Uber’s Biggest Fear, Just Happened in Seattle
The car sharing company is not happy about what happened in Seattle. The City Council voted to approve the bill that allows the Uber, Lyft or any other ride-hailing apps drivers to form unions.

The car sharing company is not happy about what happened in Seattle. The City Council voted to approve the bill that allows the Uber, Lyft or any other ride-hailing apps drivers to form unions. Council passed this bill with 9-0 for the ordinance, and this is the first time this kind bill accepted in Unites State and this is a significant victory for App-Based Drivers Association ( ABDA ) of Seattle. Other drivers Association from other states were following this bill, and they are also very thrilled about the result. It is highly possible other States will follow.
Uber now have hundreds of thousand drivers and this number rising every month. Uber is the boss of the market. But the drivers not happy with working with Uber. And they are a little bit right, Uber is a company worth about 50 billion, and they earn their money from the drivers who works long hours to support their family or life, drivers are the ones who invest the on the vehicles that Uber pretty rich. If you think Uber had nearly 160.000 drivers at the beginning of 2015, and this number doubles up every six months.
But the problem is Uber doesn’t good to the drivers who make them rich. Uber charge %30 percent from the driver and can cancel their agreement with the drivers any time they want; they don’t let drivers collect any tip. For conclusion, they don’t appreciate the effort of all those people. And finally, with this bill Uber and other car-healing companies will learn to more fair to the people who works long hours to make them earn a lot of money.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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