diff --git a/docs/make.jl b/docs/make.jl
index 71c4468c1..009a67a26 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -48,6 +48,7 @@ makedocs(
         "Introduction" => "index.md",
         "FAQ" => "FAQ.md",
         "Rule configurations and calling back into AD" => "config.md",
+        "Types that represent embedded subspaces" => "embedded_subspaces.md", 
         "Opting out of rules" => "opting_out_of_rules.md",
         "Writing Good Rules" => "writing_good_rules.md",
         "Complex Numbers" => "complex.md",
diff --git a/docs/src/embedded_subpaces.md b/docs/src/embedded_subpaces.md
new file mode 100644
index 000000000..6a9af9d39
--- /dev/null
+++ b/docs/src/embedded_subpaces.md
@@ -0,0 +1,116 @@
+# Types that represent embedded subspaces
+_Taking Types Representing Embedded Subspaces Seriously™_
+
+To paraphrase Stefan Karpinski: _"This does mean treating sparse matrixes not just as a representation of dense matrixes, but the alternative is just too horrible."_
+
+
+Consider the following possible `rrule` for `*`
+```julia
+function rrule(::typeof(*), a, b)
+    mul_pullback(ȳ) = (NoTangent(), ȳ*b', a'*ȳ)
+    return (a * b), mul_pullback
+end
+```
+
+This seems perfectly reasonable for floats:
+```julia
+julia> _, pb = rrule(*, 2.0, 3.0)
+(6.0, var"#mul_pullback#10"{Float64, Float64}(2.0, 3.0))
+
+julia> pb(1.0)
+(NoTangent(), 3.0, 2.0)
+```
+and for matrixes
+```julia
+julia> _, pb = rrule(*, [1.0 2.0; 3.0 4.0], [10.0 20.0; 30.0 40.0])
+([70.0 100.0; 150.0 220.0], var"#mul_pullback#10"{Matrix{Float64}, Matrix{Float64}}([1.0 2.0; 3.0 4.0], [10.0 20.0; 30.0 40.0]))
+
+julia> pb([1.0 0.0; 0.0 1.0])
+(NoTangent(), [10.0 30.0; 20.0 40.0], [1.0 3.0; 2.0 4.0])
+```
+
+and even for complex numbers (assuming you like conjugation [which we do](@ref complexfunctions))
+```julia
+julia> _, pb = rrule(*, 0.0 + im, 1.0 + im)
+(-1.0 + 1.0im, var"#mul_pullback#10"{ComplexF64, ComplexF64}(0.0 + 1.0im, 1.0 + 1.0im))
+
+julia> pb(1.0)
+(NoTangent(), 1.0 - 1.0im, 0.0 - 1.0im)
+
+julia> pb(1.0im)
+(NoTangent(), 1.0 + 1.0im, 1.0 + 0.0im)
+```
+
+So far everything is wonderful.
+Isn't linear algebra great? We get this nice code that generalizes to all kinds of vector spaces.
+
+What if we start mixing it up a bit.
+Let's try a real amd a complex
+```julia
+julia> _, pb = rrule(*, 2.0, 3.0im)
+(0.0 + 6.0im, var"#mul_pullback#10"{Float64, ComplexF64}(2.0, 0.0 + 3.0im))
+
+julia> pb(1.0)
+(NoTangent(), 0.0 - 3.0im, 2.0)
+
+julia> pb(1.0im)
+(NoTangent(), 3.0 + 0.0im, 0.0 + 2.0im)
+```
+
+That's _an_ answer.
+It's consistent with treating the reals as being an embedded subspace of the complex numbers.
+i.e. treating `2.0` as actually being `2.0 + 0im`.
+It doesn't feel great that we have escaped from the real type in this way.
+But it's not wrong as such.
+
+Over to matrixes, lets try as `Diagonal` with a `Matrix`
+```julia
+julia> _, pb = rrule(*, Diagonal([1.0, 2.0]), [1.0 2.0; 3.0 4.0])
+([1.0 2.0; 6.0 8.0], var"#mul_pullback#10"{Diagonal{Float64, Vector{Float64}}, Matrix{Float64}}([1.0 0.0; 0.0 2.0], [1.0 2.0; 3.0 4.0]))
+
+julia> pb([1.0 0.0; 0.0 1.0])
+(NoTangent(), [1.0 3.0; 2.0 4.0], [1.0 0.0; 0.0 2.0])
+```
+
+This is also _an_ answer.
+This seems even worse though: no only has it escaped the `Diagonal` type, it has even escaped the subspace it represents.
+
+Further, it is inconsistent with what we would get had we AD'd the function for `*(::Diagonal, ::Matrix)` directly.
+[That primal function](https://github.com/JuliaLang/julia/blob/f7f46af8ff39a1b4c7000651c680058e9c0639f5/stdlib/LinearAlgebra/src/diagonal.jl#L224-L245) boils down to:
+```julia
+*(a::Diagonal, b::AbstractMatrix) = a.diag .* b
+```
+By reading that primal method, we know that that ADing that method would have zeros on the off-diagonals, because they are never even accessed
+(a similar argument can be made for the complex part of a real number).
+
+---
+
+Consider the following possible `rrule` for `sum`
+```julia
+function rrule(::typeof(sum), x::AbstractArray)
+    sum_pullback(ȳ) = (NoTangent(), fill(ȳ, size(x)))
+    return sum(x), sum_pullback
+end
+```
+
+This seems all well and good at first:
+```julia
+julia> _, pb = rrule(sum, [1.0 2.0; 3.0 4.0])
+(10.0, var"#sum_pullback#9"{Matrix{Float64}}([1.0 2.0; 3.0 4.0]))
+
+julia> pb(1.0)
+(NoTangent(), [1.0 1.0; 1.0 1.0])
+```
+
+But now consider:
+```julia
+julia> _, pb = rrule(sum, Diagonal([1.0, 2.0]))
+(3.0, var"#sum_pullback#9"{Diagonal{Float64, Vector{Float64}}}([1.0 0.0; 0.0 2.0]))
+
+julia> pb(1.0)
+(NoTangent(), [1.0 1.0; 1.0 1.0])
+```
+That's not right -- not if us saying this was `Diagonal` meant anything.
+If you try and use that dense matrix, to do gradient descent on your `Diagonal` input, you will get a non-diagonal result:
+`[2.0 1.0; 1.0 2.0]`.
+You have escape the subspace that the diagonal type represents.
\ No newline at end of file