AI applications weirdly missing from today’s tech landscape

Despite remarkable advances in AI technology over the past few years, we’re seeing surprisingly few applications that leverage these capabilities beyond the ubiquitous chatbot interface. This disconnect between AI’s potential and actual implementation points to possible blind spots in how developers and companies are approaching AI application design, raising important questions about creativity, technical barriers, and market incentives in the rapidly evolving AI landscape.

The big picture: Three years after GPT-3.5‘s release, AI applications remain predominantly limited to chatbot interfaces despite transformers and embedding technologies enabling much more diverse interaction possibilities.

• Semantic search for consumer products like books, movies, and clothing using embedding technology is notably absent from major platforms like Amazon and Netflix.
• The ability to “browse latent spaces” — navigating AI-generated representations of content — remains largely unexplored in mainstream applications.

Key missing applications: Several obvious AI-enhanced text features have yet to be widely implemented despite their technical feasibility.

• Real-time fact checking, contextualization, and alternative viewpoints could significantly enhance reading experiences.
• Automated visualization tools like diagram generation and sentiment analysis wheels remain underdeveloped.
• Educational features like automated quiz generation and outline creation are technically possible but rarely implemented.

Potential innovation areas: AI-assisted debate moderation represents another untapped application with significant potential.

• Real-time bullet point summaries and argument trees could make complex discussions more accessible.
• Automated fact-checking, chart retrieval, and fallacy detection could substantially improve the quality of public discourse.

Behind the limitations: Several factors might explain this apparent innovation gap in AI applications.

• Technical barriers to implementation may be higher than they appear to outside observers.
• Market incentives might not align with developing diverse AI applications beyond chatbots.
• Organizational expertise in building sophisticated software might be lacking despite AI model availability.

Why this matters: The apparent fixation on chatbot interfaces suggests we may be underutilizing AI’s capabilities and missing opportunities to solve different types of problems with the technology we already have.

• This innovation gap could indicate a collective failure of imagination in how we interact with artificial intelligence.
• Understanding and addressing these limitations could unlock significant new value from existing AI technologies.