August 24, 2026

Top headlines

1. WSJ: Nvidia to Invest $6 Billion to Compete with DeepSeek and Kimi K3

   Background

   Nvidia has become the biggest winner of the AI boom by all but monopolizing the market for the graphics processing units (GPUs) used to train AI. But a run of open-source large language models (LLMs) such as China's DeepSeek and Kimi K3, which deliver strong performance on relatively little compute, is undermining the old formula that AI can only be built on vast GPU clusters. For Nvidia, that threatens demand for its hardware itself.

   Why it matters

   By committing $6 billion directly to the model-ecosystem race rather than just selling hardware, Nvidia is shifting the yardstick for AI infrastructure investment from GPU specs to software and model competitiveness.

   So what

   Organizations weighing GPU budgets should first see which models and platforms Nvidia concentrates its investment on, then adjust their hardware options accordingly.

2. Harvey's Kimi K3 move signals growing traction among US businesses; expert notes customization edge could outweigh geopolitical concerns

   Background

   Harvey is an AI start-up that specializes in automating legal work; its main customers are large law firms and corporate legal teams. Kimi K3 is a large language model built by China's Moonshot AI, noted for handling long documents in one pass and for being easy to customize. Concerns about the data security and geopolitical risk of Chinese AI models have been raised steadily in the US, so any American company putting one into production draws scrutiny.

   Why it matters

   If a Chinese model wins a real contract in a field as data-sensitive as legal and compliance work, companies in other industries may start to weigh performance and cost ahead of geopolitical risk.

   So what

   When legal or strategy teams bring in an outside AI solution, they should write the model's developer and the location of data processing into the contract, and first check for conflicts with internal security policy.

3. Is it legal to train AI models on copyrighted books? It's complicated

   Background

   AI models have improved by training on huge volumes of data: web text, books and articles. Publishers and authors have sued major AI companies such as OpenAI and Meta for using their work in training without permission, but courts have yet to rule clearly on whether AI training counts as 'fair use' under existing copyright law. Legislatures in many countries are still debating AI copyright rules, so the regulatory gap persists.

   Why it matters

   If courts find that AI training infringes copyright, companies will have to rebuild their training data or pay licensing fees, which directly affects the cost of AI services and the pace of model development.

   So what

   Organizations adopting AI solutions or building their own models should review the training-data sources and licensing policies of the models they use now, and prepare scenarios with their legal team so contract terms can be adjusted as the lawsuits are decided.