Bill Lewis spent 25 years on the floors of stock exchanges, climbing from clerk to trader. Then the algorithms arrived and eliminated his job. Now, after a career shift into ride-hailing, he’s encountering those same algorithmic overlords in a completely different context. This time, he’s not fighting them from behind a trading terminal. He’s fighting them from behind the wheel of a Prius.
According to a conversation with Lewis, a Pennsylvania-based rideshare driver, the transition from Wall Street to gig work reveals something uncomfortable about the modern economy: the same forces that disrupted one industry have simply relocated to another, wearing a different mask.
When Optimization Replaces Opportunity
Lewis drives for Uber and Lyft full time, averaging 75 hours a week, mostly ferrying the same regulars to work and handling weekend tourism traffic in the Poconos. It’s steady work, the kind that once seemed like a refuge from the algorithmic unemployment that ended his trading career. But the algorithms never really left. They just changed their function.
Now they dictate which rides he sees, how much he earns per trip, and essentially whether his work is worth doing at all. A long-distance ride that looks profitable on the surface might strand him miles from his next customer, making the fuel cost to get back into town economically disastrous. So Lewis has adapted. He refuses the trips that the algorithm suggests might not be worth it. He’s learned to calculate fuel costs faster than most people calculate tips.
“I take between 22 and 28 rides a day,” he explained. “For the most part, I’m within 25 miles of my house every day.” The calculation is merciless: every mile matters, every route needs optimization, every decision feeds into a spreadsheet of profitability that determines whether he can keep the lights on.
The Gas Problem Nobody’s Fixing
Rising fuel costs have transformed the economics of rideshare driving. A fill-up that cost Lewis $22 before geopolitical tensions spiked now costs $31 for the same tank in his Prius. That’s a 40 percent increase. For drivers operating regular sedans that get 25 to 30 miles per gallon instead of his hybrid’s 50 mpg, the math becomes impossible.
Lewis has responded with the kinds of micro-optimizations that define precarious work: taking back roads instead of highways to save miles, rejecting rides that have poor return-trip economics, and watching every expense with the intensity of someone who can’t absorb unexpected costs. He fills up six to seven times a week. That’s not casual driving. That’s the work of someone who has done the math and knows exactly how tight the margins have become.
Here’s where it gets interesting. Four years ago, when Russia invaded Ukraine and gas prices spiked, Uber and Lyft responded by adding a gas surcharge to rides, charging passengers an extra $0.45 to $0.55 per trip. Lewis estimates that kind of pass-through would add about $80 to his weekly earnings. It would be enough to make a real difference.
But this time around? The apps have offered cashback programs through partner apps or their own debit cards. Lewis doesn’t use either. For someone living paycheck to paycheck on rideshare income, these solutions feel like they were designed by people who don’t understand how people actually live.
The Algorithmic Trap Nobody Escapes
What strikes you about Lewis’s situation isn’t just the financial squeeze. It’s the repetition. He escaped one algorithmic system that made his skills obsolete, only to find himself trapped in another one that’s arguably more unforgiving. At least on the trading floor, the algorithms were confined to financial instruments. Now they govern his access to work itself.
The ride-hailing platforms optimize for passenger experience and corporate efficiency, not driver sustainability. That’s not a conspiracy. It’s how capitalism works. But it means that drivers like Lewis are constantly recalibrating their own behavior to survive within a system designed to minimize their bargaining power. He’s not just driving. He’s constantly gaming the algorithm, predicting which trips the system will throw at him, and making split-second decisions about whether accepting them will help or hurt his bottom line.
And he’s doing this on top of his actual job, which is driving people around safely and reliably.
“If you’re driving a regular car that gets 25 to 30 miles per gallon, I don’t see how this kind of work is profitable,” Lewis said. He’s not complaining. He’s being honest about the arithmetic. When your expenses scale faster than your income, and the company controlling your access to work refuses to adjust compensation, you’re not in a sustainable situation. You’re in a triage situation.
The question nobody in the technology industry seems particularly interested in answering is simple: What happens when an entire class of workers becomes too poor to afford the work they’re doing?
