SpawnLabs isn't just an AI that writes code.
It's a platform for people with deep domain knowledge to turn their insight into working systems instantly.
If you're in healthcare, finance, logistics, education, energy, agriculture, or research, you bring the expertise, the lived context, the "I know this space better than anyone else" understanding.
Spawn handles the rest.
All the boilerplate, deployment, infrastructure, and technical glue that normally stops ideas from becoming real? Gone.
That's why experts across industries use SpawnLabs: because it lets them convert years of specialized knowledge into live software without spending years becoming engineers.
And Internally, We Use SpawnLabs the Same Way.
Across the company, everyone uses Spawn: to build, to test, to iterate, to research.
But we also have a personal frontier, a problem we're obsessed with not because it's commercially easy, but because it represents the edge of human understanding.
We call this internal project Albert.
Most people use AI to automate tasks.
Albert exists to automate discovery.
Albert reads papers, synthesizes ideas, writes code, tests hypotheses, and spawns specialized sub-agents to explore new directions. He's built on the exact same SpawnLabs platform our users have, we're simply applying it to the hardest problem we know.
Every secure system you interact with, your banking app, WhatsApp messages, government communications, medical records, blockchain signatures, online payments, relies on a fundamental assumption:
Certain mathematical problems are so hard that even the fastest computers can't solve them in a reasonable amount of time.
The biggest of these problems live in a space called NP, a class of puzzles where it's easy to check if an answer is correct but extremely hard to find the answer in the first place.
Imagine trying to find the one correct key out of a lockbox containing 10,000,000,000,000 possible keys.
Or trying to find a route through 200 cities that's shorter than every other possible route, there are more possible routes than atoms on Earth.
Or imagine a Sudoku with thousands of squares: verifying a solution is fast, but finding one could take ages.
Digital security rests on the belief that no one can solve these problems quickly.
We don't actually know whether these problems are inherently hard, or whether we simply haven't discovered the right methods yet.
This is one of the deepest open questions in all of computer science.
It affects everything from:
We're not claiming Albert will magically "solve" NP-complete problems. The point isn't to flip the world upside down overnight.
The point is this:
There are patterns, shortcuts, heuristics, and structural insights that humans haven't seen, because the search space is too large.
Algorithmic breakthroughs don't require collapsing all of complexity theory.
Even a single new heuristic or structural insight could touch almost every layer of the modern stack:
weaken existing cryptographic systems,
accelerate scientific simulations,
improve optimization in logistics,
reshape how we design large-scale AI systems.
These breakthroughs are rare today because human researchers cannot explore the possible idea-space fast enough.
Albert can.
Our why
We want anyone, whether they're a doctor, trader, researcher, energy analyst, or founder, to understand that:
SpawnLabs isn't just for building apps. It's a platform for pushing the boundaries of what's possible in your field.
And Albert is our proof, an internal demonstration of how deep those boundaries can go.