What’s next for the gig economy?

It’s been a rough year so far for Uber. But, while many shake their heads at the startup’s corporate culture, the rest of the gig economy is likely quietly applauding them for testing the regulation around transport, employment and more.

What’s next for the gig economy?

Because, if there’s one thing the wide-ranging companies that make up the gig economy have in common – across accommodation apps, food delivery startups and laundry on demand — it’s their impact on work. Indeed, the catch-all term for companies such as Uber, Airbnb and the like has shifted from “sharing economy” to the perhaps more realistic “gig economy”, with workers treated as private contractors. This gives companies more flexibility to meet fluctuating demand while avoiding pesky employment benefits.

Regulators are pushing back, with Uber facing court challenges in the UK and elsewhere, but the gig economy promises other technology-sparked changes to how we work. Alongside the “platforming” of contractor employment, roles are being broken down into easily automated work, and algorithms are set to become your boss.

Work as a platform

Many Brits have already eschewed the nine-to-five in favour of flexible working. Findings by insurance firm Direct Line suggest that the number of contractors has grown in number by 23% over the past decade – now up to one in seven workers.

gig_economy_-_courier

But, rather than filing invoices at the end of a long project, some contractors are finding work via online platforms. Many are drawn to app-organised roles such as Uber and Deliveroo, with others leaning towards on-demand tasks from services like Upwork, TaskRabbit or Amazon’s Mechanical Turk.

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Helen Poitevin, research director at Gartner, believes such platforms will extend beyond less skilled work into consulting, professional services, retail and more. “A lot of the big-name consulting firms are looking into ways to have more of a sharing economy kind of platform to provide their services,” she told Alphr.

“And in retail, you see places where people are sharing workers across retail brands to kind of fill in at the last minute,” she added. Here, different brands in a local area share shop-floor staff where they’re most needed, so staff from one can fill in at another if someone calls in sick, for example.

Poitevin said such a system helps ease or even automate the distribution of work, making it easier to spread staff without hiring more managers. “These kinds of platforms and technology capabilities that are matching up what somebody’s looking for and the person who can provide that service take out the need for that kind of corporate layer… to actually manage the distribution of work.”

Keeping track of all that work and ensuring you also get paid could be organised using blockchain, the public ledger system at the heart of Bitcoin that’s now being used to track bank and government transactions.

“The barrier to entry is very low, the technology certainly enables that kind of easy access to work”

“I think some of the structure may come from blockchain, enabling some of that traceability, accountability and ownership… to help also with the payments and exchange, knowing how much you should be paid for given work that you deliver,” Poitevin explained. “It could be one way of structuring all of that.”

Alex Rosenblat, a researcher at Data and Society, said that shifting employment onto a digital platform can benefit workers as it can make it easier to find a job – but there may be downsides. “The barrier to entry is very low, the technology certainly enables that kind of easy access to work,” she said, warning that it can also remove employment protection.

Automation friendly

As with the rest of the economy, people in gig markets are vulnerable to automation. Automation removes the middleman between the person providing the service and receiving it, said Poitevin – but “some of the work that gig workers are doing might end up being automated”.

Uber is heavily investing in self-driving cars, hoping to phase out human drivers, and other one-off gig tasks, such as those on Amazon’s Mechanical Turk or Upwork, are often the sorts of repetitive work that face AI competition.

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Transcriptionists, personal assistants and those doing bits and pieces of repetitive work for remote employers could find themselves competing against machine-learning bots – once the technology catches up, that is. On the other hand, the gig economy also employs many doing very human work, such as cleaning or babysitting, which isn’t likely to be handed over to machines anytime soon, Roomba vacuum cleaners aside.

gig_economy_roombaRoomba, coming to potentially steal a job from you soon.

However, Poitevin doesn’t see much difference between gig workers and other parts of the labour market, pointing out that it’s invented new jobs that didn’t exist before. “Nobody thought that they were going to have a job as an Airbnb host, and some people are making their only income on platforms like that,” she said. “I think it’s creating more opportunity because of the technology rather than taking away jobs per se.”

Rosenblat said that it’s not job losses in terms of numbers that’s concerning, but the quality of the work. Automation “just tends to accelerate the gap between high value and low-value work. And so, it’s not that people will work less. It means they work more at lower quality jobs.”

Management by algorithm

While automation is often at the forefront of conversations about work and technology, Rosenblat argues that, in the more immediate future, we’ll see not only AI workers but algorithmic management. “Gig economy work under Uber was billed as mass entrepreneurship enabled by technology, but what I found in my research is that drivers are managed by an algorithm or the algorithm is the boss,” she said.

The “grading system” is already used across gig economy platforms – it’s the star rating that Uber drivers and Airbnb hosts are given, and if their grade drops too low, they could be out of work. “It’s entirely possible that you too will be rated on a one to five scale,” Rosenblat said. “In many workplaces, that’s already the case.” Many workers are also being gamified, be it sales figures or other analytics – with the right data collection, it’s easy to flip those impact measurements to algorithmic management rather than human.

That requires a steady flow of data measuring the right aspects of your role, giving employees feedback to keep them supplying work as desired, she noted. “[It’s] not like all workers react in the same way to algorithmic management. For some, the same practices and policies that cause other drivers to freak out are perfectly fine.”

It’s difficult to know what all of this means for employment, especially as regulators are struggling to keep up, but in time the employment innovations created by gig economy firms mean we all might find work on digital platforms, be paid over blockchain, and not only toil with automated systems but also be managed by them. And while trading in your current boss for an algorithm may sound appealing, at least there’s more to your performance appraisal than a score out of five delivered by a chatbot.

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