A native G-machine --- physical, or chemical, or biological, but not a repressed simulation over the imperative cpu-memory architecture --- is the dream of every lazy-functional programmer of great devotion. If only it became the dominant computing architecture! People would say, Haskell is high-level assembly because it is the closest to the bare metal (or bare protein) and still supports all sorts of abstractions. People would ask, why is my C program five times slower than Haskell, and people would answer, that's because C is compiled to a stack of seven monad transformers (one of which is ContT to support longjmp), and because you should use more linked lists and fewer arrays. We truely rule the world when C programmers have to consult us for performance tips. A necessary condition for dominance is existence. Here is my crazy imagination of a native G-machine. It is worded as biotech but may as well be molecular computing or nanotech. The heap is a soup of nutrients. A node is a molecular structure made from the nutrients. A thunk is a lot of nodes linked up. Nodes and thunks are suspended in the soup. Allocation means constructing nodes and links from nutrients, and deallocation means unlinking nodes and eventually dismantling them back into nutrients. As a side effect, memory upgrade is cheap and convenient: Just go buy a can of Campbell soup (my favourite is the alphabet soup) and pour into your heap. There are 24-hour convenient stores at every street corner --- pervasive computing has never been so pervasive before. A thunk is nodes linked together and suspended in the soup. A theorem in graph theory asserts that all finite graphs can be embedded without crossing edges in 3D space, and we take advantage of this to space out the nodes in a complicated thunk. Still, it is not easy, being NP-hard and all. There is a relaxation heuristic for this: It simply requires nodes to be a bit repulsive to each other and links to be elastic, and they will sort things out themselves. (When they don't, a bit of shaking up helps tremendously.) Evaluation is performed by a small organism crawling over a thunk and performing its thunk-rewriting duty as per the G-machine. It applies enzymes to construct and deconstruct nodes and links from nutrients. It performs primitive arithmetic on its own. It maintains its own stack, inside or outside its body (to be investigated). It is possible to unleash several such evaluators for parallel computing. It is possible to enable reproduction to boost parallelism. To add garbage collection, roots send out a periodic (or sustained) signal to all connected nodes. Nodes receiving the signal do not self-destruct. Nodes not receiving the signal invokes their built-in self-destruct mechanism to dissolve themselves back into nutrients. There may be better schemes. Interacting with the outside world is open, but Andrew Gordon's thesis contains translations from monadic I/O to other I/O schemes, e.g., continuations, streams, etc. Perhaps one of them can be adapted. Debugging can be done by making evaluators send radio signals concerning operations they perform; then a second computer can log these and you can review them. You can also use radio signals to instruct the evaluators to perform unusual operations on demand. The existence of a native G-machine is a necessary but not sufficient condition for world dominance. We still need to make it fast, compact, and cheap. There may also be better but totally different ways to realize a native G-machine.