At the early stages of TAOS, it is expected that it is mostly traders, other finance professionals, and academic people including researchers that can most directly benefit from the system, its algorithms’ output and data. This is especially so in the DeFi digital asset space where new technologies have highly uncertain projections.

But the TAOS idea and applicability is much grander than that. “Risk,” whether applied to everyday life or financial markets, is a very poorly understood concept, and this is what TAOS focuses on. People tend to attribute unfortunate events to bad luck (or good fortune, maybe) rather than attempting to take control of the risk themselves.

Risk is a tricky concept: On one hand, if a bad event does not happen, then one might think you do not have to account for it. But that is the wrong conclusion. On the other hand, if one manages risk very well, then one does not observe a bad outcome, which obviously makes one to wonder if the risk management was just waste of resources. Again the wrong conclusion. The role of a risk manager is not an easy one.

TAOS is also strongly developed with AI in mind: AI needs a lot of data to be used, and such amounts of data are hard to collect (and maintain) from the real world to the accuracy required. This problem is solved by creating hundreds or thousands of simulation runs that bring about the true nature of risk. This is what TAOS does.

Think of driving a rally car in darkness through winding mountain roads. Would you rather have the AI-assistant see all the available data from its sensors in real-time, and drive “lights on” – or would you be satisfied with sampled data that would allow you to see a section of the road only at every few moments? Should be an easy choice.