The challenge with ML and DL systems is that it's difficult to know a-priori what will and won't work. The math would indicate that there is nothing a suitable

The challenge with ML and DL systems is that it's difficult to know a-priori what will and won't work. The math would indicate that there is nothing a suitable DL system cannot learn, however in practice certain neural architectures can only learn certain inputs. The cycle time to develop a new system is long, and data is unfortunately scarce. Developing a DL system to solve a problem involves guesswork as to the impact of innovating on any one of dozens of components. Which is to say, it is and will be difficult to create a business based on applying a novel DL method to a particular problem space. We're seeing a consistent trend that focusing on the other aspects of the problem such as data, tooling, or end to end services tends to be much more successful.