Artificial intelligence has advanced by leaps and bounds in recent years. Models like GPT-4 and Gemini 2.0 are transforming entire sectors, from language processing to complex problem solving, and establishing themselves as essential tools in our daily lives. This progress has been driven by a fundamental resource: data. The vast amount of information gathered from the internet and other sources has allowed these technologies to learn, evolve and surprise the world. However, even this seemingly inexhaustible resource can hit a limit.

As we told you this afternoon, industry experts are already talking about an unexpected challenge: exhausting the data necessary to train the most advanced models. This situation threatens to slow down the technology race, which until now seemed to have no ceiling. As computing power continues to grow, the amount of useful data is not growing at the same pace, and technology companies are being forced to rethink their strategies.

Is this the end of the era of artificial intelligence? In this special, we explore the causes of this phenomenon, its possible consequences and solutions that could redefine the future of one of the most important technologies of our time.

Why is data so important to artificial intelligence?

Machine learning, the foundation of modern artificial intelligence, has a basic principle: models learn through data. From language patterns to complex images, all of the model’s knowledge is derived from the information it was trained with. This process begins with pre-traininga critical phase in which models process large amounts of unlabeled data to identify general patterns. For example, in language models like GPT, preschool allows you to learn language structures, grammar, and contextual relationships from billions of words extracted from the Internet.

Once the pre-training session is complete, models move to specific trainingor fine-tuning, where they focus on specific tasks using smaller, carefully selected datasets. The difference between the two phases is not only technical, but also in scale: while pretraining requires massive and diverse volumes of data, targeted training can achieve good results with several thousand labeled examples. This combination of broad and specialized learning is what led to the creation of such powerful tools as GPT-4 or Gemini 2.0.

But not all data is the same. For the models to generalize, They need information that is not only rich, but also diverse and of high quality. Redundant, biased, or unrepresentative data can limit a model’s ability to adapt to new situations. This is why massive data sources like the Internet have been so important to the development of AI. Open access to millions of websites, books, articles, and other content allowed developers to feed their models with the equivalent of centuries of human knowledge.

However, this approach was not always possible. In the early stages of AI development, models relied on specific and limited datasets such as image databases or hand-collected texts. Massive access to the internet has changed the rules of the gameenabling the creation of today’s great models. However, over time, this seemingly unlimited resource can show signs of depletion.

Data exhaustion: A limit that (almost) no one expected

For years, data has been considered the “fuel” of artificial intelligence. Without them, models like GPT-4 or Gemini 2.0 would simply not be possible. However, this seemingly inexhaustible resource could encounter its limits. In the words of Ilya Sutskever, co-founder of OpenAI: “We only have one Internet.” This statement, made during the NeurIPS 2024 conference, reflects a problem that some experts already predicted, but whose practical impact is beginning to show: the amount of available useful data is not growing at the same pace as the demands of those the most advanced. models.

years ago Researchers have already warned that the exponential growth in model size would eventually hit a barrier: lack of new data. This applies not only to the volume, but also to the variety and quality of data needed for pre-training. Reworking the same resources does not add significant value, and models begin to show less ability to improve as they reuse already known information. This phenomenon, known as data saturationit was noted in 2020 by OpenAI, but at the time it was seen as a remote problem.

Today, this problem is no longer theoretical. Traditional resources such as the Internet have reached a plateau in their growth, and much of their content is redundant or irrelevant to AI. Moreover, the exponential increase in model size—which has gone from billions to trillions of parameters in recent generations—has accelerated this exhaustion. Data, like fossil fuels, is a finite resource and we are beginning to understand its limits.

Besides, This problem is exacerbated by increasing restrictions on internet data usage. Platforms like Reddit, Twitter or media outlets have started to limit access to their content, either by charging a fee or outright banning its use in training artificial intelligence models. This trend responds to both economic and ethical concerns, particularly regarding the unauthorized use of data generated by third parties. These limitations further reduce the availability of information for the development of advanced models, forcing companies to look for alternatives such as synthetic data or to rethink their development strategy.

Synthetic data: a solution to burnout?

As the exhaustion of useful data and limitations on its access become major obstacles to artificial intelligence, the industry has begun to explore alternatives. One of the most promising is synthetic dataan approach that seeks to generate artificial information designed specifically for training artificial intelligence models. Although the idea of ​​creating “fake” data may sound counterintuitive, it has the potential to solve many of today’s limitations by being virtually limitless and customizable.

Synthetic data is generated using advanced algorithmssuch as generative adversarial networks (GANs) that produce information simulating the characteristics of real data. For example, images of human faces that do not belong to any real person or datasets that mimic financial transactions can be generated without compromising sensitive information. These techniques allow you to create data designed to meet specific needs, such as reducing bias or balancing unequal data sets.

The advantages of this approach are clear. By not being dependent on external resources, Synthetic data eliminates issues related to copyright or legal restrictions. Moreover, its generation allows us to address the inherent biases of the original data and create more balanced and diverse sets of information. However, its use is not without problems. One of the main challenges is to ensure that the synthetic data adequately represent reality.. If a model is trained only with artificially generated information, it runs the risk of developing biases or limitations that affect its performance in real-world situations.

Despite these limitations, Synthetic data is emerging as a viable solution for the future of AI. Companies like NVIDIA are already using them to train models in autonomous driving simulations, and interest in the technology continues to grow. While they don’t completely replace real data, their ability to supplement existing files could make a difference in an environment where access to original information is increasingly complicated.

The next era: autonomous agents and the future of AI

With data dwindling and constraints growing, industry experts say the future of artificial intelligence may not depend solely on existing data. Instead of training models on massive bases of static information, the focus is beginning to shift towards autonomous agents, systems capable of acting, reasoning and generating their own data in real time. This vision represents a major development in artificial intelligence that could redefine the way we interact and work with these technologies.

An autonomous agent is not limited to answering questions or performing specific tasks. These systems are able to analyze the environment, identify problems and find solutions on their own using data generated at any given moment.. Instead of relying on a large amount of pre-trained information, for example, an autonomous agent could run simulations or collect data directly from its environment to make more accurate decisions. This ability to adapt in real time not only reduces dependence on historical data, but also opens up new possibilities in fields such as robotics, business management or even space exploration.

Projects like Gemini 2.0 from Google DeepMind are leading this change. According to the people responsible for its development This model combines advanced language skills with logical reasoning and real-time learningwhich is a step towards truly autonomous systems. Additionally, companies like OpenAI are already exploring how to integrate these agents into practical applications, from advanced personal assistants to strategic planning tools for businesses.

However, this transition is not without problems. The autonomy of these systems raises important ethical and technical questions. What happens if the agent makes unexpected decisions or generates bad data? How can we ensure that they operate within safe and ethical boundaries? These issues underscore the need for a clear framework to regulate its development and use, especially as AI continues to gain power and importance in our society.

Are we ready for the future of artificial intelligence?

Artificial intelligence is at a crossroads. The depletion of data available for preschool and the increasing restrictions on the use of traditional resources have revealed the limits of a model of development that until now seemed unstoppable. However, as we have seen, the end of a stage does not necessarily mean the end of progress. Exploring alternatives such as synthetic data and autonomous agents opens the door to a new era in which AI could rely less on human data and more on its ability to create and manage its own information.

Despite these possibilities, the path to this future is not without challenges. Reliability of synthetic data, autonomy of intelligent agents and the need to create clear ethical and technical frameworks They raise questions that do not yet have a definitive answer. What limits should we put in place for artificial intelligence that is able to make decisions for itself? How can we make these technologies work for society and not against it?