What a finite learning device has recorded and what will hold value for it on future tasks are not the same quantity. We develop a typed accounting for finite-state learning devices that separates four components: a training-side fit functional Φfit, the record-correlation stock JD=I(M;D)
Nearby in the stack
, an update-side search ledger
σM
, and an operational capital value
V(M;T,b)
. This value is the work gap between an informed protocol class and a blind class obtained by deleting the memory-read port and re-optimizing from scratch. (I) Separation: for every
n
, there is a device family on which record correlation and world correlation grow by
nln2
while the capital gain is exactly zero. In the
flat∗
regime, data-free updates never increase
V
. (II) Capitalization ledger: an exact
flat∗
extraction identity and a universal ledger identity give, for (F5
′
)-stable
M
-local updates under a no-discarded-record-correlation condition (f), the bound
ηcap≤1
for the capitalization efficiency
ηcap=ΔV/(kTσM)
, together with necessary and sufficient conditions for equality. (III) Value retention: for the retention gap
Lgen
and retention ratio
ρgen
(the former carries no sign constraint; the latter is defined for positive training-side value and is not confined to
[0,1]
) we give a two-layer alignment domain: an exact exchange rate between value and the side-information-adjusted record fit
I(M′;D∣Y)
without any record-side-information independence assumption, and a raw record-stock exchange rate under a joint side-information neutrality condition
(M,D)⊥Y
, whose boundary is marked by an explicit one-time-pad witness. These are statements about finite-device value retention under task-distribution shift, not a theory of statistical generalization.