Prediction: ‘AI startup’ won’t be a thing anymore

r/Entrepreneur

8/15/2025

Content Summary

The post discusses the prediction that 'AI startup' will no longer be a distinct category in the future, similar to how 'internet startup' or 'mobile startup' have become obsolete. The author argues that AI is becoming basic infrastructure, and companies will use it as a standard tool rather than defining themselves by it. The discussion highlights that the focus should shift from AI itself to solving real-world problems, with successful businesses being those that understand their industry and use AI effectively. Many comments support this view, emphasizing that customers care about results, not the technology behind the product. Some debate whether AI will eventually become so ubiquitous that it's no longer a distinguishing factor, while others argue that new AI-focused startups will still emerge in specialized fields.

Opinion Analysis

Mainstream opinion: Most commenters agree with the prediction that AI will become a standard infrastructure, and that successful businesses will focus on solving real problems rather than just using AI. There is a general consensus that customers don't care about the technology behind a product but care about its effectiveness and ease of use. Many also agree that AI is becoming overused as a marketing buzzword, and that true innovation lies in applying AI to solve specific industry challenges.

Controversial opinions: A few commenters disagree, arguing that AI will continue to be a distinct category for years to come, and that there will still be many AI-focused startups in specialized fields. Some believe that AI is still in its early stages and that we haven't even seen the true potential of AI yet. Others suggest that AI will become so integrated into daily life that it will create entirely new industries and opportunities for startups.

SAAS TOOLS

SaaS	URL	Category	Features/Notes
Launchetize	Not mentioned	Product marketing tool	Helped with value proposition and launch strategy
Model Context Protocol (MCP)	Not mentioned	API integration tool	Allows linking existing APIs with AI clients like Claude
OpenAI	Not mentioned	AI model provider	Used by some startups for AI capabilities
Hugging Face	Not mentioned	AI model repository	Used for pre-trained models
Stripe	Not mentioned	Payment processing	Integrated with AI tools
Google Suite	Not mentioned	Productivity suite	Integrated with AI tools
MongoDB	Not mentioned	Database service	Integrated with AI tools
AWS	Not mentioned	Cloud computing	Standard infrastructure for many startups
Chatbots	Not mentioned	AI interface	Overused and disliked by some users

USER NEEDS

Pain Points:

Overuse of AI as a marketing buzzword
VCs focusing on AI pitches without real business models
Users not caring about the technical details of how a product works
Founders building businesses around the term 'AI' instead of solving real problems
Misuse of AI in areas where it's not necessary
Poor user experience with chatbots
Privacy concerns with AI-driven personalization

Problems to Solve:

Making AI an invisible, standard part of products
Focusing on solving specific industry problems rather than just using AI
Improving user experience with AI interfaces
Building trust with users through transparent AI practices
Creating value through real problem-solving rather than hype

Potential Solutions:

Using AI as a tool rather than the main selling point
Focusing on customer needs and execution over technology
Developing clear, simple value propositions that don't rely on AI jargon
Building products that solve real problems in specific industries
Improving AI interfaces to make them more intuitive and user-friendly

GROWTH FACTORS

Effective Strategies:

Focusing on solving real problems rather than just using AI
Building strong customer relationships and understanding their needs
Emphasizing execution and business model viability over technology
Avoiding AI as a marketing buzzword and instead highlighting practical benefits
Leveraging AI as a tool to enhance existing solutions rather than as a standalone product

Marketing & Acquisition:

Building value propositions that focus on problem-solving rather than technology
Targeting enterprise customers who care about results, not the underlying tech
Using clear, simple language to communicate value to non-technical users
Building trust through transparency and ethical AI practices

Monetization & Product:

Prioritizing real business models over AI hype
Focusing on measurable outcomes and ROI for customers
Developing products that are easy to use and integrate into existing workflows
Ensuring AI is used to enhance, not complicate, the user experience

User Engagement:

Building communities around specific industry problems rather than AI technology
Engaging users through clear communication of value and practical benefits
Encouraging feedback to continuously improve products and address user needs
Creating a sense of trust and reliability through consistent performance and ethical practices

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