Only question is: if executed properly, would this get VC funding?
##Idea:
Youertube.com: Enhance my youtube experience by hyper-personalizing my video content. Use AI to find out about me, my interests, my situation, and recommend videos that will help me. Create a chat-gpt link interface to built out prompts for the problems I want to solve and find video content to solve them.
##Evaluation:
Concept Overview:
Youertube.com aims to create a hyper-personalized video content recommendation platform using AI. The idea addresses the growing demand for personalized content but faces challenges with execution, competition, and market feasibility.
Market Profitability:
Market Size: Online video streaming market projected at $184.3 billion by 2027, but hyper-personalized services represent a smaller niche.
Revenue Streams: Subscription fees, content creator partnerships, and targeted advertising.
Profit Margins: High potential margins but strained by continuous development and marketing costs.
Uniqueness Factor:
Key Differentiators: AI-powered hyper-personalization with a ChatGPT-style interface.
Competitive Advantage: Unique AI features could differentiate it, but execution is critical.
Real-World Success Potential:
Barriers to Entry: High technological demands, including advanced AI and a user-friendly interface.
Scalability: Potentially scalable but challenging due to market saturation.
Market Trends: Personalized content is in demand, but competitors like YouTube and Netflix already leverage AI effectively.
Competition:
Direct Competitors: YouTube, Netflix, Hulu.
Indirect Competitors: TikTok, Instagram, and other algorithm-driven platforms.
Risk Factors:
Market Risks: Saturated market; users may prefer established platforms.
Operational Risks: Dependence on AI and user data raises privacy and regulatory concerns.
Financial Risks: High upfront costs for development and marketing with unclear user acquisition strategies.
Overall Assessment:
Strengths: Growing demand for personalized content; innovative AI can attract tech-savvy users.
Weaknesses: Intense competition and reliance on advanced AI could limit differentiation and adoption.
Opportunities: Partnerships with creators and brands for targeted advertising.
Threats: Established competitors may quickly adapt and overshadow new entrants.
Agree with this sentiment, erosion of trust and potential issues. The illusion of facts and knowledge is a great moral hazard that AI companies are willing to step around while the market share battles play out. More responsible AI companies, stronger government policy, better engineering and less dumb users are all part of the solution here.
This is more solvable from an engineering perspective if we don't take the approach that LLMs are a hammer and everything is a nail. The solution I think is along the lines of breaking the issue down into 2-3 problems: 1) Understand the intent of question, 2) Validating the data in resultset and 3) provide a signal to the user of the measure to which the result matches the intent of the intention.
LLMs work great to understand the intent of the request; To me this is the magic of LLM - when I ask, it understands what I'm looking for as opposed to google has no idea, here's a bunch of blue links - you go figure it out.
However, more validation of results is required. Before answers are returned, I want the result validated with a trusted source. Trust is a hard problem..and probably not in the purview of the LLM to solve. Trust means different things in different contexts. You trust a friend because they understand your worldview and they have your best interest in mind. Does an LLM do this? You trust a business because they have consistently delivered valuable services to their customers, leveraging proprietary, up-to-date knowledge acquired through their operations, which rely on having the latest and most accurate information as a competitive advantage. Descartes stores this mornings garbage truck routes for Boise IA in its route planning software - thats the only source I trust for Boise IA garbage truck routes. This, I believe is the purpose for tools, agents and function calling in LLMs, and APIs from Descartes.
But this trust needs to be signaled to the user in the LLM response. Some measure of the original intent against the quality of the response needs to be given back to the user so that its not just an illusion of the facts and knowledge, but a verified response that the user can critically evaluate as to whether it matches their intent.