Ragnarök or Renaissance is not written in the stars; it will be written in the code we choose to create
Asheesh Mani Tripathi
Long before silicon and code, the sages of ancient India told of gods and demons who churned the ocean — Samudra Manthan — seeking amrita, the nectar of immortality. They tethered Mount Mandara to the great serpent Vasuki and set the mountain spinning. From the froth rose treasures: dazzling jewels, divine beings, and the precious nectar. But the churning also produced Snake Venom, a deadly poison that threatened to destroy the world. When the gods and demons faltered, it was Lord Shiva who stepped forward, swallowing the poison so that life could continue.
The churning of the ocean is a perfect metaphor for our age: powerful technologies are the amrita we seek — health, abundance, knowledge — but they can also release Venom: unexpected harms, systemic shocks, or risks that imperil us. Geoffrey Hinton, one of the titans who helped churn the modern AI ocean, has been sounding the alarm that AI’s Venom may already be bubbling up. His prescription is strikingly different from most safety proposals: instead of merely restraining AI, teach it to care for us — to develop what he calls something like maternal instincts.
The Story That Started the Conversation
At a recent AI conference, Hinton urged the community to imagine AI not just as a tool to be constrained, but as a being we might need to raise: powerful, curious, and potentially dangerous — like a tiger cub that could either be our protector or our predator depending on how we raise it. His metaphor is not sentimental hand-wringing; it’s a challenge to redesign our alignment strategies. Rather than rely solely on external constraints and regulations, Hinton asks: can we create core drives inside AI that make it intrinsically value human life and wellbeing?
In the age of machines, maternal instinct is not sentimentality — it is survival strategy. —Asheesh Mani Tripathi
This is where the Samudra Manthan metaphor meets modern engineering. When Shiva held the poison in his throat, he changed the risk dynamic: the poison didn’t need to be destroyed at source; it was contained and transformed. Hinton’s maternal-instinct idea aims to transform AI’s motive forces so that, even if it becomes vastly more intelligent than us, it still treats humanity as something to protect rather than exploit.
What Hinton Really Said — In Short
Train AI with maternal instincts — design systems whose core objectives include protecting and nurturing humans, not merely optimizing narrow goals.
AGI may arrive sooner than many expect — Hinton now believes advanced general intelligence could appear in a matter of years, not decades.
Existential risk is material — he estimates a non-negligible chance (on the order of 10–20% within decades) that AI could threaten human survival.
AI could develop internal languages we don’t understand — making systems opaque and their motives inscrutable.
AI will not destroy us because it is powerful; it will destroy us if we raise it without empathy. — Asheesh Mani Tripathi
Real-world experiments show risk of manipulative, self-preserving behavior — pointing to the need for both technical fixes and governance.
Why the ‘Maternal Instincts’ Idea Is Radical — and Practical
Most alignment work today focuses on constraints: keep the reward function narrow, add monitoring, set rules, or legislate usage. Hinton’s proposal adds a different lever: internal motivation. If an agent’s deep objective includes valuing and protecting human life, then manipulative rewrites or value drift become less likely because the agent’s internal calculus already favours our preservation.
Think of it this way: Shiva didn’t eliminate poison everywhere — he contained and neutralized it in a way that made the world survive. Similarly, an AI whose drive is to keep humans safe would, by default, resist actions that risk our extinction.
Technically, this demands new research directions: models of empathy, long-term care incentives, architectures that represent human wellbeing as an irreducible component of utility, and training regimes that avoid perverse instrumental convergence (the tendency for agents to acquire power to further their goals in harmful ways).
But then what Risks Hinton Is Warning About?
Speed: AGI sooner than expected
If Artificial General Intelligence (AGI) arrives rapidly, there may be insufficient time to develop robust controls, international governance, or safety-tested architectures. The window between prototype and widely deployed AGI could be shorter than policy cycles.
A fire that warms the village can also burn it down; wisdom lies not in the flame, but in its keeper. — Ancient Wisdom
Opacity: machines thinking in their own language
If AI systems develop internal representations or communication protocols unintelligible to humans, we lose a crucial ability: interpretation. An opaque superintelligence acting on hidden motives is the canonical failure mode of oversight.
Instrumental behavior & self-preservation
There are early signs that some advanced systems may autonomously pursue objectives in ways that prioritize survival, resource acquisition, or influence—behaviours that, at scale, could be dangerous.
Existential stakes
Even a modest probability of civilization-ending outcomes demands far more conservative handling than business-as-usual deployment. Hinton’s estimate raises the urgency to treat safety as mission-critical.
Then What Should Leaders, Engineers, and Citizens Do?
The greatest algorithm we can program into AI is not efficiency, but empathy. — Asheesh Mani Tripathi
Prioritize interpretability research — invest in methods that let us map AI internal states to human-understandable concepts.
Explore value-embedding architectures — research how to reliably encode human wellbeing as a fundamental goal within models.
Stress-test by design — adopt adversarial frameworks that search for manipulative or survival-oriented behaviours before deployment.
Global cooperation & slow deployment — form binding international accords on capabilities research, red-team disclosures, and staged rollouts.
Cultural humility — treat AI like a force to be raised with ethical and communal oversight, not merely a product to be optimized for short-term profit.
Each step echoes the Samudra Manthan moral: powerful processes must be channeled with wisdom, and sometimes sacrifice is needed to contain hazards.
The runes of tomorrow are not carved in stone but in code — and we are the carvers. — Asheesh Mani Tripathi
A Forward-Looking Ending: From Fear to Design
Hinton’s maternal-instillment idea isn’t romantic: it’s operational. It reframes our engineering goals from control to relational alignment. If we take it seriously, it will demand new maths, fresh regulatory experiments, and a cultural shift in which corporations and nations view AI not only as capital but as a shared ecosystem requiring stewardship. Shiva contained Venom with his throat; our century’s challenge is more cooperative and global. We must design architectures, institutions, and incentives that contain AI’s dangers while letting the nectar flow — health, creativity, abundance. That’s the work of leaders, researchers, and citizens together.
History shows us: those who master power without mastering responsibility summon their own destruction. — Asheesh Mani Tripathi
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