Every July 16, LinkedIn fills up with AI Appreciation Day posts, most written by companies that make AI products. Almost none of them know who actually invented the day, and the answer has nothing to do with a lab, a product launch or a boardroom.
Vendors say hallucination rates are falling. Researchers say full elimination is mathematically impossible. Both are right, and the gap between those two statements is where enterprise AI strategies are currently breaking down.
The 15 companies building Egypt's AI: the national model, Arabic LLMs, chip startups and fintech tools, and why the best keep moving to the Gulf.
A large share of Indian investors quit onboarding before funding an account. Shyam Arora on where the process breaks, and what integrated AI changes.
Most companies starting with AI in 2026 make the same four decisions in the wrong order. Here is the sequence that actually works, and the questions to ask every vendor before you sign anything.
A CXO framework for deciding whether to build or buy your AI eval stack, with a current read on RAGAS, LangSmith, Braintrust, Langfuse, and the FREE-AI Framework.
Enterprises use multiple AI models, but costs and risks rise without control. Model routers route tasks to the right model, improving efficiency and governance.
Global AI product is marketed as multilingual. The research, the regulatory landscape, and the economics of how these models are built tell a more complicated story for Indian deployments.
What an AI vendor mean when he say hallucination-free, enterprise-grade, and 95% accurate. How to evaluate AI vendor claims: A technical guide for CTOs and AI leaders
When different AI models, trained by different companies, keep giving you the same answer, that is not a coincidence. In this piece, Chetanya Puri, Senior Machine Learning Engineer at CluePoints, explains why researchers now have a name for it, a dataset to measure it, and evidence that the problem runs deeper than anyone's system prompt.
