LLMs transcend abstraction layers
The claim, surfaced on March 23rd, is that large language models are now effectively transcending abstraction layers — the conceptual boundaries that software engineers and computer scientists have long used to separate concerns in system design, from hardware up through operating systems, compilers, runtimes, and application logic. The argument, as best as can be reconstructed from this single signal, is that LLMs don't navigate these layers so much as dissolve them. A model generating code doesn't "know" it's working at the application layer. It reaches through. It reasons about memory, about system calls, about interface design, all in the same breath, without the trained human instinct to stop at the abstraction boundary and hand off to a specialist.
If confirmed, here is what this means. The entire architecture of how we train engineers — built on the premise that abstraction layers are cognitive load management devices, that you learn one layer before you touch the next — becomes a curriculum designed for a constraint that no longer applies to machines. Teams that have structured their workflows around specialisation by layer, frontend engineers who don't touch infrastructure, backend engineers who don't touch ML pipelines, would find those divisions increasingly arbitrary when the tool assisting them has no such boundaries. The second-order effect is more destabilising: the security model of modern software is deeply entangled with abstraction layer isolation. If LLMs are generating code that casually crosses those lines, the attack surface analysis that security engineers rely on starts to look like a map drawn for a city that got rebuilt overnight.
Watch for peer-reviewed work or engineering post-mortems describing LLM-generated code that demonstrably crosses abstraction boundaries in production systems — that would shift this from philosophical provocation to operational reality.
<!-- SC_OFF --><div class="md"><p>I’ve been thinking about the current AGI discussions, especially the debate between scaling language models and building embodied systems.</p>
<!-- SC_OFF --><div class="md"><p>I’ve been saying this for quite some time now and this paper that came out recently really puts it clearly</p> <p><a
<table> <tr><td> <a href="https://www.reddit.com/r/singularity/comments/1sxffva/llms_are_a_dead_end/"> <img alt="LLMs are a Dead End"
  submitted by   <a href="https://www.reddit.com/user/preyneyv"> /u/preyneyv </a> <br /> <span><a href="https://pleasedontcite.me/learning-backwards/">[link]</a></span>
<!-- SC_OFF --><div class="md"><p>Some people are skeptics about ASI because "LLMs can't reach ASI". Let's make this clear. </p> <p>If any AI-LLM based system can
<!-- SC_OFF --><div class="md"><p>In the 1960s and 70s, Sperry and Gazzaniga ran experiments on patients who had undergone a severance of the corpus callosum as a treatment for
<!-- SC_OFF --><div class="md"><p>Several experiments and research by AI researchers say something important about where the AI field currently stands. </p> <p>First, we had
<!-- SC_OFF --><div class="md"><p>I have always thought it's absolutely absurd to anthropomorphise LLMs until very recently. It didn't make any sense to me. I saw the system from
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