The surge of generative AI has sparked a wave of startups, some of which exist solely to integrate chat-like features into other platforms. This superficial approach often fails to set them apart or validate their worth. This recalls the late 1990s when the term “internet startups” became so overused it lost its meaning. Merely branding a company as “AI” does not boost its value unless it significantly contributes by developing essential models or impactful applications and, for some investors, the term AI is becoming a turn-off.

The main problem with many new AI startups is their positioning as mere technology users rather than problem solvers. True, lasting success comes from addressing specific, critical issues that conventional solutions cannot tackle. For example, AI’s potential in education can personalize tutoring and help bridge the teacher skills gap. In customer service, advanced AI can efficiently manage interactions through multimodal responses. In architecture, engineering, and construction (AEC), applications can autonomously use AI vision technology to monitor sites, generate compliance reports, and flag issues that would be difficult or time-consuming to manage manually.

The trajectory of any new technology dictates that the most valuable solutions are those that leverage this new technology to address specific, high-impact challenges. This shift from generic to targeted applications signals real growth potential. Startups that thrive will not just wave the “AI” banner but will embed it in meaningful ways to solve pressing, real-world needs. As AI technology evolves, startups that stand out will be those demonstrating deep integration and practical use. This will mark a significant move from “AI for AI’s sake” to indispensable, value-driven solutions. Only those that prioritize true problem-solving will succeed and lead and those that fail to leverage AI technologies will quickly fall behind – like on-premises software solutions in the 90s.