Artificial Intelligence (AI) is widely regarded as one of the most changing technologies of this century. Naturally, the prospect of decentralized AI systems enabled by the web3 infrastructure holds significant appeal to the concept.
However, despite this seemingly compelling value proposal, the web3-Ai failed to achieve significant traction in the broader AI ecosystem. As the Fronier AI’s capabilities accelerate at the uninitiated rates, the window of the Web3 opportunity to become a viable foundation for the next generation AI will quickly close.
This essay will explore a controversial but critical thesis: the web3 loses the AI breed. There is a point in each technological revolution when it is too late to mounted a conviction. If the web3-AI does not change the focus from superficial trends to foundational infrastructure, the justification for developing the next generation of AI systems on decentralized platforms may disappear completely.
The web3 ai narrative fallacy
In “The Black Swan,” Nassim Nicholas Taleb introduces the concept of “narrative fall”: the tendency to build related stories around unrelated or weakly connected events. The current State of Web3-Ai is an example of a textbook. The community reward is highly performing but mainly irrelevant trends in the context of the AI-meme agents, speculative ZK-Ai-Ai agents-as if they had marked great development in the field. As some changes exist, the expansion of the gulf between the Web3-Ai and the wider AI ecosystem becomes unstable.
The attraction of the modern-driven narrative encouraged the web3 ecosystem to make the mistake of experimenting for development. As a result, capital and attention are often misguided in new skills. The illusion of momentum is masking the fact that most of what is now being built on Web3-Ai is orthogonal on the critical path of AI change.
Ai and the theory of the wave of the evolution of tech
To understand the fragile web3-Ai position, it is useful to look at how the technology tends to sprout. First, breakthroughs tend to open up in different waves. For example, mobile computing has been -catalyzed by earlier waves such as cloud infrastructure, and AI chips have emerged from innovative gaming hardware. To remain relevant to a new wave, technologies must be the premise of the preceding.
Web3-Ai lacks continuity. It does not play a significant role in the trends that have given an increase in the development of the AI Revolution. It has missed cloud computing cycles, large data engineering, and even early development of the AI model. 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 combine. History shows that the dominant infrastructure platforms are almost always retreating to some major players. Cloud computing is converting around AWS, Azure and GCP. Mobile development stabilizes around iOS and Android. Large data is combined around snowflake and databricks. AI infrastructure is likely to follow a similar pattern. If the web3-Ai is not position itself as one of the top three viable platforms, the risk of becoming unrelated to a highly concentrated scenery.
Missing basis and formation of irrelevant objects
The modern AI stack is built on four main columns: data, compute, models, and talent of research. Unfortunately, the web3 has a history that ignores all four. It lacks the deep talent of AI. There are no large Datasets of AI native to the web3. The compute infrastructure is still primitive. And, there are no widely adopted AI models that run significantly in decentralized protocols.
The lack of grounds is combined with a tendency to chase glossy objects. Web3-Ai projects do not motivate to motivate imaginary areas such as meme agents or ZKML without clear use cases. While these ideas are interesting to intellectual, they are not primarily to enable or scale significant AI capabilities. In their current form, they offer a little practical value for the promotion of AI infrastructure.
To make real development, the web3-Ai ecosystem must face this deficiency. It means investing in talent, developing data pipelines, creating good compute layers, and developing models that offer tangible advantages when deploying in decentralized systems.
AI interval between web3 and web2 rises
AI innovation is quickly combined, and the web3 has become a passive observer. None of the major milestones of AI-without pretense of pretending, advanced repair repairs, generation deprivation, rational machines, or agent agents-are involved in web3 architecture in a significant way.
Like every new discharge compound, the obstacles to catch will become more steep. All critical tooling, platforms, and infrastructure for the development of border models are currently centralized. Without urgently, coordinated efforts to change this trajectory, the Web3-Ai will be left for decades in a field advancing for months.
The risk of not interconnected
AI is, by nature itself, a centralized force. Training Frontier models require extensive datasets, massive computes, and specialized talents – all of which the run towards concentration. Decentralized successors face deep technical and economic challenges.
This does not mean decentralized AI is doomed. But the margin for the error is missing. Unless the web3-Ai accelerates noticeable, the centralized ecosystem will reach such dominance that decentralization becomes a thought. The risk does not disappear in the next AI trend; This becomes the beginning that is unrelated to the future of AI.
A wake-up call
Elastic and optimism have been embedded in web3’s DNA, and the recent efforts of more serious teams are encouraging like Nous research(distributed training), Prime Intellect(distributed training), Layerlens(benchmarking and evals), Pluralista(distributed training), Sahara (AI apps) and a little different. Some are starting to deal with the basic problems-privacy-maintaining ML, distributed training, proven recognition.
But these efforts will remain exceptions than the norm. The Web3-Ai movement is still short in talent, data, compute, infrastructure, and capital. It should leave the inconveniences and orients toward the foundation’s ability. Dealing with this fact with clarity offers an opportunity to change the course. Relaxing -it means losing the most outcome technological revolution in history.
