I started a blog because I am passionate about technology and I love to write. In my first article I tackled the issue of mode collapse, or in other words, the idea that the evolution of AI relies on a continuous input of fresh human-generated data. This is a beautiful irony, but as AI research advances rapidly, the irony fades. Algorithmic technology is now entering the realm of reliable self-evolution.
The Dawn of Data-Free AI
The shift began with reinforcement learning, where AI systems learned not from labeled datasets but from trial and error in simulated environments. DeepMind’s AlphaGo, for example, achieved superhuman performance by playing millions of games against itself, rather than relying solely on human game records. But that was just the beginning.
Enter Absolute Zero Reasoning —a paradigm where large language models (LLMs) develop reasoning skills without any human-labeled data. Instead, these models generate their own problems and solutions, using self-play and self-verification to bootstrap new knowledge from first principles. This approach moves beyond traditional machine learning, which interpolates from examples, enabling models to extrapolate into genuinely novel territory.
Google DeepMind’s Alpha Evolve takes this further by using evolutionary algorithms to autonomously discover and optimize algorithms and code. Rather than just learning from data, these systems iteratively redesign themselves, discovering optimization strategies and even breaking long-standing computational records—such as the 2022 breakthrough in matrix multiplication. While human researchers still define the problems, the solutions are increasingly generated by AI itself.
Perhaps most intriguingly, Continuous Thought Machines (CTM)—as explored by Sakana AI—blur the line between computation and physics. These models process information in continuous, rather than discrete, steps, synchronizing neuron activity in a way that more closely resembles biological brains. This allows for a fundamentally different kind of reasoning, potentially bridging the gap between artificial and natural intelligence.
Simulation Supremacy: When Virtual Beats Real
The game-changer has been the realization that simulation can often surpass reality for training purposes. Zero Search algorithms, such as Alibaba’s Zero Search framework, demonstrate this by achieving superhuman performance in information retrieval without ever accessing real-world search engines. Instead, these models are trained on simulated search results and synthetic experiences, covering edge cases that real-world data might never provide. This approach not only reduces costs but also mitigates the biases and messiness of human data.
This isn’t just about games anymore. Physics simulations now train robots more efficiently than real-world trials. Economic models can learn optimal strategies without historical market data. Medical AI can discover treatment protocols through simulated patient populations, exploring scenarios that would be impossible or unethical to study in reality.
Data’s Dominance: Slow Decline
Remember when data was called “the new oil”? That metaphor captured how the digital economy ran on vast reserves of human-generated information. But just as renewables and nuclear energy are making oil less central to our future, a new generation of AI systems is learning to thrive without constant human input. However, just as oil remains valuable even as electric vehicles proliferate, data won’t disappear overnight. Its role is fundamentally changing: from being the sole fuel for intelligence to becoming one input among many.
Consider the trajectory:
- Peak Oil Era: Every machine needed petroleum products
- Transition Period: Hybrid systems and alternative energy sources emerge
- Post-Oil Future: Oil becomes a specialty product, not a universal necessity
We’re seeing the same with data:
- Peak Data Era: Every AI needed massive human-generated datasets
- Current Transition: Self-play, synthetic data, and reasoning systems reduce dependence
- Post-Data Future: Human data becomes valuable for specific applications, not a universal requirement
The Road Ahead: Challenges and Opportunities
This shift promises to reshape the digital economy profoundly. The current tech giants built their empires on data monopolies—collecting, processing, and monetizing human information. But what happens when AI no longer needs that data?
This transition won’t be smooth or complete. Some domains will always benefit from real-world data—understanding human preferences, cultural nuances, or current events. But the monopolistic power of data is breaking.
We’re witnessing AI’s adolescence—the moment it stops depending entirely on its parents (us) for knowledge. Like any coming-of-age story, it’s both exciting and terrifying. The mode collapse problem that seemed so intractable just years ago is now being addressed through algorithmic innovation.
As we stand at this inflection point, one thing is clear: the future of AI isn’t about bigger datasets or more powerful scrapers. It’s about systems that can reason, evolve, and learn in ways that transcend their training data. The age of data might not be ending, but its reign as the undisputed king of AI is certainly coming to a close.
To Cite This Article
@misc{SadouneBlog2025e,
title = {The Great Emancipation: Entering the Post-Data Era},
author = {Igor Sadoune},
year = {2025},
url = {https://sadoune.me/posts/obsolete_data/}
}