Artificial intelligence (AI) is widely considered as one of the most transformative technologies of this century. Naturally, the possibility of decentralized AI systems promoted by web infrastructure has a significant conceptual attraction.
However, despite this apparently convincing value proposal, web3-AI has failed to achieve significant traction in the broader the ecosystem. As the border capabilities accelerate at unprecedented rates, the window of opportunity for web3 becoming a viable base for next -generation AI is closing quickly.
This essay explores a controversial but critical thesis: Web3 is losing the AI race. A point arrives in each technological revolution when it becomes too late to set up a credible interruption. If web3-AI does not change the focus of surface trends to fundamental infrastructure, justification for building the next generation of AI systems on decentralized platforms may disappear completely.
The web3 ai narrative fallacy
In “The Black Swan”, Nassim Nicholas Taleb introduced the concept of “narrative fallacy”: the tendency to build consistent stories about unrelated or weakly connected events. The current state of web3-AI is an example of textbook. The community rewards highly performative trends but to a large extent irrelevant in the context of the AI market: AI meme agents, ZK-AI speculative prototypes, as if they mark substantial progress in the field. While there is some innovation, the expansion of the Gulf between web3-AI and the broader the Ecosystem is becoming unsustainable.
The attractiveness of narrative innovation has encouraged the web3 ecosystem to confuse experimentation with progress. As a result, capital and attention are often assigned to novelty instead of fundamental capacity. The illusion of impulse is to mask the fact that most of what is being built today on web3-AI is orthogonal to the critical path of AI innovation.
Ai and the theory of the wave of technological evolution
To understand the fragile web 3-AI position, it is useful to see how technology tends to evolve. First, advances tend to develop in interdependent waves. For example, mobile computer science was catalyzed by previous waves such as cloud infrastructure, and IA chips emerged from innovations in games hardware. To remain relevant in a new wave, technologies must be based on the previous ones.
Web3-AI lacks that continuity. He did not play a significant role in the trends that gave rise to the generative revolution of AI. The cloud computing cycles, large -scale data engineering and even early AI model development were lost. As a result, web3-AI suffers from a fundamental vacuum: it is trying to set up a wave without a surf table.
Second, infrastructure technology markets tend to consolidate. History shows that dominant infrastructure platforms are almost always reduced to some important actors. Cloud computing converged around AWS, Azure and GCP. Mobile development stabilized around iOS and Android. Big Data joined around Snowflake and Databricks. The infrastructure of AI will probably follow a similar pattern. If web3-AI is not positioned as one of the three main viable platforms, it runs the risk of becoming irrelevant in a highly concentrated landscape.
Missing foundations and build irrelevant things
AI Modern AI is based on four fundamental pillars: data, computation, models and research talent. Unfortunately, web3 has historically ignored the four. It lacks a deep talent. There are no native web3 native AI data sets. The calculation infrastructure remains primitive. And, there are no widely adopted AI models that are executed significantly in decentralized protocols.
This lack of foundations is aggravated by a tendency to pursue bright objects. Web3-AI projects gravitate disproportionately to speculative areas such as memes or zkml agents without clear use cases. While these ideas are intellectually interesting, they are not fundamental to allow or climb significant abilities. In their current form, they offer little practical value to advance in AI infrastructure.
To make real progress, the web3-AI ecosystem must face this fundamental deficit. That means investing in talent, building data pipes, creating efficient computer layers and developing models that offer tangible advantages when implemented in decentralized systems.
The gap between web3 and web2 is increasing
The innovation of AI is rapidly aggravating, and web3 has been a passive observer. None of the main milestones of the AI (previous prison without service, advanced adjustment, an aquatic recovery generation, reasoning engines or agent frames) has involved web architectures in a significant way.
As each new release is composed in the last, barriers to catch up more steep. All critical tools, platforms and infrastructure to build border models are currently centralized. Without urgent and coordinated efforts to change this trajectory, web3-AI will leave decades in a field that advances in months.
The risk of irrelevance
AI is, by its very nature, a centralizing force. Border training models require vast data sets, huge computation and specialized talent, all of which the trend towards concentration. Decentralized alternatives face deep technical and economic challenges.
This does not mean that the decentralized AI is convicted. But the margin of error is to disappear. Unless web3-AI accelerates dramatically, the centralized ecosystem will reach such domain that decentralization becomes a late occurrence. The risk is not being lost the next trend of AI; It is becoming fundamentally irrelevant in the future of AI.
A CALL OF ATTENTION
Resilience and optimism are integrated into web3 DNA, and recent efforts of technically more serious teams are encouraging, such as Nous research (distributed training), the main intellect (distributed training), layers (comparison and evaluation), pluralis (distributed training), Sahara (AI applications) and a concern of others. Some begin to address the central problems: ML of preservation of privacy, distributed training, verifiable inference.
But these efforts remain exceptions instead of the norm. The web3-AI movement still has the lack of talent, data, computation, infrastructure and capital. You must abandon distractions and orient yourself towards fundamental capacity. Facing this reality clearly offers the opportunity to change course. Ignoring it means to miss the most consistent technological revolution in history.
