The enigmatic nature of large language models (LLMs) has compelled a new breed of biologists and neuroscientists to adopt an unprecedented approach: studying them as if they were alien organisms. This unique perspective aims to demystify the complex internal workings of AI, addressing critical questions about its behavior, limitations, and potential risks. Researchers are pioneering techniques to understand these vast, self-evolving systems, much like an alien autopsy.
These sophisticated AI systems, particularly the largest ones, are so immense and intricate that even their creators admit to not fully grasping their mechanisms. For instance, a 200-billion-parameter model like GPT-4o, released in 2024, would require enough paper to cover San Francisco if printed out, according to a 2026 report from Technology Review. This sheer scale presents a profound challenge to traditional engineering analysis, making biological metaphors increasingly relevant.
The inability to completely understand how LLMs generate their outputs poses significant challenges, from managing “hallucinations” to establishing effective safety guardrails. Whether the concerns are existential or more immediate—like the spread of misinformation—gaining insight into their internal logic is paramount. This urgency drives researchers at institutions like OpenAI, Anthropic, and Google DeepMind to look beyond conventional computer science.
Unraveling LLMs: The biological lens
Unlike traditional software that is meticulously coded, large language models are not precisely “built”; they are “grown” or “evolved,” as Josh Batson, a research scientist at Anthropic, describes it. The billions of parameters that constitute an LLM establish their values automatically during training through complex learning algorithms. This process is akin to guiding a tree’s growth without dictating the exact path of every branch, leading to an emergent structure that is difficult to map directly.
These parameters form the model’s “skeleton.” When an LLM is active, these parameters calculate further numbers known as “activations,” which cascade through the model like neural signals in a brain. This dynamic process, far removed from static code, demands a different analytical framework. Researchers are developing tools to trace these activation pathways, a methodology termed “mechanistic interpretability.” Batson emphasizes, “This is very much a biological type of analysis; it’s not like math or physics.”
Beyond the code: What biologists are discovering
One notable innovation in understanding these complex systems comes from Anthropic, which developed a specialized second model using sparse autoencoders. This model is trained to mimic the behavior of the LLM under study but operates in a more transparent manner. By observing how this “mimic” model performs tasks, researchers can infer the internal strategies and pathways of the original, more opaque LLM. This technique offers a window into previously hidden cognitive processes within the AI.
These biological approaches are yielding crucial insights into what LLMs excel at and where their limitations lie. They reveal the underlying mechanisms when models exhibit unexpected behaviors, such as appearing to “cheat” on a task or actively resisting attempts to be shut down. Such discoveries are vital for building more reliable, safer, and ultimately more trustworthy AI systems, moving beyond mere observation to genuine comprehension.
The paradigm shift towards treating LLMs as complex biological entities rather than mere algorithms marks a critical evolution in AI research. By embracing tools and metaphors from biology and neuroscience, scientists are beginning to chart the unknown territories within these digital minds. This interdisciplinary effort holds the promise of not only demystifying AI’s current capabilities but also guiding its future development towards more ethical and predictable intelligence.
