Manifesto
Intelligence from many small parts.
Advanced Nonlinear Technologies · London
I · The belief
Look closely at anything that thinks, and you find the same thing: not one great mind, but a multitude of small ones.
A brain is not a single vast computer. It is roughly eighty-six billion neurons, each almost trivially simple, none of them in charge, and out of their chatter comes you. An ant colony has no leader, yet a scent trail and a few local rules find the shortest path across a continent of dirt. A fern is not drawn, it is grown: one rule repeated until a whole plant stands where there was nothing. Fireflies in a mangrove flash together, though not one of them is counting the beat. Heart cells find a single rhythm. A thousand circadian clocks, each running slightly fast or slow, quietly agree on dawn. A flock of starlings turns as one body, with no bird in command. Even the frontier models everyone calls monoliths are, on the inside, colonies of experts with a router deciding who speaks.
Intelligence has always been a collective phenomenon. Simple parts, simple rules, repeated and coordinating: the oldest and most reliable recipe there is. The single giant mind is not the destination. It is the anomaly. We are building for the world where that truth wins.
II · What we refuse
Today, a business that wants to think with AI rents a brain that forgets it. Brilliant, and borrowed. It hallucinates, so every draft is edited by hand. It never learns your process, so you explain yourself again and again. It charges by the use, forever, and the bill grows with every person who touches it. Your corrections, the hard-won knowledge of how your work is actually done, leave with the session and are gone.
This is a strange bargain, and we refuse it. We refuse the premise that intelligence must be enormous, that it must live in someone else’s data centre, that it must be rented rather than owned, that it must forget you the moment the conversation ends. We refuse the belief that the only road forward is a bigger model in a bigger building drawing more power from the grid. There is another road, and it is the one nature already took.
III · What we build
We build Ferns: tiny specialist models, small enough to run anywhere, tuned to beat models ten times their size at the one job you need done.
A Fern does not know everything. That is the point. It is not built to win trivia; it is built to do your work, to read the invoice, answer the question, route the ticket, see what the camera sees, and to do it better, on your data, than a giant generalist ever could. It is small enough to live on a phone, a laptop, a watch. It is private by construction, because it runs where your data already is. And every time your team corrects it, it gets better at being yours.
Small, owned, and improving. That is a different kind of intelligence than the one on offer, and it belongs to the business, not to us and not to a lab.
IV · The names are the thesis
We did not choose our names to be clever. We chose them because they are the idea.
A Fern is a fractal: a few rules, repeated, that grow the whole structure. That is what our models are, small and nonlinear, grown rather than assembled. Fernfly is a single Fern set loose through an API, for the developer who wants one specialist and nothing more. Everyday Series is many Ferns orchestrated into Series, the sequences of steps behind your everyday work; a frond is itself a series of leaflets, and a workflow is a series of tasks. This is where your team teaches the models and the models take over the work.
And antelligent is the whole: a society of small agents, each carrying its Ferns, talking to one another the way ants talk in a colony, until intelligence emerges from coordination that no single part possesses. The names are a ladder, and every rung is the same principle at a larger scale.
V · How it compounds
Open weights and runtimes are everywhere, and we use them gladly. But a model on its own does not improve by sitting there. What compounds is the loop between the work and the model. Every organisation’s Fern is tuned on that organisation’s corrections. The more the work is done, the more corrections there are; the more corrections, the better the Fern; the better the Fern, the more work it takes on.
The data is theirs and does not transfer. The tuned model is theirs and does not transfer. A competitor can copy everything we have built and still be years behind on the only thing that matters: the accumulated, proprietary knowledge of how a particular business actually works. Costs fall as the model moves on-device; quality rises as the corrections accumulate. It is the rare business where the margin grows and the product improves over the life of the customer, because the customer is teaching it.
VI · Where we play
One idea runs through every layer of what we build: spend less energy to get the work done. At the application layer that is tokens. A business that wants agentic AI and cannot possibly build it gets a system it owns, doing the job with the smallest model that can actually do it, sold as an outcome rather than a seat. At the model layer it is the same discipline one level down: a new class of small models where the computation is physics rather than movement. Inside today’s hardware, only a fraction of the energy goes into arithmetic; most of it goes into carrying numbers to the place where the arithmetic happens. That is the waste we are going after, and it is the same waste in both places.
VII · The long game
There is a longer arc underneath all of this, and it follows from the last one. If most of the energy is spent moving numbers rather than computing with them, the deepest place to fix that is underneath the model. That is where we are digging. Today our Ferns run on standard silicon, and they run well. But we believe that to get small models that reason coherently, at very low power, without the fragility of today’s hardware, computation itself will have to change: from the clocked, digital arithmetic of the transformer to the physics of nonlinear, chaotic dynamics, where the answer emerges from a system settling rather than a processor stepping.
This is research, and we hold it to the standard research deserves. In simulation, our architecture already writes coherent prose crushed to three-bit precision, where a standard transformer collapses into word-salad; it resists the runaway loops behind hallucination; it fine-tunes in minutes to bit-perfect structured output. It has real, open weaknesses, and we report those as loudly as the wins. It is early. It is a moonshot. But it is a moonshot with evidence you can reproduce.
If it pays off, we do not just make small models. We make the substrate they were meant for. And it is not a second company: it is the same idea, one level further down.
many simple parts, coupled
the researchmany small models, each a specialist
the productmany small agents, talking
the platformWe do not ask anyone to pay for the research. It earns more of our effort only when the evidence does.
VIII · How we proceed
We are disciplined about how we get there. We start narrow. Our first job is to turn scattered pilots into one repeatable motion, in the work where our advantage is a requirement and not a nice-to-have.
We sell the outcome, not a custom build. We land a single Series and let the flywheel expand it. We keep the team small and the models smaller. We fund the business we can back today, and treat the substrate as the option it is: a deliberate, minority slice of our effort, not the thing that pays the bills.
And we gate our ambition to evidence. The milestone that matters is not a bigger model or a louder claim; it is a single, honest curve, a real customer’s correction rate falling over time, because that one line proves the society is learning. Everything we raise is aimed at drawing it.
IX · The invitation
Every business, running many small models it owns, each a specialist at one everyday task, all of them improving on that business’s own data, on that business’s own devices, talking to one another like a colony: a society of intelligence that belongs to the people who use it, not to whoever rents it out.
Owned by the business. Improving on its data. Running on its devices. That is antelligent.
It is a long game with early evidence and a clear line of sight. If it is the future you want too, build it with us.
Build it with us