Publié : 17 December 2025
Actualisé : 20 hours ago
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📋 Table of Contents
Larry Page was right. Back in 2000, he envisioned an AI capable of understanding and answering all our questions, a vision that’s now taking shape with LLMs like Gemini. Does this 25-year-old prediction make him an AI prophet? My take is that his vision was insightful, but it didn’t account for the colossal ethical and societal challenges that come with such power.
🤖 Larry Page’s Visionary Prediction
In 2000, when Google was just a young search engine, Larry Page stated that artificial intelligence would become the ultimate version of Google. His idea was simple: an AI capable of understanding the entire web and providing accurate answers to any question. A prophetic statement, when we observe the current capabilities of language models.
The emergence of Transformers in 2017, an innovation from Google, was a turning point. This model architecture made it possible to build LLMs like ChatGPT and Gemini, bringing reality closer to Page’s vision. These models are capable of processing and understanding natural language with impressive accuracy.
Artificial intelligence would be the ultimate version of Google. If we had the ultimate search engine, it would understand everything on the web. It would understand exactly what you want and give you the exact right answer. That’s artificial intelligence: being able to answer any question, simply because almost everything is on the web, isn’t it?
This quote perfectly summarizes Page’s ambition: to transform Google into an omniscient entity capable of meeting all our information needs. But that’s where the problem lies. The ability to answer *any* question raises crucial questions about the veracity, bias, and ethics of the information.
🧠 Gemini and the Return of Sergey Brin
Google isn’t resting on its laurels. The company continues to integrate LLMs into its services, and Sergey Brin’s return to contribute to the development of Gemini 3 Pro testifies to the strategic importance of AI for Google. However, from an engineering perspective, integrating these models on a large scale poses considerable challenges in terms of computing resources and inference challenges. Training these models requires massive amounts of data and considerable computing power, which represents a significant cost.
🤔 Current Limitations and the Scale Factor
Despite the rapid progress, LLMs are not perfect. They can generate incorrect, biased, or even absurd answers. Furthermore, their ability to understand context and reason in a complex manner remains limited. The scale factor plays a crucial role: the larger the model and the more extensive the dataset it is trained on, the better its performance. However, this approach has its limits, particularly in terms of cost and complexity.
Key Point: Larry Page’s vision is becoming a reality, but the technical, ethical, and societal challenges remain considerable.
🔮 Projection and Risks
Optimistic scenario: In the near future, LLMs will have overcome their current limitations. They will be able to provide accurate, unbiased answers tailored to the needs of each user. AI will become a powerful tool for education, research, and solving complex problems, thus helping to improve the lives of billions of people.
Pessimistic scenario: The uncontrolled development of LLMs leads to a proliferation of misinformation and large-scale manipulation. The biases present in the training data translate into discrimination and inequality. Massive job automation leads to an unprecedented economic and social crisis. The concentration of power in the hands of a few technology companies threatens democracy and individual freedoms. The stakes are high: we must regulate the development of AI to avoid these excesses.
❔ Frequently Asked Questions
Larry Page foresaw AI a long time ago, right?
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