Research
We're working out what Enterprise AI becomes next — in the open, in plain language, and honestly enough to say what we don't know yet.
Flagship program
Retrieval answers questions. Learning accumulates capability.
Today's enterprise AI mostly retrieves: you ask, it finds, it answers, it forgets. LAG is our wager on what comes after — systems where every interaction leaves something behind. A correction becomes a rule. An outcome becomes a preference. A mistake becomes a boundary.
The organization doesn't just use intelligence — it accumulates it. We're building LAG into the learning layer of our platform and writing up what we find as we go, including the parts that don't work.
Compound interest applies: every month the loop runs is learning your competitors don't have.
Abstract & first paper — publishing 2026
The manifesto
“The first generation digitized business. The second automated work. The next will continuously learn.”
The long-form version — why we think the learning enterprise is inevitable, what it demands, and what we're doing about it — is being written now, with the same care we'd give a client's brand.
Publishing soon — subscribe below to read it first
What we're pulling on
What does it mean for a company — not a model — to be intelligent? Where does that intelligence live, and who tends it?
How feedback becomes improvement in production systems: what to keep, what to forget, and how to know the difference.
The shapes that hold up under real enterprise load — knowledge, memory, agents, and the seams between them.
Patterns we’ve built more than once, written down so the next team — ours or yours — doesn’t start from zero.
The unglamorous findings from the workshop floor: evals, guardrails, failure modes, and what actually broke.
Essays
A response · July 2026
Chamath Palihapitiya says the price of intelligence is collapsing. Our response: what lands at the bottom of the curve is brilliant, amnesiac — and the moat moves to memory.
Read the essay →Research · July 2026
The program behind our wager: systems where every action becomes memory, every outcome becomes learning, and March is measurably better than January.
Read the essay →Argument · July 2026
Low-code agent builders optimise for the median demo. Your edge lives in the twenty percent they abstract away — which is why the agentic layer has to be built and owned, not configured.
Read the essay →Position · July 2026
The cost of building software has collapsed — so the old bargain of generic tools plus an army of managed services is over. Build the thing that fits, own it, and let it stay with you.
Read the essay →Thesis · July 2026
Open models have closed the agentic gap. The enterprise that fine-tunes its own weights turns institutional memory into an asset nobody can rent back to it.
Read the essay →Stress test · July 2026
Maybe AI is a bubble. Maybe the labs go bust. The enterprise that built — rather than rented — keeps everything: the code keeps running, the weights don't expire, and the bill goes down.
Read the essay →Inside view · July 2026
AI broke the maths of the billable hour. What replaces T&M — outcomes, subscriptions, virtual FTEs — why the giants are already pivoting, and the bet we placed ourselves.
Read the essay →One email when we publish. No newsletters, no nurture sequences — we're too busy building.