Why AI is NOT the Next Web3
Jake Miller
CEO | AI-Powered Solutions for Regulated Industries | Driving Innovation with AI in Healthcare, Finance, and Beyond
Defining the “Next Web3” When we talk about something being "the next Web3," we’re often referring to a concept that has two different implications. On one hand, Web3 is associated with a fad or speculative frenzy, where people rush into the space with the hope of cashing in on the hype, often without fully understanding its underlying technology or practical applications. On the other hand, Web3 represents an ambitious vision for the next phase of the internet—a decentralized and user-empowered ecosystem built on blockchain technology. If we consider AI in the context of these definitions, it’s clear that the comparison falls flat.
If the term “next Web3” implies that AI is merely a short-lived trend, it’s an inaccurate assessment. AI is not just a buzzword or speculative technology; it is deeply integrated into real-world use cases and has the potential to fundamentally change how businesses operate, customers interact with brands, and how industries evolve. However, if we’re defining the “next Web3” as the next stage of the internet—then AI certainly qualifies. But that’s where the comparison ends. AI is much more accessible, tangible, and understandable to both businesses and consumers than Web3 ever was.
AI’s Accessibility vs. Web3’s Complexity
Web3, for all its hype, was notoriously difficult for the average person to wrap their head around. Blockchain, decentralized apps (dApps), and cryptocurrency wallets required not just technical understanding but also significant behavioral changes from the user. For most people, it felt too abstract, and even those in the business world struggled to comprehend how these technologies could apply to their companies.
AI, on the other hand, while it does have complexities, is something that business leaders can relate to. They may not fully grasp how machine learning models are built, but they can easily envision and understand AI’s applications—from automating tasks and enhancing customer experiences to improving decision-making with data insights. AI is something business professionals can map onto their mental models of what technology can do for their business. They can see it in action, whether it's in chatbots, voice assistants, or personalized marketing campaigns. Unlike Web3, which remains largely an abstraction for many, AI’s value is far more tangible and integrated into our day-to-day lives.
AI Goes Beyond an Underlying Technology
Web3 was essentially a stack of technologies, specifically blockchain, promising to reshape everything from finance to governance. While its ambitions were lofty, it never evolved beyond a toolkit for technologists. AI, on the other hand, is not just about the underlying technology—it’s about how those technologies interact with people and solve real-world problems. AI manifests itself in forms that we use every day, whether it's in consumer-facing products like Siri and Alexa or business tools like Salesforce's AI capabilities. AI has the power to solve business problems, improve workflows, and drive innovation across industries. It’s more than just a set of technologies—it’s a transformative force that’s already reshaping industries.
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Integration into Consumer Technology
One of the starkest differences between AI and Web3 is the degree to which these technologies have integrated into consumer technology. Despite the hype, Web3 failed to create mainstream adoption outside niche crypto communities. While blockchain powers certain apps, Web3 technology hasn’t become a visible or seamless part of our daily digital lives.
In contrast, AI has become ubiquitous. Whether we notice it or not, AI powers everything from recommendation algorithms on Netflix to personalized ads on social media. AI is already embedded into consumer technology in a way that’s both obvious (e.g., virtual assistants like Alexa) and invisible (e.g., background algorithms driving search engines and personalization). This level of integration has made AI indispensable in ways Web3 has never been.
AI Solves Real Business Problems
While Web3 and blockchain were pitched as solutions looking for problems, AI is more of a problem-solving technology. AI is transforming industries by tackling real business challenges like data processing at scale, customer support automation, and predictive analytics. Companies can see clear, measurable ROI with AI because it addresses practical, everyday problems.
The business community understands this: whether it's automating tedious tasks, enhancing customer service, or improving product recommendations, the benefits of AI are tangible and demonstrable. Web3, on the other hand, struggled to define concrete business problems it could solve, leaving many business leaders unconvinced.
Final Thoughts
AI is not just the next buzzword in tech—it’s a transformational technology that is already revolutionizing industries. Comparing it to Web3 oversimplifies its significance and potential. Unlike Web3, AI is accessible, practical, and widely adopted across sectors. While both technologies aim to reshape the digital landscape, AI is already succeeding where Web3 struggled: in being integrated into the tools and processes that drive real business value.
AI is not a “fad”—it’s the next step in how businesses and consumers interact with technology. While Web3 was a toolkit for technologists, AI is a tool for everyone.
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1moIn business terms, AI is a way to cut costs, while Web3 is another way to manage users interactions in the Internet. Web3 is more for ecosystems and institutions, while AI - for particular businesses
IT Consultant | Expert in Software Outsourcing, IT Staff Augmentation, and Offshore Office Expansion | Delivering High-Quality Web & Mobile Application Solutions
3moUseful tips
Interesting! There are many layers to this discussion. While I agree that AI is not the next Web3, there are notable parallels in terms of ups and downs, accessibility, and insustainable power consumption. AI has experienced two significant winters in its history, with the most recent one lasting a decade. At one point, neural networks were almost dismissed as fads until the modern computer of GPUs and big data revitalized AI techniques developed in the 1980s. Web3 aims to address decentralization, but it remains uncertain whether it can achieve this goal in its current form. Especially if in insists on memecoin 'innovations' 😅 If it cannot, we need to quickly find alternative decentralization of compute or otherwise, the AI you see will likely be powered by only a few major 'hype' scalers #ChipWars
Making great ideas into great products
3moI think #Ai is not going away, just like #Web3 hasn't, but moving into become a more "mature" technology for implementation but we can't argue that it isn't falling victim to it's own success very much like "Web3" did. That means being implemented in the "right" way and not viewed as a magic bullet, like so many thought leaders proposed it as or CEOs' at HIMSS touted it as. No it won't fix your monolithic Java 6 application but yes it could create a basic web app with some help or augment a human's approach to analyzing x-rays faster for cancer. We are moving back to putting a "human's needs" first rather than technology first. We don't hear consultants barreling into a meeting and saying, "look Mongo is the way... what was the problem again". To say this Web3 had to mature not only in its' offerings to include things like Private Data Collections (PDC) and GRPC utilization but ALSO the development community support. Don't forget the Window's Phone... With that said we are seeing the same thing occurring with AI, a lot of implementations were "all cloud", which is a misunderstanding of local resource utilization. Yes I can throw a million GPUs at a problem but maybe a localized approach would be better...