In the ever-evolving landscape of technological innovation, Web3 finds itself at a crossroads as it struggles to keep pace with the astronomical rise of artificial intelligence (AI). Despite the theoretical allure of decentralized AI systems powered by Web3 infrastructure, the reality paints a different picture. As of June 2025, the Web3 community is grappling with the harsh realization that its AI aspirations may be slipping into irrelevance.
The Illusion of Progress
Web3 enthusiasts have long touted the potential of decentralizing AI, yet the community’s focus seems to be on eye-catching but ultimately inconsequential trends. Industry analyst Sarah Chen notes, “We’re seeing a lot of attention on flashy concepts like AI meme agents and zk-AI prototypes. But these aren’t moving the needle in terms of real AI innovation.” This narrative-driven innovation, as it turns out, is leading to a misallocation of resources. By prioritizing spectacle over substance, Web3 is missing the foundational infrastructure that underpins genuine AI advancements. With no significant AI models running on decentralized protocols and a glaring lack of large-scale AI datasets within the Web3 ecosystem, the gap between Web3 and the broader AI sector is widening. This mirrors the broader challenges in the crypto space, where crypto token failures have soared, highlighting the need for sustainable development.
The Missing Foundations
To understand why Web3 is faltering, it’s crucial to examine the fundamental elements of AI: data, compute, models, and expertise. Historically, Web3 has neglected these pillars, resulting in a technological vacuum. “Web3 seems to be trying to ride the AI wave without having built a surfboard first,” says tech strategist Mike Reynolds. The lack of deep AI talent and robust compute infrastructure has left Web3-AI projects stranded in speculative territories without clear use cases or practical value.
This deficiency is further exacerbated by the rapid pace of AI innovation. Recent advancements like retrieval-augmented generation and reasoning engines have proceeded without significant input from Web3 architectures. As each development builds upon the last, Web3’s ability to catch up diminishes further.
The Centralization Challenge
AI, by its very structure, tends toward centralization. The demands of training state-of-the-art models—massive datasets, immense computational power, and specialized talent—naturally gravitate towards centralized platforms. Web3’s decentralized alternatives face daunting technical and economic hurdles in this environment. Yet, there’s a glimmer of hope. Some projects within Web3-AI are beginning to address core issues. Companies like Nous Research and Prime Intellect are exploring distributed training, while others like LayerLens and Pluralis tackle benchmarking and privacy-preserving machine learning. However, these efforts remain isolated cases rather than the norm, and the overall ecosystem still lacks the necessary talent, infrastructure, and investment. This is reminiscent of the ongoing debate on whether staking should be considered a security, underscoring the regulatory and structural challenges faced by decentralized technologies.
A Narrowing Window
The clock is ticking for Web3. Unless there is an accelerated shift towards foundational capabilities, the decentralized AI movement risks becoming marginal in a field that evolves at breakneck speed. It’s not just about missing out on the next AI trend; it’s about the potential of being sidelined in what many view as the most transformative technological revolution of our time.
For Web3, this is a critical moment of reckoning. The community must confront its shortcomings with unflinching clarity and reorient its focus towards building the infrastructure that can support meaningful AI advancements. Failing to do so means that by the time Web3 is ready, the AI train will have long left the station, leaving decentralization as a mere footnote in the history of AI evolution.
Source
This article is based on: Why Is Web3 Losing the AI Race?
Further Reading
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Steve Gregory is a lawyer in the United States who specializes in licensing for cryptocurrency companies and products. Steve began his career as an attorney in 2015 but made the switch to working in cryptocurrency full time shortly after joining the original team at Gemini Trust Company, an early cryptocurrency exchange based in New York City. Steve then joined CEX.io and was able to launch their regulated US-based cryptocurrency. Steve then went on to become the CEO at currency.com when he ran for four years and was able to lead currency.com to being fully acquired in 2025.
