Repurposing digital assets through evolutionary logic
The discovery of repurposed biological structures offers a framework for Swiss SMEs to maximise their existing software and data.

A recent video by @tilscience on TikTok explains a study published in Nature regarding Cambrian Period vertebrates. Researchers found that Myllokunmingia, a jawless fish from 518 million years ago, possessed four functional eyes. The study suggests that our modern pineal gland is the evolutionary descendant of this second pair of eyes.
Why it matters
This discovery is a lesson in how biological complexity is rarely discarded, only repurposed. For a business owner, the parallel is clear. Innovation often involves taking an existing asset and shifting its function to meet a new environmental demand. We see this in digital marketing too. A tool designed for one specific task might lose its primary function but gain a new, vital role in a larger system. The evolutionary transition from an image-forming eye to a light-sensitive pineal gland proves that utility is not a fixed state. It depends on the context of the organism or the market. You should not view your existing digital infrastructure as a set of static tools. Instead, look for ways to redirect their energy when their original purpose becomes obsolete.
evolution... never throws anything away, it recycles
— @tilscience
What it changes in practice
In practice, nothing changes for your business on Monday morning. A discovery in paleontology does not alter your immediate conversion rates or your local SEO rankings. However, the mindset required to navigate market shifts should change. Many Swiss SMEs treat their marketing stack as a collection of fixed instruments. They use a tool for its original purpose until it stops working. The Myllokunmingia study suggests a better way. Instead of abandoning a tool that no longer performs its primary task, you should analyse its secondary capabilities. A platform used for simple email broadcasts might actually hold the key to deeper customer data analysis. You should look for these hidden functions. Evolution succeeds by repurposing, not by starting from scratch every time the environment shifts.
What we would do
We do not treat digital assets as single-purpose tools. When a client's marketing channel loses its direct effectiveness, we look for the secondary value within the data or the audience it has already built. We apply the same principle of repurposing found in the Myllokunmingia fossils to your existing tech stack. This prevents the waste of throwing away functional infrastructure just because its primary role has shifted.
- The Inventory ShiftWe map every current software subscription to its secondary data outputs rather than just its primary interface.
- The Channel PivotWe redirect traffic from declining social platforms into owned databases to preserve the underlying customer intelligence.
- The Resource ReallocationWe move budget from exhausted acquisition methods into the optimisation of existing high-value customer segments.
Within a year, automated model training will make manual asset repurposing obsolete. This happens if your data architecture becomes sufficiently structured for direct machine ingestion. At that point, you will stop reorganising files and simply feed raw datasets into local LLMs.
Key takeaways
- Biological complexity is often repurposed rather than discarded.
- Digital tools should be analysed for secondary data outputs when their primary function fails.
- Structured data architecture will eventually automate the process of asset reallocation.
Frequently asked
How do we identify the secondary value in a tool we already use?
You must map your current software subscriptions to their data outputs rather than just their user interfaces. This reveals what information they collect even if they no longer drive direct conversions.
Does this approach require a complete overhaul of our current tech stack?
No, it requires a change in how you view your existing infrastructure. The goal is to redirect current resources instead of buying new ones from scratch.
What is the actual benefit of preparing data for local LLMs now?
Structuring your data today ensures that you can feed raw datasets into automated models tomorrow. This prevents you from being stuck in manual reorganising cycles when automation becomes the standard.