AI让你做事更快了，但这并不意味着你更有效率——你只是把“慢的那部分”外包给了其他人。当个人因工具提速而组织整体并未跟上时，真正的瓶颈从个人转移到了协作环节。
While the agriculture industry is increasingly open to adopting artificial intelligence, its underlying data remains fragmented, inconsistent, and poorly structured, hindering the effective deployment of AI tools for farming and crop management.
The EU's Digital Markets Act (DMA), designed to swiftly regulate Big Tech, has faced delays and bureaucratic hurdles, slowing its enforcement against major companies like Apple, Google, and Meta.
Enterprise AI adoption is stagnating due to organizations struggling with data quality, governance, and integration challenges rather than a lack of technological capability. Many companies fail to establish the necessary data infrastructure and workflows to make AI tools effective at scale.
Anthony Pompliano argues that major AI laboratories have effectively been nationalized, even if they are not yet aware of it.
AI can generate code efficiently but fails to deliver a good product because it lacks understanding of user needs, business context, and product strategy. Writing code is only one part of product development, which requires empathy, decision-making, and holistic design thinking that AI currently cannot replicate.