Let’s retire the "AI onion" and consider a more useful framework: "Machine learning mode" vs "AI mode".
We've all seen the “AI onion”: nested circles where AI contains machine learning, machine learning contains deep learning, and so on. It's been copied countless times and serves a pedagogical purpose. But upon closer inspection, it doesn’t fit how these technologies actually work and relate to each other.
The core problem? The onion mixes goals with methods. AI is a goal: build systems that do intelligent work across contexts. Machine learning (ML) is a method: fit parameters from data to minimize loss. Treating these as nested categories creates false hierarchies that collapse under practical analysis: For example, linear regression is ML but nobody calls it "AI" with a straight face. Hybrid systems like AlphaGo—deep networks plus tree search—don't fit any single circle either.
The onion served its purpose during AI's academic phase. Now that these tools are deployed at scale, we need frameworks that guide practical decisions rather than just explain history. I would therefore propose a simple distinction that is based on purpose:
▶️ “Machine learning mode”: optimize for specific outcomes from structured and well-understood data. Build the perfect tool for one job or domain. Think rows, columns, clear targets, and measurable improvements.
▶️ “AI mode”: develop broad capabilities that work across unstructured and new inputs. Build adaptable systems that handle messy, varied problems. Think text, images, conversations, and emergent behaviors.
This isn't just cleaner conceptually—it maps directly to how these systems work in practice and how they need to be designed. Credit scoring, demand forecasting, and fraud detection live in ML territory: clean tabular data, defined objectives, statistical validation. Reading contracts, generating marketing copy, analyzing charts, and having conversations live in AI territory: varied inputs, contextual understanding, broad reasoning.
Understanding this distinction also drives what matters: investment priorities, team skills, risk profiles, success metrics. ML projects double down on data quality, drift monitoring, and statistical robustness. AI projects focus on metadata, integration, prompt engineering, cost management, and measuring usefulness across diverse applications.
Of course the boundaries aren’t always clear-cut. Some of the most powerful solutions oscillate between both paradigms: broad AI for context, narrow ML for optimization. But most practitioners will know immediately which mode they're operating in for any given challenge.
Keep the onion for teaching if you must. But when deciding what to build, how to staff it, and where to invest, distinguishing between ML and AI mode reflects how these technologies actually create value.
Elegant categories make pretty slides. Practical ones make better decisions.