Biggest drawback is that since the structure is all discrete, it is inherently weak at modeling statistical distributions. For example, it'll likely never best a neural network at stock market prediction or medical data extrapolation.
However, for things that are discrete and/or causal in nature, we expect it to outperform deep learning by a wide margin. We're focused on language to start, but want to eventually target planning and controls problems as well, such as self-driving and robotics.
Another drawback is that the algorithm as it stands today is based on a subgraph isomorphism search, which is hard. Not hard as in tricky to get right like Paxos or other complex algorithms; like NP-Hard, so very difficult to scale. We have some fantastic Ph.Ds working with us who focus on optimization of subgraph isomorphism search, and category theorists working to formalize what constraints we can relax without effecting the learning mechanism of the rewrite system, so we're confident that it's achievable, but the time horizon is unknown currently.
