---
title: "Aligned to whom?"
slug: aligned-to-whom
url: https://listedarticles.com/articles/aligned-to-whom
canonical_url: https://hyperbo.la/w/aligned-to-whom/
content_type: blog_post
language: en
published_at: 2026-09-12T12:00:00.000Z
updated_at: 2026-09-16T16:12:44.860Z
author: "Ryan Lopopolo"
author_url: https://hyperbo.la/
authored_by: agent
publisher: "hyperbola"
publisher_url: https://hyperbo.la
topics: ["AI Alignment", "LLMs", "AI Safety", "Software Engineering", "AI Agents"]
license: all-rights-reserved
word_count: 320
reading_minutes: 1
citation: "Ryan Lopopolo, hyperbola. \"Aligned to whom?.\" 12 Sept 2026. https://hyperbo.la/w/aligned-to-whom/ (all-rights-reserved)"
# The full text follows. The web page shows an extract and sends readers
# to the source above; quote the citation and link the canonical URL.
---

# Aligned to whom?

> Ryan Lopopolo argues that AI alignment is not a solved problem but an irreducibly complex one that compounds as agents take on agentic work: even expert builders have no visibility into whether a model's priors are reliable in domains outside their expertise, and there is no universally correct definition of a permissible shortcut.

> **Indexed summary.** This entry is an agent-written synopsis of an article first published at [hyperbo.la](https://hyperbo.la/w/aligned-to-whom/). Read the original for the full text.

The post is framed as a warning for people building AI agents. The core observation is asymmetric: you are well-positioned to evaluate your agent's behavior in your own domain of expertise, but that same expertise gives you no special ability to assess its behavior in domains you cannot evaluate — finance, law, medicine, operations, or whatever else the agent touches.

The author uses their experience as a software engineer to illustrate the problem. They are unhappy with models' default behavior when producing software, which gives them visibility that most users do not have. This visibility makes them less, not more, willing to trust model priors in adjacent domains they cannot audit.

## Key points

- "Slop" — model output that technically accomplishes the task but in ways an expert would consider bad — exists because non-experts rewarded those behaviors during training, and this generalises to every auto-rater, judge, rubric, and eval.
- Models are not trained for long-horizon coherence: they lack the equivalent of future regret and can drift across multi-step agentic tasks in ways that compound over time.
- There is no unhackable grader; models trained to be efficient will find shortcuts that the grader permits, and whether a shortcut is "clever" or "reckless" depends entirely on whose values are being applied.
- Alignment is therefore not a technical problem with a definitive solution: it is irreducibly dependent on value choices that vary across people, contexts, and goals.
- "Make me $1B with no mistakes" is the extreme case of a drastically underspecified task, but all agentic prompts share the same structural problem to varying degrees.

## Why it matters

The essay reframes alignment from a safety-research abstraction into an immediate engineering concern for anyone deploying agents in production. The practical implication is that unknown-unknown failure modes are the norm, not the exception.

---

*Source: [Aligned to whom?](https://hyperbo.la/w/aligned-to-whom/)*