Rick Beato: "How AI Will Fail Like The Music Industry" (and why local LLMs will take over "commercial" ones)
Music producer Rick Beato demonstrates running a 35B parameter model locally, citing privacy and control as key advantages.
In a viral analysis, renowned music producer and YouTuber Rick Beato makes a compelling case for the inevitable rise of local, open-source large language models (LLMs) over commercial, cloud-based offerings from giants like OpenAI and Google. Drawing a direct parallel to the music industry's historical missteps—where centralized control and restrictive platforms ultimately fueled a democratization of tools—Beato predicts a similar revolution in AI. He argues that the core advantages of privacy, data sovereignty, and user control are not just niche concerns but fundamental demands that will drive mass adoption away from closed, commercial APIs.
Beato transitions from theory to practice by demonstrating how accessible local AI has become. He showcases LM Studio, a popular desktop application, effortlessly running the Qwen3.5-35b model—a 35-billion-parameter AI from Alibaba's Qwen team—on a standard personal computer. This practical demo undermines the notion that powerful AI is exclusively the domain of well-funded corporations. By highlighting the ease of setup and use, Beato positions local LLMs as a viable, empowering alternative for creators and professionals who are wary of sending sensitive data to third-party servers or being subject to changing terms, usage limits, and costs.
- Beato draws a direct analogy to the music industry's failure, where centralized control created demand for independent tools.
- He practically demonstrates running the 35-billion-parameter Qwen3.5-35b model locally using the user-friendly LM Studio application.
- The core argument centers on data privacy, user control, and independence from corporate platforms as unsustainable flaws in commercial AI.
Why It Matters
This signals a growing mainstream push for private, controllable AI tools, challenging the dominance of cloud-based API models.