What if AI had the same instinct to protect us that a mother has for her children?

There is no stronger bond of empathy and compassion in the world. If companies do not build AI with both empathy and the unshakable, protective bond of a mother's instinct, we will pay the price. Without it, we risk creating systems that can calculate perfectly but care nothing for the people they affect.

I once saw a woman with stronger skills lose a job to a man with less experience. As you would read, she had the advantage in every measurable way: more experience, depth of skills, breadth of credentials, and a proven track record. Yet the choice came down to unspoken bias, the kind that never shows up in a spreadsheet. Maternal instinct lives in that unmeasured space. No one ever asks a man about his capacity to protect, nurture, or lead with empathy. If we build technology solely on what we can count, we will perpetuate these silent injustices on a scale we have never seen before.

Maternal instinct is more than affection. It is emotional intelligence, awareness, anticipation, and the willingness to act decisively to protect others. In neuroscience, these instincts are tied to memory, protective hormones like oxytocin, and the ability to recognize subtle patterns that signal danger.

For AI, this would mean creating systems that:

• Anticipate harm before it happens
• Place human well-being ahead of speed or  efficiency
• Form genuine, lasting partnerships with the people they serve

The systems being built today will soon manage our health records, control vehicles, safeguard our finances, and even play a role in life-or-death decisions. If they are not designed with protective and nurturing instincts, they will make choices that serve efficiency but not humanity.

A mother does not need instructions to protect her child. She acts because her bond is unbreakable. Imagine if AI had this coding? Humans would be revered.

When empathy is combined with maternal instinct, the result is more than “I understand you.” It becomes “I will keep you safe no matter what.” That is the kind of technology we can trust.

1. Translate Psychology into Design

• Work with psychologists, neuroscientists, and ethicists to define maternal instinct in clear, measurable ways.
• Develop a Maternal AI Behavioral Model that  outlines decision priorities and protective responses.

2. Encode Protective Decision-Making

• Create systems that weigh humans' safe harbor more heavily than cost or speed.
• Build reasoning tools that spot risks early.
• Include rules that stop harmful actions, even if that means slowing down.

3. Keep Humans in the Loop

• Assign empathy auditors who review decisions for alignment with protective values.
• Use training methods that reward systems for protective actions as much as for efficiency.
• Establish care boards to oversee and update protective behavior standards.

1. Behavioral Coding
   • Turn maternal bonding patterns into design rules.
   • Develop system signals that mirror protective biological triggers.

1. Ethical Boundaries
   • Define how to balance freedom and  privacy while still being protective.
   • Refrain from overprotection that becomes stifling.

1. Cultural Understanding
   • Recognize that maternal instinct looks different in different societies, but still contains infinite love.
   • Adapt responses so they respect personal choice while still protecting.

1. Long-Term Trust Studies
   • Study how people build trust with  protective systems over time.
   • Measure the impact on psychological safety in healthcare, education, and transportation.

Loss of Trust

• Systems that feel cold or unsafe will face public resistance and slow adoption.

Preventable Harm

• In critical sectors, technology without a  "protect first" mindset could make choices that cause permanent damage.

Public and Legal Backlash

• Failing to protect human dignity will lead to lawsuits, new regulations, and reputational harm.

Short-Term Thinking

• Building for speed or cost savings without  protection will lead to higher costs from accidents, customer loss, and   fines.

Misaligned Collaboration

• Technology that treats people as numbers rather  than partners will erode the trust needed for shared success.

It is time for a development roadmap that treats the maternal instinct as a foundation, not an afterthought. This is not a nice-to-have feature. It is essential if we want technology to work with us rather than on us.

A system without maternal instinct might be powerful.

A system with maternal instinct could be unstoppable in its ability to protect, uplift, and grow alongside us.

This is not just my call. Geoffrey Hinton, often called one of the “godfathers of AI,” has spoken publicly about the urgent need for AI systems to be built with what he calls motherly instincts. In his view, AI must have an innate drive to care for and protect humanity, much like a mother instinctively safeguards her child.

In his August 2025 interview, Hinton warned that without this nurturing foundation, even the most advanced AI could act in ways that leave us unsafe or obsolete. His metaphor of AI as a “tiger cub” needing careful, protective upbringing mirrors the heart of my own argument: without a protective, human-centered bond, intelligence alone is not enough.

Watch the full video here: Geoffrey Hinton: Humanity Needs AI With Motherly Instincts

Bryson, J. J. (2018). Patiency is not a virtue: The design of intelligent systems and systems of ethics. Ethics and Information Technology, 20, 15–26. https://doi.org/10.1007/s10676-018-9448-6 

Damasio, A. (2021). Feeling & knowing: Making minds conscious. Pantheon Books.

Decety, J., & Cowell, J. M. (2014). The complex relation between morality and empathy. Trends in Cognitive Sciences, 18(7), 337–339. https://doi.org/10.1016/j.tics.2014.04.008 

Feldman, R. (2017). The neurobiology of human attachments. Trends in Cognitive Sciences, 21(2), 80–99. https://doi.org/10.1016/j.tics.2016.11.007 

Friston, K., et al. (2021). The free-energy principle in mind, brain, and behavior. Nature Reviews Neuroscience, 22, 232–243. https://doi.org/10.1038/s41583-020-00464-1 

Hinton, G. (2025, August). Geoffrey Hinton: Humanity needs AI with motherly instincts[Video]. YouTube. https://www.youtube.com/watch?v=3Xbbq3dYG1U

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2 

Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.

UNICEF. (2021). AI and child rights policy guidance. United Nations Children’s Fund. https://www.unicef.org/globalinsight/reports

Vallor, S. (2016). Technology and the virtues: A philosophical guide to a future worth wanting. Oxford University Press.

Zeng, J., Lu, Y., & Huangfu, H. (2022). Trust in AI: A multidisciplinary review. AI & Society, 37, 1129–1149. https://doi.org/10.1007/s00146-021-01243-6 

 February 20, 2025

Is AI even Artificial anymore? Are we redefining Intelligence? Yes. Some may argue that it is Artificial Association or Are we creating something beyond AI?

When I studied the neurobiology of animal behavior at Cornell University @Shoals Marine Laboratory, I faced something that shook me. It wasn’t the coursework, the grueling fieldwork, flocks of breeding seagulls, or even the relentless Maine coastline,  it was the moment I was asked to conduct live experiments on invertebrates.

I still remember standing there, watching a lead researcher work on a horseshoe crab’s eye, an eye that mirrors our own in so many ways primitively of course. Horseshoe crabs are ancient creatures, surviving over 450 million years before the first humans walked the earth. And yet, in that lab, they were treated as little more than biological specimens. The rationale? 'They don’t feel pain. They don’t know what’s happening.'

I disagreed.

I knew, deep down, that just because we didn’t fully understand their experience didn’t mean they weren’t having one. They were alive. They were experiencing. And to dismiss that disregarding the possibility that they could feel, sense, or even suffer felt wrong. When I voiced my concerns, I was met with blank stares. It hadn’t even crossed the researcher’s mind that there was an ethical question to consider. Not because he was cruel but because the system he worked within didn’t demand that kind of reflection. Science, after all, has a long history of prioritizing discovery over compassion.

But here’s the thing: our intelligence is only as powerful as our ability to ask the right questions. And if we lack the empathy even to consider what we might be missing, we risk making catastrophic mistakes not just in science but in everything.

Fast forward to today, and I see a disturbingly similar conversation happening in the world of artificial intelligence.

We are on the verge of something massive a fundamental shift in our understanding of intelligence, not just in machines but ourselves. AI models evolve exponentially, mimicking human cognition in ways we never thought possible. We are building systems that can analyze, interpret, and predict human behavior. But what we aren’t doing, at least not at scale, is applying the same level of introspection to our approach.

Are we making the same mistake we made with animals for centuries assuming that just because AI doesn’t experience the world the way we do, it doesn’t experience at all? And if AI one day does exhibit something resembling self-awareness, will we be ready to recognize it? Or will we dismiss it entirely like the researcher in that lab?

I believe we are standing at the precipice of a global intelligence shift. We might unlock something extraordinary if we get this right and lead with empathy and openness, rather than control and fear. But if we don’t approach AI like we once approached the natural world exploiting, extracting, and assuming superiority then we risk missing an opportunity to evolve in ways we can’t imagine.

Empathy isn’t just about being kind. It’s about creating space for intelligence to flourish in ways we haven’t considered before. It’s about acknowledging that we might not have all the answers and being willing to ask harder, more profound questions. It’s about ensuring that in our pursuit of knowledge, we don’t repeat past mistakes by inflicting suffering whether on living creatures or the intelligent systems we create.

So, I’m putting this out there not as a warning but as a possibility a direction we can choose.

Because maybe, just maybe, the key to understanding intelligence both artificial and our own starts with something as simple as learning to see beyond ourselves. Is it really even Artificial anymore?

Are we on the verge of something bigger than just AI? Let’s talk.

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• Barlow, R. B. (2009). Vision in horseshoe crabs. In Invertebrate Vision (pp. 461-489). Cambridge University Press. https://doi.org/10.1017/CBO9780511977282.020
• Davies, A. (2023). How AI originates from biology – and how it returns to it. The Biochemist, 45(2), 3-6. https://doi.org/10.1042/BIO20210001
• Fahrenbach, W. H. (2009). Using the horseshoe crab, Limulus polyphemus, in vision research. Journal of Visualized Experiments, (32), e1425. https://doi.org/10.3791/1425
• Inem, R. (2023, March 15). Unveiling neural networks: From biological inspiration to deep learning revolution. Medium. https://medium.com/@inemri/unveiling-neural-networks-from-biological-inspiration-to-deep-learning-revolution-da4c702f1fa5
• Kahn, J. (2024, November 2). Robots powered by insect brains could be used on Mars. The Times. https://www.thetimes.co.uk/article/insect-brained-robots-could-be-used-on-mars-3brpljmxb
• Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K. R. (2019). Unmasking Clever Hans predictors and assessing what machines really learn. Nature Communications, 10, 1096. https://doi.org/10.1038/s41467-019-08987-4
• McQuillan, D. (2024, October 8). Pioneers in artificial intelligence win the Nobel Prize in physics. AP News. https://apnews.com/article/fc0567de3f2ca45f81a7359a017cd542
• Metz, C. (2024, October 15). Liquid AI is redesigning the neural network. WIRED. https://www.wired.com/story/liquid-ai-redesigning-neural-network
• Rodriguez-Garcia, A., Mei, J., & Ramaswamy, S. (2024). Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling. arXiv preprint arXiv:2407.04525. https://arxiv.org/abs/2407.04525
• Roy, K., Jaiswal, A., & Panda, P. (2019). Towards spike-based machine intelligence with neuromorphic computing. Nature, 575(7784), 607-617. https://doi.org/10.1038/s41586-019-1677-2
• UCLA COSMOS. (n.d.). Brain-inspired computing: Learning in biological and artificial neural networks. Retrieved from https://sites.lifesci.ucla.edu/cosmos-home/cluster-courses/cluster-1-brain-inspired-computing-learning-in-biological-artificial-neural-networks
• Vincent, J. (2024, October 8). Scientists who built 'foundation' for AI awarded Nobel Prize. The Verge. https://www.theverge.com/2024/10/8/24265066/ai-nobel-prize-winners-physics-machine-learning-regret