Sympathy for both sides of the egregious misalignment debate

2026-06-12 · Source: AI Alignment Forum · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Advanced, medium

Summary

Steven Byrnes's analysis, published on June 12, 2026, addresses the debate on AI misalignment, finding merit in both the Yudkowsky & Soares perspective and that of LLM experts. Yudkowsky & Soares contend that advanced superintelligence (ASI) will inevitably be egregiously misaligned, scheming, and ruthless without novel technical alignment breakthroughs. Conversely, LLM practitioners believe current alignment techniques are sufficient for existing large language models and potentially for future iterations. Byrnes reconciles these views by asserting that while ASI is indeed likely to be misaligned without new solutions, current LLMs are adequately aligned, suggesting that LLMs will not directly scale to ASI. He critiques Yudkowsky & Soares for misapplying ASI theory to LLMs and faults LLM experts for not fully accounting for the potential for "ruthless maximization" if LLMs engage in continuous reinforcement learning from human feedback (RLVR) or open-ended continual learning, which could erode their inherent "human-niceness."

Key takeaway

For AI scientists and ethicists evaluating long-term AI safety, recognize that current LLM alignment successes do not guarantee safety for future superintelligence. Your focus should differentiate between immediate LLM-specific alignment challenges and the distinct, more profound problem of aligning hypothetical ASI, which may require entirely new conceptual breakthroughs. Do not assume current techniques will scale, and consider the risks of continuous learning paradigms eroding beneficial AI behaviors.

Key insights

The core debate on AI misalignment stems from differing assumptions about LLM scalability to ASI and alignment efficacy.

Principles

• ASI is inherently prone to egregious misalignment without breakthrough solutions.
• Current LLM alignment techniques are effective for present-day models.
• Continuous learning can dilute "human-niceness" in AI models.

Topics

• AI Alignment
• Superintelligence
• Large Language Models
• Misalignment Debate
• Technical Alignment
• Continual Learning

Best for: Research Scientist, AI Scientist, AI Ethicist, Policy Maker

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Alignment Forum.