Weekly AI Digest: The Future of Expertise, Google’s Anthropic Investment, and AI Fairness Benchmarks

Week 11, 2025

Welcome to week 11 AI Digest! This edition covers how generative AI is reshaping the value of expertise, details on Google’s investment in Anthropic, and new AI benchmarks aimed at reducing model bias.

We’ll also explore a research paper on Visual Reinforcement Fine-Tuning (Visual-RFT) and its impact on vision-language models. Let’s do it!

How Generative AI Could Change the Value of Expertise

The rise of generative AI is redefining what it means to be an expert. With AI capable of generating high-quality insights, professionals across various industries provably need to rethink their roles.

The latest Harvard Business Review article explores how expertise might shift towards judgment, problem framing, and AI-assisted decision-making rather than traditional knowledge accumulation.

➡️ Read more: HBR: How Gen AI Could Change the Value of Expertise

Google’s Investment in Anthropic

Google has deepened its commitment to AI by significantly expanding its investment in Anthropic, one of OpenAI’s biggest competitors.

This move is part of Google’s strategy to ensure it remains at the forefront of AI development while diversifying its partnerships beyond its own Gemini models. It will be interesting to see how this will coexist with Google’s own LLM ecosystem.

➡️ Read more: Inside Google’s Investment in Anthropic

New AI Benchmarks for Fairness

Two new AI benchmarks aim to tackle bias in machine learning models, offering a more structured way to assess fairness and mitigate harmful outputs. These benchmarks focus on diverse datasets and real-world applications to measure where AI systems fail in equitable decision-making.

➡️ Read more: These Two New AI Benchmarks Could Help Make Models Less Biased

Paper of the Week: Visual Reinforcement Fine-Tuning (Visual-RFT)

This week’s featured research explores Visual Reinforcement Fine-Tuning (Visual-RFT), an extension of Reinforcement Fine-Tuning (RFT) applied to vision-language models. Unlike traditional fine-tuning methods that rely on vast datasets, Visual-RFT enables large models to learn effectively from limited data using verifiable reward mechanisms.

This research marks a shift towards data-efficient and reward-driven training methods — a trend that we will probably see for the next years.

➡️ Explore the research: Visual-RFT Paper

Why This Matters

From redefining expertise to advancing AI fairness and training methodologies, the AI landscape continues to evolve rapidly. As we witness major investments, ethical considerations, and breakthroughs in AI efficiency, staying informed is more crucial than ever.

As always, I’d love to hear your thoughts — drop a comment or share your insights on these AI news.