Let’s get back to the observation that “most of the other known mechanisms of evolutionary change such as genetic drift, neutral mutation, gene duplications, transposons, horizontal gene transfer by plasmids, and others have no direction or goal at all and are in fact random.” As I explained, this is insufficient reason for declaring that evolution has no goal.

Nevertheless, some people may be troubled by the fact that the mechanisms of evolution are random (with regard to fitness). Not me.

If we are to design organisms and have them reach a goal across deep time, we must remember that the environment that life finds itself in is ever-changing and fluctuating. In fact, if we factor for deep time, we must factor for catastrophic changes like potential asteroid impacts. What this means is that our designs must be placed into life forms that are versatile and plastic enough to adapt to a wide range of challenges. And it is the random nature of these evolutionary mechanisms that facilitates such adaptation, where evolution can be viewed as an exploratory process guided by random changes that are constrained by the front-loaded state. In fact, I have even suggested that evolution can be viewed as a learning process.

If these evolutionary mechanisms were not random and instead guided, then all the potential solutions to all possible environmental challenges would have to be encoded in the original cells and then propagated with extreme fidelity for millions of years until they were needed. As I explain in The Design Matrix:

Maybe a designer developed a better solution: Let the population of cells be the computer. This population can then be thought of as a neural network, where all cells are connected, at the very least, through the same genetic program called “survive.” There is no need to install a computer in each cell that monitors the environment and programs specific changes in the genome in response to environmental challenges. That objective is carried out by the population of cells, where different solutions to an environmental challenge are put on the table through a random mutagenic walk, and the solution that works ends up changing the population. Variation among a population followed by natural selection is exactly the type of strategy a designer might employ when endowing cells with the ability to adapt and learn against the backdrop of a sea of contingency.

Furthermore, consider the mechanisms of gene duplication and lateral transfer. These are very smart ways of making sure that evolution and adaptation occurs. Both mechanisms also echo front-loading. As I write in The Design Matrix:

Gene duplication solves the design problems cited above simply because cells can retain the core designed state while the duplicate is free to mutate and explore new solutions. As long as the original is eff ectively retained, the pathway to the new function is retained and propagated. It is a beautiful solution for a front-loading designer. In one process, we both propagate the original design and set things up to unpack secondary designs without erasing the original design. Stability and change, all in one package.

I should also add that both gene duplication and lateral transfer are dependent on molecular machines, meaning that we can view evolution as something that has been facilitated with nanotech devices. And the material used to build these machines? Once again, it’s those amazing proteins.

Evolution as a clever learning process made possible through machinery built from ingenious design material is a robust teleological perspective. From here, it is a question of nailing down the goal.