AI Compresses Away the PMF
The same mechanisms that make AI reliable also make it overlook the variance where new ideas often begin.
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As all kinds of artificial intelligence emerge and make their ways into our lives, market research seems easier than ever. Shoot a question to AI, it summarizes markets in seconds, compare competitors, analyze different products or service, and surface common themes across thousands of pages.
I once asked AI "How do knowledge workers manage their content system? What are their pain points and solutions?". Before finishing half verse in my mind, the answers were ready, structured, and polished, with cited resources. "Fragmentation. Difficulty in retrieval. High maintenance. Lack of context, synthesis, or integration in the workflow." Every answer was reasonable and every conclusion was defensible. Yet the more I searched, the less I felt I was discovering.
People on Reddit said that the most resonant experiences are not more features, but frictionless guidance and deliberate simplicity. The conclusion was given by my agentic workflow user-insights. Yet all general AI leave it out, even Reddit's own AI-powered Q&A feature.
The problem isn't accuracy. It is compression. Every AI summary amplifies agreement and suppresses variance. This isn't a "flaw" in a particular model. It's a consequence of how modern LLMs are trained and optimized: they learn from existing data, compress vast distributions into useful representations, and are further tuned to produce reliable, broadly acceptable responses.
Models are advertised to offer better answers, but they are rewarded for giving safer ones. It's useful when the goal is understanding the present consensus, but much less powerful when the goal is discovering the future possibilities.
Optimization for reliability inevitably comes with a cost: the loss of variance. Opportunity comes from preserving the variance that is compressed away.
Opportunity doesn't begin with averages. It begins with anomalies and non-consensus. It stems from a niche group, whose struggles can't fit the algorithm or be solved by standard solutions. But the friction remains and these outliers are making noise, looking for solutions, even waving their cash, just waiting for someone to build exact what they need.
LLM generation is largely about prediction; whereas originality and opportunity are not, they are about variance and possibility.
So next time if you have a new idea, other than polishing it in your head and researching via AI, get into the real world and listen to the real voice. Spot the tiny but real, resonant yet unsolved issues that people are willing to pay. Start with your network, where your background carries weight, your empathy runs deep, and real sparks ignite. Find the people in your orbit who are already in the trenches, facing real demands, and book a coffee chat.
It's the ultimate shortcut. While AI offers a quick start, it’s not a fast track to actual accomplishment. This offline approach seems outdated time-wasting, and might not be the final roadmap, but it is the best entry point. Do not let the averages from past determines your choice in the future. The variance compressed away is where originality, entrepreneurship, and new opportunities often begin.