Deployment and markets

This is not an eleventh layer. It is the commercial system that surrounds the ten technical ones and decides whether any of their capability becomes an adopted workflow, a measured outcome and a return. It sits beside the physical map, not above it — and a product can be technically available and commercially undeployable for reasons that have nothing to do with whether it works.

What this section answers

Who buys the system. Which business problem it solves. Who integrates and operates it. Who carries implementation and liability risk. How value is measured and monetised. And, separately from all of that, who captures the economic surplus — because a technically essential supplier can capture very little, and an integrator who invented none of the technology can capture a great deal.

1 · The adoption pipeline

Eleven stages between a customer problem and a renewal. Technical readiness gets a deployment to about stage three; everything after that is integration, approval and change management.

2 · Buyer and stakeholder map

The buyer is not always the beneficiary, and the beneficiary is not always the person affected. Seven roles with genuinely different incentives, any of which can stop a deployment.

3 · End markets

Fifteen markets with different regulatory gates, integration costs and willingness to pay. They are listed in rough order of how quickly capability has converted into deployment, not by size.

4 · Systems integrators and the channel

The participants that turn a product into a working deployment. Most of them are not technology companies, and in physical markets they capture more of the value than the vendors do.

Who is actually in the value chain

Consultants and engineering firms; systems integrators; managed-service providers; distributors and channel partners; industry-specific software providers; robot integrators and automation specialists; training and change-management providers; certification, testing and audit organisations; insurers and financiers; and maintenance, repair and lifecycle-service providers.

None of these appear in the ten technical layers, and no deployment at scale happens without several of them. Their margin comes out of the same customer budget as the vendor's.

5 · Business models

Eleven ways the same capability gets sold. The model chosen decides what the vendor is exposed to far more than the technology does.

6 · Unit economics and return

What has to be measured before a deployment can be called economic. Deployment and integration costs are the lines most often under-counted.

Additionally, for embodied systems

7 · Regulation, safety and liability

The gate that decides which markets open, and in what order.

Three separate questions, routinely merged

Is it permitted? Sector regulation — medical device approval, financial supervision, automotive type approval, aviation certification. Is it safe enough to deploy here? A safety case for this specific installation, which is site work rather than product work. Who pays when it is wrong? Liability allocation between vendor, integrator, operator and insurer, which is unsettled in most markets and will determine which business models are viable in several of them.

A capability can clear the first and fail the third. Insurance pricing can remove a technology from a market without any regulator banning it.

8 · Operation, maintenance and lifecycle

What happens after the deployment, which is where most of the lifetime cost and most of the durable revenue both sit.

The part that is not a launch

Monitoring and support; incident response; exception handling by people; spares, repair and refurbishment; recalibration and requalification after any change; software and model updates, staged and reversible; retraining of staff as procedures change; and eventually replacement, resale or disposal. For physical systems the maintenance line is higher in year three than in year one, and it is the line that decides whether a leasing or as-a-service structure works at all.

9 · Value capture by layer

Where the surplus lands. Technical necessity and pricing power are different properties, and this is the distinction the whole report is built to make.

10 · Investment implications

What follows from the sections above, stated as claims that can be argued with.

Claims this section refuses to make

Each is common, each is convenient, and each fails on contact with an actual deployment. Market sizing built by multiplying jobs or wages by an assumed automation percentage is the most persistent of them, and it is not a method.

Every market-size and productivity claim in this section is deliberately qualitative. A defensible one would need the use case, customer, geography, adoption rate, price, implementation cost, time horizon and source stated together — and where this report cannot state all of those, it does not give a number.

This report draws its diagrams and tables with JavaScript. Here is the substance without it.

Layer 1 · Energy, power and utilities
Generation, transmission, substations, electrical distribution gear and thermal management. An AI accelerator is a machine for converting electricity into inference — that is the whole business.
Binding constraint. The chokepoint is long-lead-time electrical equipment and turbine hot-section castings, not generation. Global heavy-duty turbine capacity runs 60–70 GW a year against roughly 110 GW of outstanding orders, and the constraint is specialised foundries and skilled welders laid off during the lean 2010s.
Layer 2 · Raw and processed materials
Ore, brine, gas and by-product streams, and the refining, separation and transformation steps that turn them into something a fab, a transformer or a magnet can actually use.
Binding constraint. For most of these materials the chokepoint is refining, separation and qualification rather than reserves in the ground — but not for all of them: high-purity quartz is geologically concentrated, and by-product metals like gallium and germanium cannot be produced to order at any price. Mining is geographically diverse; refining is not. China holds 86–90% of rare earth separation, 48% of copper smelting against 8% of copper mining, and two mines in one North Carolina district supply roughly 80% of the world’s high-purity quartz.
Layer 3 · Semiconductor production ecosystem
Design software and IP, wafer fabrication equipment, materials and chemicals, the foundry itself, and advanced packaging and test. Everything required to turn sand into a working advanced logic or memory die.
Binding constraint. The bottleneck moved. For years it was lithography; it is now advanced packaging — bonding a logic die to twelve stacks of HBM on a silicon interposer. TSMC controls over 90% of global CoWoS output, and CoWoS allocation is effectively the revenue ceiling for every accelerator vendor.
Layer 4 · Compute, memory and networking
The accelerators, the memory that feeds them, and the networking that binds them into a single training fabric. Highest revenue growth in the infrastructure stack, and the shortest moat half-life.
Binding constraint. Three separate chokepoints, and memory is currently the tightest. The most profitable chip company in history is guiding margins down because its memory suppliers raised prices — value transferring up the supply chain against the buyer’s wishes is what a genuine bottleneck looks like.
Layer 5 · Data centres, cloud and edge
The physical buildings and the operating businesses that sell capacity: hyperscale cloud, colocation and GPU-rental neoclouds. Four genuinely different business models that are routinely conflated.
Binding constraint. This is no longer a technology sector. It is a leveraged infrastructure sector wearing technology multiples. Once the build-out is financed with debt, SPVs and depreciating collateral rather than operating cash flow, the right analytical toolkit is project finance, not software.
Layer 6 · Data and knowledge infrastructure
The data assets and the systems that govern them: ingestion, storage, cataloguing, cleaning, annotation, provenance and curation. It supports evaluation, retrieval and monitoring as much as training — and it was previously compressed into a single card inside the model layer.
Binding constraint. The binding constraint here is legal rather than physical. Clear, transferable rights to train on third-party content are contested across several jurisdictions simultaneously, and lineage sufficient to evidence those rights cannot be retrofitted once data is already mixed into a corpus. That is a chokepoint with no lead time to shorten.
Layer 7 · AI models and inference
Frontier training, post-training and alignment, inference serving, and the data pipelines that feed all of it. Where capital expenditure is converted into capability.
Binding constraint. There may not be one. Model weights are a depreciating asset; a fab is not. The durable assets at this layer are proprietary data and distribution, not the weights themselves — which is why the vertically integrated players look structurally stronger than the pure-play labs.
Layer 8 · Agentic software and applications
Orchestration frameworks, agent runtimes, vector and operational databases, observability, security, and the application software customers actually pay for.
Binding constraint. Data gravity and systems of record. The scarce input is context, not intelligence — model capability is available to everyone at collapsing prices, but a company’s customer records, permission structures and process knowledge are not. Whoever owns the context owns the deployment.
Layer 9 · Connectivity and communications
The communications layer that links racks, buildings, continents and machines. It was missing from the earlier map: optics sat inside compute silicon, carrier networks appeared nowhere, and the path between the data centre and the edge was drawn as an arrow.
Binding constraint. The chokepoints are geographic rather than manufactured. Metro conduit sits on rights of way that would not be granted again, and three firms outside China can lay a transoceanic system. Neither constraint is relieved by capital expenditure, which is precisely what makes them durable.
Layer 10 · Embodied AI and autonomous systems
Robots, vehicles, drones and devices, plus the actuators, reducers, sensors and batteries beneath them. Three industries at three completely different stages of maturity, routinely conflated.
Binding constraint. The geography inverts here. Layers 3 and 4 concentrate in the Netherlands, Taiwan, Japan and Korea. Layer 10 concentrates in China, which holds 63% of humanoid component supply and over 70% of rare earth magnet production. Adding this layer to a semiconductor-heavy portfolio adds a second, oppositely-directed geopolitical exposure — it does not diversify the first.