Everything in this room is given away. The paper that named the era, annotated for organisations. The long history of attention. The method in full. The books we keep returning to. Take your time, and take what is useful.
Attention has been studied seriously for one hundred and thirty five years. The field of artificial intelligence discovered it formally in 2017. The two lineages converge on the same question and answer it from opposite ends. Our work sits at the join.
One hundred and thirty five years of attention
The cognitive tradition asked how the human mind directs focus.
The machine tradition asked how a system might learn to focus its own.
Both arrived at the same answer in 2017.
The paper that opened the AI era was a research artefact. We have spent considerable time reading it as something else: a guide to how attention, hierarchy, and parallel processing might be redesigned in the organisations of the next decade. What follows is a small selection of passages from the original paper, with our commentary.
This is the technical heart of the paper, and it has a remarkable organisational analogue. An organisation is also a function that maps queries to outputs by weighting which information sources are most compatible with the question being asked. A good organisation routes the right query to the right person with the right context, and weights their input correctly. A bad organisation routes everything to everyone and weights nothing. The transformer's contribution was to make this routing explicit, parallel, and differentiable. The same operations could redesign how decisions get made inside companies.
Multi-head attention is the discovery that one perspective is not enough. The model gets better when several different attention mechanisms run in parallel, each looking at the same data through a different learned lens, and the results are concatenated. This is exactly how good consulting works, when it works. A senior team brings several perspectives to the same problem in parallel, integrates them, and produces a synthesis that no single perspective could have reached. Most organisations operate as single-head systems. They have one dominant lens, usually financial, and miss everything the other lenses would have caught.
Recurrent networks process information one step at a time, like a hierarchy passing memos up and down. Self-attention sees the whole context at once. This is the deepest structural insight of the paper for organisational work. Most companies are built on recurrent communication. Information goes up the hierarchy, gets compressed, comes back down, gets misinterpreted. The companies that will thrive in the agentic era will be the ones that flatten this. Where context is shared, where everyone sees the whole sequence, where decisions can be made closer to where the work happens.
A short list, updated quarterly. Not a syllabus on artificial intelligence. A syllabus on people, work, attention, and the longer arc of how technology either lifts or diminishes us.
Two ways the brain attends to the world. The mode you choose determines what you can see. Foundational text on why attention is the substrate of every other capacity.
The distinction between labour, work, and action. Reads as if it were written for the agentic era. The most important book about what humans are for that we know of.
The pattern that connects every technology revolution to the social one that follows. Helps locate where we are in the AI cycle.
The original text on knowledge work and what it asks of organisations. Drucker saw most of this fifty years ago and most companies still have not caught up.
The paper the firm is named for. Eight authors, eight pages, the architecture that opened the modern era of artificial intelligence. We annotate it above.
The cybernetic view of organisations. How information flows, how feedback loops work, how a company is more like a living system than a machine.
Thinking in centuries rather than quarters. The intellectual posture every senior leader needs and almost none cultivate.
The cost of mistaking computation for understanding. A counterweight to the techno-optimism that dominates AI discourse.
The medium is the message. Every new technology rewrites the human relationships around it. AI is the most consequential medium since print, and we are not yet asking the right questions about it.
The political economy of the AI industry, told without flattery. Required reading for anyone deploying these systems into their organisation.
The first sentence of the paper is a diagnosis. The dominant approach is too complex. The second sentence is a thesis. Simpler architecture, focused on the one thing that mattered (attention), would outperform. Almost every successful organisational redesign we have ever seen follows this structure. Diagnose where the complexity is unnecessary, identify the one mechanism that matters, build around that. Most companies inherit elaborate machinery from a previous era and never ask whether the machinery is still doing the work.