The AI revolution was supposed to change everything. Instead of delivering on the many predictions hinting at change, it is fast becoming a graveyard of abandoned tools and broken promises. The amount of heavy investing the AI industry has experienced in the last couple of years warrants more than the level of real-world impact we've seen. The numerous investments in the AI space have built a sector that is proliferated with tools with no lasting utility. According to a report by Aim Media House, 90% of AI startups fail within their first year of operation despite the sector attracting over $100 billion in investments in 2024 alone. While there are the top AI companies like OpenAI, Anthropic, and Google with their flagship products like ChatGPT, Claude AI, and Gemini, respectively, there are countless other AI applications with surface-level utility. Many of these companies are evaluated based on hype, and not practical usage, and they end up failing. fail attracting Builder AI, a Microsoft-backed UK-based AI startup once valued at $1.5 billion, collapsed back to zero three months ago. Ghost Autonomy, an autonomous driving company that attempted to build self-driving capabilities using large language models (LLMs), failed despite raising $239 million and filing an impressive number of patents. There are many more examples like this. But despite all of these, we've seen a considerable amount of improvement on existing technology with AI, which makes me wonder: what if the problem isn't AI itself, but how we've been building and rewarding AI systems? Misaligned Incentives When you think about it, you can see how the small guys would struggle to build startups that stand the test of time in the AI world. What I'm driving at is the monopolization of AI development by big tech. The industry is being led by tech giants like Google, Amazon, Microsoft, NVIDIA, etc. According to The Guardian, it is August, and big tech has spent $155 billion already on AI this year. spent Meta, Amazon, Alphabet, and Microsoft plan to spend as much as $320 billion combined on AI technologies in 2025. While this is exciting and promising for the industry, it results in independent creators building on platforms they don't own and with tools that can be deprecated at any moment. spend Tokenization was introduced into AI to democratize the industry, but it has instead amplified a speculation problem. Most projects in the blockchain industry have launched AI tokens based on whitepapers, promises, and, most times, on vibes rather than demonstrated utility. According to Bitget News, 68% of AI tokens launched in 2024 experienced substantial price drops, losing over 80% of their value within six months of launch. A major reason for this is that we are in a market that rewards marketing over engineering and potential over performance. Bitget News Shift focus back to traditional AI again, and you'd notice the launches follow a familiar pattern. They announce the product, generate initial buzz, and hope for viral adoption. There's no core requirement to prove impact or track real-world utility, and no consequences for failing to deliver on promises. The solution to these isn't more AI tools; it's instead a fundamental restructuring of how AI creates and captures value. Shift Towards A Utility-First AI Economy Shift Towards A Utility-First AI Economy The AI industry is slowly adapting to a new model that moves away from the traditional approach. Instead of launching products first and proving their utility later, this new model requires AI Agents to prove real-world impact before receiving rewards. The centralized platforms on which these products are launched are changing, too. Instead of these platforms extracting value from creators, they now enable direct creator ownership and monetization. This ties value to actual usage and demonstrates performance, instead of speculation-driven tokenomics. This new approach champions a simple but revolutionary concept: every interaction, task completed, and problem solved becomes part of an immutable record that displays genuine impact. This way, only useful agents will survive and thrive. And with blockchain technology, it's easy to track agent performance by using smart contracts to automatically verify utility and create records of impact on the blockchain. This transparency benefits everyone. Users are able to identify genuine agents, builders get fair compensation for creating value, and investors are able to make decisions based on data rather than hype. Market Validation Market Validation This new model isn't theoretical. It is happening right now and works too, as evidenced by the market's remarkably enthusiastic response. On August 11, 2025, Xaleb Protocol launched on Binance Alpha. This is an AI Agent that allows anyone to create, tokenize, and launch utility-driven AI influencers without prior coding experience. The project is at the forefront of the utility-first AI agent economy, and its performance in the market has given credence to the appetite for this new approach. Upon launching on Binance Alpha, XCX opened at a $50 million Fully Diluted Valuation (FDV), an immediate demonstration of strong initial confidence from the market. Within hours, buying pressure saw the valuation shoot to $90 million as traders recognized the project's unique positioning. At the time of writing, XCX is stabilized at approximately $60 million—a 20% premium on its launch price. I think this is significant because the token isn't experiencing the typical post-launch crash that plagues speculative projects. Industry Implications of XCX's Success Industry Implications of XCX's Success XCX's performance on Binance Alpha signals a broader market shift towards utility-based AI projects. This is more than just one project's success. Institutional backing from MEXC, HashKey, Mirana, Foresight Ventures, and Amber validates the investment community's confidence in the utility-first model. There is a clear appetite for AI projects that prioritize substance over hype as investors, users, and creators actively seek alternatives to the speculation-driven models that have dominated the sector. This trend will also likely extend beyond the cryptocurrency markets. Traditional AI companies may need to start adopting similar utility-providing and transparency mechanisms to maintain competitive positioning. The combination of Proof-of-Utility mechanisms, creator-focused economics, and community governance is creating a framework that addresses long-standing issues in AI development. The Future of AI Economics The Future of AI Economics A utility-first model points toward a fundamental transformation in how AI is developed and the economics surrounding it. The implications extend beyond any single project or platform. We'll see the traditional model of AI, where these tools act in isolation, give way to more integrated agent ecosystems where AI entities will operate autonomously within economic frameworks. PwC's Workforce Transformation Practice Leader, Anthony Abbatiello, predicts that AI agents will revolutionize the workforce, while also suggesting that "Centers for agents will replace Centers of Excellence", indicating a shift towards more distributed and agent-based AI systems within organizations. Anthony Abbatiello These agents wouldn't just perform tasks; they'll also participate in value creation, capture fair compensation for their contributions, and continuously improve via feedback from the market. The true potential of AI may finally be actualizing, but through a different path than originally expected. Instead of the proliferation of the market with general-purpose tools by centralized corporate development, we're seeing the emergence of decentralized creators building specialized, utility-focused agents. The AI revolution is shifting into another gear, and this time, it's based on utility, not promises.

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