Philosophy, Not Just Ethics, Eats AI

Today, regulation, litigation, and emerging public policies represent exogenous forces mandating that AI models embed purpose, accuracy, and alignment with human values. But companies have their own values and value-driven reasons to embrace and embed philosophical perspectives in their AI systems. Giants in philosophy, from Confucius to Kant to Anscombe, remain underutilized and underappreciated resources in training, tuning, prompting, and generating valuable AI-infused outputs and outcomes. As we argue, deliberately imbuing LLMs with philosophical perspectives can radically increase their effectiveness.

This doesn’t mean companies should hire chief philosophy officers … yet. But acting as if philosophy and philosophical insights are incidental or incremental to enterprise AI impact minimizes their potential technological and economic impact. Effective AI strategies and execution increasingly demand critical thinking — by humans and machines — about the disparate philosophies determining and driving AI use. In other words, organizations need an AI strategy for and with philosophy. Leaders and developers alike need to align on the philosophies guiding AI development and use. Executives intent on maximizing their return on AI must invest in their own critical thinking skills to ensure philosophy makes their machines smarter and more valuable.

Google’s revealing and embarrassing Gemini AI fiasco illustrates the risks of misaligning philosophical perspectives in training generative AI. Afraid of falling further behind LLM competitors, Google upgraded the Bard conversational platform by integrating it with the tech giant’s powerful Imagen 2 model to enable textual prompts to yield high-quality, image-based responses. But when Gemini users prompted the LLM to generate images of historically significant figures and events — America’s Founding Fathers, Norsemen, World War II, and so on — the outputs consistently included diverse but historically inaccurate racial and gender-based representations. For example, Gemini depicted the Founding Fathers as racially diverse and Vikings as Asian females.

These ahistorical results sparked widespread criticism and ridicule. The images reflected contemporary diversity ideals imposed onto contexts and circumstances where they ultimately did not belong. Given Google’s great talent, resources, and technical sophistication, what root cause best explains these unacceptable outcomes? Google allowed teleological chaos to reign among rival objectives: accuracy and diversity, equity, and inclusion initiatives.3 Data quality and access were not the issue; Gemini’s proactively affirmative algorithms for avoiding perceived bias toward specific ethnic groups or gender identities led to misleading, inaccurate, and undesirable historical outputs. What initially appears to be an ethical AI or responsible AI bug was, in fact, not a technical failure but a teleological one. Google’s trainers, fine-tuners, and testers made a bad bet — not on the wrong AI or bad models but on philosophical imperatives unfit for a primary purpose.

As intelligent technologies transition from language models to agentic AI systems, the ancient Greek warrior/poet Archilochus’s wisdom — “We don’t rise to the level of our expectations; we fall to the level of our training” — becomes a strategic warning. When paired with statistician George Box’s cynical aphorism — “All models are wrong, but some are useful” — the challenge becomes even clearer: When developing AI that independently pursues organizational objectives, mere “utility” doesn’t go far enough. Organizations need more. Creating reliably effective autonomous or semiautonomous agents depends less on technical stacks and/or algorithmic innovation than philosophical training that intentionally embeds meaning, purpose, and genuine agency into their cognitive frameworks. Performance excellence depends on training excellence. High-performance AI is contingent upon high-performance training.

We argue that AI systems rise or fall to the level of their philosophical training, not their technical capabilities. When organizations embed sophisticated philosophical frameworks into AI training, they restructure and realign computational architectures into systems that:

• Generate strategic insights rather than tactical responses.
• Engage meaningfully with decision makers instead of simply answering queries.
• Create measurable value by understanding and pursuing organizational purpose.

These should rightly be seen as strategic imperatives, not academic exercises or thought experiments. Those who ignore this philosophical verity will create powerful but ultimately limited tools; those embracing it will cultivate AI partners capable of advancing their strategic mission. Ignoring philosophy or treating it as an afterthought risks creating misaligned systems — pattern matchers without purpose, computers that generate the wrong answers faster.

AI’s enterprise future belongs to executives who grasp that AI’s ultimate capability is not computational but philosophical. Meaningful advances in AI capability — from better reasoning to more reliable outputs to deeper insights — come from embedding better philosophical frameworks into how these systems think, learn, evaluate, and create. AI’s true value isn’t its growing computational power but its ability to learn to embed and execute strategic thinking at scale.

Every prompt, parameter, and deployment encodes philosophical assumptions about knowledge, truth, purpose, and value. The more powerful, capable, rational, innovative, and creative an artificial intelligence learns to become, the more its abilities to philosophically question and ethically engage with its human colleagues and collaborators matter. Ignoring the impact and influence of philosophical perspectives on AI model performance creates greater and greater levels of strategic risk especially when AI takes on a more strategic role in the enterprise. Imposing thoughtfully rigorous philosophical frameworks on AI doesn’t merely mitigate risk — it empowers algorithms to proactively pursue enterprise purpose and relentlessly learn to improve in ways that both energize and inspire human leaders.

Michael Schrage and David Kiron – https://sloanreview.mit.edu/article/philosophy-eats-ai/