What it is
A second opinion is only a second opinion if it can disagree. A second tool bought from the same vendor, or the same model run twice, gives two answers that fail in the same places. The cross-check still appears in the process, and it no longer catches anything.
What it pushes on in the Lab
In the Lab, this pressure raises the failures that AI models relay to one another, because one model's blind spots repeat across every case. It caps the pathway where models cross-check each other's outputs, so the cross-check cannot grow stronger. It also raises record contamination pressure.
Who feels it
The reviewer who trusts two agreeing systems feels safer than they are. The clients feel it when a worker acts on an error that both systems share.
What answers it
An answer has to make the second check genuinely different from the first, or stop one model's output from becoming another model's input unchecked. Each lever below does at least one of these.
Levers in the Lab that push the other way on something this pressure pushes on:
- Pattern:Connection authorization
- Pattern:Peer-edge governance
- Pattern:Cross-model verification
The list leaves out levers the Lab has retired, levers it keeps as counter-examples, and any lever no network offers.
Where it starts switched on
Networks in the Lab that start with this pressure switched on:2
The evidence behind its effects
The Lab cites no claim from the evidence registry for this pressure's effects. The Lab's authors set the direction and size of each effect.