Where Does Consumer AI Stand at the End of 2025?

Full Title

Where Does Consumer AI Stand at the End of 2025?

Summary

The episode reviews the state of consumer AI at the end of 2025, highlighting key product launches, user behavior shifts, and the growing dominance of major players.

It looks ahead to 2026, discussing potential for startups, the impact of multimodality, and the potential breakout of scalable consumer AI applications.

Key Points

• ChatGPT leads the consumer AI market with 800-900 million weekly active users, significantly outpacing competitors like Gemini, Claude, and Grok.
• Gemini is showing strong growth (155% year-over-year for desktop users) fueled by viral models like Nano Banana and integration into Google's ecosystem.
• Multimodal models, particularly in image and video generation (e.g., Sora, VO3, Nano Banana Pro), have been highly viral and influential in shaping consumer AI usage.
• Product nuances, such as intuitive interfaces and engagement loops (like ChatGPT's trending themes), are proving more critical for user adoption than raw model quality alone.
• The consumer AI market appears to be consolidating towards a "winner-take-most" dynamic, with a small number of products dominating everyday usage.
• Startups are still finding opportunities, especially by building on top of foundational models and focusing on specific, opinionated use cases or specialized interfaces.
• The integration of AI into existing productivity workflows and personal data (e.g., calendar, email, documents) is seen as a promising area for future growth, potentially leading to "everything apps."
• Social features within AI products are currently facing challenges, with engagement primarily driven by productivity or entertainment rather than genuine social connection.
• The development of more sophisticated multimodal models capable of "anything in, anything out" processing is a key trend to watch, potentially merging text, image, and video capabilities.
• The cost of compute and the strategic decisions labs make regarding its allocation (training vs. inference, entertainment vs. utility) present ongoing challenges and opportunities.
• Startups have an advantage in building opinionated products that push boundaries, as large labs are often constrained by internal incentives and risk aversion.
• Enterprise adoption of AI, particularly through platforms like ChatGPT, is growing rapidly and could translate into increased consumer adoption.
• The app store model for AI is emerging as a significant new channel for consumer AI products.

Conclusion

The models have reached a quality level that enables the development of truly scalable consumer applications.

2026 is poised to be a significant year for consumer AI builders, moving beyond consumers simply using products to consumers actively building and creating with AI.

Startups continue to have a strong advantage in creating innovative, opinionated products by leveraging advanced models and focusing on specific niches.

Discussion Topics

• How do you see the balance between large labs and startups shaping the future of consumer AI innovation?
• What are the most critical product nuances that will determine the success of consumer AI applications going forward?
• Beyond productivity and entertainment, what unmet needs could AI uniquely address in social interactions and community building?

Key Terms

LLM

Large Language Model, a type of AI designed to understand and generate human-like text.

Multimodal Models

AI models that can process and understand information from multiple types of data, such as text, images, audio, and video.

Viral Models

AI models or features that gain rapid and widespread popularity and usage, often due to novel capabilities or striking outputs.

Winner-Take-Most

A market dynamic where a single product or company captures a disproportionately large share of the market, leaving only small niches for competitors.

Prosumer

A user who both consumes and produces content or products, often using advanced tools.

Agentic Model

An AI system designed to act autonomously to achieve specific goals, often involving planning and executing a series of actions.

Prompt Engineering

The process of designing and refining input text (prompts) to guide AI models to produce desired outputs.

Compute

The processing power required to run AI models, encompassing both training new models and performing inference (generating outputs).

Inference

The process of using a trained AI model to make predictions or generate outputs based on new input data.

SaaS

Software as a Service, a software distribution model where a third-party provider hosts applications and makes them available to customers over the Internet.

Timeline