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Understanding displacement, funding paradoxes, and the future of meaningful work The AI conversation is stuck in the wrong place. Most headlines either fuel panic, “AI is coming for your job!” Or blind optimism, “AI will make everything easier!”. Neither perspective gives professionals what they actually need: clarity and reassurance. Yes,

Why AI Expectations Are Out of Control You’ve seen the headlines: AI is revolutionizing marketing, copywriting, analysis, customer service—entire workflows, all at once. The hype is everywhere, and the pressure to “catch up” is mounting across every industry. But when professionals open up tools like ChatGPT for the

Why Putting People At the Centre Is the Real Key to AI Success The phrase “AI-first” is everywhere—from corporate roadmaps to investor decks—but behind the buzz lies a growing concern: in the race to adopt artificial intelligence, are we sidelining the very people it's meant to

Addressing engineering bias in AI training methodologies requires a deliberate shift from engineering-centric prompt design to intuitive, conversational training techniques. Embracing such methodologies not only benefits historically underserved groups—women and older professionals—but also provides a universally accessible foundation for AI skill development. Redefining AI Literacy Beyond Engineering Norms

Engineering Bias and Invisible Defaults in Training Methodologies Engineering bias emerges primarily from how users are trained to interact with AI—not inherently from the AI tools themselves. While AI systems can be effectively used in conversational, intuitive, and exploratory ways—styles of interaction that resonate strongly with older professionals

The generative AI industry suffers from an overlooked but critical obstacle: engineering bias. Rather than adapting AI tools to match how most people naturally learn—through conversation, intuition, and incremental guidance—the industry overwhelmingly pressures mainstream users to think and behave like engineers. Certification programs, corporate training, and AI tutorials

Introduction Over the past five years, prompt engineering became nearly synonymous with effectively interacting with AI systems. Driven by necessity, early large language models (LLMs) such as GPT-2 and GPT-3 required carefully structured prompts to produce reliable outputs. Engineers and early adopters developed a quasi-formal discipline of prompt engineering, akin