Your bear case is cogently argued, yet I find it way too tethered to a narrow view of LLMs as static tools bound by pretraining limits and jagged competencies.
The evidence suggests broader potential. LLMs already power real-world leaps, from biotech breakthroughs (e.g., Evo 2’s protein design) to multi-domain problem-solving in software and strategy, outpacing human baselines in constrained but scalable tasks. Your dismissal of test-time compute and CoT scaling overlooks how these amplify cross-domain reasoning, not just in-distribution wins.
Regarding programming, your current view also risks largely underestimating the vast potential of these models. See this from YC where they mention 1⁄4 of the founders claim 95% of their codebase is already written by LLMs. This has been my experience as well as a software engineer. You need to know how to steer the ship, but if you do, this tech comfortably makes you 10x.
I’m also skeptical of your claim that agency stalls at complexity; current models orchestrate complex workflows (e.g., agentic systems in logistics) with growing adeptness. Are you underweighting these strides because they don’t fit a clean AGI narrative, or do you see a ceiling I’m missing?
What’s your take on LLMs bridging inferential gaps across domains, say from code to ethics, where human steering already yields outsized returns?
Your bear case is cogently argued, yet I find it way too tethered to a narrow view of LLMs as static tools bound by pretraining limits and jagged competencies.
The evidence suggests broader potential. LLMs already power real-world leaps, from biotech breakthroughs (e.g., Evo 2’s protein design) to multi-domain problem-solving in software and strategy, outpacing human baselines in constrained but scalable tasks. Your dismissal of test-time compute and CoT scaling overlooks how these amplify cross-domain reasoning, not just in-distribution wins.
Regarding programming, your current view also risks largely underestimating the vast potential of these models. See this from YC where they mention 1⁄4 of the founders claim 95% of their codebase is already written by LLMs. This has been my experience as well as a software engineer. You need to know how to steer the ship, but if you do, this tech comfortably makes you 10x.
https://www.youtube.com/watch?v=IACHfKmZMr8
I’m also skeptical of your claim that agency stalls at complexity; current models orchestrate complex workflows (e.g., agentic systems in logistics) with growing adeptness. Are you underweighting these strides because they don’t fit a clean AGI narrative, or do you see a ceiling I’m missing?
What’s your take on LLMs bridging inferential gaps across domains, say from code to ethics, where human steering already yields outsized returns?