
DeepSeek R1 marks a turning point for the AI industry by accelerating the commoditization of LLMs. While it may disrupt the economics of the current landscape, it doesn’t change the fact that simply scaling data and compute doesn’t move us closer to general intelligence. DeepSeek demonstrates that better algorithms can make LLMs cheaper but it doesn’t solve the fatal flaw in state-of-the-art methods: Reliability. Reports are already emerging that, like other LLMs, R1 hallucinates, contains biases in its training data and exhibits behavior that reflects China’s political views on certain topics such as censorship and privacy.
Claims that pre-trained LLMs demonstrate genuine logical reasoning are questionable as is their capacity for agency and adaptability – requirements for operating safely and autonomously in the real world. These limitations are particularly problematic for companies like OpenAI, Google, Microsoft, and Nvidia that are heavily dependent on LLMs. The race to the bottom will make it ever-clearer that bigger and cheaper models do not equate to better, smarter, explainable, reliable agentic machine intelligence.
Advancements like DeepSeek that affect LLMs and GenAI are disconnected from what we’re building as they are fundamentally different architectures. Based on scientific principles that explain how biological intelligence works, Genius empowers agents with cognition. The benefit of this approach is intelligent agents that are not only more efficient but, more critically, reliable enough to navigate and continuously adapt to complex dynamic systems like the real-world.