Company executives in every industry are quickly becoming aware that AI will have a dramatic impact on their industry. They are reading the tea leaves and realize that the sheer scale of the disruption will give those who gain early mover advantage in their space outsized returns in terms of both market share and revenue. To this end companies ranging from stealth start-ups to big tech companies like Google, Apple, Tesla and Facebook, and even including non-tech companies like Ford, GM, Toyota, Goldman Sachs and more are rushing to recruit AI scientists.
Too bad most of these AI positions will go unfilled. There are simply not enough scientists trained in AI related fields to go around and this shortage is different then they types of skills shortages Silicon Valley frequently experiences. Every so often the technology industry experiences a skills shortage when either a) a tech boom happens that expands the need for engineers and/or b) a new programming language (or technique) is developed that requires that existing engineers learn the new language. Tech booms always end and software engineers can easily learn new programming languages. Both just take time.
But while a C++ engineer can fairly easily learn Java which creates a “new java engineer” who can then be employed by a company writing code in java, the same does not hold true in the AI space. AI scientists possess different skill sets then software engineers and come from a completely different pool of candidates, a vastly smaller pool of candidates. And unlike the supply of software engineers that can be increased by ramping up education and training, the same cannot be said for AI scientists. And this brings me back to my original point which is that most of the open AI positions will go unfilled.
So what this means is that competition for AI talent, while already stiff, is only going to become more competitive. The companies that are going to win this war for talent will be the ones that are trying to do big and exciting things; The AI equivalent of putting a man on the moon, a rover on Mars, cracking DNA or curing cancer.
So if you want to build an AI team it is important to really understand the big and exciting "thing" you are doing because the truth is that most companies, start-ups included, are actually not trying to put a man on the moon but have a business model that is increasingly reliant on AI which often times has some really exciting applications which need to be articulated really well to the scientist you are trying to recruit.
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