No, the OP mostly fails to 'get it'.

First, beating up on Lisp is next to
irrelevant to the role, potential or
current, of math in business or
computing.

Second, the OP fails to make a solid
case for the power of math to make
a powerful, valuable contribution to
the solution of real problems for which
we do believe we need software.
In particular, in spite of the claim of
the OP, I doubt that linear programming
did much if anything to defeat Germany
in WWII; why? Because both the Kantorovich
Nobel prize for his
work on the 'transhipment' problem
and Dantzig's work on his simplex algorithm came
after WWII was over.

Third, the OPs recommendation for math
for applications is too meager;
what he listed is all good, but we
need more to be as successful
as we should wish.

Here's a simple description of the
potential of math for 'information
technology' (IT) business, i.e., the
money making kind: We have a lot of
input data and want to process it,
that is, manipulate it,
with software to generate output
data that is valuable in business,
the money making kind.

So, a question is how to process the
data? We want powerful means that
will convert the input data to valuable
output data; did I mention valuable
in the sense of business, the money
making kind?

Well, the traditional means in business
IT has been to take some work that
is well understood just as manual work,
that is, in principle could be done
manually. Then take those means and
write software to do the corresponding
data manipulations. There nearly always
the means were just intuitive, obvious.

So, what IT did with the input data was
nearly always just intuitive, i.e., what
was well understood from manual efforts.
Some of the big examples were bookkeeping,
accounting, payroll, order entry,
inventory control, materials requirements
planning (MRP), customer relationship
management (CRM), airline and hotel
reservations. Here, again, we have
means well understood manually, and IT
wrote software to do essentially the
same thing.

So, we are taking input data and
manipulating it to get valuable
output data. So, for more valuable
output data, we want more powerful
means of manipulating the data.

So, where do we find the more powerful
means? E.g., we are flying airplanes
and want to get the work done, that is,
meet the published schedule, but at
minimum cost. Turns out, can do much
better, save maybe 15% of direct
operating costs, where just the fuel
savings alone are finger lick'n
good, with integer linear programming
set partitioning, i.e., quite a lot
of applied math, including one of the
early and main leads to the question
of P versus NP.

Another example? Okay, how to climb,
cruise, and descend an airplane for
minimum cost within a given time window.
There are various approaches, but a
start is deterministic optimal control
theory, which is some quite good and
advanced applied math.

E.g., once the FedEx Board wanted
some revenue projections. A mess up
had the representatives of crucial
Board Member General Dynamics (GD)
pack their bags and get plane reservations
back to TX, which would have killed
FedEx. Here's what pleased the GD guys
and saved FedEx: We know the present
revenue, and we know the revenue at our
planned target market. So, the question
is how to interpolate between these two.
So, let t denote time, say, in days,
y(t) the revenue at time t, and
b the revenue per day when are serving
all the planned market. Then make a
'virality' assumption, that is, that the
rate of growth in revenue is directly
proportional to (a) the number of current
customers talking and (b) the number
of potential customers listening
(or the corresponding revenue). With
y'(t) the calculus first derivative of
y(t), that is, y'(t) is the rate of
growth in revenue, in dollars per day,
we have that there exists some constant
k so that

y'(t) = k y(t) ( b - y(t) )

There is a closed form solution via
freshman calculus, and the result is
a lazy S curve that rises asymptotically
to the full market revenue b. So, with
this bit of math, there is only
one guesstimate, k. So, draw some
curves for various values of k
and from other considerations pick
a reasonable value of k. Or estimate
k from the data on the growth so far. Done.
The GD guys stayed, and FedEx was
saved. True story.

There are many more examples, and
many more to be found and executed.

Net, the role of the math is to find
more powerful means of processing the
input data and, thus, yield more valuable
output data, valuable in the sense of
business, the money making kind.

For the relevant math, there is nearly
no limit. Fortran, Lisp, C, recursion,
etc. have essentially nothing to do with
the value or role of the math. The math is before
the software development, leads to
telling the programmers what to program,
that is, what data manipulations to do.
The data manipulations can be programmed
in nearly any programming language although
some languages are much easier for the
programmer than others.