The future is already here — it's just not very evenly distributed. (William Gibson)
Good AI talent is hard to find. Companies like Google, Amazon, Facebook, IBM, Netflix, and Uber employ hundreds of data scientists and machine learning engineers, monopolizing the talent. Right now, it's becoming increasingly difficult to begin AI initiatives and build teams. Credit to TOPBOTS for curating some pretty incredible statistics on the issue.
Microsoft recently acquired GitHub for $7,5 billion, which is nearly 30 times its annual recurring revenue. Can you guess the reason behind this astronomic amount?It’s quite simple: developers.
However, not all developers are valued equally. There is a privileged group of AI developers that companies are ready to pay astonishingly huge amounts for acquiring.
The numbers speak for themselves:
- Only about 22,000 Ph.D.-level computer scientists around the world have the required experience to build cutting-edge AI systems.
- Of those, only about 3,000 are currently looking for a job. In contrast, at least 10,000 related positions are open in the U.S. alone.
- Six technology companies employ 54% of all deep learning specialists.
- Fresh Ph.D.s and others with limited work experience are regularly paid between $300,000 to $500,000 a year or more in salary and company stock.
- The average salaries of Google’s DeepMind staff in the UK amounted to $345,000 per employee in 2016.If you are thinking about building an AI team, you will face challenges both finding and competing for top talent. You may be weighing out options to build versus buying a team. We say both. Partner to get initial use cases off the ground and learn through execution. As you discover what you need and encounter limitations, you can be building your team over time, with less risk of falling behind.
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