The human price of dynamic pay
At first, the job offered a sense of stability and autonomy, with a fixed 25 per cent commission and predictable earnings. That changed when Uber introduced dynamic pricing, which he experienced as a loss of control and transparency.
“When I started, I was on a fixed rate of 25 per cent commission,” Khurram explained. “Then they brought in this dynamic commission, so you’re not sure how much money they’ll take out of every trip”. The system also rolled a portion of holiday pay directly into each fare, which he said made it almost impossible to predict his income.
Despite recognising that many fares were not financially viable after deducting fuel, maintenance, and insurance, Khurram felt forced to accept nearly every job: “You keep thinking…if I won't accept it, someone else will”. The fear of losing work or being penalised by the algorithm shaped his decisions throughout the day. He gave the example of a 240-mile Heathrow–Birmingham return job that paid just £90, and a 31-mile round trip from Mayfair to Heathrow for £22. Both were well below what he considered sustainable.
Such experiences left Khurram convinced that the company relied on inexperienced or desperate drivers. “They capitalise on those kind of drivers … new in the industry. They just want to make money no matter what,” he said. “Any right-minded person wouldn’t do half the jobs offered to them in a day.”
He also described confusion around what determined the price. “It could be time of the day, demand in the area, driver’s availability, driver’s distance from the job, customer’s rating, my rating. There's so many things”, he said, noting that Uber often withheld key information, such as multiple stops, until after he had accepted a job. When drivers raised concerns, he said, Uber’s response was to hide behind the algorithm: “the system … we understand [it] is the system, but you're still controlling that system.”
The result was longer hours, greater fatigue, and growing tension at home. “We are more under pressure than before,” he explained. His working day extended from ten to twelve hours, with few breaks.
“I am sleeping at least two hours less than before, because I have to spend a couple of hours extra on the road now to make money. So, where I was doing 10 hours in past, I'm doing 12 hours now… So, we are thinking more about our work than our families… And the support from [Uber] is again, minimum”.
The fatigue, he said, also created safety risks: “So, we are doing more wrong things on the roads … we are parking wrong, we are having arguments for no reason because we are frustrated. And when you're frustrated, you make mistakes… even the customers have noticed it, like the drivers are more aggressive now than before… You're tired, you're spending long hours … your family's questioning like, why you are … spending this much time out there?”
He linked this to frustration and exhaustion, describing a workforce under continuous strain with minimal support from Uber.
Khurram’s story illustrates how dynamic pricing erodes transparency and pay stability, forcing workers into longer hours to maintain income while shifting commercial risk entirely onto them. For him, the system represents not flexibility but dependency, a constant balancing act between acceptance rates, algorithmic pressure, and personal exhaustion.
Khurram’s experience exposes the financial and psychological cost of unpredictable pay. This is also outlined in the experience of other drivers, including the story of Zara (case study 4) that further reveals how gender and safety intersect with these same stressors. Khurram’s account also demonstrates how variable take-home rates and information withholding intensify acceptance pressure, extending working hours and transferring fatigue and safety risks onto workers.
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