The very lowest-level operations on FPGAs can run more efficiently than emulating them on an CPU. The higher level operations, which is usually what we want, are slower, as a 'gate' in an FPGA is approximately an order of magnitude slower, more power hungry, and expensive than a gate in a CPU (or GPU). modern CPUs excel at executing data-dependent operations of a wide variety while exploiting as much parallelism as available, while GPUs excel at churning through a huge amount of memory doing parallel arithmetic operations: for both of theses tasks an FPGA is going to be worse. For an FPGA to be the better option you need a task which is specialised enough that the speedup from designing a specialised circuit to do it overcomes the intrinsic slowdown from it being implemented in an FPGA as opposed to raw silicon, and these are not your typical software tasks, usually much more hardware-type tasks.

The two areas where FPGAs excel are having extremely low and predictable latency, and having lots of extremely flexible high-bandwidth IO (still less bandwidth than the memory or PCI-E bandwidth of a GPU, but it's close and it's much more flexible). For computation throughput, however, there's only a few niche applications, though one important one is simulating the logic which is going to be implemented in a CPU, GPU, or ASIC to validate it before the expensive process of actually etching some silicon.