In response to the potential data shortage, AI companies are considering training their models on AI-generated data. However, studies have shown that using AI-generated content can degrade the output quality of the models. There is ongoing debate about the severity of this issue, with some arguing that AI algorithms could become more efficient and produce better outputs with less data or computing power.
Key takeaways:
- AI researchers warn that companies like OpenAI and Google are rapidly running out of human-written training data for their AI models, which could limit the models' ability to improve.
- This lack of data is seen as an existential threat for AI tools that rely on large amounts of data, often pulled from publicly available online archives.
- Researchers predict that large language models could run out of fresh data as soon as 2026, and AI companies may resort to training their models on AI-generated data, which has been found to erode output quality.
- There is ongoing debate about the potential impact of this issue, with some suggesting that AI algorithms could become more efficient and produce better outputs with less data or computing power.