Most of what is written about artificial intelligence misses the point. The story is not the technology. The story is the people, and the work, and what survives the transition.
A field changed when eight researchers focused on what mattered. The same lesson now applies to organisations.
In June of 2017, eight researchers at Google published a paper that quietly changed everything. They argued the field had been building the wrong machinery for years. You did not need recurrent networks. You did not need convolutions. You needed attention. The architecture they proposed was simpler than what had come before, not more complex. The thesis was that focus on the right thing was more powerful than the elaboration of the wrong things.
Eight years later, every modern AI system descends from that paper. The transformer architecture they introduced underpins every model you have heard of. The lesson, however, is not really about machine learning. It is about how progress actually happens in any field. Most of the time, it happens by removing complexity rather than adding it. By attending to what matters and ignoring what does not.
We are at the same moment again, this time with organisations.
The intelligence era is arriving, faster than most leaders are prepared for. Every company we work with is being asked to deploy agents into their operations, redesign their workflows, rethink their headcount, restructure their org charts. Almost none of them know how. The big consultancies sell strategy decks that gather dust. The system integrators run two-year projects that miss the point by the time they ship. The AI-native firms speak in tokens and embeddings rather than outcomes. Everywhere, the noise is overwhelming. And in the noise, the actual question gets lost.
The actual question is not how to deploy more technology. It is how to focus an organisation on what genuinely matters, and to design the agents and the people and the work around that focus.
This is what attention means in the context of an organisation. Not the cognitive load on your inbox. Not the executive bandwidth lost to meetings. Attention is the firm's collective capacity to direct its energy at the things that produce real outcomes for the people it exists to serve. Most companies spend their attention badly. They optimise the visible at the expense of the meaningful. They mistake activity for progress. They confuse the instruments of work, the apps and the org charts and the quarterly metrics, with the work itself.
The agentic era will reward the organisations that can focus their attention on what matters. It will punish the ones that cannot. This is not a technology problem. It is a leadership problem, an organisational design problem, a culture problem. The technology is the smaller half of the work.
We named this firm 1706 Studio because we believe the parallel is real. The paper that opened the AI era was not the most technically complex piece of research published that year. It was the simplest one with the right insight. The firms that win the next decade will not be the ones with the most technology. They will be the ones with the clearest attention.
Our work is to help leaders find that clarity. Not to sell them more software. Not to add to the noise. To help them see, with care, what genuinely matters in their organisation right now, and to design the people, the agents, and the work around that.
We believe the human is at the centre. Always. The agents serve the work. The work serves the people. The firm exists to design the relationships between all three.
This essay will be replaced quarterly with new thinking. The reading list grows over time. The annotated paper is below, for those who want to read what we read. None of this is service. It is an open invitation to think with us.
If any of it lands, you know how to find us.
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.
We do not publish case studies. The work belongs to the people who commissioned it. What follows are short, anonymised notes on the kinds of questions we have been asked, and the angles we have taken on them.
A venture studio asked us to help redesign itself from the inside out, around the assumption that agents would be first-class participants in its operations from day one. We did not build them an AI tool. We did something more difficult and more useful: we codified the operating model of the firm into a structure that an agent could read, reason about, and act on, with humans coordinating the relationships and judgement that agents cannot. The result is a chief of staff agent that holds context across the entire firm, coordinates project teams, and removes a category of friction the partners had been carrying for years.
Operating model · Chief of staff · Friction removalA founder running several companies in parallel asked us to design what an executive-tier agent might look like for someone in their position. The technical challenge was straightforward. The harder question was philosophical: which decisions should remain entirely human, which should be agent-assisted, and which could be agent-led with human approval? The work produced a working agent and, more importantly, a written constitution for how the founder would relate to it. The relationship turned out to matter more than the technology.
Founder operations · Agent constitution · Human–agent relationshipA US-based organisation working to help ordinary people get genuine value from AI, and to defend them against its harms, asked us to build the agentic backbone they would need. The work matters because most AI infrastructure is being built for enterprises and the wealthy. Almost no one is building thoughtful tools for the people who will be most affected by the transition and least equipped to navigate it. The brief was technical. The motivation was civic. We took it because the question of who AI ends up serving is the most important question in the field.
Civic infrastructure · Defensive AI · The other 95 per centA note about the work itself rather than a specific engagement. We have been struck, across every project this season, by how little of the difficulty is technical. The hard part is almost always organisational, almost always about people, almost always about questions of authority and judgement that have been carried unexamined for years. The agentic transition is forcing companies to be honest about how they actually work. The companies willing to look at this clearly are finding the deployments easier than they expected. The ones still arguing about model selection are finding the deployments impossible. We are taking notes.
Methodology · Field observations · The honest workA 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 actually 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 actually 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 future of work is not human against agent. It is not human replaced by agent. It is the human at the centre, augmented by a network of agents that handle the work below the threshold of judgement.
The skills that survive automation are the ones that require relationship, context, and accountability. The firm builds organisations around those skills, not against them.
The companies that win the next decade are not the ones with the largest headcount. They are the ones whose people do work agents cannot, supported by agents that do work people should not have to.
Senior advisory at the top, operational delivery underneath, with the methodology that connects them owned by the firm. No handoffs. No translation gaps. The strategy ships.
They fail because the technical work and the organisational work happen separately. The model is right, the org is wrong. We work both layers as one.
Twenty years building at the frontier of digital. Microsoft, Nokia, UEFA, Art Basel. Perplexity Fellow. Publishes Ground Truth on the orchestration layer thesis. Brings the strategy, the technical depth, and the senior advisory practice.
Operational leadership at scale across Microsoft, Nokia, and Minecraft Studios. Brings the delivery model, the financial discipline, the network of builders, and the business framing that keeps the firm focused on outcomes the buyer can act on.
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.