Universal problems, like managing personal addresses, persist because they are too boring for top talent to solve. Technologists who could build solutions are drawn to higher-leverage, more interesting projects, leaving these obvious-but-unglamorous opportunities unaddressed.
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Some business ideas, like a "what's on campus" app or a universal group organizing tool, seem obvious yet consistently fail. These are "mirage opportunities" where a fundamental assumption about user behavior is flawed. If many have tried and failed, it's a signal to stay away.
During tech gold rushes like AI, the most skilled engineers ("level 100 players") are drawn to lucrative but less impactful ventures. This creates a significant opportunity cost, as their talents are diverted from society's most pressing challenges, like semiconductor fabrication.
AI lowers the barrier to entry, flooding the market with "whiteboard founded" companies tackling low-hanging fruit. This creates a highly competitive, consensus-driven environment that is the opposite of a "good quest." The real challenge is finding meaningful problems.
Kara Swisher argues that friction is critical for moving forward. The tech industry's obsession with creating seamless, easy experiences is misguided. Hardship and challenges are what lead to growth, cognitive health, and true innovation, whereas frictionless AI can lead to mental atrophy.
Businesses invest heavily in recruiting top talent but then micromanage them, preventing them from using their full cognitive abilities. This creates a transactional environment where employees don't contribute their best ideas, leaving significant value unrealized.
Instead of optimizing for a quick win, founders should be "greedy" and select a problem so compelling they can envision working on it for 10-20 years. This long-term alignment is critical for avoiding the burnout and cynicism that comes from building a business you're not passionate about. The problem itself must be the primary source of motivation.
The process of struggling with and solving hard problems is what builds engineering skill. Constantly available AI assistants act like a "slot machine for answers," removing this productive struggle. This encourages "vibe coding" and may prevent engineers from developing deep problem-solving expertise.
Maintain a running list of problems you encounter. If a problem persists and you keep running into it after a year, it's a strong signal for a potential business idea. This "aging" process filters out fleeting frustrations from genuinely persistent, valuable problems.
Many businesses avoid adopting new tools like online scheduling because they fixate on potential outlier problems (e.g., a complex booking). This "paralysis by analysis" over imaginary scenarios prevents them from capturing the majority of leads who would benefit from convenience, ultimately costing them business.
