Joanna Partridge writes that food delivery riders are demanding transparency from platforms like Deliveroo, Uber Eats, and Just Eat regarding the algorithms used to determine wages and job allocation. Workers claim that increased automation and "dynamic pricing" have significantly lowered their earnings even when workloads remain consistent. The group is seeking help from academics via the Workers' Observatory to research these opaque systems and challenge deteriorating working conditions in the gig economy.
- Some riders report pay per order has dropped steadily between 2023 and 2026 despite constant delivery volumes.
- A landmark class action lawsuit against Uber in Amsterdam alleges that algorithms violate data protection laws by manipulating earnings based on driver behavior.
- Deliveroo uses a machine-learning system named "Frank" to predict order timings and allocate deliveries.
- Riders have reported issues with automated facial recognition technology preventing them from logging on due to injuries or poor lighting.
- Some riders report pay per order has dropped steadily between 2023 and 2026 despite constant delivery volumes.
- A landmark class action lawsuit against Uber in Amsterdam alleges that algorithms violate data protection laws by manipulating earnings based on driver behavior.
- Deliveroo uses a machine-learning system named "Frank" to predict order timings and allocate deliveries.
- Riders have reported issues with automated facial recognition technology preventing them from logging on due to injuries or poor lighting.
As artificial intelligence continues to advance and outperform humans in specific tasks like mathematics or complex gaming, the question arises whether human cognition will remain unique. Tom Griffiths argues that intelligence is not a single linear scale but a multifaceted trait shaped by different constraints. While AI excels at processing vast amounts of data using scalable hardware, human intelligence is uniquely defined by biological limitations such as short lifespans and limited neural capacity. These constraints have forced humans to develop specific strengths in pattern recognition, social cooperation, and efficient learning from minimal experience. Ultimately, rather than seeing AI as a direct rival on all fronts, we should view it as a different kind of entity with its own set of capabilities and weaknesses.
- Intelligence is multifaceted rather than a single scale like height.
- Human intelligence is shaped by biological constraints such as lifespan and brain size.
- AI intelligence is driven by data volume, scalability, and machine communication.
- Different underlying architectures lead to different methods of problem-solving.
- Humans and AI are likely to be companions with distinct capabilities rather than total competitors.
- Intelligence is multifaceted rather than a single scale like height.
- Human intelligence is shaped by biological constraints such as lifespan and brain size.
- AI intelligence is driven by data volume, scalability, and machine communication.
- Different underlying architectures lead to different methods of problem-solving.
- Humans and AI are likely to be companions with distinct capabilities rather than total competitors.
