When most people say they are proficient with AI, what they really mean is that they have used ChatGPT. While ChatGPT is an excellent tool and OpenAI deserves enormous credit for making AI accessible to hundreds of millions of people, equating ChatGPT with AI proficiency is like saying you are proficient with computers because you can use Microsoft Word. It is a start, but it is nowhere near the full picture.
The Multi-Model Reality
The AI landscape in 2026 is genuinely multi-model. Anthropic's Claude excels at long-form analysis, nuanced reasoning, and following complex instructions. Google's Gemini has deep integration with search and multimodal capabilities that make it the best choice for certain research tasks. Mistral and other open-source models offer privacy advantages and customisation options that matter for sensitive enterprise use cases. Meta's Llama models power countless specialised applications.
Each model has distinct strengths, weaknesses, and optimal use cases. A truly AI-proficient professional knows which model to reach for depending on the task. They understand that Claude might be the better choice for synthesising a 50-page report, while Gemini might be better for tasks requiring real-time information, and a fine-tuned open-source model might be necessary when data cannot leave the organisation's infrastructure.
Beyond Chat Interfaces
True AI proficiency also extends beyond chat interfaces entirely. The most effective AI users understand how to work with AI through APIs, how to use AI-powered features embedded in their existing tools, how to leverage AI agents that can take actions autonomously, and how to build simple automations that chain multiple AI calls together. They understand concepts like temperature, token limits, context windows, and system prompts — not at an engineering level, but well enough to get dramatically better results.
Building Multi-Model Literacy
Developing multi-model literacy does not mean becoming an expert in every AI system on the market. It means understanding the landscape well enough to make informed choices. It means knowing the difference between a model that is good at coding and one that is good at creative writing. It means understanding why you might get different answers from different models and knowing how to evaluate which answer is better. This is the level of AI proficiency that separates professionals who use AI effectively from those who are just going through the motions.