It certainly does look that way for certain classes of problems, as witnessed by the evolution of GPT language models, where the model gets better through sheer use of compute resources.

For many combinatorial problems however, improvements in algorithms can often produce bigger strides than just throwing brute force compute at the problem. Take Mixed Integer Programs (MIPs) -- roughly the optimization-equivalent of SATs -- used for airline scheduling, optimal assignment problems and such. In slide 12 [1] (there are other sources that corroborate), the author notes that MIP solver performance between 1988-2017 had improved 2,527,768,000x.

17,120x was due to machine improvements (single core). 147,650x was due to algorithmic improvements. Multiple cores can also provide a performance boost up to a point, before saturating due to coordination costs. The author notes that "A typical MIP that would have taken 124 years to solve in 1988 will solve in 1 second now".

The biggest improvements in MIP algorithm performance have been due to improvements in solver heuristics (!), because the fastest computations are those that don't have to be performed at all -- i.e. that are eliminated via heuristics.

[1] http://www.focapo-cpc.org/pdf/Linderoth.pdf