This article was drafted together with AI and reviewed by an editor. It draws on an external article, which is credited at the bottom.
The three articles we referred to seem to have different topics, but all of them deal with "when to doubt the way you are doing things now." We organized them our own way and bundled them into three lenses.
Lens 1 — Stagnation signal: diligent but stuck in place?
Someone who makes the best choice available at each moment can, oddly, stay in the same place for a long time. An article that borrowed the robotics concept of "local minima" introduces signals for recognizing this state. We use three of them. You spent more time but the result is the same / You keep going back and forth between plan A and plan B / Your reason for continuing is "because we've done it so far." If two or more apply, we run an experiment of making one big change of direction. It means deliberately accepting that things may get worse in order to look at a different path.
Lens 2 — The praise trap: the idea everyone likes
Both AI and people are generous about my ideas. The article we referred to calls the type that everyone says is good but fails a "tar pit." Our standard is simple. Is there anyone who says they will pay, and what are they using to solve it now? Praise is not evidence; current substitutes and willingness to pay are. With AI, as discussed earlier, we ask, "If it fails, what would the reason be?"
Lens 3 — Outcome-based pricing: from what you used to what you solved
The article we referred to reports that in the software industry, pricing that charges for "what was solved" rather than "what was used" is spreading. This approach lightens the customer's burden, but it also means that the risk shifts from the customer to the provider. Before adopting it, we check three things: Can we define "solved" as a number? Can we measure that number ourselves? How large are the variables we cannot control? If even one is unclear, a compromise that mixes a flat fee with a performance bonus is safer.
Summary
The three lenses start from the same question. Is our present confidence resting on evidence, or on inertia? AI cannot answer this question for us, so a human does.
Further reading
This article was rewritten from our own team's perspective and standards, drawing on the concerns raised in the articles below. We did not follow their wording or structure, and where we could not verify a figure an original article cited against the primary data, we marked it as such. For some of the original articles, the full text opens only after logging in, so we read them based on the publicly available portion.
- learn.bizni.usIs It Time to Get Worse for a Moment? A Checklist for Escaping Local Minima (Korean) (BIZNIUS LEARN:US)
- learn.bizni.usThe More AI Praises an Idea, the Riskier It Is (Korean) (BIZNIUS LEARN:US)
- learn.bizni.usCompanies That Charged for What You Used Have Started Charging for What You Solved (Korean) (BIZNIUS LEARN:US)
Also checked against other sources: