Web3 has been through its fair share of hype cycles, but the intersection of artificial intelligence and decentralised networks is shaping up to be more than just another passing trend. Dom’s post on X didn’t hold back—either networks become platforms for an AI-powered internet, or they risk fading into irrelevance. It’s a blunt assessment, but one that many in the space are starting to take seriously.
For years, Web3 has been synonymous with blockchain scalability, DeFi, and tokenomics. The conversation has largely centred on Layer 1 and Layer 2 solutions, each vying for dominance with promises of faster transactions, lower fees, and enhanced security. But with AI’s rapid progress, the focus is shifting. Networks that don’t integrate AI capabilities could find themselves obsolete, overshadowed by platforms that enable self-generating content, automated smart contracts, and AI-driven governance.
The implications are significant. Web3’s decentralisation ethos has long been about removing middlemen, ensuring transparency, and fostering open access. AI, on the other hand, is largely being developed in silos, controlled by a handful of dominant players. The challenge is clear: can decentralised networks harness AI without compromising their core principles? Or will AI itself become the gatekeeper of a new digital landscape?
Some projects are already making moves. AI-powered smart contracts are becoming more sophisticated, able to adjust their terms based on real-time data. Decentralised autonomous organisations (DAOs) are exploring AI-driven decision-making models, reducing the inefficiencies of human governance. Even decentralised social platforms are leveraging AI to filter spam and enhance user experience. But these are still early-stage experiments. The real shift will come when AI-generated content—text, video, and even code—becomes seamlessly integrated into Web3 platforms, creating a truly self-writing internet.
The urgency in Dom’s message isn’t just about innovation—it’s about survival. Networks that don’t evolve risk losing users, developers, and relevance. The Web3 landscape is competitive, and with AI set to redefine digital interactions, the gap between adaptable platforms and stagnating ones will widen quickly.
Scepticism remains, particularly around how decentralised AI can be achieved without slipping into the same centralisation issues that Web3 sought to solve. AI requires vast amounts of data and computational power, resources that are currently concentrated in a few key players. The challenge for Web3 is to develop decentralised AI frameworks that are as efficient as their centralised counterparts while staying true to the principles of transparency and user control.
One of the biggest concerns is the black-box nature of AI models. Traditional AI systems make decisions in ways that are often opaque, raising questions about bias, accountability, and trust. Web3 offers a potential antidote through on-chain verification and open-source AI development, but whether these solutions can scale effectively remains to be seen. If Web3 is to integrate AI meaningfully, it needs to ensure that AI systems operating within decentralised networks are transparent and auditable, rather than reinforcing the very power structures it set out to disrupt.
Meanwhile, traditional Web2 giants are also pivoting towards AI, and they have the advantage of scale, user base, and funding. If Web3 projects fail to integrate AI effectively, users may opt for AI-enhanced Web2 experiences instead, where convenience often trumps decentralisation. It’s a harsh reality that Web3 innovators must contend with—offering idealistic principles isn’t enough if the technology lags behind.
Developers are already exploring ways to bridge the gap. Decentralised AI marketplaces are emerging, allowing users to train and deploy models without relying on centralised providers. AI-generated NFTs, dynamic DAOs, and self-improving decentralised applications are beginning to push the boundaries of what’s possible. But for Web3 to truly consolidate around AI, there needs to be a fundamental shift in how networks position themselves—not as static infrastructures, but as evolving ecosystems designed to support intelligent, self-generating interactions.
There’s also a social and ethical dimension to consider. The promise of Web3 was to give users more control over their digital identities, data, and financial interactions. AI, when applied incorrectly, could erode these gains, reinforcing surveillance, manipulation, and bias. Striking the right balance will require more than just technological innovation—it will demand robust governance models, community oversight, and a commitment to building AI tools that align with Web3’s original vision of a decentralised, user-driven internet.
The coming years will determine whether Web3 and AI become complementary forces or competing paradigms. If Dom’s take is right, there’s no middle ground—networks that fail to adapt will wither, while those that embrace AI will redefine what decentralisation means in an era of intelligent automation. The challenge now is not just building the infrastructure but ensuring that the AI-enhanced Web3 future is one worth fighting for.
