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AI Native OS
Built In Austin Texas
Built In Austin Texas
This is the next peel on the labor efficiency onion I've been working on my entire career.
I've been solving the same problem my entire career.
I just didn't realize it until I started using AI to rebuild my last company—and saw there was another layer to peel.
Every company I've built has been about labor efficiency:
Each time, I'd peel one layer of the onion. Get more efficient. Scale. Sell.
Then start over with the next layer.
At Done, we built what would eventually become an 800-person offshore team under Porch.
The team handled customer service, fulfillment coordination, back-office operations. All the work that makes a business run but doesn't directly generate revenue.
Offshore labor was cheaper than US labor. But it still ate margin.
And scaling to that size meant all the problems that come with large teams:
I'd optimized human labor as far as it could go.
We sold to Porch. Went public. I got paid.
And I thought I was done.
After the going public, I started a small family office. Moved into commercial real estate.
9 AM to 3 PM days. Meetings with brokers. Due diligence. Lease negotiations.
It was mind-numbing.
Not because real estate is bad. People make fortunes in it.
Because it's slow. Decisions take months. "We'll know in 90 days" was the standard response.
In tech, you ship something and know if it works by next week.
In commercial real estate, you wait six months to find out if a lease got signed.
I couldn't do it.
I started rebuilding Done's tech stack. Just for fun.
Better architecture. Modern patterns. Everything I wished I'd built the first time.
Then I asked myself the question that changed everything:
If I'm rebuilding Done, how do I solve the labor cost problem?
Hundreds of people doing necessary work. All eating margin. All requiring management, training, coordination.
What if AI could do some of that work?
Not assist with it. Not make humans faster.
Actually do it.
I started building AI workflows to replace parts of the offshore team's work.
Customer service responses. Order status lookups. Refund processing. Address changes.
All the repetitive, structured work that humans were doing.
That's when it hit me:
This isn't about rebuilding Done.
This is the next peel on the labor efficiency onion I've been working on my entire career.
Not onshore → offshore (geographic arbitrage)
Not offshore → nearshore (quality improvement)
Not nearshore → automation (eliminate some tasks)
Offshore → No shore.
Synthetic labor that doesn't require:
Labor that scales without adding headcount.
Every previous layer was optimization:
This is substitution:
Replace $3,000/month human workers with $500/month AI employees.
Not 10% better margins. A fundamentally different cost structure.
Lesson 1: Human labor has a floor cost
Even offshore, even optimized, you can't get below a certain cost per unit of work.
You're paying for time, not outcomes. And humans can only move so fast.
Cerebral changes this: You pay for execution capacity, not hours. Volume scales without proportional cost increase.
Lesson 2: Management overhead scales with headcount
Large teams need managers. Managers need directors. Directors need VPs.
Every layer adds cost and slows decision-making.
Cerebral changes this: AI employees don't need management hierarchies. They need workflow design and governance policies.
Lesson 3: Quality variance is expensive
Some agents are great. Some are mediocre. Some need constant supervision.
You can't predict which is which until they're already trained and working.
Cerebral changes this: Deterministic execution means consistent quality. Every AI employee executes the same workflow the same way.
Lesson 4: Institutional knowledge walks out the door
Your best agent quits. They take years of learned experience with them.
Now you're training someone new from scratch.
Cerebral changes this: Workflows are the institutional knowledge. They don't quit. They don't forget. They compound over time.
Looking back at every company I built:
Company 1: Manual processes → Documented workflows
Company 2: Documented workflows → Offshore execution
Company 3: Offshore execution → Automation-assisted
Company 4: Automation-assisted → Synthetic execution
Each one was a step toward this.
I just didn't see the pattern until I tried to rebuild Done and realized the real problem wasn't the product.
It was the cost structure of human labor.
I'm not building this because AI is trendy.
I'm building it because I've spent 20 years trying to solve labor efficiency and kept hitting the same ceiling:
You can't scale human labor without scaling human costs.
Offshore helped. Automation helped. Better processes helped.
But they all hit a floor.
Synthetic labor doesn't have that floor.
It scales like software. It costs like infrastructure. It executes like labor.
That's the category I've been trying to build my entire career without realizing it.
Now I'm building it.
And I'm not stopping until it's the foundation other companies build on.
Every company I built before was practice for this one.
I didn't know it at the time. But looking back, the pattern is obvious:
Each one taught me one piece of what synthetic labor needs to actually work:
Done gave me the experience of building what became an 800-person team - showing me the ceiling of human labor optimization.
Porch gave me the journey that proved the value is in the build, not the exit.
Commercial real estate gave me the contrast that showed me I need fast iteration, not slow returns.
Cerebral is where all of it comes together.
Not as the next company. As the category I've been building toward.
The infrastructure for no-shore labor.
And this time, I'm building it to last.
Ben Jenkins
Founder