Intelligence Plus Action

The analogy

Eric Glyman frames what makes an AI tool valuable through Stephen Hawking: profound intelligence paired with a body that had lost interaction with its own spinal cord and the use of certain limbs, leaving amazing reasoning with real limitations on its output.¹ Mapping this to AI systems produces two questions: "Do you have enough signals of inputs feeding back into it, so it can process and act on a wide variety of things?" And, "Do you have the ability to go act on and get feedback on things?"¹ A system's practical value depends less on what it can theoretically reason about and more on whether it sits inside a rich loop of data flowing in, action flowing out, and feedback flowing back.

The corollary: narrow tools are weak

Tools that are very narrow, and that cannot learn from, benefit, or improve many different areas of how a business operates, have far more limited utility than the reasoning-only framing suggests, even though narrow point tools can still work well in specific clever niches. This reasoning underlies two related claims: narrow AI sales-development tools built only on outbound and response data will struggle, because the actual value sits in the full loop of wins, losses, longer-run outcomes, and real intent signals, not just the outbound motion; and data infrastructure teams matter as much as model teams, because tool selection turns less on the specific use case and more on whether an organization has well-structured, connected, interoperable data, the substrate that lets intelligence actually act.¹

Why it matters

The frame reorients AI strategy away from model access, which is increasingly commoditized through an API, and toward wiring: which inputs can be fed to a system and which actions it is actually allowed to take. A useful internal tool built on a hundred thousand transcripts of real customer interactions is the intelligence-plus-rich-input-signal half of the loop; an agentic system that can act on a business process and get feedback is the act-and-feedback half.¹ The frame operates at the level of an individual tool or workflow, complementing the more macro claim that value accrues to whoever can sell an outcome rather than a tool, since a system can only be sold as an outcome if it can actually act, not merely advise.

Grounded in the physical world

The same intelligence-plus-action loop appears in physical AI, with far higher stakes, since a physical agent that makes an irreversible mistake, dropping a reagent or damaging a part, cannot simply undo it the way a software agent can. Closed-loop agentic systems, where a physical agent takes an action, observes the result, and adjusts, are named as the domain that stress-tests this dependency hardest: even a ninety-five percent success rate at each step in a physical task compounds down to roughly sixty percent success across a ten-step chain, making reliability at scale the binding constraint on the action half of the loop.² The digital version of the same dependency, reasoning that cannot act is only half a system, is the same statement made under real-world physics rather than software.

Tensions

More action surface means more risk, and current practice generally keeps a human in a supervisory role as the governor on how much of the loop can be closed autonomously. The frame is also intuitive but under-specified: how much signal and action wiring is enough, and where the marginal return on adding more inputs flattens out, is not addressed by the analogy itself.