>>11757542Example two
At party with friends. One of their cell phone battery died, every talking about bad battery life, but not willing to get dumb phone. Later looked battery tech. Found many things, but one tech that I liked had working prototype that was heavily patented with high promise, but shelved. Wondered why given tech was not too far off and had good prospects. It was a sweet spot, significantly better then current things, but realistically could hit market in a few years as it had no hard tech barriers. Unlike others that had only small gains, or were still in experimental phases. It looked like a sure thing, so I looked into the company to find out why they weren't pushing these. CEO had long history of selling small gains and dragging it out for maximum profit, not the risk taker to capitalized on the tech. Marked related patents and terms for tracking and waiting.
Find problem: cell phone batteries died too much
Find solution: many new battery tech being researched, lots of options
Adjust for agency: market wants sweet spot, CEO unlikely to sell tech that meets sweet spot
Map money paths: the patent is the key to the money here, follow it and related things
Market mismatch and hype: other types are better know, CEO is dumb
Pitfalls and competitions: nothing will happen till CEO changes, someone buys patents, or public demands tech they don't know exists
Re-evaluate: Wait for obstacle to be removed then invest in the next battery revolution
It helps that I know a lot about electrical chemistry and global manufacturing. So I can compare battery potential with mass production feasibility. That came for learning and pouring over a lot of data, but I was motivated to prove my Econ Professor wrong about market prediction. I mean the physics on this planet is finite, and individuals and groups have reasonable patterns most of the time, so why can't it all come together. Big data with a more human perspective, for lack of time to go into all the details
