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DeepSeek’s R1 and OpenAI’s Deep Research just redefined AI — RAG, distillation, and custom models will never be the same
DeepSeek's R1 model release and OpenAI's new Deep Research product will push companies to use techniques like distillation, supervised fine-tuning (SFT), reinforcement learning (RL), and retrieval-augmented generation (RAG) to build smarter, more specialized AI applications. From pricing and performance to hallucination risks and the importance of clean data, here’s what these breakthroughs mean for anyone building AI today.
Less talked about, however, is how they’ll push companies to use techniques like distillation, supervised fine-tuning (SFT), reinforcement learning (RL), and retrieval-augmented generation (RAG) to build smarter, more specialized AI applications. “I fully expect the cost to go to zero,” said Ashok Srivastava, chief data officer of Intuit, a company that has been driving AI hard in its tax and accounting software offerings like TurboTax and Quickbooks. DeepSeek and OpenAI’s Deep Research are more than just new tools in the AI arsenal—they’re signals of a profound shift, where enterprises will be rolling out masses of purpose-built models, extremely affordably, competent, and grounded in the company’s own data and approach.
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