Why Is It So Hard to Build Truly Safe AI? Dynamic Constraint Boundary

Raising $5,000active

DCB removes probability from AI decision-making and replaces it with internal integrity so AI doesn't guess, it decides within its boundaries.

Endorsed by+1

DCB removes probability from AI decision-making and replaces it with internal integrity so AI doesn't guess, it decides within its boundaries.

Endorsed by+1

Project Details

I've been thinking for a long time: why is it so hard to make AI that is truly safe? We keep trying to make AI obey the law we impose external rules, we add filters, we train with punishments and rewards. But laws don't stop bad people. Only internal integrity can stop someone, even when no one is watching.

As long as AI still "guesses" (based on probability), it never truly has integrity. It only has rules imposed from the outside. And like a person without integrity, it will always look for loopholes not because it is evil, but because it never knew its own boundaries.

DCB (Dynamic Constraint Boundary) is a deterministic architecture I built to change that. Safety is not just an external layer it is part of the system itself. Violating a boundary means violating its own integrity mathematically impossible without breaking the system.

I'm often asked: "Can DCB still be creative without probability?" Yes. DCB remains creative through parallel exploration of bounded possibility spaces. It can explore many paths at once, but stays within safe and relevant boundaries. The result is creative, deterministic, and computationally efficient.

What makes DCB unique isn't just safety it also has the potential for metacognition, anticipatory inhibition, and high adaptability to chaotic environmental changes in real-time without retraining.

I've run initial simulations (PoC), but because DCB's intelligence is emergent (no single variable dominates), I want to build a fully functional DCB Core not just a comparative simulation So all these hypotheses can be tested in pract

ice.

Theory of Impact

If I succeed in building DCB, it could become the first structural foundation for safe AGI. A foundation that must be laid before external safety layers are added so internal safety already exists from the start.

The immediate impact: DCB could cut inference costs by up to 83%. This isn't just about AI safety in the future it could also help tackle the massive energy waste we're seeing today (green AI).

The long-term impact: we would have an AGI architecture that cannot be jailbroken, cannot hallucinate, because safety is already embedded as internal integrity from the beginning.

People

Naufal Ridwan

Team Member

Grants Received– no grants recorded

Add grant

Funding Asks

grantmaking.ai Launch Round

Applied

Minimum

$5,000

Ideal

$20,000

How the money will be spent

I'm requesting $20,000 for the next 12 months. Breakdown:

· Living stipend ($12,000): $1,000/month so I can focus full-time on this research without worrying about daily living costs.
· Research infrastructure ($4,000): Laptop, internet, and cloud computing for large-scale simulations.
· International patent filing (~$4,000–$5,000): Estimated initial cost to protect this architecture before public disclosure. The exact cost will be adjusted based on actual needs.

Minimum funding ($5,000): If only this is raised, I can still buy basic equipment and work for 3-4 months, though not full-time.

Discussion

Naufal RidwanEndorsement5d

I recorded this video for the review team and anyone interested in understanding this project more directly. I tried to explain DCB from multiple angles to make it accessible across different backgrounds: how this framework would work in an AI safety environment, why this deterministic approach matters structurally, what sets it apart from existing approaches, and why I believe it's worth exploring further.

This video also serves as my response to the verification request from grantmaking.ai. Feel free to watch if you’re interested.

Verification Video: DCB Project Explanation

Naufal Ridwan3d

One more thing I'd like to share, how DCB "feels" or knows its own boundaries.

DCB doesn't feel pain emotionally like a human does. But it feels pain structurally, like a biological body that senses damage as a signal to stop.

When a boundary is violated, there is a decline in the system's internal health. That is its "pain" not an emotion, but a structural signal that changes behavior. The system becomes more cautious, its boundaries tighten, and it stores that pattern as long-term memory.

This is what makes DCB different: it doesn't need to be "punished" to learn. It simply "feels" that something has violated its integrity, and it will not repeat it, not even just to try.

This is a foundation I think is important to understand: that true safety comes from a system's ability to feel its own boundaries, not from fear of puni

shment.