The AI-Web3 Convergence Is Overhyped, Here's What Investors Should Actually Focus On

In short, the hype cycle around the AI-Web3 convergence is at full throttle. You see it everywhere – conferences like Istanbul Blockchain Week's Dealflow Den, VCs tripping over themselves to fund "decentralized AI" startups, and breathless articles promising a revolutionary future. Except, let’s face it, a lot of it is just hype.

I'm not saying there's nothing there. There's potential. That said, the level of enthusiasm that’s out there is perilously out of touch with reality on the ground. As a data-driven urban analyst, I receive my salary to cut through the fluff. And today, I feel like there’s way too much noise.

Funding Frenzy or Fool's Gold?

The total amount of money being dumped at the dream of AI-Web3 is mind gobbling. You see firms like TON Ventures, Polychain, and Animoca Brands (all reportedly at events like Dealflow Den) sniffing around early-stage projects. Are these investments really built on good fundamentals, or simply FOMO?

Let's look at the data. While funding for general AI startups has seen some justification based on revenue and adoption, the AI-Web3 subset is largely pre-revenue, pre-product, and often, pre-anything. The reality is that many of these projects are built on shaky ground. They attempt to rubberstamp AI into Web3 applications, but many times, it fails to provide any meaningful advantages. Here’s what you should start with: Is tokenizing AI the solution to a problem that really exists? Or is it just a PR move to attract hot money?

• The Problem: Many AI-Web3 projects lack clear problem-solution fit.
• The Risk: Overvaluation and potential for significant losses.
• The Question: Are investors truly understanding the underlying technology, or are they just chasing the buzzwords?

Technical Challenges: The Elephant In The Room

Even putting aside the motivations behind such a merger, the technical hurdles to a true AI-Web3 convergence are massive. I'm talking Everest-sized.

• Scalability: Both AI and Web3 face scalability challenges independently. Combining them exacerbates these issues. Can decentralized AI models truly handle the demands of real-world applications? The answer, for now, is a resounding no.
• Data Availability and Quality: AI models are only as good as the data they're trained on. Securing high-quality, unbiased data in a decentralized environment is incredibly difficult. How do you ensure data integrity without centralized control?
• Compute Power: Training and running complex AI models require significant computational resources. Distributing this across a decentralized network is inefficient and costly.

Think about this. We are creating these smart systems that require unprecedented levels of data and processing capability. These systems as a whole need this sort of smooth coordination while remaining in a decentralized and trustless system. It would be as if we were trying to build a highspeed train on top of quicksand. It seems almost revolutionary until you consider the built-in practical challenges, which are huge.

It feels like the early days of the dot-com boom. So everyone was trying to get on the internet without regard for whether their business plan was viable. We all know how that ended.

Focus On Real Utility, Not Hype

It's simple: real utility.

Avoid the temptation to pursue every new buzzword. Prioritize projects with practical solutions to existing challenges and obvious, tangible applications. Put aside the idea of “decentralized AI” just for the beauty of it. Focus on areas where AI can genuinely enhance Web3 applications:

• Enhanced Security: AI can be used to detect and prevent fraud and malicious activity in Web3 networks.
• Improved User Experience: AI-powered chatbots and personalized recommendations can make Web3 applications more user-friendly.
• Data Analysis and Insights: AI can be used to analyze on-chain data to provide valuable insights into market trends and user behavior.

Don’t misunderstand me — events such as Dealflow Den and Istanbul Blockchain Week are immensely worthwhile. They provide a unique platform for promising startups to connect with investors, garner exposure, and showcase their groundbreaking ideas. It’s on you to conduct your own due diligence and sift the wheat from the chaff.

Be wary of projects that promise the world but lack a clear roadmap and a strong team. Seek out those with a track record of delivering, and that have an intimate knowledge of the nuances in both AI and Web3 technologies.

In short, the AI-Web3 convergence is not a slam-dunk. It’s a very hard field, a very tough field, a very high failure rate. Prioritize actual utility and perform careful due diligence. By avoiding the buzz, you’ll greatly increase your odds of finding the next great opportunity. Or, at the very least, prevent you from losing your shirt. Remember, true innovation is rarely flashy. Yet it’s most likely to be found in the quiet, unglamorous work of addressing real problems.

In conclusion, the AI-Web3 convergence is not a guaranteed success. It's a complex and challenging field with a high risk of failure. But by focusing on real utility, conducting thorough due diligence, and avoiding the hype, you can increase your chances of finding the next big thing. Or, at the very least, avoid losing your shirt. Remember, true innovation is rarely flashy. It's often found in the quiet, unglamorous work of solving real problems.