1233 summarised stories about Enterprise AI, each linking back to the original source. Browse all topics →
Simon Willison’s Weblog 4 weeks ago 11
voxium says a new role at a large company has used Claude Code to generate essentially all specs, code, tests, and related work, but the team dislikes the setup and feels progress is slow. Engineers are reportedly working 12 to 13 hours a day just to submit code, while management claims pushing code is not the bottleneck. The result is widespread unreading/repetitive use of the tool and pressure to ship more despite the perceived slowdown.
TechCrunch 4 weeks ago 48 ● 5 sources
TechCrunch Disrupt 2026 announced a pricing deadline for tickets and related registration benefits. Current pricing ends on Sept. 25 at 11:59 p.m. PT, and attendees can save up to $200 before prices rise. After that deadline, ticket prices increase and the event information is positioned as a way to network and attend sessions in San Francisco on October 13–15.
Fortune 41
A Harvard Law School graduate says their first law-firm job billed clients over $600 per hour for manually preparing signature-line documents across 18 files. They cite that initial billing rate as proof of how the practice rewarded non-legal work. The author argues AI tools are already changing legal practice and would reduce reliance on traditional big-firm training and infrastructure, expanding access to higher-quality legal services.
Fortune 50
Axios founders Jim VandeHei, Mike Allen, and Roy Schwartz say artificial intelligence has made corporate communication heavier instead of lighter, leading to more decks, longer emails, and “work-like activity” that needs extensive review. They argue companies should audit what to delete, automate, and prioritize on at least a monthly cadence, compared with VandeHei’s earlier quarterly practice. The result is a push for more frequent simplification of work processes and clearer boundaries for saying no, rather than treating AI as an efficiency fix by default.
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19 October 2026
The day’s biggest thread was the messy middle between “agents that act” and the systems that prove they won’t act badly. Hugging Face policy chief Irene Solaiman warned that embedded AI evaluators and today’s benchmarking are still too immature to trust without better reproducibility and clearer comparisons, especially as evaluation is shifting from model-only tests to full-system setups that include the harness and agent—raising cost and forcing standardization. That concern landed alongside a Princeton-led paper on emerging agent-evaluation trends: ExploitBench runs can burn tens of thousands of credits, and the authors argue the field needs more independent, multi-layer evaluation, including real-world and human-impact studies, plus a better evaluation ecosystem.
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