The gap that firms are trying to bridge is _huge_. I work in finance (slow finance: insurance), but finance technical enough to have our own PhD-level quants building code. They are running simulations taking hours, so scaling up on # cores would seem the first step to take. But it's not that the quants don't know parallelization, it's the IT-architecture not allowing to use AWS at all. And well, many cores on your own premises is hugely expensive because they are idle most of the time. And as a sidestep, most quants are not the first you think of to woo architectures' support. (This is probably easier for firms that trade for own profit.)
Point being: improvements in AI / machine learning and other statistical techniques are huge, and analytical demand exists, but 'old corporations' need cultural change before they can start working with and embedding startup-like technology in their workflows.
Point being: improvements in AI / machine learning and other statistical techniques are huge, and analytical demand exists, but 'old corporations' need cultural change before they can start working with and embedding startup-like technology in their workflows.