Data can be leveraged by different people in different specialties to solve real-world problems. A neurogeneticist is working in an e-commerce start-up to solve the problem of how to plan a delivery route across the busiest parts of the city. He is building his algorithm to mimic the way human genes convey information. A competitor, meanwhile, has hired a nuclear physicist who worked in the Large Hadron Collider to solve the same problem. Both are approaching the challenge as a data problem, after all.

Here are three parameters to gauge whether a job can be done by robots:The job is driven by a clear set of rules and scenarios. That’s how robots are programmed.Efficiency and productivity are the main measures of the job.The role of soft skills — collaboration, influencing, negotiation etc — is limited.Do the same rules apply to an industry? How does your industry measure up?

Alphabet is home to a dozen innovative companies. Look at the company that is simply called X, which runs “moonshot” projects. Google X chooses to focus on the future as it chooses what problems to solve. In keeping their innovation projects in-house, they use their massive cash reserves to focus on problems that would not get venture capital funds or those that have very long time horizons.