reveal that leading platforms in various tech domains undergo consolidation. If Web3-AI is unable to establish itself as one of the top three competing architectures, it may indeed fade into obscurity.

In conclusion, for Web3-AI to remain pertinent, it must not only attract talent but also invest in data infrastructures, computational capabilities, and practical models to harness the potential advantages of decentralized systems. Ignoring these essentials runs the risk of being left behind in a rapidly advancing field.