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Person 1E6ABA

1E6ABA started with the mission of Teenager Financial Education. 1E6ABA pointed out that Wall Street possesses Information Inequality. While I agreed, I asked how we would solve it since information devalues when widespread. 1E6ABA first pointed out Content + Technology + Anywhere in the middle.

Koreans also have a language barrier. The sources and the quality of the sources differ a lot. Why would the Korean Govs and Korean Banks promote any terrible news for the Korean economy? While that is understandable, as an individual, that can have a detrimental effect. See 1997 Asian financial crisis The quality of the news dilutes drastically through the language barrier. 1E6ABA's team's financial expert used Bloomberg as their primary source while in the industry.

1E6ABA exemplified Robinhood and Robo-advisors, promoting passive investments. Toss Invest also tries to solve this information inequality in the financial market, but they target active investors (who are more straightforward to profit from via transaction fees) 1E6ABA's team first found a middle ground as a non-profit organization.

1E6ABA thinks a good ranking and curation may be the solution. Finding Information among Data.

1E6ABA first targeted the Korean market but now considers the American market due to sheer market volume.

1E6ABA says selling a financial product is the easiest. Most banks already spend at least $300 to acquire a customer. Possible side revenues include selling curated content with subscriptions, charging portions of the margin, or providing access to analyst information (Bloomberg model.)

Recently closed 4Minfunding,estimatedburnrate4M in funding, estimated burn rate 1.2M a year approx. Team of a financial expert, engineer, UX designer, PM, media expert, media researcher, and engineering intern.

1E6ABA's experience at Google involved multiple teams, from Chrome to TensorFlow. Learned a lot about soft skills. The TensorFlow team had weird, intertangled relationships across groups. For example, the Tensorflow compiler team wanted to make everything as static as possible, whereas the Tensorflow Python team wanted to make everything as dynamic as possible.

Current startup indeed holds more risk, but it comes with a thrill. The place makes the person.

💙I love my job because...

People are brilliant and kind. A huge portion of my job satisfaction.