Philosophy Eats AI
Authors: Michael Schrage and David Kiron Published: January 16, 2025 — MIT Sloan Management Review (Winter 2025) Reading time: ~32 minutes
Central Thesis
Just as Marc Andreessen declared “software is eating the world” (2011) and Jensen Huang updated it to “AI is eating software” (2017), this article argues that philosophy is eating AI. As a discipline, data set, and sensibility, philosophy increasingly determines how digital technologies reason, predict, create, generate, and innovate.
The enterprise challenge: will leaders use philosophy as a deliberate resource for AI value creation, or default to tacit, unarticulated philosophical principles? Either way — philosophy eats AI.
Core Framework: Four Philosophical Lenses
| Lens | Question it asks |
|---|---|
| Teleology | What should AI models achieve? (purpose, goals) |
| Epistemology | What counts as knowledge? How does AI know and represent what it knows? |
| Ontology | How does AI represent reality? |
| Ethics | What values and responsibilities govern AI? |
The authors argue ethics receives disproportionate attention vs. the other three, which equally shape value creation.
Key Arguments
Philosophy is embedded in every LLM
Developers may not consciously articulate their philosophies, but there’s no avoiding questions of purpose, knowledge, semantics, aesthetics, and ethics when using AI for supply chains, CRMs, accounting, or HR systems. LLMs and GenAI make these tacit philosophical assumptions into explicit AI parameters for training, fine-tuning, and learning.
Philosophical training correlates with AI leadership
Notable AI investors and innovators with formal philosophical backgrounds: Stephen Wolfram, Palantir’s Alex Karp, and Peter Thiel. “It’s not an accident.”
Western vs. Eastern philosophical traditions produce different AI
- LLMs tuned to Western moral principles → responses rooted in explainable utility and distributive justice
- GenAI cultivated from Eastern philosophies (Taoism, Confucianism) → emphasis on detachment and relational ethics
Practical example: nudge theory in HR
An HR chatbot trained using libertarian paternalism (Sunstein & Thaler) can nudge employees toward successful behaviors while preserving autonomy. This is a concrete way philosophical commitments become product decisions.
Virtuous cycle
“When designers prompt the model to think better, its responses prompt humans to think better.” — Kiron
Evidence base
Six years of global executive surveys and interviews with multinational company executives provide empirical support that philosophy-driven training and investments directly impact AI’s economic returns.
Notable Quotes
“Philosophy’s ultimate AI impact might not be in making these intelligences more ethical or better aligned with current human values, but in transcending our current perceived limitations and inspiring new frontiers of understanding and capability… By decade’s end, we’ll be getting philosophical insights and inspirations from nextgen LLMs that will shock and inspire people. That’s what happens when philosophy eats AI.” — Schrage
“Re-reading Socrates, Rawls, Mill, Anscombe, Wittgenstein and/or Confucius will be central to boosting Return on GenAI investment.” — Schrage
“There’s too much emphasis on ethics at the expense of other foundational philosophical perspectives.” — Schrage
Relevance to MSBAi
This article speaks directly to the program’s positioning as an AI-native, human-centered analytics master’s. Potential touchpoints:
- BADM 576 (ML II / LLMOps): Philosophical framing of LLM training, fine-tuning choices, and ontological assumptions baked into model outputs
- Agentic AI elective: Teleological questions — what should agents be designed to achieve?
- AI-Native Strategy (strategy/ai_native_strategy.md): The L-C-E framework implicitly rests on epistemic and teleological assumptions worth surfacing
- Practicum: Students should be able to articulate the philosophical assumptions in their AI design choices
- Competitive differentiation: Few analytics programs teach students to interrogate the philosophical foundations of AI systems — this is a differentiator
References from Article
- Burgis on Peter Thiel’s philosophy; Westberg on Alex Karp; Fei-Fei Li, The Worlds I See (Flatiron Books, 2023); Stephen Wolfram, “How to Think Computationally About AI, the Universe, and Everything” (2023)
- Awwad, “Influences of Frege’s Predicate Logic on Some Computational Models,” Future Human Image Journal 9 (2018)
- C. McGinn, “Intelligibility,” colinmcginn.net (2019)
- J. Del Ray, “The Making of Amazon Prime” (Vox, 2019)
- T. Schaul, “Boundless Socratic Learning With Language Games,” arXiv (Nov. 2024); “AI Systems Reflect the Ideology of Their Creators,” Discover Magazine (Oct. 2024)
Companion Resources
- MIT IDE Q&A (open access): https://ide.mit.edu/insights/is-philosophy-the-next-llm-training-frontier/
- Video summary: https://sloanreview.mit.edu/video/philosophy-eats-ai-what-leaders-should-know/
- Full article (paywall): https://sloanreview.mit.edu/article/philosophy-eats-ai/