The Seed of Intelligence: How Nature and Seed Logic Will Shape True AGI

Eight years ago, I was sitting on a couch discussing technology with a friend. We were deep into theoretical topics, things like designing new network protocols for secure communication via different methods, a recent radio-based hack that triggered all 156 emergency sirens in Dallas, and the differences between supervised and unsupervised training in machine learning.

Most people in the industry today know this term →

Seed Dataset:

\ a small, initial set of data used to train or guide machine learning models often curated or labeled to bootstrap learning, classification, or data expansion

It’s typically the first step in building a modern model. That term and conversation sparked an idea that I scribbled down on a sheet of yellow legal paper a simple but powerful realization:

True AGI will need to be pretrained on seed datasets and instinctive wiring just like how humans are preloaded with information when they are born

Which led to the idea of what is needed for true AGI as we compare AGI to human intelligence.

Seed Logic:

\ the foundational rules, instincts, or heuristics preloaded into an intelligent system before it begins learning.

This is what I call the foundational layer of what will be AGI.

Think about it; humans aren’t born as blank slates.
We come into the world with innate wiring:

• A sense of spatial awareness

• Primitive logic and reasoning

• Facial recognition

• Emotional triggers like fear or joy

These hardwired instincts form the basis for how we interpret and interact with the world.

Most LLMs and generative AI models don’t have that.
They’re statistical engines trained on massive datasets of text and while they’re powerful, they still hallucinate. A lot.

But here’s what’s wild:
Today’s best models don’t learn everything from scratch either.
They’re pretrained on filtered, instruction-tuned, or curated datasets just like the “seed” knowledge I envisioned back then.

Some of the top AI researchers today argue that “foundational models” need baked-in reasoning frameworks or symbolic logic; the same thing I was getting at eight years ago:
Give the model a foundation to build off of, not just data to pattern match.

And when you think about it...
The best tech we’ve ever built mimics nature.
From neural networks mimicking the brain to reinforcement learning mirroring how we learn through trial and error we're not building from scratch, we're building from what already works.

Biomimicry:

\ the design and production of materials, structures, and systems that are modeled on biological entities and processes.

Take flight, for example.

Birds have been perfecting aerodynamics for millions of years.
When humans wanted to take to the skies, we didn’t invent flight from scratch we studied wings, lift, and gliding patterns.

• The Wright brothers looked at how birds tilted their wings to steer, which led to the concept of wing warping a core principle in early flight (Smithsonian Air & Space).

• Modern aircraft wings (airfoils) are inspired by the curvature of bird wings, optimizing lift and reducing drag (NASA Glenn Research Center).

• Drones and VTOL aircraft often mimic the flight mechanics of hummingbirds, bees, and even bats because nature’s design is already proven through evolution (National Geographic).

And it doesn’t stop with flight.

Nature has inspired some of the most powerful breakthroughs in tech:

• Velcro was invented after studying how burrs stuck to dog fur (Velcro Official History).

• Sharkskin-inspired materials are now used on swimsuits and ship hulls to reduce drag and bacteria (Nature - Bioinspiration in Marine Design, Sharklet Technologies).

• Termite mounds inspired architects designing sustainable buildings with natural airflow systems (Biomimicry Institute).

• Spider webs influenced next-gen materials that are light, flexible, and insanely strong (Scientific American).

Even in computing:

• Neural networks are modeled after the structure of the human brain (Deep Learning, Goodfellow et al.).

• Genetic algorithms mimic evolution random mutations, selection, and survival of the fittest to solve complex optimization problems (Holland, J.H., 1975).

• Swarm intelligence in robotics and traffic systems is modeled on the behavior of ants, birds, and fish moving collectively with simple rules (IEEE Swarm Intelligence Research).

When we follow nature, we’re not copying; we’re learning from billions of years of R&D we didn’t have to pay for.

So it’s no surprise that when we talk about AGI, the same principle applies.

We shouldn’t expect true intelligence to emerge from a blank slate because humans aren’t blank slates either.
We have biases, instincts, logic patterns, and the ability to override them.

If we want to build an AI that thinks like us, or better than us, it needs more than just data it needs the right starting point.

A seed.

Diagram: The role of seed data in AGI

                      +-------------------------+
                      |     Seed Knowledge      |
                      |  (Rules, logic, bias,   |
                      |   instincts, ethics)    |
                      +-----------+-------------+
                                  |
                                  v
               +------------------+------------------+
               |   Pretraining on Curated Datasets   |
               |  (text, symbols, images, math, etc) |
               +------------------+------------------+
                                  |
                                  v
                   +-----------------------------+
                   |    Reasoning Framework      |
                   |(Symbolic Logic, Constraints)|
                   +-----------------------------+
                                  |
                                  v
                      +--------------------+
                      |   Fine-Tuning via  |
                      |   Environment or   |
                      |    Human Feedback  |
                      +--------------------+
                                  |
                                  v
                      +--------------------+
                      |  Intelligent Agent |
                      |   (Emergent AGI?)  |
                      +--------------------+

This seed will contain all preloaded logic / rules that it can follow. Which paths to take when “learning”. How it will handle unforeseen obstacles. What it can and cannot do.

Examples of Curated "Seed" Logic for AGI:

We have built reasoning before into many different systems such as for defense companies, but I think the ability for a system to have "Seed" logic that can be refined as it learns plus true reasoning with "Morals" will be the advent of AGI.

What’s Next?

This is just the beginning of my new Thursday series: AI & the Future of Security, where I explore the big ideas shaping how we think about artificial intelligence, digital trust, and what comes next in cybersecurity and society.

Coming soon:

• The Ethical Seed: How to Preload Morality into AI Systems

• How Vector Databases Make LLMs Smarter (and Less Dangerous)

• Should Your AI Have a Memory?: Balancing Privacy, Identity, and Persistence

• Can AGI Be Trusted with Autonomy?: Designing Constraints that Actually Work

• [REDACTED]

Follow along if you’re into AGI alignment, human-centric AI, and the security implications that come with intelligent systems. I’ve spent nearly a decade building real tech and I’m fascinated by what it means to do it responsibly.

Sure, you could Google it. Or ask ChatGPT.
But OneLogin’s blog and learning center already have the answers and fewer hallucinations.