Artificial Intelligence (AI) is widely considered one of the most transformative technologies of this century. Of course, the prospect of decentralized AI systems driven by web3 infrastructure has significant conceptual appeal.
However, despite this seemingly compelling value proposition, Web3-IA has not achieved a meaningful traction in the wider AI ecosystem. As Frontier AI capabilities accelerate with unprecedented rate, the web3 option closes to become a viable foundation for the next generation AI quickly.
This essay explores a controversial but critical thesis: Web3 loses the AI race. There is a point in any technological revolution when it gets too late to mount a credible disturbance. If web3-IA does not shift focus from superficial trends to basic infrastructure, the rationale for building the next generation of AI systems on decentralized platforms can disappear completely.
Web3 AI Narrative Fallacy
In “The Black Swan”, Nassim Nicholas Taleb introduced the concept of “narrative error”: the tendency to construct coherent stories around non -related or weakly connected events. The current state of Web3-IA is a textbook example. Society rewards very performative, but largely irrelevant trends in the context of the AI market-ai-Mememe agents, speculative ZK-II prototypes-as to mark significant progress in the field. While there is some innovation, the expanded golf between web3-IA and the wider AI ecosystem becomes unsustainable.
The connection of narrative-driven innovation has called on the web3 ecosystem to celebrate experimentation for progress. As a result, capital and attention are often awarded to news rather than basic capacity. The illusion of momentum is to mask the fact that most of what is being built today in Web3-Ai is orthogonal to the critical path to AI innovation.
AI and the wavy theory of technical evolution
To understand the fragile position of the Web3-II, it is useful to look at how technology tends to develop. First, breakthroughs tend to unfold in interdependent waves. For example, mobile computing was catalyzed by previous waves as a cloud infrastructure, and AI chips came out of innovations in gaming hardware. To remain relevant in a new wave, technologies must be grounded in earlier.
Web3-IA is missing that continuity. It did not play a meaningful role in the trends that gave rise to the generative AI revolution. It missed the cycles with cloud computing, large-scale data technique and even early AI model development. As a result, the web3-ai suffer from a basic vacuum-it tries to run on a wave without a surfboard.
Secondly, infrastructure technology markets tend to consolidate. History shows that dominant infrastructure platforms almost always shrink to a few big players. Cloud Computing converging around AWS, Azure and GCP. Mobile development stabilized around iOS and Android. Big Data gather around snowflakes and daters. AI infrastructure is likely to follow a similar pattern. If web3-IA does not position itself as one of the top three viable platforms, it risks becoming irrelevant in a very concentrated landscape.
Lack of basic elements and build -up of irrelevant things
The modern AI stack is built on four basic columns: data, calculation, models and research talent. Unfortunately, web3 has historically ignored all fours. It lacks deeply AI talent. There are no large AI data sets resident in web3. Calculate infrastructure is still primitive. And there are no widely adopted AI models that run meaningfully on decentralized protocols.
This lack of basic elements is worsened by a tendency to hunt shining objects. Projects in Web3-II-U-Relatives Aweize on Speculative Areas such as MEME agents or ZKML without any cases of clear use. Although these ideas are intellectually interesting, they are not core to enable or scale meaningful AI capabilities. In their current form, they offer a little practical value to promote AI infrastructure.
To make real progress, the Web3-II ecosystem must confront this basic deficit. This means investing in talent, building data pipes, creating effective calculation layers and developing models that offer concrete benefits when implemented on decentralized systems.
AI -Gorge between Web3 and Web2 increases
AI -Innovation exacerbates quickly and web3 has been a passive observer. None of the largest AI-MilEleste-U-overwhelmed prior, advanced fine-tuning, fetch-augmented generation, reasoning engines or agent frames-have involved web3 architectures in a meaningful way.
As each new release connections lately will be barriers to catching up on steeper. All critical tools, platforms and infrastructure to build border models are currently centralized. Without urgent, coordinated efforts to change this track, web3-ai will be left for decades in a field that goes on for months.
The risk of irrelevans
AI is in itself a centralization power. Educational boundary models require huge data sets, tremendously calculated and specialized talent – as all tendency to concentration. Decentralized alternatives face deep technical and financial challenges.
This does not mean that decentralized AI is doomed. But the margin of error disappears. Unless web3-ai accelerates dramatically, the centralized ecosystem will reach such dominance that decentralization becomes a reflection. The risk does not miss the next AI trend; It becomes fundamentally irrelevant in the AI future.
A waking call
Resilience and optimism are embedded in Web3’s DNA, and the recent efforts of more technically serious teams encourage such as Nous Research (Distributed Training), Prime Intellect (Distributed Training), Layerlens (Benchmarking and Evals), Pluralis (Distributed Training), Sahara (AI Apps) and a handful other. Some begin to tackle core problems-privacy-preserving ml, distributed training, verifiable inference.
But these efforts remain exceptions rather than the norm. The Web3-II movement is still short on talent, data, calculation, infrastructure and capital. It must give up distractions and orient itself to basic capacity. Facing this reality with clarity gives a chance to change course. Ignoring it means missing out on the most consistent technological revolution in history.
