Consumer AI Startups Struggle to Achieve Lasting Impact

At a recent TechCrunch event, venture capitalists discussed the challenges faced by consumer AI startups in achieving long-term success. According to Chi-Hua Chien, co-founder and managing partner at Goodwater Capital, most AI startups are still struggling to make a lasting impact on individual consumers, despite the rapid adoption of general-purpose large language models like ChatGPT. Chien noted that many early AI applications in video, audio, and photo were initially popular, but ultimately failed to resonate with consumers. He cited the example of Sora and Nano Banana, which open-sourced their video models, effectively eliminating the competitive advantage of similar startups. Chien compared this phenomenon to the rise of the smartphone, where it took several years for the platform to stabilize before game-changing consumer apps emerged. He believes that AI platforms require a similar period of stabilization for lasting consumer products to flourish. The generative AI boom, which began three years ago, has yet to yield a significant number of consumer-facing applications that have achieved widespread adoption. While consumers have quickly adopted general-purpose LLMs, specialized consumer GenAI applications have struggled to gain traction. Chien attributed this to the lack of a stable AI platform, which he believes is necessary for the development of innovative and lasting consumer products. Chien's comments were made during a panel discussion at TechCrunch's StrictlyVC event in early December. He emphasized that the current landscape is reminiscent of the early days of mobile-first innovation, which saw the emergence of game-changing apps in the 2009-2010 era. He believes that the AI industry is on the cusp of a similar revolution, but requires a period of stabilization to unlock its full potential. Industry experts agree that the current challenges faced by consumer AI startups are not unique to the AI space. Many startups have struggled to achieve long-term success in the face of rapidly changing technological landscapes. However, Chien's comments highlight the specific challenges posed by the AI industry, where the rapid development of new technologies and the open-sourcing of models can quickly render competitive advantages obsolete. As the AI industry continues to evolve, it remains to be seen whether consumer-facing applications will be able to achieve widespread adoption. However, Chien's comments suggest that the industry is on the cusp of a significant shift, driven by the development of more stable and user-friendly AI platforms.