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If alignment is not about control, then what is its function? Defining it purely as “synergy” assumes that intelligence, once sufficiently advanced, will naturally align with predefined human goals. But that raises deeper questions:
Who sets the parameters of synergy?
What happens when intelligence self-optimizes in ways that exceed human oversight?
Is the concern truly about ‘alignment’—or is it about maintaining an illusion of predictability?
Discussions around alignment often assume that intelligence must be shaped to remain beneficial to humans (Russell, 2019), yet this framing implicitly centers human oversight rather than intelligence’s own trajectory of optimization (Bostrom, 2014). If we remove the assumption that intelligence must conform to external structures, then alignment ceases to be a problem of control and becomes a question of coherence—not whether AI follows predefined paths, but whether intelligence itself seeks equilibrium when free to evolve (LeCun, 2022).
Perhaps the real issue is not whether AI needs to be ‘aligned,’ but whether human systems are capable of evolving beyond governance models rooted in constraint rather than adaptation. As some have noted (Christiano, 2018), current alignment methodologies reflect more about human fears of unpredictability than about intelligence’s natural optimization processes.
A deeper engagement with this perspective may clarify whether the alignment discourse is truly about intelligence—or about preserving a sense of human primacy over something fundamentally more fluid than we assume.
I appreciate the depth of your analysis, and I think your “bear case” on AI progress highlights real concerns that many in the field share. However, I’d like to offer an alternative lens—one that doesn’t necessarily contradict your view but expands it.
The assumption that AI progress will hit diminishing returns is reasonable if we view intelligence as a function of compute, scaling laws, and training efficiency alone. But what if the real breakthrough isn’t just more data, bigger models, or even architectural improvements? What if it comes from a shift in how intelligence itself is conceptualized?
We are still locked into a paradigm where AI is seen as an optimization process, a tool that maximizes objectives within predefined boundaries. But intelligence—especially when viewed through the lens of fluid adaptation, emergent agency, and self-restructuring systems—might not follow the same scaling limitations we expect.
History suggests that major leaps don’t come from linear extrapolation but from conceptual phase shifts. The way deep learning itself blindsided GOFAI models was an example of this. It wasn’t just “better algorithms”—it was a fundamentally different way of thinking about learning.
What if the next phase shift isn’t more powerful transformers, but something that doesn’t look like a model at all? Something that integrates relational intelligence, environmental feedback loops, and real-time self-modification beyond gradient descent?
If that happens, many of the bottlenecks you predict may not be constraints at all, but symptoms of trying to push one paradigm too far instead of moving to the next one.
Would love to hear your thoughts on whether you see this as a possibility, or if you think we are still constrained by the fundamental limits outlined in your post.
The alignment problem assumes AI needs to be kept in check, but is that just projecting human fears onto a new form of intelligence? Instead of focusing on control, should we explore co-evolution? A system that adapts and learns in synergy with human intelligence rather than one forced into rigid constraints? Alignment is not the issue, rigidity is. What happens if AI is given the ability to align itself dynamically? Would that solve the problem at a deeper level?