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Shyp involves multiple layers of complexity—once it picks up an item, it takes it to a warehouse, packs it up, then hands it off to a major courier such as UPS for delivery—but it's the couriers who define the face-to-face experience for customers. "Our service has so many touch points—showing up at your home and shipping anything anywhere in the world," says CEO and cofounder Kevin Gibbon. "It could be really expensive, like a painting or something like that. We felt that given how complicated the actual job is, the best course is to transition these folks."

Still, by moving away from the contractor model, the company gains the ability to exert more control over the Shyp experience without fear of legal repercussions. It can get more involved in training and coaching couriers, managing the hours they work, and generally treating them like full-blown team members rather than freelancers. It will also begin to pay workers' compensation, unemployment, and Social Security taxes for couriers. They'll continue to use their own vehicles, but Shyp will cover costs such as fuel.

Aren't employees more expensive than contractors? Sure, which is one big reason why on-demand startups have shied away from hiring them. But Gibbon says that Shyp's satellite drivers and warehouse workers are already employees, so hiring couriers isn't a dramatic departure. And its profit margins are such that there's room for the extra cost. "We felt that with everything we can bring operationally, it'll be a net positive," he told me. "If someone has a better experience, they're much more likely to tell someone else about it."

Uber has long claimed it’s a technology company, not a transportation company. Uber’s drivers are promoted as entrepreneurs and classified as independent contractors. The company claims to provide only a platform/app that enables drivers to be connected with passengers; as an intermediary, the company relies on the politics of platforms to elude responsibility as a traditional employer, as well as regulatory regimes designed to govern traditional taxi businesses.

Drivers must submit to a system that molds their interactions, controls their behavior, sets and changes rates unilaterally, and is generally structured to minimize the power of driver (“partner”) voices. Drivers make inquiries to outsourced community support representatives that work on Uber’s behalf, but their responses are based on templates or FAQs.

Uber uses surge pricing to lure drivers to work at a particular place at a particular time, without guaranteeing the validity of the surge incentive if they do follow it. Surge is produced through an algorithmic assessment of supply and demand and is subject to constant dynamism. The rate that drivers are paid is based on the passenger’s location, not their own. Even when they travel to an active surge zone, they risk receiving passengers at lower or higher surge than is initially advertised, or getting fares from outside the surge zone. Drivers will be locked out of the system for varying periods of time, like 10 minutes, 30 minutes, etc. for declining too many rides. They also get warnings for “manipulating” surge.

As a first example, consider how workplace monitoring generates data that companies can use to automate the very tasks workers are being paid to perform. When Uber drivers carry passengers from one location to another, or simply cruise around town waiting for fares, Uber gathers extensive data on routes, driving speed, and driver behavior. That data may prove useful in developing the many algorithms required for autonomous vehicles—for example by illuminating how a reasonable driver would respond to particular traffic or road conditions.

with GPS data from millions of trips across town, Uber may be able to predict the best path from point A to point B fairly well, accounting not just for map distance, but also for current traffic, weather, the time of day, etc. In other words, its algorithms can replicate drivers’ subtle, local knowledge. If that knowledge was once relatively rare, then Uber’s algorithms may enable it to push down wages and erode working conditions.

By managing drivers’ expectations, the company may be able to maintain a high supply of drivers on the road waiting for fares. The net effect may be to lower wages, since the company only pays drivers when they are ferrying passengers.

Originally founded in Germany in 2012, Fairmondo aims to federate and expand to create a global online marketplace, but with ownership firmly in hands of their local users. The German coop currently gathers over 2000 members who have invested over 600,000 euros in shares. It is open both to professional and private sellers and the products on offer have no general restrictions unless they are illegal or run counter to Farmondo’s values. The core values are fairness and the promotion of responsible consumption. Rather than having to find fairly sourced products from a variety of places, Fairmondo practically gathers them in federated, democratic platforms. The fairness of the products in question is assessed by a shared criteria which remains open to discussion and improvement by the members and the Fairmondo user base. The platform also includes certain products which are not necessarily fair trade, for example books, with more than two million on offer.

The economic democracy ethos surrounding ownership and control of the platform goes beyond the practices of most cooperatives. Fairmondo calls this “Cooperativism 2.0” and asks all new Fairmondo chapters to adapt the following seven Core Principles:

Consent and majority consensus:  90% of Fairmondo constituents must agree prior any modification to the general principles.

Unlike the traditional economy, the gig economy doesn’t rely on CVs or letters of recommendation. You build your reputation on one platform at a time — and your reputation is often the route to higher earnings (A service user is more likely to choose someone with 100 five-star ratings than just one or two). Platforms don’t want people to leave, so they don’t let workers have ownership over their own ratings. Leaving a service means starting over.

More recently, we’ve been exploring the “how” of ratings portability: what technology, data, user experience and investment might be needed to make this real.Our design team, along with our policy intern and developer James Darling, have been conducting user research and prototyping possible technical solutions for ratings portability. Here’s where we’ve got to so far.

“Cab” drivers didn’t have visible habits around their ratings, weren’t checking them frequently and when we spoke about them, they told us that this wasn’t something they’d considered before or something they were particularly concerned about. They were confident in their skills and ability to find work outside of their platforms, and viewed ratings more as performance indicators for their platform owners — the main fear being a drop below 3.5 stars, where they might be dropped from the platform completely.

The machines are not just cleverer, they also have access to far more data. The combination of big data and smart machines will take over some occupations wholesale; in others it will allow firms to do more with fewer workers. Text-mining programs will displace professional jobs in legal services. Biopsies will be analysed more efficiently by image-processing software than lab technicians. Accountants may follow travel agents and tellers into the unemployment line as tax software improves. Machines are already turning basic sports results and financial data into good-enough news stories.

There will still be jobs. Even Mr Frey and Mr Osborne, whose research speaks of 47% of job categories being open to automation within two decades, accept that some jobs—especially those currently associated with high levels of education and high wages—will survive (see table). Tyler Cowen, an economist at George Mason University and a much-read blogger, writes in his most recent book, “Average is Over”, that rich economies seem to be bifurcating into a small group of workers with skills highly complementary with machine intelligence, for whom he has high hopes, and the rest, for whom not so much.

In California, Uber, Lyft, TaskRabbit, and a half-dozen other companies are lobbying to defang a court ruling that could make it difficult to avoid reclassifying such workers as employees. And in Washington, the Republican-dominated National Labor Relations Board has made moves to undo an Obama-era precedent that could make big employers legally liable for contract workers even if they have only indirect control over them.The GOP takeover in Washington is one reason the Temporary Workers of America, a union of bug testers for Microsoft Corp., gave up on what had been, for people in the software world, an almost unheard of unionization victory, says the group’s founder, Philippe Boucher.

Boucher and his ex-colleagues are among a growing population of tech workers, including many Uber drivers, Amazon.com warehouse loaders, and Google software engineers, who lack the rights and perks of those companies’ full-fledged employees.

Google parent Alphabet Inc. now has fewer direct employees than it does contract workers, some of whom write code and test self-driving cars.

But Flex operates year-round, not just during the holiday season, which suggests there’s another reason for it: It’s cheap. As the larger trucking industry has discovered over the past decade, using independent contractors rather than unionized drivers saves money, because so many expenses are borne by the drivers, rather than the company.

The company doesn’t share information about how many drivers it has, but one Seattle economist calculated that 11,262 individuals drove for Flex in California between October 2016 and March 2017, based on information Amazon shared with him to help the company defend a lawsuit about Flex drivers.

“A lot of these gig-type services essentially rely on people not doing the math on what it actually costs you,”