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4 resources
Artificial Analysis describes a benchmark developed with Google DeepMind for agents handling visually complex inputs and creating document deliverables across 200 tasks in 10 industries. Scores and cost estimates depend on its rubrics and evaluation harness; benchmark development is a partnership, not wholly independent of the model maker.
Daily writing and research publication covering AI, business strategy, and how knowledge workers actually use new tools, plus its own suite of AI products.
Why I recommend it: I read Every when I want thinking about AI that goes beyond hype cycles. The essays are long but they change how you work.
How to Decide, Delegate, and Build Systems That Remember. A practical book for knowledge workers who want to use AI as a real operating layer.
Why I recommend it: I am always looking for resources that treat AI as an operating layer rather than a toy. This book is a practical frame for deciding what to delegate and what to keep human.
Harvard Business School AI Institute breakdown of new research on agentic AI: how autonomy and context integration shift the cost structure of knowledge work, expanding both productivity and the scope of what workers take on.
Why I recommend it: Read this before you assume AI just speeds up your current tasks. The useful takeaway for job seekers: agents lower the cost per step, so the valuable human skills become specifying goals clearly and verifying output. Practice describing outcomes, not keystrokes, and put "agent workflow design" language in your resume bullets.
