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- Uber Drivers are Training for the Future
Uber Drivers are Training for the Future
๐ฑ Setapp's all-in-one solution + ๐ Slow journey to true AI predicted
Hi everyone,
Uber is getting creative with its workforce, using driver data to help train its AI, an intriguing move, but what does it mean for privacy and innovation?
Meanwhile, Andrej Karpathy shares his thoughts on the future of AI agents, suggesting a slow approach. Could this be the path to more sustainable advancements?
Let's get right into it.
In this issue:
๐ค In Partnership: One plan for every app you need
๐คฟDeep Dive: Uber pays drivers to train AI
๐คPowered by: The brief for founders
๐ผ๏ธAI Art: Examples of great and trending AI art
๐คฟDeep Dive: Karpathy predicts slow road to true AI agents
๐คSupported by: Stop fraud in its tracks
โ๏ธTool Snapshots: Tools for AI, no-code, and productivity
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๐คฟ DEEP DIVE
Uber and Uber AI Solutions Pilot Driver Data Tasks
Intelligence: Uber launched a US pilot that pays drivers and couriers to complete inโapp data tasks that help train AI models, with a broader rollout planned by the end of 2025.

Tasks include uploading photos, recording short voice clips, and submitting documents for AI training and data labeling.
Each task pays a fixed amount based on complexity and time, with payments appearing in driver balances within 24 hours, while US pay rates are not yet finalized.
Task availability will vary based on client demand for data labeling work.
The pilot was first tested in India and is set to expand across the US by the end of 2025.
The program is separate from Uberโs autonomous ride efforts, such as the Waymo collaboration, and runs through Uber AI Solutions, which serves clients including Aurora and Tier IV.
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๐ผ๏ธ AI ART
Examples of great and trending AI art

Images by Quiet-Assistance-519
https://www.reddit.com/r/midjourney/s/qNBEzZiRxF
๐คฟ DEEP DIVE
Andrej Karpathy and OpenAI Agent Timelines
Intelligence: OpenAI cofounder Andrej Karpathy said on the Dwarkesh Podcast that functional AI agents are likely a decade away, arguing the industry is building tools beyond current model capabilities.

Karpathy said todayโs agents lack sufficient intelligence, are not multimodal enough, struggle with computer use, and do not have continual learning or reliable memory.
He estimated about a decade to resolve these gaps and noted he is now building an AI-native school at Eureka Labs.
He prefers human-AI collaboration where agents pull API docs, verify correct usage, ask when uncertain, and help users learn rather than dump large code outputs.
He criticized visions of fully autonomous agents that render humans unnecessary and spread low-quality AI slop.
In a follow-up post on X, he said the industry is overshooting tooling relative to present capability, while his timelines are five to ten times more conservative than common Silicon Valley hype but still optimistic compared to AI skeptics.
Context from peers includes investor hype around 2025 as the year of the agent and Scale AI executive Quintin Auโs estimate of roughly a 20 percent error per action, implying about a 32 percent chance of getting five steps all correct.
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โน๏ธ ABOUT US
The Intelligent Worker helps you to be more productive at work with AI, automation, no-code, and other technologies.
We like real, practical, and tangible use-cases and hate hand-wavy, theoretical, and abstract concepts that donโt drive real-world outcomes.
Our mission is to empower individuals, boost their productivity, and future-proof their careers.
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