4 — Is AI Inevitable?

Predicting the future is easy, but accurately predicting the future isn’t. Anyone willing to research what it was like for inventors and pioneers who introduced many of our now-ubiquitous technologies can find predictions and responses of their contemporaries which are now, in hindsight, laughable. Here are a just a few of my favorites:

1876: “The Americans have need of the telephone, but we do not. We have plenty of messenger boys.” — William Preece, British Post Office.

1903: “The horse is here to stay but the automobile is only a novelty — a fad.” — President of a Michigan bank, in denying an investment loan to Henry Ford’s partner.

1946: “Television won’t be able to hold on to any market it captures after the first six months. People will soon get tired of staring at a plywood box every night.” — Darryl Zanuck, 20th Century Fox.

Speaking of accurate predictions, let’s understand that in each of the above examples, the technology already existed, but the question of “will this be adopted on a large scale?” had yet to be settled. Just because something exists, doesn’t mean it will necessarily be used. In today’s times, technology investors are constantly challenging inventors, who can prove that something can be done, to demonstrate why it should be done.

Analysts in the AI space are noting that the rate of innovation and improvement is actually outpacing Moore’s “law” (an approximate doubling every two years in processor capabilities). Stanford’s “AI Index 2019” has even found “the time required to train a network on cloud infrastructure for supervised image recognition fell from about three hours in October 2017 to about 88 seconds in July 2019.”

While current and upcoming AI innovations are sure to be impressive to machine learning insiders, it does not necessarily mean that a given breakthrough will translate into the larger workforce, or make a real difference in whatever industry it’s intended for. As an underground construction professional who has for the last 13 years focused on trenchless technologies and pipeline robotics, I have seen far too many novel innovations, each with their own measurable value proposition, fail to take off. With many of these, the promoters of the technology might have even been diligent to quantify the extent of the value added with their innovation, to publish case studies of results for early adoptors, even to the point where some (like myself) would consider it to be a “no brainer” scenario which would surely make a huge impact on my industry, only to see (years later) these companies or product offerings fail to take hold, for whatever reason (strategic marketing mistakes, bad business practices, or bad luck etc).

As underground infrastructure professionals seek to navigate the uncertain future caused by the Covid-19 global pandemic, where we are seeing water and wastewater utilities’ (in the US at least) experience an unprecedented loss in revenue on the magnitude of tens of billions of dollars, and who have already been dealing with a wave of retirements among industry professionals, and the loss of that institutional knowledge, there absolutely will be an increased demand for solutions that enable organizations to do more with less, which is a classic problem AI can solve. Nevertheless, it will be essential for those with AI solutions to offer to communicate them responsibly to the stakeholders in the industry, and to communicate in a way that addresses many of the valid concerns about the workforce disruptions caused or perceived by AI.