The practice filter: benchmark or claim?
"What is AGI, really?" introduced a first practice test: anyone using "AGI" should be able to say which definition they mean. For concrete progress claims, that alone isn't enough - you also need a checkable basis for measurement: on which dataset, compared to what, confirmed by whom.
What research labs actually publish
Serious research generally doesn't publish a single "AGI achieved" moment, but narrow, empirically tested progress on concrete benchmarks - say, a higher success rate on a specific task class, compared against a clearly named prior version. That's less spectacular than a headline, but it's exactly how you tell real progress from a bare announcement.
What companies actually deploy
In business practice, tightly scoped AI agents with clear approval processes dominate (see "Human in the loop: when AI needs approval" and "Security in production use") - not fully autonomous generalists. That's not a sign of being behind, it's a deliberate architecture choice that makes sense today: scoped tightly enough to work reliably, with human oversight exactly where the error chain from "The gap to today" would otherwise strike.
The four-step filter for any AGI-adjacent claim
The previous modules add up to a repeatable test you can apply to any announcement: which definition is meant (see module 1)? Which benchmark or dataset is the claim based on? What's it compared against - is there a clear baseline? And has the result been reproduced or confirmed independently? If a clear answer to any of these four questions is missing, skepticism is warranted (related pattern: see "Spotting AI hype vs. real value").
What this already means for your business today
The whole course adds up to a clear stance: there isn't one single AGI definition (module 1), the gap to today's state is concretely nameable (module 2), the open hurdles have active but incomplete research answers (module 3), and at the same time real, AGI-adjacent capabilities already work productively today (module 4). The practical consequence: don't wait for a single big breakthrough - use the reliable building blocks available today in a targeted way, with the four-step filter as a tool to separate substance from announcement.