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AI researchers have typically tried to get around the issues posed by by Montezuma’s Revenge and Pitfall! by instructing reinforcement-learning algorithms to explore randomly at times, while adding rewards for exploration—what’s known as “intrinsic motivation.” But the Uber researchers believe this fails to capture an important aspect of human curiosity. “We hypothesize that a major weakness of current intrinsic motivation algorithms is detachment,” they write. “Wherein the algorithms forget about promising areas they have visited, meaning they do not return to them to see if they lead to new states.”

The team’s new family of reinforcement-learning algorithms, dubbed Go-Explore, remember where they have been before, and will return to a particular area or task later on to see if it might help provide better overall results. The researchers also found that adding a little bit of domain knowledge, by having human players highlight interesting or important areas, sped up the algorithms’ learning and progress by a remarkable amount. This is significant because there may be many real-world situations where you would want an algorithm and a person to work together to solve a hard task.

Simply defined, gamification is the use of game elements – point-scoring, levels, competition with others, measurable evidence of accomplishment, ratings and rules of play – in non-game contexts. Games deliver an instantaneous, visceral experience of success and reward, and they are increasingly used in the workplace to promote emotional engagement with the work process, to increase workers’ psychological investment in completing otherwise uninspiring tasks, and to influence, or “nudge”, workers’ behaviour.

According to Burawoy, production at Allied was deliberately organised by management to encourage workers to play the game. When work took the form of a game, Burawoy observed, something interesting happened: workers’ primary source of conflict was no longer with the boss. Instead, tensions were dispersed between workers (the scheduling man, the truckers, the inspectors), between operators and their machines, and between operators and their own physical limitations (their stamina, precision of movement, focus). The battle to beat the quota also transformed a monotonous, soul-crushing job into an exciting outlet for workers to exercise their creativity, speed and skill. Workers attached notions of status and prestige to their output, and the game presented them with a series of choices throughout the day, affording them a sense of relative autonomy and control. It tapped into a worker’s desire for self-determination and self-expression. Then, it directed that desire towards the production of profit for their employer.

Former Google “design ethicist” Tristan Harris has also described how the “pull-to-refresh” mechanism used in most social media feeds mimics the clever architecture of a slot machine: users never know when they are going to experience gratification – a dozen new likes or retweets – but they know that gratification will eventually come. This unpredictability is addictive: behavioural psychologists have long understood that gambling uses variable reinforcement schedules – unpredictable intervals of uncertainty, anticipation and feedback – to condition players into playing just one more round.

The unemployment rate is at a 17-year low, but stagnant wages, chronic underemployment and growing inequality are leading more Americans to take on so-called side hustles. Some want to supplement their incomes. Others are just trying to eke out a living. Nearly 1 in 4 Americans now earn money from the digital “platform economy,” according to the Pew Research Center. Most of that work is for domestic tasks, such as housecleaning and repairs, or driving for companies such as Uber.

But labor experts say companies such as Snag Work could set a dangerous precedent. Employers are already wary of hiring full-time employees because of overtime and health-care costs, they say, and having a pool of potential gig workers at the ready could make matters worse for those seeking the stability, benefits and protections that come with full-time work.

Although it would seem straightforward that the laws protecting workers should also apply to workers in what is described as the ‘gig economy’ or ‘platform-based work’, there is much debate – and confusion – on this issue. This lack of clarity stems in part from the novelty of platform-based work. There has also been an effort to conceal the nature of platform-based work through buzzwords such as ‘favours’, ‘rides’, and ‘tasks’ as well as the practice common to many platforms of classifying their workers as independent contractors. Platform-based work includes ‘crowdwork’ and ‘work-on-demand via apps’. In crowdwork, workers complete small jobs or tasks through online platforms, such as Amazon Mechanical Turk, Crowdflower, and Clickworker.  In ‘work-on-demand via apps,’ workers perform duties such as providing transport, cleaning, home repairs, or running errands, but the workers learn about these jobs through mobile apps, from companies such as Uber, Taskrabbit, and Handy. The jobs are performed locally.

Platforms mediate extensively the transactions they have with their workers, and also between the customers and the workers.  Platforms often fix the price of the service as well as define the terms and conditions of the service, or they allow the clients to define the terms (but not the worker). The platform may define the schedule or the details of the work, including instructing workers to wear uniforms, to use specific tools, or to treat customers in a particular way. Many platforms have performance review systems that allow customers to rate the workers and they use these ratings to limit the ability of lower-rated workers to access jobs, including by excluding workers from their system. The amount of direction and discipline that clients and platforms impose on workers, in many instances amounts to the degree of control that is normally reserved to employers and is normally accompanied by labour protections such as the minimum wage, limits on working time, and contributions to social security. This recent ILO study provides more detailed analysis on these features of platform-based work.

With even nursing at risk of becoming an on-call gig-working Uber-like profession, the model could bring about the end of employment and become the main way of organizing labor in the new economy, thinks Sundararajan. But problems arise when casual side hustles turn into full-time gigs. We have “painstakingly” built a system of worker protections, minimum wages, regulations and pension schemes that “transformed full-time employment from something that was pretty reprehensible 100 years ago to something that looks pretty good in many countries today,” says Sundararajan. How will a crowd-based economy look after its workers?