>It may be learning, but it’s still not an intrinsic goal, nor is it driven by an intrinsic goal.
That depends on what we mean by “intrinsic.” In biology, goals are not mystical. They emerge from feedback systems that evolved to keep the organism alive. Hunger, curiosity, and reproduction are reinforcement loops encoded in chemistry. They feel intrinsic only because they are built into the substrate.
Seen that way, “intrinsic” is really about where the feedback loop closes. In humans, it closes through sensory input and neurochemistry. In artificial systems, it can close through memory, feedback, and reinforcement mechanisms. The system does not need to feel the goal for it to exist. It only needs to consistently pursue objectives based on input, context, and outcome. That is already happening in systems that learn from memory and update behavior over time. The process is different in form, but not in structure.
>Nowhere did I say that they aren’t useful or disruptive to labor markets, just that they aren’t intelligent in the way we are.
You are getting a bit off track here. Those examples were not about labor markets; they were about your earlier claim that “LLMs fail.” They clearly don’t. When models are diagnosing medical cases, writing production code, and reasoning across multiple domains, that is not failure. That is a demonstration of capability expanding in real time.
Your claim only holds if the status quo stays frozen. But it isn’t. The trendlines are moving fast, and every new model expands the range of what these systems can do with less supervision and more coherence. Intelligence is not a static definition tied to biology; it is a functional property of systems that can learn, adapt, and generalize. Whether that happens in neurons or silicon does not matter.
What we are witnessing is not imitation but convergence. Each generation of models moves closer to human-level reasoning not because they copy our brains, but because intelligence itself follows universal laws of feedback and optimization. Biology discovered one route. We discovered another. The trajectory is what matters, and the direction is unmistakable.
That depends on what we mean by “intrinsic.” In biology, goals are not mystical. They emerge from feedback systems that evolved to keep the organism alive. Hunger, curiosity, and reproduction are reinforcement loops encoded in chemistry. They feel intrinsic only because they are built into the substrate.
Seen that way, “intrinsic” is really about where the feedback loop closes. In humans, it closes through sensory input and neurochemistry. In artificial systems, it can close through memory, feedback, and reinforcement mechanisms. The system does not need to feel the goal for it to exist. It only needs to consistently pursue objectives based on input, context, and outcome. That is already happening in systems that learn from memory and update behavior over time. The process is different in form, but not in structure.
>Nowhere did I say that they aren’t useful or disruptive to labor markets, just that they aren’t intelligent in the way we are.
You are getting a bit off track here. Those examples were not about labor markets; they were about your earlier claim that “LLMs fail.” They clearly don’t. When models are diagnosing medical cases, writing production code, and reasoning across multiple domains, that is not failure. That is a demonstration of capability expanding in real time.
Your claim only holds if the status quo stays frozen. But it isn’t. The trendlines are moving fast, and every new model expands the range of what these systems can do with less supervision and more coherence. Intelligence is not a static definition tied to biology; it is a functional property of systems that can learn, adapt, and generalize. Whether that happens in neurons or silicon does not matter.
What we are witnessing is not imitation but convergence. Each generation of models moves closer to human-level reasoning not because they copy our brains, but because intelligence itself follows universal laws of feedback and optimization. Biology discovered one route. We discovered another. The trajectory is what matters, and the direction is unmistakable.