because traders are used to seeing such predictions fail.
Reuters was trading FX electronically since the early 1990s. At the tier one IB I worked for the IT budget was 500m USD a year (across products), and that was in 1997! Huge resources were thrown at automation. However, to this day, large trades in FX (> 10m USD notional) are still almost exclusively performed by humans over a telephone or over the bloomberg messaging system.
That's because, no matter how much you automate stuff, there is still the 1% "edge case" scenario where something goes wrong, and when that happens, you most definitely want a human that you can "look in the eye", when you have that sort of execution risk. Remember that markets move really fast and there is a lot of risk in big trades that "go wrong" because unwinding said trade will almost certainly cost one of the sides a fortune.
Also, high finance is not just about what you know. It's inevitably about who you know, about "illogical" factors such as salesperson charisma, entertainment, and most importantly, a credible personality type that understands the edge case risks. These things are very hard to replicate with a machine. You'll say they should be, that these things are unfair, but they remain a fact after many attempts at removing them have failed.
As for AI, let's for now call it what it is: machine learning. Learning from the past. That's fine for recognising stop signs at different distances, angles and degrees of noise. But in finance, the past is often misleading. Sure there's trend, but there are also very big instabilities in the historical correlation matrix. Paradigms shift without you even realising it. The constant is change. AI is not good enough at that, yet.
BTW, that's not to say machines are not making inroads. It's becoming almost impossible to get a decent trading job now with knowing at least R and Python to a comfortable degree, and good quant programmers cost a fortune. There's massive demand.