Tag: AI thought leadership

This blog explains how Mixture of Experts models work, why they improve performance and efficiency and how they compare to traditional model architectures.

This blog explores how organizations progress through stages of AI maturity, from early experimentation to operational transformation and why growth happens step by step.

If 2023–2024 were the years of pilots and prototypes, 2025–2026 will be about orchestration, governance and scale. The signal across serious researchers is consistent: adoption is widespread and business impact concentrates where companies redesign workflows, measure outcomes and hard-wire trust and controls into the stack. McKinsey reports that ~80% of companies use generative AI (GenAI), yet most still aren’t seeing material earnings contribution, because scaling practices and operating models lag the hype. This gap is a roadmap that can be leveraged by Frontier Firms and individuals looking for an advantage (or many) in today’s AI-powered world.

The demand for high-quality instructional videos has never been greater. Everybody needs them. Whether you are onboarding new users or rolling out complex technical products, there is always a lot of pressure to deliver awesome and accurate videos. But let’s be realistic—the resources and deadlines are always tight. This is where advances in AI are completely transforming the way content is created and consumed these days. AI tools are now making it possible to restructure time-consuming production steps for creators. They can reduce human error to significantly improve the overall learning experience for learners.

Hollywood provides clues on reasoning based on the storytelling of many films. We can use the analogies in film scenes to show how Large Language Models (LLMs) arrive at conclusions, which are more accurately described as hypotheses.