Why Drive.ai is going to struggle to disrupt public transport

Drive.ai has announced that it is to begin trialling a self-driving car taxi service in Frisco, Texas this Summer. The trial is to last 6 months as the organization works closely with the Frisco authorities to finalize the details of the routes and to ‘educate’ the public about how they can be used. But although the news has widely been presented as a step forward for the wider adoption of self-driving cars, the story in fact exposes the way in which self-driving car engineers are struggling to properly disrupt. And that’s before it has even begun.

Drive.ai’s announcement comes shortly after a number of high profile incidents involving self-driving cars. In March, a woman was killed by an Uber self-driving car in Arizona. In May, a Waymo van was involved in a collision in Arizona too. This puts a little more pressure on Drive.ai, and means the trial will be watched particularly closely. Any further issues will only do more to make the wider public resistant to autonomous vehicles. The more issues that appear, the more the very concept of self-driving vehicles begins to look like a Silicon Valley pipe dream. It starts looking like a way for tech entrepreneurs to take advantage of underfunded public infrastructure in the name of disruption and innovation.

And this is precisely the problem with the forthcoming Drive.ai trial. For the trial to work, Drive.ai are dependent on the support and collaboration of the Frisco authorities. Yes, there are some positives to this – there’s an argument that the future of public life depends on a sort of hybrid of entrepreneurialism and state support. But we’re not just talking about using machine learning or deep learning to better understand how to deploy resources more effectively, how to target those most in need of support. In this instance, we’re talking about a slightly clunky system. It’s a system everyone recognises as clunky – after all, that’s why public ‘education’ is needed.

Disruption should be frictionless. Self-driving taxis aren’t.

Whatever you think of Uber and Airbnb, both organisations have managed to disrupt their respective industries by building platforms that make certain transactions and interactions frictionless. However, when it comes to self-driving taxi services, things are much different. They’re not frictionless at all. That’s why Drive.ai are having to work with the Frisco authorities to sell the idea to the public. Disruptive tech works best when people immediately get the concept. It’s the sort of thing that starts with wouldn’t it be great if... No one thinks that about self-driving cars. The self-driving bit is immaterial to most users. Provided their Uber drivers are polite and get them to where they want to go, that’s enough. Of course, some people might even like having a driver they can interact with (god forbid!).

Sure, you might think I’m missing the point. Self driving cars will be more efficient, right? The cost savings will be passed on to end users. Of course it might – but seen in perspective, lots of things have become more efficient or automated. It doesn’t mean we’re suddenly all feeling the benefits of our savings.

More importantly, this isn’t really disruption. You’re not radically changing the way you do something based on the needs of the people that use it. Instead you’re simply trying to shift their expectations to make it easier to automate jobs. In many instances we’re seeing power shift from public organizations to those where technical expertise is located. And that’s what’s happening here.

Artificial intelligence needs to be accessible to be impactful

Essentially, the technology is stuck inside the Silicon Valley organizations trying to profit from it. We know for a fact the deep learning and artificial intelligence are at their most exciting and interesting when its accessible to a huge range of people. In the case of Drive.ai, the AI is just the kernel around which all these other moving parts depend – the investment, infrastructure, and acceptance of the technology.

Artificial intelligence projects work best when they seem to achieve something seamlessly, not when they require a whole operation just to make it work. The initiatives being run by Drive.ai and its competitors are a tired use of AI. It’s almost as if we’re chasing the dream of taxi cabs that can drive themselves simply because we simply should. And while there’s clearly potential for big money to be made by those organizations working hard to make it work, for many of the cities they’re working with, it might not be the best option. Public transport does, after all, already exist.

Drive.ai needs users to adapt to the technology

Perhaps Drive.ai might just make this work. But it’s going to be difficult. That’s because the problems of self-driving cars are actually a little different to those many software companies face. Typically the challenge is responding to the needs of users and building the technology accordingly. In this instance, the technology is almost there. The problem facing Drive.ai and others is getting users to accept it.

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