AI startups that initially gained attention with flashy demos are now pivoting toward developing sustainable, revenue-generating technologies, exemplified by companies like Tome transitioning to Lightfield and Character AI shifting its business model. This trend reflects the broader challenges in commercializing AI, emphasizing the need for adaptability and pragmatic strategies to achieve long-term growth amid rapid technological change.
AI startups that initially attracted attention with flashy demos are now shifting focus toward developing technology that generates sustainable revenue. A prime example is Keith Paris’s startup Tome, which in early 2023 rapidly grew to 25 million users with its AI-powered presentation tool. Despite raising $80 million from prominent investors, Tome struggled to convert its primarily student and small business user base into paying customers, resulting in stagnant revenue around $3 million annually. The product also failed to gain traction among professionals due to its lack of integration with relevant data and context.
Recognizing the need for change, Paris sought advice from billionaire entrepreneur Stewart Butterfield, who recommended a leaner team and a fresh product with strong customer traction. Following this guidance, Paris shut down Tome in March 2025, laid off most employees, and retained a small core team to develop a new venture. Eight months later, he launched Lightfield, an AI tool designed to assist salespeople by automating tasks like call summarization, follow-up emails, and client management. This pivot has proven successful, with Lightfield experiencing 80% monthly revenue growth and securing 1,000 paying customers, while continuing to attract investment from previous backers.
This trend of pivoting after significant funding is becoming common in the AI startup ecosystem. Many companies that raised large sums during the AI boom are either completely overhauling their original products or expanding into new, more profitable areas. For instance, Pika shifted from AI video generation to building AI agents and avatars, while Poolside moved from training AI coding models to planning a large data center project, although the latter faced setbacks. These shifts reflect the challenges startups face in finding viable business models amid rapid technological innovation.
Character AI offers another illustrative case. Founded by former Google DeepMind researchers, it initially focused on AI chatbots modeled after real and fictional characters, raising around $200 million from top investors. After Google acquired key founders and licensed the technology for $2.7 billion, the company faced lawsuits related to harmful content generated by its chatbots. Under new leadership, Character AI settled legal issues, restricted underage users, and transitioned to a bootstrapped model owned by employees. It is now exploring monetization through advertising and in-app purchases while expanding into AI-generated audio stories, comics, and interactive entertainment.
Overall, the AI startup landscape is witnessing a shift from hype-driven launches to pragmatic business strategies aimed at sustainable growth. Founders are learning to pivot or evolve their offerings based on market feedback and revenue potential, often after raising substantial capital. This evolution underscores the challenges of commercializing AI technologies and highlights the importance of adaptability in the fast-changing tech environment. For more detailed insights, readers can refer to Rashi Shrivastava’s article on Forbes.com.