CONCEPT
Destination Signal vs. Channel Signal
The distinction — implicit in Shannon's framework, consequential in human-AI collaboration — between the artifact the channel is supposed to deliver and the incidental information the transmission process generates as a byproduct.
In Shannon's original framework, the destination signal is everything: the voice message, the data packet, the intended content. Any signal generated by the channel itself — the static, the distortion, the artifacts of transmission — is noise to be eliminated. The goal is maximum destination signal, minimum channel signal. But in the human-AI collaboration, this framework misses something crucial. The traditional software development process delivered two things simultaneously: a working artifact (destination signal) and an education about the system that produced it (channel signal). The errors encountered during debugging, the unexpected behaviors, the failed hypotheses were noise from the artifact's perspective but information from the developer's perspective. The smooth AI interface delivers the artifact and suppresses the education — preserving destination signal while eliminating channel signal. The loss is invisible in the short term and devastating in the long term, because the channel signal was the mechanism by which expert mental models were built.
