Kashani said that besides rawing photo-realistic images and holding seemingly sentient conversations, AI has failed on many promises and even created a wave of AI skepticism.

“We can become too cynical and watch from the sidelines as winners emerge, or find a way to filter noise and identify commercial breakthroughs early to participate in a historic economic opportunity. There's a simple framework for differentiating near-term reality from science fiction. We use the single most important measure of maturity in any technology: its ability to manage unforeseen events commonly known as edge cases. As a technology hardens, it becomes more adept at handling increasingly infrequent edge cases and, as a result, gradually unlocking new applications,” he said.

He said that today's AI can achieve very high performance if it is focused on either precision or recall. In other words, it optimises one at the expense of the other (i.e., fewer false positives in exchange for more false negatives, and vice versa). But when it comes to achieving high performance on both of those simultaneously, AI models can’t cope.

He said that Delivery Autonomous Mobile Robots (AMRs) are the first application of urban autonomy to commercialise, but robo-taxis still await an unattainable hi-fi AI performance.

“The rate of progress in this industry, as well as our experience over the past five years, has strengthened our view that the best way to commercialise AI is to focus on narrower applications enabled by lo-fi AI, and use human intervention to achieve hi-fi performance when needed. In this model, lo-fi AI leads to early commercialization, and incremental improvements afterwards help drive business KPIs,” Kashani said.

By targeting more forgiving use cases, businesses can use lo-fi AI to achieve commercial success early, while maintaining a realistic view of the multi-year timeline for achieving hi-fi capabilities, he said.