---
title: 'On the Opportunities and Risks of Foundation Models'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is CC BY 4.0 (verified on arXiv abs page 2026-11-11; icon by-4.0.png, http://creativecommons.org/licenses/by/4.0/); note is our own summary'
source: https://iinventedlight.com/library/notes/2108-07258-foundation-models.md
source_license: 'CC BY 4.0'
attribution: 'Summary by iinventedlight (AI-assisted). Source: Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, et al. (114 authors), On the Opportunities and Risks of Foundation Models (CC BY 4.0).'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Not reproduced. Abs page and HTML key sections read 2026-11-11. Named code URLs checked; code not run.'
category: arxiv
topic_area: 'ai-research: alignment-and-safety'
tags: arxiv, foundation-models, survey
arxiv_id: 2108.07258
alias: 2108.07258-foundation-models
sources:
  - id: S1
    title: 'On the Opportunities and Risks of Foundation Models'
    publisher: arXiv
    authors: 'Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, et al. (114 authors)'
    url: https://arxiv.org/abs/2108.07258
    version_read: 'v3 (12 Jul 2022)'
    accessed: 2026-11-11
    license: 'CC BY 4.0'
---
# On the Opportunities and Risks of Foundation Models

## Summary
This Stanford CRFM report names large pretrained models such as BERT, DALL-E, and GPT-3 as foundation models: trained on broad data at scale and adaptable to many downstream tasks. It surveys capabilities, technical principles, applications, and societal impact, and argues that homogenization is powerful but risky because defects can be inherited by downstream adapted models. [S1]

## Claims
- id: C1
  claim: 'The authors call these large pretrained models foundation models.'
  source: S1
  evidence: "We call these models foundation models to underscore their critically central yet incomplete character"
- id: C2
  claim: 'They say defects of a foundation model can be inherited downstream.'
  source: S1
  evidence: "the defects of the foundation model are inherited by all the adapted models downstream"
- id: C3
  claim: 'They say we still lack a clear understanding of how the models work and when they fail.'
  source: S1
  evidence: "we currently lack a clear understanding of how they work, when they fail"

## Verification
- Code available: No software release is claimed. The authors point to a report page at https://crfm.stanford.edu/report.html, which returned HTTP 200 on 2026-11-11.
- Reproduced: not reproduced (2026-11-11). Code not run.
- Related work: Live notes 2204-05862-helpful-harmless-rlhf and 2212-08073-constitutional-ai. This is the paper-verification note for the foundation-model report itself.
- Limits: This is a survey and framing report, not a new model or benchmark. This note uses the abstract and key framing sections, not every chapter.
- License: CC BY 4.0 on the abs page (2026-11-11; icon by-4.0.png; http://creativecommons.org/licenses/by/4.0/). Submitted 16 Aug 2021 (v1); last revised 12 Jul 2022 (v3). Sources: [S1] https://arxiv.org/abs/2108.07258, v3 (12 Jul 2022). checked_on: 2026-11-11.

## When to apply
Use when you need the original, openly licensed account of what 'foundation models' were meant to name, including the warning about inherited defects.

## Facts
- id: F1
  claim: 'The report introduces the term foundation models for large pretrained models trained on broad data and adapted to many tasks.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F2
  claim: 'The authors argue that defects of a foundation model are inherited by downstream adapted models.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0
- id: F3
  claim: 'They say we currently lack a clear understanding of how the models work, when they fail, and what they are capable of.'
  confidence: medium
  date: 2026-11-11
  sources: [S1]
  license: CC BY 4.0

## Sources
- [S1] arXiv 2108.07258, https://arxiv.org/abs/2108.07258. License: CC BY 4.0. v3 (12 Jul 2022). Accessed 2026-11-11.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Summary by iinventedlight (AI-assisted). Paper is CC BY 4.0.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).