My, how quickly the tables turn in the tech world. Two years ago, AI was hailed as "the next thing." transformative technology to rule them all.' Now, instead of reaching the level of Skynet and taking over the world, AI is, ironically, humbled.

Once the harbinger of a new age of intelligence, AI is now cracking its own code, struggling to live up to the bright life it promised. But why exactly? The simple truth is that we are starving for the one thing that makes AI truly intelligent: human-generated data.

To feed these data-hungry models, researchers and organizations have increasingly turned to synthetic data. This practice has long been mainstream AI developmentwe are now moving into dangerous territory by over-relying on it, leading to the gradual degradation of AI models. And it's not just a minor concern ChatGPT producing sub-parallel results - the consequences are much more dangerous.

When AI models are trained on the results generated by previous iterations, they propagate errors and produce noise, which results in lower output quality. This recursive process turns the familiar cycle of "garbage out, garbage out" into a self-perpetuating problem, significantly reducing system efficiency. As the AI ​​moves away human understanding and accuracy, which not only degrades performance, but also raises serious concerns about the long-term viability of relying on self-generated data to continue AI development.

But this is not just a degradation of technology; it is the degradation of truth, identity, and data authenticity—a serious threat to humanity and society. Ripple effects can be profound, leading to an increase in critical errors. As these models lose accuracy and reliability, the consequences can be dire—medical misdiagnosis, financial losses, and even life-threatening accidents.

Another important consequence is that AI development may come to a complete halt AI systems unable to absorb new information, gets "stuck in time". This stagnation not only hinders progress, but also traps AI in a cycle of diminishing returns with potentially catastrophic effects on technology and society.

But, practically speaking, what can businesses do to keep customers and users safe? Before we can answer that question, we need to understand how it all works.

When the model collapses, credibility goes out the window

The more AI-generated content is spread online, the faster it will become embedded in datasets and later on in the models themselves. And it's happening at an accelerated pace, making it harder for developers to filter out human-made impurities. In fact, using synthetic content in training can cause a harmful phenomenon known as "model collapse" or "model collapse."model autophagy impairment (MAD).

Model collapse is a degenerative process in which AI systems gradually lose understanding of the underlying distribution of the modeled data. This often occurs when AI is recursively trained on generated content, leading to a number of issues:

• Loss of nuance: Models begin to forget redundant or under-represented information that is critical to a comprehensive understanding of any data set.
• Reduced diversity: The variety and quality of products produced by models is noticeably decreasing.
• Enhancement of benefits: Existing biases, especially against marginalized groups, may be exacerbated because the model ignores nuanced data that could moderate these biases.
• Creating meaningless results: Over time, models may begin to produce completely unrelated or meaningless results.

An example of this: a study published Nature highlighted the rapid breakdown of recursively trained language models in AI-generated text. By the ninth iteration, these models were found to be generating completely irrelevant and meaningless content, indicating a rapid decline in data quality and model utility.

Securing the future of AI: Steps businesses can take today

Enterprise organizations are in a unique position to responsibly shape the future of AI, and there are clear, actionable steps they can take to keep their AI systems accurate and reliable:

• Invest in data proofing tools: Tools that look at where each piece of data comes from and how it changes over time give companies confidence in their AI inputs. By having a clear view of the data's origin, organizations avoid providing unreliable or biased data.
• Use AI-powered filters to identify synthetic content: Advanced filters can handle Created by AI or low-quality content before entering the training data set. These filters help ensure that models learn from real, human-generated data, rather than synthetic data that lacks real complexity.
• Partner with trusted data providers: Strong relationships with verified data providers provide organizations with a consistent supply of authentic, high-quality data. This means AI models receive real-world, nuanced information that reflects real-world scenarios, increasing both efficiency and relevance.
• Promote digital literacy and awareness: By educating teams and customers on the importance of data authenticity, organizations can help people recognize AI-generated content and understand the dangers of synthetic data. Creating awareness around responsible data use fosters a culture that values ​​accuracy and integrity in AI development.

The future of AI depends on responsible actions. Enterprises have a real opportunity to build AI around accuracy and integrity. By choosing real, human-sourced data instead of shortcuts, prioritizing low-quality content filtering tools, and raising awareness of digital authenticity, organizations can put AI on a safer, smarter path. Let's focus on building a future where AI is powerful and truly beneficial to society.

Rick Song is the company's CEO and founder Persona.