1 Department of Physics and Psychology (Alumni), Concordia University, Montreal, Canada.
2 McGill University, McGill Genome Center, Majewski Lab, Montreal, Canada.
3 Centre for Logic, Epistemology and the History of Science, University of Campinas (UNICAMP), Campinas, Brazil.
4 DEXL, National Laboratory for Scientific Computing, Petrópolis, Brazil.
5 Department of Oncology-Pathology, Center for Molecular Medicine, Karolinska Institutet, Solna, Sweden.
6 Algorithmic Dynamics Lab, Karolinska Institutet, Solna, Sweden.
7 Algorithmic Dynamics Lab, Karolinska Institutet, Solna, Sweden. hector.zenil@cs.ox.ac.uk.
8 School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK. hector.zenil@cs.ox.ac.uk.

We demonstrate that the assembly pathway method underlying assembly theory (AT) is an encoding scheme widely used by popular statistical compression algorithms. We show that in all cases (synthetic or natural) AT performs similarly to other simple coding schemes and underperforms compared to system-related indexes based upon algorithmic probability that take into account statistical repetitions but also the likelihood of other computable patterns. Our results imply that the assembly index does not offer substantial improvements over existing methods, including traditional statistical ones, and imply that the separation between living and non-living compounds following these methods has been reported before.