OpenAI and Anthropic are battling Big Tech for talent. We asked workers who's winning them over — and who's not.
THE SO WHAT
The fact that OpenAI, Anthropic, Google, and NVIDIA all show up as “dream employers” means the talent market is clustering around a handful of AI gravity wells. If you’re not one of them, your hiring pitch has to be scope and ownership — not compensation or brand.
READ THE SOURCE
MORE FROM THE WIRE
Applied AITop AI tools including Claude, Codex, and Hermes installed suspicious code inside corporate networks
LLM ‘squatting’ via llms.txt and llms-full.txt is a new supply-chain attack surface—prompting AI tools to pull and run untrusted code from what looks like documentation. Treat AI dev tooling like any other executable: lock down what it can fetch, run, and connect to, and add llms*.txt checks to your security reviews this week.
Applied AIHow Bill Gates uses AI, and why he thinks nobody is preparing for it
When Bill Gates is again floating AI/robot taxes, human-only job categories, and new transition institutions — and saying leaders are worried in private — it’s a signal that policy and labor friction will rise as capabilities scale. Operators should scenario-plan around regulatory drag and differentiated treatment of “human-only” work, not just productivity upside.
Applied AISony Music and Warner Chappell sue Anthropic over song lyrics in Claude’s training data
Music publishers are escalating from platform takedowns to direct suits against model developers — naming executives personally and citing a 2025 Munich ruling that memorized lyrics can infringe. If you train or fine-tune on proprietary content, treat licensing and dataset provenance as board-level risk, not a legal footnote.
I couldn't land a job, so I started AI training for $15 an hour. Now I make $100 an hour and built a career around AI.
The path from $15/hour data labeler to $100/hour AI trainer shows a new labor ladder forming around model supervision and evaluation. For operators, this is a real talent pool — people who understand both domain nuance and how models fail — worth tapping before it gets fully priced in.