There is a Brian Scalabrine line that I love:
“I’m closer to LeBron than you are to me.”
Scalabrine was never close to LeBron James in the usual basketball sense. He was a role player in a league organised around stars. But after people repeatedly claimed that they could beat him, he accepted the challenge. In 2013, a group of accomplished amateur players faced him one-on-one. Scalabrine beat them comfortably.
At first glance, that sounds like empty bravado. But it is actually a statement about geometry.
Most people imagine basketball ability as an evenly spaced ladder. LeBron is near the top. Scalabrine was somewhere toward the bottom of the NBA. A very good amateur might therefore be just below him.
It turns out, distances are not evenly distributed.
Scalabrine and LeBron occupied very different positions inside the same professional regime. Yet, they had both crossed the thresholds required to read, process, and play the game at NBA speed. The amateur challengers were simply outside that regime.
So while their ranking was visible, this discontinuity was not.
I think the same thing happens when we judge organisations operating with AI.
Distance Along Which Axis?
For four years, my job leading AI inside Aurecon has been, in one form or another, to help us operate at the edge. One question has occupied much of our time: what’s next?
That doesn’t mean we were trying to turn an engineering consultancy into a frontier model laboratory. Even though we did some great work in synthetic data generation, model fine-tuning, and AI assistants, we were not training foundational models, operating the same compute infrastructure, or conducting research at the same scale as a frontier lab.
That distance was obvious. But our purpose has been different.
Our goal has been to stay close enough to the frontier that we could encounter new capabilities early, build the intuition required to understand what they meant, quickly test them against real domain problems, reject what did not work, try to operationalise what did, and carry the learning into the next cycle.
That distinction leads to my first, slightly provocative, idea:
An organisation can be far from the frontier in resources and much closer to it in operating practice.
This is difficult to express because we tend to collapse organisational AI capability into one maturity score. But distance only makes sense once we specify the axes.
On capital, compute, model research, and specialist concentration, an applied organisation may be very far from a frontier lab.
On experimental velocity, frontier awareness, evaluation practice, domain translation, deployment capability, and speed of learning, that geometry can look very different.
As I argued in Where Capability Actually Lives in Agentic Engineering, useful agent capability does not live in the model alone. It is distributed across the tools, verifiers, control flow, output contracts, interfaces, and review structures surrounding it.
Organisational capability is distributed in much the same way.
It does not live in the number of AI licences an organisation has purchased. It does not live in one impressive demonstration. It does not even live entirely in the people labelled as the AI team.
It lives across technical and domain talent, evaluation, deployment pathways, institutional memory, leadership permission, and finally the repeated contact between these things.
Some capability also sits beyond the formal boundary of the organisation, in the relationships through which it learns.
The distinction between a vendor and a partner is not merely contractual. A vendor gives an organisation access to a product. A genuine partner participates in capability formation.
That has been the significance of our strategic partnership with Nomic.ai. The value was not simply access to its platforms, but the ability to work alongside the people building them as new capabilities and workflows took shape—bringing real engineering problems into the conversation while learning directly as the technology evolved.
From the outside, that distinction is almost invisible. A procurement list cannot distinguish a purchased tool from a shared practice and its accumulated tacit knowledge.
Two organisations can therefore have access to the same model or the same platform, but still possess radically different capabilities.
Far in scale. Closer in practice.
A conceptual two-axis diagram compares three generic organisational positions. The horizontal axis represents model-building scale and resources. The vertical axis represents frontier-learning practice and adaptation rate. An applied organisation operating at the edge and a conventional adopter occupy similar horizontal positions, but the applied organisation sits much higher. The applied organisation and a frontier AI lab are far apart horizontally, but both sit within the shaded frontier-learning regime. Brackets mark their large difference in resources and smaller difference in learning practice. Positions are relative and are not measured values.
The Edge Is a Different Regime
Most AI maturity models imply a ladder.
An organisation begins by becoming aware of AI. It then experiments, pilots, deploys, scales, and eventually transforms. Each stage appears to be another step along the same path. We like this description, it is simple, straightforward, but I think it hides an important phase change.
At some point, an organisation stops merely conducting AI projects and becomes a system that continuously converts advances in AI into organisational capability. This is what happens when you move from one-off, disconnected experiments to building structures that get reinforced with every idea, every exploration, and every deployment.
After the loop exists, every serious experiment can leave something behind: an evaluation, a reusable tool, a deployment pattern, a better interface, a clarified policy, a harness improvement, a new domain dataset, or simply better intuition about what is now possible.
In Broad Creation, Narrow Authority, I described these outputs as stepping stones. Exploration compounds only when experiments are visible, attributable, and reusable; otherwise, an organisation accumulates a graveyard of private prototypes rather than a shared capacity to discover.
This is what makes the edge a different operating regime.
An organisation is not merely further along the same roadmap. It has in fact developed machinery that helps it generate the next roadmap.
Is the Curve Exponential?
I think “exponential” points toward the right intuition, but perhaps at the wrong object.
There is probably not one clean exponential curve relating AI activity to organisational maturity. What we are seeing is the combination of at least three nonlinear effects: a threshold, multiplication, and compounding.
Small horizontal change. Large vertical effect.
A conceptual S-curve relates the strength of an organisation's learning system to its adaptation velocity. The curve remains low through disconnected experiments, rises sharply through a threshold formed by direct access, real domain problems, permission to experiment, credible evaluation, and reusable infrastructure and memory, then levels into a compounding frontier-learning system. The diagram has no numeric scale and does not represent a measured mathematical function.
The platform ceiling
There is another threshold hidden inside the technology itself.
At scale, organisations tend to mature along the dimensions their default platforms make practicable. A chatbot teaches a particular relationship with AI: ask a question, receive an answer, refine the prompt. It can help people become better at prompting, synthesis, and conversational iteration.
But it does not naturally teach them how to embed verification, maintain persistent state, decompose work into executable tasks, construct recursive improvement loops, compare alternative trajectories, or turn failures into evidence for the next run.
Those ideas can be discussed inside a chatbot. But they are not embodied in the working environment.
A platform can therefore become a ceiling on organisational learning. If an organisation’s primary AI surface is chat, it may become increasingly proficient at chat while remaining unable to practise the system-level capabilities that define more advanced work.
Richer platforms change not only what the technology can do, but also what an organisation can learn to do.
When tools, workflows, evidence, decomposition, verification, evaluation, and iteration become first-class parts of the environment, people can begin constructing and inspecting a different class of system. The platform stops being merely a delivery surface for capability.
It becomes a curriculum.
Once the environment makes these practices available, people begin to develop different intuitions about what AI work can be: not just a conversation with a model, but a process that can be decomposed, instrumented, tested, and improved.
The organisation crosses from using AI to learning through AI.
That resembles a phase transition more than a smooth maturity ladder.
Multiplication
The relevant capabilities reinforce, or constrain, one another.
Organisational AI capability depends on the interaction between technical and domain talent, experimental velocity, credible evaluation and benchmarking, and, crucially, organisational permission.
Talent without permission remains trapped in isolated experiments. Experimentation without evaluation produces demonstrations that cannot be trusted. But faster experimentation also creates more evidence; better evaluation makes deployment safer; and successful deployment creates permission for more ambitious work.
Modest improvements across several connected parts of the system can therefore produce a much larger change in the whole.
That is one reason differences in operating practice that look small from the outside can translate into enormous differences in actual maturity.
Compounding
The third effect is the most important.
For a team operating near the frontier, the more it knows today the quicker it can understand tomorrow.
When a new model, harness, or technique appears, the team may already possess:
- representative domain tasks;
- evaluation harnesses;
- known failure modes;
- deployment environments;
- security and governance patterns;
- relationships with practitioners;
- a vocabulary for interpreting the advance;
- and enough experience to distinguish a meaningful capability from an impressive demo.
The arrival of something new does not reset the process. It enters an existing learning system.
A more conventional adopter encounters the same model as a largely new event. It may need to establish access, rebuild confidence, identify use cases, obtain approval, educate stakeholders, and construct evaluation from the beginning.
The technology has advanced equally for both organisations but the amount of organisational learning it produces has not.
The gap is a difference in learning rate.
Once learning rate diverges, the distance widens even when everyone has access to the same underlying technology.
Distance in Learning Cycles
This suggests a different way to think about organisational distance.
Instead of comparing features, headcount, budgets, or maturity labels, we might ask:
How many difficult learning cycles would one organisation need to reproduce another organisation’s operating capability?
The answer is not necessarily the same in both directions.
A team already operating near the frontier may be able to inspect and adopt a conventional practice quickly. The reverse journey may require years of experimentation, failure, relationship-building, infrastructure, and tacit learning.
Two organisations are not separated only by what they have built but also what they have had to learn in order to build reliably.
This is the organisational version of the Scalabrine problem.
An observer sees a set of organisations using similar products and platforms and assumes they occupy nearby positions. But one may have crossed into a different operating regime: it understands the reads, recognises the failure patterns, and has internalised the tempo of the game.
An applied organisation is not equivalent to a frontier laboratory. It may nevertheless share enough of the frontier’s learning machinery to absorb and translate its advances. A conventional adopter may still be trying to understand the visible outputs of that machinery.
Why the Gap Is Hard to See
Foundation models make this distance unusually difficult to judge because they compress visible differences.
Two organisations can each ‘build’ a fluent assistant, create a nice interface, or automate an apparently sophisticated task. To a casual observer, their capabilities may look similar.
The same geometry applies to people.
Organisations evaluate talent through local reference classes: titles, job families, visible outputs, and the KPIs that currently matter. Someone working close to the technical frontier may therefore be seen as an enthusiastic dabbler, because colleagues see a demonstration or strategic role rather than the years of reading, building, failing, and developing intuition behind it.
On axes such as technical judgment, experimental practice, familiarity with failure modes, and the ability to turn new capabilities into working systems, an embedded practitioner may be closer to a frontier researcher than to someone occupying a seemingly adjacent organisational role.
I am not trying to prove equivalence here. Just noting that role labels are poor coordinates for determining which regime of practice someone inhabits.
The edge often exists inside an institution before the institution knows how to recognise it. Those people can become the bridge by which the wider organisation crosses the threshold.
The Frontier Firm Beneath the Frontier Firm
Microsoft’s Frontier Firm framing describes organisations structured around intelligence on demand, human-agent teams, and people increasingly responsible for directing and managing agents.
David Beauchemin extends that idea in Innovate at the Frontier.
His central proposition is that the frontier is a practice rather than a place: a combination of visibility into real work, guardrails that allow speed without abandoning control, and a learning flywheel through which improvement compounds.
Human-agent teams are one visible form of the Frontier Firm. The underlying condition is the ability to continually absorb, test, govern, and operationalise new forms of intelligence.
It is possible to purchase agents and remain a conventional organisation.
It is possible to deploy copilots everywhere while preserving the same slow approval structures, fragmented learning, disconnected data, and annual planning cycles beneath them.
The presence of AI does not necessarily make an organisation frontier-operating. The more consequential question is whether every deployment improves the organisation’s ability to make the next deployment.
The edge is a rate, and not a stock, of capability formation.
This changes what AI leaders should try to build.
The objective is not to complete a finite AI transformation programme or produce a plan on a page. It is to create the conditions under which transformation can remain continuous: direct contact with important problems, bounded experimental freedom, reliable evaluation, shared memory, deployment and recovery infrastructure, and governance expressed in the systems where work happens.
The organisation does not need to predict every valuable application in advance. It just needs to become good at discovering, testing, promoting, and learning from them.
Operating at the edge is not a badge an organisation earns permanently. The frontier moves: models, interfaces, and the unit of useful work all change, and old intuitions expire. An organisation can possess sophisticated systems and still drift away if its learning loop slows.
Staying close therefore requires direct contact with the technology, permission for domain experts to experiment, and governance that bounds consequences without placing a committee between every practitioner and every question.
The aim is not permanent novelty, but sustained contact with change and enough operational discipline to turn it into trustworthy practice.
From experience, that is a difficult thing to balance. Exploration, speed, governance, and scale. Staying at the edge requires all of these to not just be there, but also just the right amount of tension between them in order to remain active.
The Relative Distance
Scalabrine’s point was not that he was almost LeBron.
It was that conventional rankings obscured a more important boundary. He and LeBron had both learned to operate inside the same professional game. The gap between them was real, but it existed within that regime.
The gap between Scalabrine and the challenger crossed the boundary of the regime itself. The organisational analogy I have made throughout this article should be read just as carefully.
An applied organisation operating at the edge is not equivalent to a frontier AI laboratory. Their resources, objectives, and absolute capabilities may be profoundly different.
But the applied organisation may have crossed into the same broad learning regime. It encounters frontier capability directly.
It is capable of interpreting it. It can quickly test it. It can translate it into its own, specialised domain. It can recognise failure and understand its conditions. It can turn experiments into real, lasting infrastructure. And, most importantly, each cycle makes it more capable of completing the next.
Outside that regime, each new advance can feel like another technology to adopt. Inside it, each advance becomes fuel for the learning system already in motion.
There is also a hopeful implication: nonlinearity cuts both ways.
It allows distance to compound, but it also means that closing the distance does not require retracing every step.
Yesterday’s frontier is continually being packaged into today’s models, tools, and practices. A small number of well-chosen changes—direct access, real problems, credible evaluation, permission to experiment, and a way to retain what is learned—can change an organisation’s learning rate surprisingly quickly.
It might not immediately inherit all the tacit judgment of those already operating at the edge. But it can cross into the same learning regime.
Once that happens, small steps can begin to cover very large distances.