Why Is Web3 Losing the AI Race?

Artificial intelligence (AI) is widely regarded as one of the most transformative technologies of this century. Naturally, the prospect of decentralized AI systems powered by Web3 infrastructure holds significant conceptual appeal.

However, despite this seemingly compelling value proposition, Web3-AI has failed to achieve meaningful traction in the broader AI ecosystem. As frontier AI capabilities accelerate at unprecedented rates, the window of opportunity for Web3 to become a viable foundation for next-generation AI is rapidly closing.

This essay explores a controversial but critical thesis: Web3 is losing the AI race. There comes a point in every technological revolution when it becomes too late to mount a credible disruption. If Web3-AI doesnt shift focus from superficial trends to foundational infrastructure, the rationale for building the next generation of AI systems on decentralized platforms may disappear entirely.

The Web3 AI Narrative Fallacy

In “The Black Swan,” Nassim Nicholas Taleb introduced the concept of the “narrative fallacy”: the tendency to construct coherent stories around unrelated or weakly connected events. The current state of Web3-AI is a textbook example. The community rewards highly performative but largely irrelevant trends in the context of the AI market—AI meme agents, speculative zk-AI prototypes—as if they mark substantial progress in the field. While some innovation exists, the widening gulf between Web3-AI and the broader AI ecosystem is becoming unsustainable.

The allure of narrative-driven innovation has encouraged the Web3 ecosystem to mistake experimentation for progress. As a result, capital and attention are often misallocated to novelty rather than foundational capability. The illusion of momentum is masking the fact that most of what is being built today in Web3-AI is orthogonal to the critical path of AI innovation.

AI and the Wave Theory of Tech Evolution

To understand Web3-AI‘s fragile position, it’s helpful to look at how technology tends to evolve. First, breakthroughs tend to unfold in interdependent waves. For example, mobile computing was catalyzed by prior waves like cloud infrastructure, and AI chips emerged from innovations in gaming hardware. To remain relevant in a new wave, technologies must be grounded in previous ones.

Web3-AI lacks that continuity. It did not play a meaningful role in the trends that gave rise to the generative AI revolution. It missed the cycles of cloud computing, large-scale data engineering, and even early AI model development. As a result, Web3-AI suffers from a foundational vacuum—it is trying to ride a wave without a surfboard.

Second, infrastructure technology markets tend to consolidate. History shows that dominant infrastructure platforms almost always shrink to a few major players. Cloud computing converged around AWS, Azure and GCP. Mobile development stabilized around iOS and Android. Big data coalesced around Snowflake and Databricks. AI infrastructure will likely follow a similar pattern. If Web3-AI doesnt position itself as one of the top three viable platforms, it risks becoming irrelevant in a highly concentrated landscape.

Missing Fundamentals and Building Irrelevant Things

The modern AI stack is built on four fundamental pillars: data, compute, models, and research talent. Unfortunately, Web3 has historically ignored all four. It lacks deep AI talent. There are no large-scale AI datasets native to Web3. Compute infrastructure is still primitive. And, there are no widely adopted AI models running meaningfully on decentralized protocols.

This lack of fundamentals is compounded by a tendency to chase shiny objects. Projects in Web3-AI disproportionately gravitate toward speculative areas like meme agents or zkML without clear use cases. While these ideas are intellectually interesting, they are not core to enabling or scaling meaningful AI capabilities. In their current form, they offer little practical value for advancing AI infrastructure.

To make real progress, the Web3-AI ecosystem must confront this foundational deficit. That means investing in talent, building data pipelines, creating efficient compute layers, and developing models that offer tangible advantages when deployed on decentralized systems.

The AI Gap Between Web3 and Web2 is Increasing

AI innovation is compounding rapidly, and Web3 has been a passive observer. None of the major AI milestones—unsupervised pretraining, advanced fine-tuning, retrieval-augmented generation, reasoning engines, or agentic frameworks—have involved Web3 architectures in a meaningful way.

As each new release compounds on the last, the barriers to catching up become steeper. All critical tooling, platforms, and infrastructure for building frontier models are currently centralized. Without urgent, coordinated efforts to change this trajectory, Web3-AI will be left decades behind in a field that advances in months.

The Risk of Irrelevance

AI is, by its very nature, a centralizing force. Training frontier models requires vast datasets, enormous compute, and specialized talent—all of which trend toward concentration. Decentralized alternatives face deep technical and economic challenges.

This does not mean decentralized AI is doomed. But the margin for error is vanishing. Unless Web3-AI accelerates dramatically, the centralized ecosystem will reach such dominance that decentralization becomes an afterthought. The risk is not missing out on the next AI trend; it is becoming fundamentally irrelevant in the AI future.

A Wake-Up Call

Resilience and optimism are embedded in Web3s DNA, and recent efforts by more technically serious teams are encouraging such as Nous Research( distributed training), Prime Intellect( distributed training), LayerLens( benchmarking and evals), Pluralis( distributed training), Sahara (AI apps)and a handful of others. Some are starting to tackle core problems—privacy-preserving ML, distributed training, verifiable inference.

But these efforts remain exceptions rather than the norm. The Web3-AI movement is still short on talent, data, compute, infrastructure, and capital. It must abandon distractions and orient toward foundational capability. Facing this reality with clarity offers a chance to change course. Ignoring it means missing out on the most consequential technological revolution in history.