DeepSeek’s AI Training Secret: Gemini Connection Raises Industry Eyebrows

The AI world is buzzing with accusations that DeepSeek’s new R1 model might have gotten a little too cozy with Google‘s Gemini outputs during training. AI researchers claim they’ve spotted telltale signs—linguistic patterns, reasoning traces, and word choices that look suspiciously familiar. Like finding your roommate’s homework in someone else’s handwriting.

DeepSeek won’t say where their training data comes from. That’s convenient. Meanwhile, developers are playing detective, analyzing R1’s “thought traces” during problem-solving. The verdict? They smell like Gemini 2.5 Pro. This isn’t DeepSeek’s first rodeo either. Their older models sometimes forgot who they were and claimed to be ChatGPT. Awkward.

DeepSeek’s models keep forgetting their own identity—last time they claimed to be ChatGPT.

Google noticed. They’ve started scrambling their Gemini traces, making them harder to copy. OpenAI and Anthropic are doing the same, basically putting locks on their digital homework. Nobody wants their expensive AI research becoming someone else’s cheap shortcut.

The technical evidence remains circumstantial but compelling. No smoking gun, just a room full of smoke. DeepSeek R1 shows Gemini’s preferences in word choices and thinking patterns. That’s like writing an essay and accidentally using your teacher’s favorite catchphrases.

What makes this messier? Third-party platforms like Relay.app and Make.com let users connect DeepSeek and Gemini for business workflows. These platforms enable deep integrations that allow automated data exchange between models. Great for automation, potentially great for data crossover too. These integrations make it ridiculously easy to pipe outputs from one model into another. Microsoft’s investigation uncovered data exfiltration activities linked to DeepSeek in late 2024, adding fuel to the distillation fire.

The industry’s getting nervous. Proprietary model behavior is valuable intellectual property, and everyone’s suddenly worried about knowledge leaking like water through a sieve. Leading labs are building defensive walls, changing how they share model outputs. This incident raises serious questions about real-time analysis of model outputs being used to train competitive systems.

This whole saga highlights a dirty secret in AI development: synthetic output training. It’s faster and cheaper to learn from another model’s homework than to do original research. Distillation, they call it. Sounds fancy, works great, ethically questionable.

The AI community watches, whispers, and waits. DeepSeek stays silent about their methods while Google fortifies its digital fortress. Welcome to the new AI arms race, where your competitor’s chatbot might be your unwitting teacher.