The 10-second building permit is already here!

Leading with Perspective, Leila Banijamali

A friend recently shared a video clip of Jeff Bezos describing a future where a building plan is scanned by AI and the entire construction project is approved in 10 seconds.

The conference was in Miami. His point was simple: with today’s technology, there is no obvious reason this should not already be happening.

It is an exciting vision.

It is also not the current reality.

My co-founders and I spent years studying exactly this problem at Stanford AI Lab. That research ultimately led us to launch Symbium, which focuses on automating regulatory compliance for permitting systems.

Everyone wants the future Bezos described. The key question is what kind of AI can actually deliver it.

Why the “10-second permit” is harder than it sounds

The challenge is not simply scanning a set of plans quickly. The real difficulty lies in the nature of building codes themselves.

Building permits sit at the intersection of law, engineering, and life safety. That combination creates several constraints that most AI discussions overlook.

Let me unpack three of them:

1. Building code decisions must be deterministic

Predictive AI models are probabilistic by design.

Ask the same question several times and the system may produce slightly different answers. That variability is acceptable when drafting emails or generating marketing copy.

It is not acceptable when determining whether a structure complies with fire separation rules, structural loads, or electrical safety requirements.

Building codes behave more like tax law than conversational language. Given the same inputs, the system must produce the same answer every time.

That requirement points toward deductive AI systems, where explicit logic determines outcomes rather than statistical predictions.

2. Permit decisions must be legally defensible

A permitting decision is not just a technical judgment. It is a legal action taken by a government authority.

When a permit is issued or denied, a jurisdiction must be able to show:

• Which code provisions applied
• How the submitted design was evaluated
• Why the project passed or failed

This creates a need for traceable reasoning.

Predictive AI models generate answers based on patterns learned from data. They rarely produce a transparent chain of logic that can be audited or defended in a legal context.

For regulators, the question is not only whether an answer is correct. It is whether the decision can be explained and defended.

3. Real construction plans are messy data

The final challenge is less glamorous but equally important.

Most demonstrations assume clean inputs. Real building plan submissions look very different.

A single permit application may include:

• Hundreds of pages of drawings
• Multiple file formats
• Handwritten annotations
• Inconsistent labeling
• Jurisdiction-specific requirements

Before compliance can even be evaluated, a system must extract structured information from these documents.

In many cases, interpreting the plans is harder than applying the code.

What is actually possible today with Symbium

Despite these challenges, meaningful progress is already happening.

Systems built on deductive regulatory logic can automate compliance checks for well-defined project types with consistent inputs.

At Symbium, this approach already enables instant permitting for high-volume project categories across more than 200 jurisdictions.

These projects share a key property: their compliance rules can be expressed as explicit logic rather than probabilistic inference.

In other words, the path to instant permitting does not begin with AI guessing whether a design is compliant.

It begins with systems that can prove compliance deterministically. The inputs may not be as Bezos describes but the output is still instant!

The future of instant permits is still getting better!

The vision Bezos described is compelling. A world where complex regulatory approvals happen instantly and accurately upon plan upload would unlock enormous efficiency in housing, infrastructure, and clean energy deployment.

That future is not science fiction.

But reaching it requires the right architecture. Deductive AI systems designed for legal and regulatory reasoning will likely play a central role.

The destination is clear.

The real work lies in building the systems that can get us there safely, reliably, and at scale.

And the conversation around that future will likely look very different even a year from now. See you there!

Leila Banijamali is the CEO and Co-Founder of Symbium and a former Stanford CodeX Research Fellow. Symbium is a regulatory technology company that uses computational law to automate compliance verification and enable instant permitting. Symbium serves over 270 jurisdictions across California and Colorado and is expanding nationally.