Working in HealthTech, we spent years building and training our own AI models — collecting datasets, labeling them, checking accuracy, teaching machines to see and understand what once required a human mind. We built AI systems for diagnostics, prediction, and decision support. But now, the paradigm has shifted completely. What used to be a specialized tool — designed to detect a tumor or classify tissue — has become infrastructure. Intelligence itself is now accessible, like electricity or the internet: you plug in, and you can think with it.
We’re living in the age of LLMs — Large Language Models — when they’ve evolved from academic curiosities into platforms for thought. What started with early statistical models and neural nets in the 2010s exploded in 2022, when OpenAI released ChatGPT. A million users in five days, a hundred million in two months. Today, ChatGPT handles over 2.5 billion queries every day and stands as the fastest-growing product in tech history. It’s not “just a chatbot.” It’s a new cognitive infrastructure — one that redefines how we work, learn, and create.
We’ve reached the point where you can literally think with the grid. Intelligence has become a utility. Just like energy once stopped being a luxury and became a basic service, intelligence is now available on demand. We already live in a world where an LLM can analyze thousands of sources in real time, compare hypotheses, explain causality, and help a human mind think faster and deeper. This isn’t task automation — it’s the expansion of cognition itself.
Nowhere is this shift more visible than in professional domains. In medicine, for instance, models like GPT-4 have passed national medical exams at the level of licensed physicians — in the U.S., the U.K., and Poland. Accuracy reaches 80–90%, and in some areas — like vascular pathology or clinical reasoning — the models outperform medical students. In 2025, the Delphi-2M model, trained on UK Biobank data and tested on over two million Danish patient records, demonstrated the ability to predict the likelihood of more than a thousand diseases up to twenty years in advance. It doesn’t just find correlations — it maps causal pathways and health trajectories, explaining its reasoning as it goes.
These breakthroughs show that LLMs are no longer just tools — they’ve become external cognitive extensions that can be plugged into any profession. For decades, professions existed as closed systems of knowledge: doctors, lawyers, and accountants acted as gatekeepers between information and people. That era is ending. When cognition itself becomes widely accessible, professions evolve from being fortresses of expertise into layers of interpretation, context, and trust.
This marks a fundamental shift in logic. The system no longer trains us to store knowledge — it gives us access to it, instantly. We stop being carriers of information and become operators of intelligence. Humanity is transitioning from Homo sapiens — who thinks alone — to Homo augmentus — who thinks together with the network.
In healthcare, this shift is especially visible. The combination of real-world data, sensors, LLMs, and new communication models makes proactive healthcare possible — medicine that doesn’t react to disease but manages risk. When intelligence is embedded in daily life, the system can interpret behavior, body signals, and context to predict health issues long before symptoms appear.
Just as electricity transformed industry and everyday life, and the internet transformed communication and the economy, Augmented Intelligence will transform the structure of civilization itself. When intelligence becomes a utility, the boundary between human and machine dissolves — and the scale of what’s possible grows exponentially. This isn’t just another technology; it’s a new form of life — one where thinking is continuous, distributed, and shared.
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