The 6 biggest hurdles slowing the pace of AI innovation
At first glance, the artificial intelligence industry seems to be on fire, with tons of consumer demand and ample investor interest. In fact, VC investment in AI startups rose from just $3.2 billion in 2014 to more than $9.5 billion just in the first five months of 2017. There are countless exciting prospects for AI development, including applications for healthcare, agriculture, and other realms of technology, but the AI industry isn’t a runaway train just yet.
AI has plenty of excitement backing it, but these significant hurdles are keeping it from achieving even more explosive growth:
One of the greatest advantages of young startups is their capacity to be lean, quick, and flexible; big companies often suffer from protracted decision-making and an inability to pivot, but smaller, nimbler companies can react quickly to new circumstances and survive new conditions more efficiently. However, AI startups don’t necessarily enjoy this advantage; because AI is so complicated, and depends on so many unknown variables, it’s hard to shift gears in the middle of a project. This can leave some AI startups dead in the water, or delay projects far past their originally outlined timelines.
The number of professionals well-versed in machine learning and innovative enough to create new features is very small. There’s a talent shortage in AI, and it’s having some profound effects on the pace of development in the industry.
Proficient AI developers can demand huge salaries, making it hard for startups to afford them, and even those with the cash may struggle to fill their open positions.
There are hundreds of interesting AI startups on the horizon or in the middle of development. That sounds like an exciting prospect for consumers hoping to get their hands on some next-generation technology, but it also presents an important problem: competition. Startups are forced to make faster decisions, go to market faster, and trim features in order to beat their competitors.
