Fast Data Science

https://fastdatascience.com/generative-ai/openai-vs-claude-vs-qwen/

How well do the newest large language models perform? How do they compare to 2025's offerings?

We evaluated the newest large language models on a law test prepared by Eugenio Vaccari. The Chinese entrants DeepSeek and Qwen have challenged the dominance of GPT, although most UK users use GPT, Gemini, and Claude. Over time we are heading towards an 80% score on the law test when the bots are combined with a RAG system (a database of English insolvency statutes and case law), when two years ago we were only around 30%. What is fascinating is that the Chinese models are delivering a similar performance to the American juggernauts, for a fraction of the cost.

We've plotted the AI models' performance on the law exam with model release date on the x-axis, so you can see at a glance how rapidly the field is advancing. I posted an earlier version of this graph a year ago, but now we have data going back to 2024 and the early days of GPT 3.5, which is very exciting.

Meanwhile, the House of Lords has put out a report recommending that the UK government implements some protections for creative industries and to force AI companies to be transparent about where their training data came from. https://fastdatascience.com/legal-ai/ai-copyright/

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https://fastdatascience.com/generative-ai/ai-bubble/

Are we in an AI bubble?

AI adoption is rising but the economy hasn't yet shown signs of improved productivity across the board. How does the conjectured "AI bubble" compare to bubbles of the past such as the dot-com bubble, and what can we expect from the next few years? This blog post does not contain any AI slop.

🌐 https://fastdatascience.com

https://fastdatascience.com/ai-for-business/predict-customer-churn-machine-learning-ai/

We've just published a deep dive into how we use Machine Learning and AI to predict customer churn.

✅ Why time splits (e.g., training on 2024, testing on data 2025) are the best way to prove your model can handle macroeconomic shifts and changing behaviors.

✅ Model performance: How we can achieve 70-80% AUC using a Random Forest model by integrating customer account-level data and web analytics.

✅ Human vs. Machine: Comparing "human-executable" scoring models with black box models.

✅ The deployment gap: Why deploying a model is often more work than building it—from batch jobs

If you’re in Retail, SaaS, or any B2C field, understanding these patterns isn't just a "nice-to-have"—it's the difference between scaling and stagnating.



🌐 https://fastdatascience.com

https://fastdatascience.com/ai-in-research/jicl-paper/

Publication Announcement: A Generative AI-Based Legal Advice Tool for Small Businesses in Distress

We are thrilled to announce the publication of our latest paper in the Journal of International and Comparative Law (JICL):

Marton Ribary, Thomas Wood, Miklos Orban, Eugenio Vaccari, Paul Krause, A Generative AI-Based Legal Advice Tool for Small Businesses in Distress. Journal of International and Comparative Law, Vol 12.2, 2025.

Small business owners often face a "justice gap" when dealing with the complexities of corporate insolvency. To address this, our team developed the Insolvency Bot, a Retrieval-Augmented Generation (RAG) system specifically designed to provide information about English and Welsh insolvency law.

In head-to-head testing against unmodified models like GPT-4, the Insolvency Bot significantly outperformed them in legal accuracy and reliability. The system leverages a curated database of 6,000 legal texts, including statutes, case law, and HMRC forms.

What's next? We are currently working with partners around the world to develop equivalents in
* Bhutan
* India
* Eight European jurisdictions



🌐 https://fastdatascience.com