Philosophy Eats AI

In 2011, coder-turned-venture-investor Marc Andreessen famously declared, “Software is eating the world” in the analog pages of The Wall Street Journal. His manifesto described a technology voraciously transforming every global industry it consumed. He wasn’t wrong; software remains globally ravenous.

Not six years later, Nvidia cofounder and CEO Jensen Huang boldly updated Andreesen, asserting, “Software is eating the world … but AI is eating software.” The accelerating algorithmic shift from human coding to machine learning led Huang to also remark, “Deep learning is a strategic imperative for every major tech company. It increasingly permeates every aspect of work, from infrastructure to tools, to how products are made.” Nvidia’s multitrillion-dollar market capitalization affirms Huang’s prescient 2017 prediction.

But even as software eats the world and AI gobbles up software, what disrupter appears ready to make a meal of AI? The answer is hiding in plain sight. It challenges business and technology leaders alike to rethink their investment in and relationship with artificial intelligence. There is no escaping this disrupter; it infiltrates the training sets and neural nets of every large language model (LLM) worldwide.

Philosophy is eating AI: As a discipline, data set, and sensibility, philosophy increasingly determines how digital technologies reason, predict, create, generate, and innovate. The critical enterprise challenge is whether leaders will possess the self-awareness and rigor to use philosophy as a resource for creating value with AI or default to tacit, unarticulated philosophical principles for their AI deployments. Either way — for better and worse — philosophy eats AI. For strategy-conscious executives, that metaphor needs to be top of mind.

While ethics and responsible AI currently dominate philosophy’s perceived role in developing and deploying AI solutions, those themes represent a small part of the philosophical perspectives informing and guiding AI’s production, utility, and use. Privileging ethical guidelines and guardrails undervalues philosophy’s true impact and influence. Philosophical perspectives on what AI models should achieve (teleology), what counts as knowledge (epistemology), and how AI represents reality (ontology) also shape value creation. Without thoughtful and rigorous cultivation of philosophical insight, organizations will fail to reap superior returns and competitive advantage from their generative and predictive AI investments.

This argument increasingly enjoys both empirical and technical support.