Is AI a bubble set for popping? One CEO thinks not, despite the challenges facing the new AI economy. Via Flickr/ tom_bullock.

No Dot-com Style AI Bubble, but Advances Yet to Come

Turkish-American AI entrepreneur Emrah Gultekin wrote an editorial in VentureBeat July 1 addressing fears among some that the burgeoning AI industry could be the “next big tech bubble to burst.” By analyzing the state of the industry and concluding that while “corrections and surprises” might mark the next few years of progress, her asserts AI is a young but fast-progressing technology that is “here to stay.”

Some skeptics of AI believe that the nascent industry is presently in a bubble similar to the dot-com boom of the late 1990s, where promising new technologies didn’t have enough real-world traction to sustain a suddenly crowded Silicon Valley tech scene and hype-driven wildcat investment in “vaporware” companies capitalizing on anything web-related (with little or no real product) crashed the tech economy for years to come.

Emrah Gultekin, CEO and co-founder of Via Medium.

While the technological innovations sparked during the web boom gave rise to today’s tech behemoths such as Amazon and Google and ultimately spurned the development of Web 2.0 in the aughts, the unrealistic expectations and runaway overvaluations of the core technologies of the dot-com bubble and its entrepreneurial acolytes alike precipitated an economic crash and a harsh reality check for the tech industry.

Staying realistic about the present state of AI and the hurdles yet to come, Gultekin dispels some of the present misconceptions and future hurdles of mass AI adoption, but also proposes that “change is on the horizon” if companies and developers tackle the necessary technological obstacles and growing pains of AI.

Co-founder and CEO of Chooch Intelligence Technologies, an AI company providing computer vision API to business customers, Gultekin identifies the challenges the AI industry presently faces, which are similar to problems tech developers faced in the dot-com boom but that needn’t foretell disaster this time if overcome.

Conceding that the industry is vulnerable to hype, Gultekin’s analysis suggests that AI, while promising, does suffer a certain degree of magical thinking outside of the tech community, writing that terms like artificial intelligence and neural networks “just [sound] sexier” to business customers and the public, who might attribute to the technology a kind of mysticism or unrealistic expectations despite the field of machine learning having “been around for decades.”

In Gultekin’s words, AI is essentially nothing more than the latest generation of a “network and database tool” that “extracts features, converts them to vectors (numbers) in layers, and stores them for easy recall”—hardly a magic one-size-fits-all business solution or the self-aware threat of the many Terminators and HALs of popular media.

In addition to problems of image and misconception, AI needs to deliver real-world products and technologies to reduce “serious pain points” in business applications and not simply “[have] something that’s cool.” Describing the current state of AI as “trying to do a mobile app before 3G or trying to do VOIP 20 years ago,” Gultekin sees AI as a technology on the verge of major development but slowed by the unprecedented challenges of developing new and revolutionary technological infrastructures.

Gultekin sees the nuts-and-bolts of AI development as an area of improvement, noting that while successfully developing AI frameworks requires a radical departure from classical coding into new machine learning disciplines dominated by the PhDs and academics that are increasingly being siphoned by top-paying companies, companies still need to hire traditional coders to turn the AI into a usable real-world product:

“Even if you get these AI frameworks to work, they’re not deployable products in themselves. Developers still need to have classical coding skills to develop products from these frameworks, which means you need to have teams of AI people and classical coders working in synchrony.”

Gultekin believes that “we’re probably five to seven years out before [AI] starts impacting people in a more significant way” and estimates that the industry is “ten to fifteen years out” from creating “a major shift” in society, estimating as well that “[w]e’ll see a slew of new companies emerging in different fields of AI over the next several years,” predicting as well that established tech giants may not dominate over the newcomers despite their present abundance of AI talent.