In the ever-evolving AI arms race, even the titans are looking over their competitors’ shoulders. Recent revelations suggest that Google quietly tapped into OpenAI's ChatGPT to help boost its own generative AI system, Bard (now known as Gemini). This surprising twist highlights just how interdependent the world of artificial intelligence has become—even among rivals.
According to internal documents, Google engineers used ChatGPT as part of a data-gathering and evaluation strategy while training Bard. This development not only shines a light on the high-stakes pressure within Big Tech to lead the AI revolution, but also raises ethical and strategic questions about using a competitor’s product to train your own.
Bard’s Bumpy Road: A Need for a Boost
When Google first launched Bard in 2023, it entered a crowded and competitive space already dominated by OpenAI’s ChatGPT. While Bard had the branding and resources of one of the world’s most powerful tech companies behind it, its debut was met with mixed reviews—especially when compared to ChatGPT’s performance.
Internally, it seems Google was aware of Bard’s shortcomings and began exploring unconventional ways to close the gap. One approach: feeding ChatGPT’s answers into Bard’s training datasets to analyze strengths, weaknesses, and patterns.
Using a Competitor's AI: Clever or Controversial?
On one hand, this strategy makes sense. In the world of machine learning, models are only as strong as the data they’re trained on. By analyzing ChatGPT's outputs, Google could identify nuances, linguistic decisions, and reasoning structures that outperformed Bard’s.
But on the other hand, it introduces a blurry ethical line. Was this fair use, or does it cross into intellectual property territory? The answer is still up for debate.
Regardless, the move illustrates a powerful truth: even AI giants aren't building in silos. The ecosystem is fluid, competitive, and increasingly collaborative—whether intentionally or not.
The Role of Scale AI
Documents also suggest that Scale AI, a data labeling and training company known for its work with top-tier models, played a significant role in curating and assessing Bard’s training inputs. Scale's involvement added another layer of human oversight, using professional annotators to judge which AI-generated responses were more helpful, logical, or accurate.
This human-in-the-loop approach remains crucial. As powerful as generative models are, they still rely heavily on human evaluation to ensure quality, fairness, and usefulness.
What This Means for Users and the AI Landscape
Ultimately, this story is less about competition and more about evolution. The AI space is moving so quickly that even the most dominant players are constantly iterating—and sometimes looking to rivals for inspiration.
For users, this could mean better products across the board. If Bard (or Gemini) improves because it learned from ChatGPT, and vice versa, the outcome is smarter, more helpful tools for everyone. It's similar to how smartphone companies borrow design cues and software features from one another—eventually raising the standard across the industry.
But it also signals a broader shift in AI development: it’s no longer just about having the biggest model or dataset. It's about agility, adaptability, and being open to learning—even from your competitors.
