大型AI实验室实际上已经被国有化，但它们自己尚未意识到这一点。核心观点认为，这些实验室虽表面独立运作，实质上已受到国家力量的控制或影响。
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.
The article argues that AI has increased individual work speed, but overall productivity hasn't improved because the time saved by one person is often shifted as a burden or bottleneck onto others in the company.
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.
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.