New Order #5: where Fibonacci and Beatty meet at Wythoff
R, 143 130 124 123 bytes



function(n){k=0:n+1
`~`=rbind
m=k-1~(k*(1+5^.5)/2)%/%1
for(i in k)m=m~m[i,]+m[i+1,]
m=m[-1:-2,]
m[order(row(m)+col(m))][n]}


Try it online!

Uses the formula \$T(n,-1)=n-1; T(n,0)=\lfloor n\cdot\phi\rfloor;T(n,k)=T(n,k-1)+T(n,k-2)\$ to construct the array (transposed), then splits the array along antidiagonals. k merely exists to prevent forcing a drop=F argument in m[-1:-2,] for the case n=1.

Thanks to Neil for pointing out a 1 byte golf.

R, 150 138 132 bytes



function(n){T[2]=1
for(j in 2:n-1)T=c(T,T[j]+T[j+1])
m=T[-1]%o%((1:n*(.5+5^.5/2))%/%1)+T[-1-n]%o%(1:n-1)
m[order(row(m)+col(m))][n]}


Try it online!

Implements the formula \$T(n,k)=Fib(k+1)\cdot\lfloor n\cdot\phi\rfloor+Fib(k)\cdot(n-1)\$ to get generate the array, then splits along the antidiagonals and extracts the nth element.

Thanks to Robin Ryder for the T[2]=1 trick for generating the Fibonacci sequence.



Both solutions are highly inefficient, creating an nxn matrix of (most likely) doubles, as R promotes integer (32-bit signed) to double automatically when overflowing, but the second one should be quite a lot faster. Taking n as a bignum should work automatically, using the call gmp::as.bigz(n), should loss of precision under doubles be worrisome, and then the language would be R + gmp.