The CFO receives the AI update before the board pack is finalised.

The dashboard looks impressive:

AI adoption is up.

Tokens processed are up.

Engineering is using coding agents.

Legal is testing contract review.

Procurement has started to use negotiation assistants.

Customer service is routing more queries through copilots.

Several business units now report “AI-assisted productivity” in their monthly performance reviews.

The CIO is pleased. The CEO is encouraged. The board sees momentum.

Then the CFO opens the cost report.

The numbers are less reassuring:

The company is spending more on AI-enabled SaaS, more on APIs, more on model access, more on cloud infrastructure, more on integration, more on data preparation, more on cyber controls, more on human verification and more on the internal teams needed to make the new tools work.

Some of the spending is useful.

Some of it is simply machine activity.

The CFO asks the first question:

“Which of this AI spend has reached the P&L?”

The room becomes more cautious.

The CIO explains that some teams are already changing model defaults, routing simpler work to cheaper models, using prompt caching, shortening context windows and reserving expensive frontier models for harder tasks. That makes sense. Coinbase recently became a useful public example of this pattern: higher token usage did not automatically mean higher AI spend once default models, routing, caching, context discipline and spend visibility were changed.

Then the COO asks the better question:

“Why are we adding AI to old workflows instead of redesigning the work?”

That question changes the meeting.

The company already owns the important assets:

• It owns the customer relationships.

• It owns the supplier relationships.

• It owns the workflows.

• It owns the exception history.

• It owns the operating data.

• It owns the decision rights.

• It owns the brand, trust, permissions, plants, systems, contracts, approvals and physical infrastructure through which work gets done.

The issue is not whether the company owns enough assets to make AI useful.

The issue is whether those assets are trapped inside old workflows built for humans, old software built for screens, old approval chains built for slower work and old metrics built to prove adoption rather than value.

The digital co-worker does not only create a cost problem, but it exposes an operating-model problem.

The Boardroom point

Large corporates already own the scarce ingredients of enterprise AI.

Not just data.

Data by itself are often inert. They can be incomplete, duplicated, badly permissioned, incorrectly labelled, stuck in different systems or divorced from the context that gives them meaning.

The real corporate asset is the operating system of the business:

• Data.

• Workflow.

• Client insight.

• Client relationships.

• Supplier insight.

• Supplier relationships.

• Exception history.

• Tacit judgement.

• Regulatory permissions.

• Brand trust.

• Physical assets.

The practical knowledge of what happens when the process map meets reality. AI does not create that operating system but it makes visible whether management understands it.

That is why the current AI debate is shifting:

The first phase of enterprise AI was about access.

The second phase was about adoption.

The third phase will be about redesigning work.

The danger is that companies measure the wrong thing at precisely the moment when the cost structure is becoming more visible.

Prompts sent are not value. Tokens consumed are not value. Agents launched are not value. AI-enabled SaaS renewals are not value. A larger AI budget is not transformation. A smaller AI budget is not necessarily discipline.

The useful metric is verified machine work.

Did the digital co-worker do something economically useful? Was it correct? Was it used? Did it remove an old step? Did it reduce rework? Did it shorten cycle time after review? Did it reduce risk? Did it improve customer outcome? Did it lower total cost after tokens, cloud, integration, verification, governance and failure are counted?

That is the boardroom question.

The AI market is now forcing that question because the pricing model is changing. Reuters reported this week that usage-based AI pricing is making corporate AI spending harder to predict even as token prices fall, and that businesses are increasingly turning to smaller, cheaper models and routing tools to reserve premium models for more complex work. Gartner has also warned that token-driven AI coding spend is becoming difficult to justify where organisations lack the maturity to measure cost against business impact.

This is not a temporary procurement annoyance.

It is the beginning of machine-work accounting.

Tokenmaxing is not transformation

Tokenmaxing is the corporate habit of treating visible AI consumption as evidence of progress. It was understandable at first.

Boards wanted to know whether employees were using the tools. CIOs needed proof that licences were not sitting idle. Business-unit leaders wanted to show experimentation. Software vendors wanted usage. Investors wanted adoption. Employees wanted speed.

So usage became the early proxy:

• More prompts.

• More tokens.

• More copilots.

• More agent runs.

• More AI-assisted documents.

• More lines of AI-assisted code.

The problem is that usage was never the same as productivity.

A company can increase token consumption while leaving the old workflow intact. It can generate more documents without making better decisions. It can produce more code while increasing review burden. It can answer more customer questions while increasing repeat contacts. It can summarise more meetings while holding the same number of meetings.

That is not transformation, it is a new layer.

The most dangerous version is not reckless AI use. It is controlled, approved, measured AI use that still measures the wrong thing.

A company can comply its way into waste. It can build dashboards, policies, approved tools, training modules and governance committees, then reward teams for activity that never becomes verified value.

The question should not be:

How much AI are we using?

The better question is:

Which old work disappeared because the digital co-worker now performs part of it safely, cheaply and verifiably?

If the old work remains, AI has not transformed the workflow, it has added a meter to it.

The workflow was always the asset

Most large corporates have an asset they do not properly value because it does not sit neatly on the balance sheet:

It is the workflow.

The workflow is not the process map. The process map is the clean version. The workflow is what actually happens:

It is how a customer exception gets resolved when the standard policy fails. It is how a supplier is treated differently because everyone knows they recover faster after a shock. It is how a claims handler spots that a loss is not really like the last one. It is how a maintenance engineer knows that a machine will fail after a particular vibration pattern even when the dashboard is still green.

It is how an underwriter reads silence in a submission. It is how a finance team knows which invoice discrepancy is harmless and which one signals fraud. It is how a buyer knows which supplier promise is credible. It is how a plant manager knows when a system can be stopped safely and when stopping it creates a larger problem.

That knowledge is rarely stored in one place.

It lives across enterprise systems, emails, spreadsheets, contracts, call notes, service records, supplier histories, customer conversations, claims files, maintenance logs, workarounds, local relationships and people’s heads.

That is why generic AI can disappoint inside large companies:

The model may be capable, but it does not automatically understand the business. It can summarise the policy without knowing how the policy behaves under stress. It can draft the contract without knowing which clause has caused disputes. It can read the supplier scorecard without knowing which supplier absorbs pressure quietly and which one fails loudly.

The scarce asset is not data alone, it is data joined to workflow, relationships and judgement.

This is important for the make-versus-buy question:

The SaaS vendor may own the interface. The corporate owns the operating reality. If the vendor brings scale, resilience, regulatory assurance, security, network effects, maintenance and deep workflow capability, buying still makes sense.

But if the vendor mainly wraps a generic interface around the corporate’s own data, customer relationships, supplier relationships and workflow history, the economics are changing.

AI-enabled development does not make software free. Maintenance, testing, security, audit, user support, integration and accountability still cost money. But AI does lower the cost of creating workflow-specific tools. That shifts the boundary.

The new question is not:

Can we build software?

The better question is:

Does the vendor own something scarce, or are we paying rent on our own operating knowledge?

Gartner has described a related pressure as “agentic arbitrage”, where AI agents complete tasks across systems and reduce the need for users to interact with traditional software interfaces. Gartner estimates that up to $234 billion of enterprise application software spend could be exposed to this dynamic by 2030, roughly 20% of enterprise application SaaS spending.

That does not mean SaaS disappears, what it means is weak SaaS has to justify itself.

The digital co-worker needs a job description

The phrase “digital co-worker” is useful only if it is taken seriously.

A human employee does not enter the company with unlimited authority. The company defines the role, reporting line, budget, access rights, approval limits, escalation process, review standard and accountability structure.

A digital co-worker needs the same discipline.

It needs a job description.

Not a marketing description. A real operating specification.

What work may it perform? Which data may it access? Which tools may it call? Which systems may it change? Which model tier may it use? How much may it spend? When must it escalate? Which outputs require verification? Which decisions require a receipt? When must it stop?

Without that, the digital co-worker becomes unmanaged machine labour.

It can generate useful work. It can also generate expensive activity, plausible error, hidden dependency and verification debt. It can operate continuously. It can create outputs faster than the company can absorb them. It can make the old bottleneck reappear as senior review.

The point is not to slow everything down.

A digital co-worker that classifies invoices inside a narrow rule set is different from a digital co-worker that drafts a board paper. A digital co-worker that retrieves policy wording is different from one that recommends declining a claim. A digital co-worker that summarises supplier contracts is different from one that changes payment terms.

Authority matters.

So does reversibility.

So does evidence.

The company should be able to reconstruct what the digital co-worker did, what it saw, which model ran, which tool was used, which data were accessed, what output was produced, who approved it and whether the result was used.

“The AI said so” is not an audit trail.

Routing is the new operating discipline

A large company would not assign every task to the most expensive partner in the firm, and it should not assign every machine task to the most expensive model.

The expensive model should be the escalation point, not the default worker.

Routine extraction, classification, comparison, drafting, reconciliation and summarisation may not need frontier intelligence. Some work can go to smaller models. Some can go to retrieval systems. Some can go to deterministic software. Some can run locally because the data are sensitive. Some should use a frontier model because the reasoning burden is high. Some should stop and escalate to a human.

Gartner’s recent comments on AI coding costs are directionally consistent with this. It recommends breaking work into smaller tasks, aligning model selection with task complexity and reserving frontier models for complex, high-value development work. Reuters reported a similar market shift, with companies using routing tools such as OpenRouter, Martian or proprietary internal gateways to assign tasks to cost-effective models and reserve premium models for complex work.

That sounds technical, yet it is not only technical, it is workforce planning for machine labour.

Routing should be based on:

• Cost.

• Risk.

• Confidentiality.

• Complexity.

• Reversibility.

• Required judgement.

• Verification burden.

• Auditability.

The objective is not to minimise tokens. A cheap answer that creates review work, litigation risk or customer damage is not cheap. An expensive answer that prevents a serious error may be good value.

The objective is verified output at the lowest total cost and acceptable risk.

That is a different management discipline.

The make-or-buy question comes back

The old software answer was simple enough.

Buy.

Software development was slow. Internal IT capacity was scarce. SaaS vendors had better product teams, faster releases, cleaner interfaces and broader benchmarking across customers. The corporate usually did not want to become a software company.

That answer still holds in many places but it does not hold everywhere anymore:

AI changes the economics of internal build.

It compresses some development work.

It accelerates prototyping.

It allows small teams to create internal tools that would previously have required larger engineering teams.

It makes workflow-specific software more plausible.

But this is where companies need discipline:

AI does not remove the cost of ownership.

It does not remove security.

It does not remove maintenance.

It does not remove regression testing.

It does not remove user support.

It does not remove legal accountability.

It does not remove audit.

It does not remove the need to keep the tool alive when the person who built it leaves.

So the right answer is not “build everything”, it is to segment the software estate.

• Buy commodity systems where standardisation is valuable.

• Buy where the vendor has network scale, regulatory depth, security maturity, resilience or superior maintenance.

• Build or customise where the workflow is proprietary, strategically material, data-rich, relationship-rich and poorly served by generic tools.

• Rent frontier intelligence where breadth matters.

• Own the proof where accountability matters.

This is where the corporate’s intangible assets become central. A workflow that depends on the company’s customer trust, supplier history, exception knowledge and risk appetite may be too important to leave entirely inside a vendor interface. Equally, a generic workflow that the vendor performs better and more cheaply should not become a vanity internal build.

The board should not ask whether the company is pro-SaaS or anti-SaaS.

It should ask whether each software decision protects, uses or leaks the operating knowledge that makes the company distinctive.

Falling token prices are not a simple win

The intuitive view is that falling AI prices solve the problem:

They do not.

They change the problem.

Inference prices have been falling quickly, although estimates vary by methodology, model tier and benchmark. A 2025 paper using data from Artificial Analysis and Epoch AI found that the price for a given benchmark-performance level was falling around 5x to 10x per year for frontier models across several benchmark categories. A 2026 paper on inference-service pricing found an approximately 600-fold decline in token prices in its dataset from 2020 to 2026, but also found that flagship models still carried a substantial reasoning premium. This is an academic preprint and should be treated as research evidence, not settled market fact.

Cheaper tokens make more machine work viable and that is the opportunity:

• They allow more experimentation, more automation, more local tools, more digital co-workers and more workflow redesign.

• They may also reduce the cost of errors if the company has good routing, verification and stop conditions.

But cheaper tokens can also expand usage faster than prices fall and that is a risk:

• More workflows become AI-enabled.

• More agents run.

• More internal tools get built.

• More supplier products add AI features.

• More SaaS vendors reprice.

• More employees become heavy users.

• More machine outputs require review.

The result may be lower unit prices and higher total dependency.

This is an enterprise version of Jevons’ paradox: the cheaper the machine work becomes, the more work is likely to be given to machines.

That does not mean the outcome is bad. It means the company must measure the right thing.

Cheaper AI is useful only if it produces more verified value, not merely more machine activity.

There is also a supply-chain question:

McKinsey estimates that data centres may require $6.7 trillion of worldwide capital expenditure by 2030 to keep pace with demand for compute power, including $5.2 trillion for AI-processing loads. McKinsey also sets out a wide scenario range for AI-related data-centre capital expenditure, which is important because the exact demand path is uncertain.

That capital has to earn a return.

If token prices fall sharply and enterprise usage expands, the market may be fine. If token prices fall faster than monetisable demand grows, parts of the AI infrastructure and vendor chain may come under pressure. If vendors subsidise usage to gain adoption, customers may build workflows on economics that later change.

No board should pretend it can forecast the AI infrastructure cycle with precision but it should stress-test its own dependency:

What happens if token prices fall and usage explodes?

What happens if token prices rise for premium reasoning models?

What happens if a vendor changes limits?

What happens if a model is withdrawn?

What happens if the cheap model is not acceptable for regulatory, security or geopolitical reasons?

What happens if a supplier’s AI economics fail?

The risk is not simply that AI becomes expensive, it is that AI becomes cheap enough to embed everywhere before the company has understood what it now depends on.

The AI supply chain changes shape

The first AI supply-chain risk was scarcity:

Could the vendor access GPUs? Could the model provider serve demand? Could the data centre get power? Could the cloud provider allocate capacity? Could the enterprise get enough rate limit to run its workflows?

That risk has not disappeared. McKinsey has separately described the AI data-centre build-out as one of the largest infrastructure build-outs in modern history and points to constraints in power, thermal equipment and long-lead components.

But another risk is now appearing, volatility:

Prices change. Model rankings change. Providers change terms. Open models improve. Closed models cut prices. Some vendors chase enterprise customers. Some may lose margin. Some applications become easier to replace. Some internal tools become more attractive. Some AI-enabled SaaS products become less defensible.

A corporate workflow that depends on one model, one vendor, one orchestration layer, one cloud platform, one data connector or one pricing assumption may look efficient but it may also be fragile.

For each important workflow, it should know:

• Which model is used.

• Which provider serves it.

• Which data are accessed.

• Which tools can be called.

• Which SaaS system sits in the path.

• Which human verifies the output.

• Which fallback exists.

• Which cost assumption makes the business case work.

The AI supply chain is not only the chips and data centres, it is the chain of decisions through which machine work becomes business action.

The CFO version

For the CFO, this is a cost-accounting problem:

The company is building a machine-work cost base. That cost base does not sit neatly in one line item. It appears in software subscriptions, cloud usage, API calls, data platforms, AI-enabled SaaS uplifts, internal engineering, vendor support, cybersecurity, legal review, model evaluation, governance, training, failed pilots and human verification.

The cost is also dynamic.

A seat may be fixed. A token is not. An agent can run more steps. A coding assistant can consume longer context. A legal review can expand across more documents. A supplier negotiation assistant can call more tools. A customer-service workflow can retry or escalate. A reasoning model can use hidden thinking tokens that make actual task cost difficult to predict.

This is why cost-per-token is useful but incomplete, the CFO needs cost per verified outcome:

• Cost per claim triaged correctly.

• Cost per invoice processed without rework.

• Cost per contract reviewed with the relevant exceptions identified.

• Cost per software change accepted after testing.

• Cost per customer query resolved without repeat contact.

• Cost per supplier risk detected before operational failure.

The CFO should also separate four types of savings.

1. Avoided work: a task disappears.

2. Faster work: the task remains but takes less time.

3. Better work: the same process produces fewer errors or better outcomes.

4. Shifted work: the apparent saving in one area reappears as compute cost, review burden, vendor dependency or downstream risk.

Only the first three create value, the fourth creates a better-looking business case.

The CFO’s central question is:

Which AI spend has produced verified value after the full cost of machine work has been counted?

The CEO version

For the CEO, this is an operating-model problem:

The company cannot become AI-capable by adding tools to existing workflows. That may improve individual productivity, but it does not necessarily redesign the company.

The CEO has to decide where the company’s operating knowledge should sit.

• Inside vendor software?

• Inside internal platforms?

• Inside local models?

• Inside data products?

• Inside human experts?

• Inside digital co-workers with controlled authority?

The answer will vary by workflow.

The CEO should start with the parts of the business where the corporate’s intangible assets are strongest: customer trust, supplier knowledge, operational exceptions, pricing judgement, regulatory insight, product judgement and service history.

Those are the workflows where generic software may not be enough.

The CEO should also be careful about theatrical AI. The useful first redesign may not be a glamorous agent. It may be removing three approval loops from procurement, reducing customer exception handling time, routing maintenance cases better, finding claim anomalies earlier, cutting invoice rework or giving frontline teams access to the right operational memory.

The CEO should insist that each digital co-worker has a role, authority and evidence trail.

It should be clear whether the digital co-worker is:

• Assisting.

• Recommending.

• Executing.

• Escalating.

• Approving.

Those are different activities and should not be hidden under one AI label.

The CEO’s central question is:

Which workflows should be redesigned because digital co-workers change the constraint around speed, cost, judgement and accountability?

The Board version

The board does not need to approve every model, gateway, workflow agent or routing rule but it does need to know whether management is turning corporate intangible assets into verified value or merely buying AI activity.

A useful board discussion should cover ten questions.

1. Which workflows have been redesigned, not merely AI-assisted?

2. Which digital co-workers have defined roles, budgets, authority limits and stop conditions?

3. Which AI usage metrics are activity measures rather than value measures?

4. Which AI outputs require human verification, and how much verification work is being created?

5. Which model-routing rules determine when work goes to cheap models, frontier models, local models or humans?

6. Which SaaS tools mainly wrap our own data, workflows, client relationships and supplier knowledge?

7. Which software should we buy, build, retire or replace with workflow-specific tools?

8. Which AI vendors, model providers or orchestration layers create concentration risk?

9. What happens if token prices fall, rise, become less transparent or vary sharply by model type?

10. Which AI benefits have reached the P&L after full cost, risk, rework and old-process removal are counted?

The board should also ask one uncomfortable question:

Are we using AI to redesign work, or to accelerate work that should no longer exist?

That question is rather central because AI can make bad workflows faster:

It can produce more reports that no one needs. It can create more code that no one can maintain. It can generate more analysis than management can absorb. It can preserve redundant controls by making them cheaper to complete. It can automate internal theatre.

The board’s job is not to celebrate usage, it is to demand proof.

The hard truth

The corporate already owns the hard part: it owns the relationships, data, workflows, permissions, operating history, physical assets and judgement that make enterprise AI valuable.

But ownership is not the same as usability:

If those assets are trapped inside fragmented systems, undocumented exceptions, unexamined workflows, vendor interfaces and human memory, AI will not automatically unlock them.

It may simply create another layer of activity.

The next phase of AI will therefore punish two types of company.

1. The first will underuse AI because it treats the technology as a controlled experiment rather than a new form of machine labour.

2. The second will overuse AI because it treats machine activity as transformation.

Both will miss the point and winners will redesign work:

They will route machine labour intelligently.

They will use expensive models where expensive models matter.

They will use cheaper models where cheaper models are enough.

They will build where the workflow is proprietary.

They will buy where the vendor owns real advantage.

They will require decision receipts where accountability matters.

They will count verified value, not tokens.

They will not tokenmax, they will make the workflow measurable.

The weekend takeaway

AI is not asking large corporates to discover whether they own valuable assets, they already do.

The real corporate asset is the operating system of the business: data, workflows, client relationships, supplier relationships, exception history, judgement, permissions and tangible infrastructure.

Digital co-workers make that asset more usable, but only if the work is redesigned.

The danger is not that companies fail to use AI. Many will use it heavily. The danger is that they use AI to accelerate old workflows, burn tokens, deepen vendor dependency and call the activity transformation.

The next software question is no longer simply buy or build, it is:

Which workflow should we redesign?

Which part should a digital co-worker perform?

Which model should do the work?

Which vendor really adds value?

Which decision needs proof?

Which old step can disappear?

The companies that win will not be the ones with the most AI activity, they will be the companies that know which machine work was worth paying for.

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🧠 Techie Corner for the Non-Techies

1. Token

A token is a unit of text or data processed by an AI model. A short prompt uses fewer tokens. A long document, coding agent or multi-step workflow can use many more. Token usage matters because many AI services are priced directly or indirectly by consumption.

2. Tokenmaxing

Tokenmaxing is the habit of maximising or celebrating AI usage as if usage itself proved productivity. It does not. The company may be consuming more machine work without producing more verified value.

3. Verified machine work

Verified machine work is AI output that has been checked, used and connected to a business outcome. A generated draft is not verified machine work. A contract review that correctly identifies the relevant clauses and reduces expert review time may be.

4. Digital co-worker

A digital co-worker is an AI-enabled system that performs part of a workflow. The label is only useful if the company defines its scope, authority, access rights, budget, escalation rules and evidence requirements.

5. Model routing

Model routing sends different tasks to different models or systems. Simple work may go to a smaller or cheaper model. Complex work may go to a frontier model. Sensitive work may stay local. Consequential work may go to a human.

6. Frontier model

A frontier model is a highly capable model near the leading edge of current AI performance. There is no single universal threshold. Frontier models can be valuable, but they are not always the right tool for routine work.

7. Small language model

A small language model is a narrower model that may be cheaper, easier to control and more suitable for bounded workflows. It is not automatically safer or better. It must still be tested against the task.

8. Context window

The context window is the amount of information the model can consider during a task. Larger context can help, but it can also increase cost and noise. More context is not always better.

9. Prompt caching

Prompt caching reduces cost by reusing repeated parts of a prompt or context rather than processing the same material from scratch each time. It is useful where workflows have stable repeated instructions or reference material.

10. Agentic AI

Agentic AI can pursue goals across several steps, often using tools, systems or external data. Its risk depends less on the word “agent” and more on what it can access and do.

11. AI gateway

An AI gateway sits between users or applications and model providers. It can manage routing, logging, cost controls, security, caching and provider selection.

12. Decision receipt

A decision receipt records how an AI-assisted output was produced: what data were used, which model ran, which tools were called, who reviewed the result and whether it was approved. Serious work needs this record.

13. Make versus buy

Make versus buy is the decision whether to build internally or purchase from a vendor. AI lowers some build costs but does not remove maintenance, security, audit, support or accountability.

14. Agentic arbitrage

Gartner uses this term for the risk that AI agents complete tasks across systems and reduce the need for users to interact with traditional SaaS interfaces. The result may pressure parts of enterprise application spending.

15. Jevons’ paradox

Jevons’ paradox is the idea that greater efficiency can increase total consumption by making a resource cheaper or easier to use. Cheaper tokens may increase total AI use.

16. Verification debt

Verification debt is the review burden created when AI generates more output than the organisation can confidently inspect, approve and act on.

17. AI supply chain

The AI supply chain includes model providers, cloud platforms, data centres, chips, power, software vendors, orchestration tools, data connectors and the people who operate them. For corporates, it also includes the workflow dependencies created by AI tools.

18. Workflow redesign

Workflow redesign means changing how work moves through the organisation, not simply adding AI to the existing process. The test is whether steps disappear, decisions improve or outcomes change.

19. Corporate intangible stack

The corporate intangible stack is the combination of data, workflows, customer relationships, supplier relationships, exception history, judgement, trust and permissions that makes the company’s work hard to copy.

20. Cost per verified outcome

Cost per verified outcome measures the full cost of producing a business result, including model use, tokens, cloud, integration, human review, rework, risk and governance. It is more useful than cost per token.

📚 Further reading

• Reuters: Cheaper AI is better: Soaring bills are reshaping how businesses choose models (2026)

  Reuters reports that usage-based AI pricing is making corporate spending unpredictable, that some businesses are turning to smaller and cheaper models, and that routing tools are becoming more important. It also reports examples and estimates from third parties.

• Business Insider: Coinbase’s CEO outlined 5 strategies to keep AI spend low without limiting tokens (2026)

  Business Insider summarises Brian Armstrong’s public comments on Coinbase’s approach to AI spend, including cheaper default models, routing, caching, leaner context and spend visibility.

• Gartner: AI coding costs will surpass average developer salary by 2028 as token consumption surges (2026)

  Gartner argues that token-driven AI coding spend is creating budget strain and recommends model-routing, task classification and disciplined operating models.

• Gartner: $234 billion in enterprise application software spend is at risk from agentic AI (2026)

  Gartner estimates that up to $234 billion of enterprise application software spend could be exposed to agentic arbitrage by 2030, equal to roughly 20% of enterprise application SaaS spending.

• McKinsey: The cost of compute: A $7 trillion race to scale data centers (2025)

  McKinsey estimates that data centres may require $6.7 trillion in worldwide capital expenditure by 2030, including $5.2 trillion for AI-processing loads. The article provides scenario ranges, which matters because demand, efficiency, regulation and supply constraints remain uncertain.

• McKinsey: The $7 trillion data center build-out: How industrials can capture their share (2026)

  McKinsey describes the AI data-centre build-out as a large industrial infrastructure challenge, with constraints around capital, energy and long-lead equipment.

• Mingdeng Du: Tiered Super-Moore’s Law: Price Evolution, Production Frontiers, and Market Competition in Large Language Model Inference Services (2026 preprint)

  This academic preprint studies token pricing across a large dataset and finds large declines in token prices, with important differences by model tier and a continuing premium for flagship reasoning models. It is useful research, but it should be treated as a preprint unless and until independently validated through peer review.

• Hans Gundlach, Jayson Lynch, Matthias Mertens and Neil Thompson: The Price of Progress: Algorithmic Efficiency and the Falling Cost of AI Inference (2025 preprint)

  This paper estimates that the price for a given level of benchmark performance has declined rapidly, around 5x to 10x per year for frontier models across several benchmark categories. It is useful for understanding the direction of inference economics, but benchmark-price declines do not automatically translate into lower enterprise workflow costs.

Last week on Something for the Weekend

Your Cyber Controls Were Built for a Slower Clock

Last week’s article argued that the Five Eyes AI cyber warning is fundamentally about time. AI may not need to invent entirely new attacks. It may only need to make familiar attacks faster than corporate governance can respond.

The central idea was governance latency: the time between recognising a credible risk and executing the decision required to contain it. The article argued that boards should stop asking only whether cyber controls exist and start asking whether those controls can perform quickly enough under pressure.

That same idea carries into this week’s piece: the issue is not whether AI tools exist, the issue is whether the company can redesign, route, verify and govern machine work before the meter becomes the strategy.

The board receives its quarterly AI report:

Usage is up.

More employees have access to copilots.

Several hundred agents have been launched.

The cost of some models has fallen.

The average response arrives faster.

The sustainability team reports that the estimated carbon intensity of each request is improving.

It sounds encouraging, then the CFO asks three questions:

1. How much of the old work disappeared?

2. How much verified value reached the profit and loss account?

3. Why did the total AI bill still rise?

The CIO explains that the company is doing much more with AI. More users. More prompts. More documents. More code. More agents. More automated decisions.

That may be true.

However, more machine activity is not the same as more business value.

The company may have made each unit of AI cheaper while buying many more units. It may have reduced the energy required for a simple request while replacing simple requests with agents that read files, call tools, reconsider their plans, retry failures and ask other models to check their work.

The environmental report may also be less certain than it appears.

For most closed AI services, the corporate user cannot see the physical machine answering the request. It cannot see the precise hardware, the number of other users sharing it, the utilisation rate, the cooling system, the electricity source or the location from which the workload was served.

The carbon number is therefore often an estimate built from assumptions and a precise decimal point does not remove that uncertainty.

The board is left with a more useful question:

The Boardroom point

AI efficiency should not be measured by how much output the machine produces. It should be measured by the verified business value produced for every unit of total resource consumed.

That includes:

Tokens.

Compute.

Electricity.

Water.

Carbon.

Cloud expenditure.

Hardware.

Human verification.

Correction and rework.

Management attention.

Operational risk.

The numerator matters as much as the denominator:

• A model that consumes little energy but produces a wrong answer is not efficient.

• A cheap agent that saves no human time is not productive.

• A fast system that creates work nobody uses has not created value.

• A smaller model that repeatedly fails may be more expensive than a larger model that completes the task correctly.

The useful measure is neither output per kilowatt nor it is not tokens per pound. It is not documents per employee either.

It is:

That is where the environmental argument and the commercial argument begin to converge: waste is expensive before anyone calls it unsustainable.

Last week, the token became a kilowatt

Last week’s Something for the Weekend followed the AI token down through the software stack:

The token looked weightless.

Underneath it sat inference.

Underneath inference sat chips, memory, networks and cooling.

Underneath those sat data centres, grid connections, power contracts, land, capital and water.

The conclusion was that the token is only the basic meter.

But a better meter is useful, verified work.

This week’s question is what happens when companies install millions of those meters without first deciding which machine work is worth paying for.

That is important because AI is becoming more efficient at the same time as it is becoming much more widely used. Those two statements are not contradictory: a more efficient engine can still consume more fuel if the company installs thousands of engines and runs them for longer.

The International Energy Agency reported in 2026 that global data-centre electricity demand increased by 17% during 2025. It estimated that consumption in AI-focused data centres increased by about 50% over the same year.

These are system-level estimates, not direct measurements of every data centre. They should therefore be treated as modelled aggregates rather than perfect counts.

But the direction is nevertheless clear:

• The machines are becoming more efficient.

• The demand for machine work is growing faster.

The IEA’s central projection has global data-centre electricity consumption rising from approximately 485 terawatt-hours in 2025 to around 950 terawatt-hours in 2030. It expects electricity consumption from AI-focused data centres to grow much faster than the broader category, roughly tripling over that period.

Terawatt-hours can be difficult to picture. So, put into terms we all see every day, the electricity consumed by the world’s data centres in 2025 was equivalent to the annual electricity use of approximately:

• 186 million typical Chinese homes;

• 180 million typical British homes;

• 143 million typical German homes;

• 101 million typical Japanese homes; or

• 47 million typical US homes.

These are alternative ways of describing the same 485 terawatt-hours. They are not numbers to be added together. By 2030, the IEA’s central projection would be equivalent to the annual electricity consumption of approximately:

• 364 million Chinese homes;

• 350 million British homes;

• 280 million German homes;

• 198 million Japanese homes; or

• 92 million US homes.

The numbers differ because a typical household does not consume the same amount of electricity in every country. Climate, home size, electric heating, air-conditioning, appliances and household size all matter.

But whichever domestic benchmark is used, the scale is difficult to ignore.

The world is moving from enough data-centre electricity to power tens or hundreds of millions of homes towards nearly twice as much within five years. Whilst that projection may change, AI demand, hardware efficiency, power availability, financing conditions and regulation are all moving quickly.

However, the boardroom implication does not depend on the exact forecast being correct:

Efficiency per task will not control the company’s total AI footprint if the number and complexity of tasks grow faster.

The digital co-worker is not a unit

Companies increasingly refer to AI agents as digital employees or digital co-workers.

Whilst that phrase is useful, it is also dangerous:

• There is no standard unit called one digital co-worker.

• One may answer short questions from employees.

• Another may review contracts.

• Another may watch transactions for fraud.

• Another may search a software repository, modify code, run tests, inspect failures, rewrite the code and try again.

• Another may communicate with customers, retrieve account information, calculate an answer, trigger a refund and create a record for human review.

Calling all of them digital workers is like calling a bicycle, a delivery van and a cargo aircraft transport. The label describes their purpose but it does not describe what they consume. The energy footprint of a digital co-worker depends on:

The model used.

The length of its instructions.

The amount of information it reads.

The number of steps it takes.

The tools it calls.

The number of times it fails.

The number of times it retries.

The length of its output.

The hardware on which it runs.

The utilisation of that hardware.

The data-centre overhead.

The electricity mix.

The human work needed to check the result.

Without those facts, an average annual carbon footprint for a digital employee would create a false sense of precision.

A corporate user should not ask:

What is the footprint of one AI employee?

It should ask:

What does this particular AI workflow consume to produce one accepted business outcome?

That outcome might be:

• A correctly resolved customer enquiry.

• A software defect repaired and passed through testing.

• A contract reviewed to an agreed standard.

• An invoice matched without later correction.

• A claim triaged and accepted by a human reviewer.

• A board paper produced with verified evidence.

• A transaction flagged and correctly investigated.

That is a functional unit which makes comparison possible because it connects the machine to the job.

The agent can spend without seeing the bill

A human employee has an employment contract:

• They have working hours.

• They have delegated authority.

• They may have a company credit card and an expense limit.

• They usually know when they have spent a day on a task.

An AI agent operates differently:

• It can work continuously.

• It can call other services.

• It can load more information.

• It can ask another model for help.

• It can abandon one route and try another.

• It can repeat the same mistake at machine speed.

• Most importantly, it does not feel the cost.

Recent academic work illustrates the problem, although the findings should not yet be treated as universal. A 2026 preprint titled How Do AI Agents Spend Your Money? studied eight frontier models performing agentic software-engineering tasks. The researchers reported that these tasks consumed around 1,000 times more tokens than the code-chat and code-reasoning comparisons they used.

Runs on the same task differed by as much as 30 times in total token consumption.

More token consumption did not consistently produce higher accuracy. In the study, performance often peaked at an intermediate level of expenditure and then stopped improving materially.

The researchers also found that the tested models were poor at forecasting their own token requirements and tended to underestimate them.

These findings come from coding tasks and may not transfer directly to law, finance, insurance, manufacturing or customer service. The paper was also a preprint at the time of writing, not a settled industry standard.

The management lesson is still important:

1. An agent may not know how much work it will consume.

2. More machine effort may not produce a better result.

3. The same task may produce materially different costs on different runs.

Where the waste hides

The visible AI invoice is only part of the cost.

Waste hides throughout the workflow.

The model is too large for the job

A frontier model may be justified for difficult research, complex reasoning or broad synthesis. Yet, it may be unnecessary for classifying documents, matching invoices, extracting defined fields or checking whether a form is complete.

Using the largest available model for every task is not a sign of technological ambition:

It is a failure to route work properly.

It is the digital equivalent of sending a long-haul aircraft to deliver an envelope.

The machine reads too much

Large context windows allow AI systems to process long documents, code repositories and conversation histories. That does not mean the company should give every model every available document.

Much of the information may be irrelevant and the model still has to process it. Better systems retrieve the relevant material first and then ask the model to reason from the smaller set.

The objective is not the smallest possible prompt. Over-compressing information can remove facts the model needs and produce worse answers.

The objective is the smallest sufficient body of evidence for a reliable result.

The agent takes the scenic route

An agent can search, inspect, plan, revise and retry. That flexibility is part of its value.

It is also a source of waste:

• An agent without a step limit may continue exploring after the economically useful point has passed.

• An agent without a retry budget may repeat the same failure.

• An agent without an escalation rule may spend more trying to solve an exception than the exception is worth.

A scenic route can be enjoyable on holiday, it is less attractive when the taxi meter is running.

The machine talks too much

Longer answers consume more output tokens and generally require more generation work.

Some tasks need detail. Many do not.

A 2026 preprint examining open-weight language models in software-development tasks found that several models produced unnecessarily verbose output. The researchers reported large energy savings after suppressing this behaviour in their tested code-generation settings without reducing measured accuracy.

The reported savings ranged from 44% to 89%.

That is a striking result, but it should not be generalised casually. The study examined a limited set of models and coding benchmarks under controlled conditions. It does not prove that every company can cut its AI energy use by the same amount.

It does demonstrate a simpler point: the machine can consume resources by saying more than the job requires.

The answer is wrong

A wrong answer consumes resources when it is produced:

It consumes more when someone checks it.

It consumes more again when the workflow is corrected.

A hallucination is not only a quality problem, it is waste with a confidence problem.

The model may consume tokens to produce the answer. A lawyer, engineer, accountant or underwriter may then consume expensive human time finding the error. The system may be called again to repair it.

Yet, the company pays for the generation and the correction. That is verification debt with an energy bill attached.

Nobody uses the output

AI makes generation cheap. It can produce more reports, summaries, options, presentations, images and code than the organisation can absorb. Output that is never read can still appear in a productivity dashboard.

It may even look impressive:

The machine was busy.

The employees were experimenting.

The token count rose.

Nothing changed.

A digital worker can look productive while creating no more value than a human worker sending emails to appear occupied. It can simply do it faster with a meter attached to it.

The old process remains

This is one of the most common ways for an AI business case to fail:

• The company introduces an AI step.

• It keeps the old step.

• It adds human review.

• It adds monitoring.

• It adds governance.

• It adds another vendor.

The process becomes faster at producing an intermediate output but more expensive overall.

If the old work does not disappear, the company may have added a layer rather than removed one.

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The greenest answer may not be the shortest

There is a temptation to make the argument too simple:

Fewer tokens must mean less energy.

Smaller models must be greener.

Shorter prompts must be better.

That is not always true:

A smaller model may fail repeatedly.

A compressed prompt may omit the facts needed for the answer.

A cheap model may create more verification work.

A short answer may be useless.

A local machine may sit underused.

A more capable model may complete a difficult task correctly in one attempt.

Recent research reinforces the need for caution. A 2026 preprint examining prompt compression across several model providers found that shortening the input did not reliably reduce estimated energy while preserving quality. In some tested settings, compression degraded task performance severely or caused models to produce much longer outputs.

The study estimated energy using a token-based proxy calibrated against local measurements. It did not directly measure the proprietary providers’ physical infrastructure. Its conclusion was therefore not that compression is bad. It was that reducing input tokens alone is not a reliable optimisation strategy.

That is the right corporate principle: do not optimise a visible metric while damaging the outcome that metric is meant to serve.

Thus, the greenest answer is not necessarily the shortest answer. It is the answer that achieves the required result with the least avoidable total consumption.

A carbon number is a model too

The same AI request can receive different carbon estimates from different methodologies. This is not surprising.

The methodologies may assume different:

Hardware.

Model sizes.

Batching levels.

Server utilisation.

Generation times.

Data-centre overheads.

Electricity sources.

Locations.

Cooling systems.

Hardware lifetimes.

Lifecycle boundaries.

Some estimates cover operational electricity only.

Others try to include emissions associated with manufacturing equipment.

Some use measured telemetry.

Others infer consumption from tokens, latency and model identity.

For a closed API, the external user usually cannot observe the whole physical system. That means environmental reporting may contain three different types of information:

1. Measured data.

2. Provider-supplied data.

3. Modelled estimates.

They should not be presented as though they carry the same level of certainty but this does not make estimation useless. Companies routinely use models where direct observation is difficult. The problem is false precision.

A credible report should say:

• What was measured.

• What was estimated.

• Which methodology was used.

• Which boundaries were included.

• Which assumptions were made.

• How uncertain the result may be.

• Whether two compared figures were calculated on the same basis.

The board should be suspicious of an environmental number that contains several decimal places but no methodology.

Measurement is beginning to catch up

The field is not starting from nothing. The Green Software Foundation ratified its SCI for AI specification in late 2025.

It extends the Software Carbon Intensity approach across AI systems, including generative and agentic workloads. It distinguishes between impacts a provider can influence and the operational impacts a consumer can influence.

The specification supports different functional units for different types of AI system:

A language model may be measured per token.

A classifier may be measured per inference.

A corporate workflow should ultimately be measured per useful result.

The Green Software Foundation has also argued that many organisations deploying agents still lack task-level tooling for energy, carbon and water. That assessment comes from an industry standards organisation rather than a comprehensive global audit, so it should not be read as a measured percentage of all companies.

It is nevertheless consistent with the practical problem faced by corporate users:

They can often see the cloud bill.

They may see token consumption.

They rarely see the full physical resources consumed by an individual business outcome.

MLCommons is developing and operating benchmark frameworks that measure performance and power together across machine-learning systems. These benchmarks improve comparison at the infrastructure level. They do not by themselves tell a board whether an AI-assisted underwriting decision, contract review or customer interaction created value.

Finance is discovering the same problem

Environmental measurement is not developing in isolation and financial management is moving in the same direction.

In June 2026, the Linux Foundation announced its intention to establish a Tokenomics Foundation focused on open standards, benchmarks and practices for AI infrastructure economics. The announcement described a planned collaboration with the FinOps Foundation and support from organisations including major technology providers and enterprise buyers.

It was an announcement of intent, not proof that a mature standard had already been delivered. Its timing is still revealing.

Enterprise AI has moved far enough into production that buyers now need a common language for comparing models, providers, tokens and outcomes.

The FinOps Foundation’s 2026 guidance makes the same operational point: AI costs do not sit only in model inference, they appear across training, tuning, retrieval, orchestration, monitoring, security, subscriptions, cloud infrastructure and agent workflows.

The FinOps guidance warns that poorly controlled agents can create compounding expenditure through repeated calls and loops. That wording should not be interpreted mathematically in every case. Not every agent bill grows exponentially.

The useful observation is that one action can trigger several more actions, each carrying its own cost. Therefore, AI spend can multiply through the architecture of the workflow.

The finance department cannot manage this by counting software seats: it needs to understand the use case.

Green versus fierce is a false choice

The debate is often framed badly:

On one side are people concerned about emissions, water and energy consumption.

On the other are people who want growth, productivity and industrial competitiveness.

The argument then becomes hugging trees versus drilling wells which misses the commercial point:

• A manufacturer does not reduce scrap because it dislikes production, it reduces scrap because wasted material destroys margin.

• An airline does not reduce unnecessary weight because it opposes travel, it does so because fuel has a cost.

• A retailer does not improve inventory turns because warehouses are immoral, it does so because idle stock consumes capital.

AI is no different:

• Unnecessary tokens consume money.

• Unnecessary context consumes compute.

• Unnecessary retries consume capacity.

• Unnecessary output consumes review time.

• Wrong answers consume trust.

The efficient AI system is not less ambitious, it is better designed.

The fiercest competitor will not necessarily use the least AI:

It will use the least wasteful AI.

It will know which work requires a frontier model.

Which work belongs with a smaller model.

Which work should run locally.

Which work needs retrieval.

Which work should be handled by ordinary software.

Which work requires a person.

Which work should not happen at all.

That is not environmental restraint imposed on corporate growth, it is operating discipline.

The rebound problem

There is an awkward complication: efficiency often increases total consumption.

The economist William Stanley Jevons observed in the nineteenth century that improvements in the efficiency of coal use could increase rather than reduce total coal consumption by making more uses economically attractive.

The modern application is sometimes called the Jevons paradox or rebound effect. The analogy should not be treated as a mechanical law.

Cheaper AI does not guarantee that every company will consume more of it, but it makes more machine work economically possible:

As cost per token falls, companies can automate tasks that were previously uneconomic.

As models improve, employees find new uses.

As agents become capable of longer workflows, one human request can trigger many machine actions.

The energy consumed by each action may fall while the total number of actions rises much faster.

This is why a board needs two sets of measures.

First, intensity:

• Cost per verified outcome.

• Energy per verified outcome.

• Carbon per verified outcome.

• Human review time per verified outcome.

Second, absolute consumption:

• Total AI expenditure.

• Total tokens.

• Total electricity attributable to the system, where it can be measured or reasonably estimated.

• Total estimated emissions.

• Total human verification time.

A company can halve the energy used per transaction and increase the number of AI-assisted transactions tenfold. So, both measures must be considered in concert: an improving ratio can coexist with a rapidly expanding total footprint.

The CFO version

For the CFO, the problem is no longer simply the AI budget, it is the economics of machine work.

The finance team needs to ask:

• What is the cost per completed outcome?

• What is the cost per accepted outcome?

• What is the difference between the two?

• How much human review is required?

• What does a failed run cost?

• Which business unit created the consumption?

• Which model and provider were used?

• Which workflows generate revenue?

• Which workflows reduce cost?

• Which workflows reduce risk?

• Which old processes have been removed?

• Which costs remain hidden in cloud, software, consulting, legal, compliance and security budgets?

The fully loaded cost of AI includes more than the provider’s invoice, it includes integration:

• Data preparation.

• Retrieval.

• Monitoring.

• Security.

• Human checking.

• Failure recovery.

• Governance.

• Vendor dependency.

• The cost of keeping the old process alive.

A falling cost per token may be good news, but it is not evidence of a rising return on investment.

The question is: which AI consumption reaches the P&L as value?

The CIO version

For the CIO, this is an architecture problem. The enterprise needs routing rules:

• Which tasks need broad reasoning?

• Which need a narrow model?

• Which require local control?

• Which can use a public cloud service?

• Which should use deterministic software?

• Which require a source record?

• Which require human approval?

• Which can be cached?

• Which contexts can be shortened safely?

• Which agents need step limits?

• Which agents need spending limits?

• Which failures should trigger another attempt?

• Which failures should trigger escalation?

• Which workflows should be terminated?

The objective is not to minimise tokens blindly, it is to reduce avoidable waste without losing the quality, security or accountability the business requires:

Use enough model for the task.

Use enough context for the answer.

Use enough verification for the risk.

Use enough evidence for the decision.

Use enough human judgement for accountability.

No more, no less.

The sustainability version

For the sustainability team, the problem is methodological credibility. A responsible AI footprint report should distinguish between:

• Direct measurement.

• Provider disclosure.

• Third-party estimation.

• Internal modelling.

It should state the boundary.

• Does the number cover only inference electricity?

• Does it include data-centre overhead?

• Does it include cooling?

• Does it include hardware manufacturing?

• Does it include training?

• Does it include storage and networking?

• Does it cover water withdrawal, water consumption or neither?

• What electricity mix was assumed?

• Was the actual serving region known?

• Was an annual grid average used or a time-specific figure?

These questions do not make reporting impossible, they make honest reporting possible. In April 2026, the European Commission opened a consultation on measuring the energy consumption and emissions of AI models and systems.

The consultation was intended to inform a measurement framework connected to the AI Act’s energy-related objectives and the possible design of an AI energy and emissions label. The Commission noted that a comprehensive assessment requires information across computational resources, electricity use and hardware.

The consultation demonstrates that policymakers see the issue but it also demonstrates that the methodology is not finished.

Companies should prepare for greater disclosure without pretending that every current estimate is audit-grade.

The Board version

The board does not need to select the best GPU and it does not need to debate every prompt. However, it does need to understand whether management is building a machine-work cost base the company can control.

A useful board discussion should cover ten questions:

1. Where is AI being used in production?

2. Which workflows consume the most tokens, compute and human review?

3. Which AI costs are fixed, variable or hidden inside other budgets?

4. Which workflows have a defined unit of useful work?

5. Which agents have budgets, retry limits and stop conditions?

6. Which tasks are routed to unnecessarily large models?

7. Which AI outputs require verification?

8. Which old processes have actually been retired?

9. How are environmental estimates calculated, and what uncertainty remains?

10. What happens to cost and total resource consumption when usage increases tenfold?

The board should then ask the harder question:

The insurance question

Waste can become loss and an uncontrolled agent may consume more than expected:

It may access the wrong data.

It may trigger unauthorised actions.

It may generate advice on which someone relies.

It may disclose confidential information.

It may continue operating after the economically useful point has passed.

It may create operational, legal, cyber, professional-liability or reputational consequences.

The resulting loss may not fit neatly into one insurance policy:

• Was it a cyber event?

• A technology error?

• A professional-services failure?

• An employee-like act?

• An unauthorised transaction?

• A failure of internal controls?

• A product-liability event?

• Or several of them at once?

That uncertainty is relevant because insurers do not only ask how likely a loss is. They also need to know where the loss sits, how large it could become, whether several policyholders could suffer the same failure at the same time and which policy is supposed to respond.

AI makes each of those questions harder. The same foundation model may sit beneath thousands of companies. The same vendor may provide models, infrastructure, data services and security controls.

Therefore, one upstream failure could create losses across many insureds, industries and policy classes at the same time. That is an accumulation problem.

It helps explain why the insurance market is moving away from leaving AI exposures silently embedded inside conventional policies. Some insurers are introducing exclusions, clarifications or sublimits, while a smaller affirmative market is beginning to offer more explicit protection for defined AI risks.

The direction is not yet uniform. Coverage will depend on the policy wording, jurisdiction, loss mechanism and facts of the claim. A reference to AI in a loss does not automatically mean that every policy will exclude it. Nor does the absence of an AI exclusion guarantee that the claim will be covered.

But the broad lesson is clear:

An AI system with poor resource discipline may therefore be difficult to underwrite. An insurer examining a material AI workflow will increasingly want to know:

• What authority the agent has.

• Which data it can access.

• Which systems and tools it can operate.

• What financial, operational and time limits apply.

• Which actions require human approval.

• How its actions are recorded.

• How often humans intervene.

• How failures are detected.

• Whether the system can be stopped.

• Whether its decisions can be reconstructed.

• Which external model, cloud and data providers it depends on.

• What happens if one of those providers changes, fails or is compromised.

Yet, an efficient and well-controlled AI system is not automatically safe or insurable.

Controls do not remove the risk, they make the risk more legible. So, insurance becomes more plausible when the company can convert an open-ended AI exposure into a defined operating risk. That requires more than a general AI policy written by the legal department.

The insurer needs to see a bounded use case. The agent should have a stated purpose, a defined population of users, approved data sources and a clear list of permitted actions. It needs limited authority.

An agent that can draft a payment instruction presents a different exposure from an agent that can approve and transmit the payment.

An agent that recommends a customer response differs from one that sends it without review:

• It needs financial and operational limits. That may include spending caps, transaction limits, restricted counterparties, maximum run times, limits on tool calls, retry budgets and automatic escalation when the system moves outside its expected behaviour.

• It needs human control at the consequential point. Not every AI action requires approval. But decisions that move money, alter legal rights, affect safety, bind the company or materially affect a customer should have an identified accountable person or a deliberately approved exception.

• It needs evidence. The company should be able to show which model ran, which version was used, what data the system accessed, what instructions applied, which tools were called, what the system produced, who reviewed it and what final action followed.

• It needs testing against failure, not merely testing for average performance. That includes hallucination, prompt injection, data leakage, model drift, inappropriate tool use, vendor outage, corrupted retrieval, excessive autonomy and failure of the human escalation route.

• It needs a stop mechanism. A kill switch sounds dramatic until a system starts taking repeated actions at machine speed.

• It needs an incident plan. The organisation should know who investigates, who contains the event, who preserves the evidence, who informs customers or regulators and when the insurer must be notified.

• It also needs an insurance map. Cyber, technology errors and omissions, professional indemnity, crime, media liability, product liability, directors’ and officers’ liability and general liability policies may each respond differently.

The company should not wait for a loss to discover that every insurer assumed another policy was covering the exposure. That may lead to:

• affirmative AI coverage for a tightly defined risk;

• an endorsement under an existing policy;

• a dedicated sublimit, retention or aggregate;

• exclusions for activities the insurer will not accept.

It may require several policies to operate together. The objective is not necessarily to find one policy called “AI insurance”.

The more realistic answer may be a coordinated programme in which the different loss pathways are expressly allocated rather than left silent. That is what could make corporate AI use more insurable.

Evidence that the company knows what the system is allowed to do, how it can fail, can see what it actually did, can limit the consequences when it goes wrong and has agreed in advance where the resulting loss should sit.

The insurable AI system will not be the one described as intelligent, it will be the one whose risk can be bounded, observed, reconstructed and priced.

The weekend takeaway

The digital co-worker does not need a reusable coffee cup, it needs a properly designed job.

The wrong model:

• reads too much;

• thinks too long;

• retries too often;

• calls too many tools;

• produces more than the company can use; then

• asks a human to work out whether any of it was right.

The right model:

• does enough work;

• uses the evidence it needs;

• reaches a defensible answer;

• creates a record; then

• stops.

The environmental argument and the commercial argument are beginning to converge:

Waste is expensive in pounds.

Waste is expensive in kilowatts.

Waste is expensive in water, carbon and human attention.

The company with the most AI may look advanced.

🧠 Techie Corner for the Non-Techies

1. Token

A token is a unit of text or data processed by a language model. Tokens are not the same as words. A word may contain one token or several, depending on the model and the text.

AI providers often use input and output tokens as part of their pricing and usage systems. A token is a useful measure of model activity.

It is not a direct, universal measure of energy because different models, hardware and serving systems can consume different amounts of energy for the same number of tokens.

2. Inference

Inference is the process of using a trained AI model to produce an answer, classification, prediction or action.

Training builds or adjusts the model. Inference uses it.

Most corporate users will not train a frontier model from the beginning. They will consume inference directly through APIs, software products, cloud platforms or internal deployments.

3. Prefill

Prefill is the stage during which a language model processes the input.

That input may include:

• The user’s question.

• System instructions.

• Documents.

• Conversation history.

• Retrieved evidence.

The model converts that information into internal representations used to generate the answer. Long or unnecessary context can increase prefill work.

4. Decode

Decode is the stage during which the model generates output tokens. Longer answers generally require more decoding work.

The energy relationship is not perfectly linear across all models and hardware, but output length is an important driver of inference cost.

5. Context window

The context window is the amount of information a model can consider during one interaction. A larger context window allows the model to read more text, code or history.

It does not guarantee a better answer. Large contexts can increase cost, latency and energy use. They may also make it harder for the model to identify the relevant material.

6. Retrieval-augmented generation

Retrieval-augmented generation, usually called RAG, is a method that searches for relevant information before asking a language model to answer. Instead of placing an entire document collection into the prompt, the system retrieves a smaller set of relevant passages.

A well-designed retrieval system can reduce unnecessary context and connect an answer to evidence. A poorly designed one can retrieve the wrong evidence and create false confidence.

7. AI agent

An AI agent is a system that can take several steps towards a goal.

It may:

• Read documents.

• Search databases.

• Call software tools.

• Write code.

• Run tests.

• Revise its plan.

• Retry failed actions.

• Escalate to a human.

The agent is not necessarily autonomous in the human sense. Its authority depends on the systems and permissions given to it.

8. Model routing

Model routing sends different tasks to different models or systems:

• A simple task may use a small model.

• A complex task may use a frontier model.

• A rules-based task may not need a language model at all.

Routing attempts to match the capability and cost of the machine to the needs of the task.

9. Functional unit

A functional unit is the thing being measured:

• “Per token” is one functional unit.

• “Per inference” is another.

For a business, stronger functional units may include:

• Per resolved enquiry.

• Per accepted contract review.

• Per completed transaction.

• Per defect repaired.

• Per claim triaged.

A functional unit makes comparisons meaningful by defining the work delivered.

10. Software Carbon Intensity

Software Carbon Intensity (SCI) is a method for expressing the carbon impact of software relative to a defined functional unit. At a high level, it considers operational energy, the carbon intensity of the electricity and the embodied emissions allocated to the software system.

SCI for AI extends the approach to AI systems and their lifecycle. The quality of the result still depends on the quality and availability of the underlying data.

11. Embodied emissions

Embodied emissions are emissions associated with making and supplying physical equipment and infrastructure. For AI, that may include manufacturing servers, chips and other hardware. These emissions are different from the operational emissions associated with electricity consumed while the system is running.

Different methodologies may include or exclude embodied emissions, which can make apparently similar carbon figures difficult to compare.

12. Carbon intensity

Carbon intensity describes the greenhouse-gas emissions associated with producing a unit of electricity.

It can vary by country, region, season and time of day. The same amount of computing performed in two locations may therefore have different estimated carbon emissions. Lower operational carbon does not necessarily mean lower water use or lower total environmental impact.

13. Water withdrawal and water consumption

Water withdrawal is water taken from a source. Water consumption is the portion not returned to the original source in a readily reusable form.

Data centres may use water directly for cooling. Electricity generation may also have its own water footprint. Water figures can differ substantially depending on which boundary and definition are used.

14. Rebound effect

The rebound effect occurs when an improvement in efficiency lowers the effective cost of an activity and encourages more of that activity. AI inference may become cheaper per task while total AI usage rises faster.

Efficiency and absolute consumption must therefore be measured separately.

15. FinOps

FinOps is a management discipline for connecting technology usage with financial accountability. It developed around cloud expenditure.

For AI, it increasingly covers model costs, tokens, infrastructure, orchestration, agents and cost per business outcome. FinOps is not simply an exercise in cutting costs.

Its purpose is to make spending visible, attributable and connected to value.

📚 Further reading

• International Energy Agency: Key Questions on Energy and AI (2026)

  The IEA’s updated analysis of electricity demand from data centres and AI.

  It reports that global data-centre electricity demand increased by 17% in 2025 and estimates that consumption in AI-focused data centres grew substantially faster. Its central projection has total data-centre electricity consumption rising from approximately 485 TWh in 2025 to around 950 TWh in 2030.

  These are modelled estimates and projections, not direct measurements of every data centre. They should not be treated as guaranteed outcomes.

• International Energy Agency: Energy and AI (2025)

  The IEA’s broader assessment of electricity demand from data centres and AI, the sources expected to supply that electricity, grid constraints and the potential for AI to improve efficiency elsewhere in the energy system. Useful for understanding the physical infrastructure and power-system context beneath individual AI workloads.

• International Energy Agency: Energy and AI Observatory (2025–)

  The IEA’s data and analysis resource for tracking the changing relationship between AI, data centres and energy. Includes an interactive map of data centre hubs alongside associated power and digital infrastructure.

• Ofgem: Average gas and electricity use explained

  Ofgem’s explanation of typical domestic electricity and gas consumption in Great Britain.

• U.S. Energy Information Administration: Residential electric bills in Hawaii and Connecticut are highest in the United States (2025)

  The EIA reports that average monthly electricity consumption per US residential customer was 865 kWh in 2024, equivalent to approximately 10,380 kWh a year.

  The figure refers to residential electricity customers and varies materially by state, climate, home type and heating or cooling arrangements.

• German Federal Statistical Office: Electricity consumption of households by household size

  Destatis reports average German household electricity consumption of 3,383 kWh in 2021, including electricity used for heating, hot water, lighting and appliances. The source is official, but the data are older than the British and US benchmarks. The German-home equivalent should therefore remain rounded and dated rather than presented as a current exact average.

• Chao Yang, Mei Chen, Wanyang Zhou, Xinyue Zhang and others:

  The Role of Online Consumer Reviews in Shaping Chinese Consumers’ Purchase Intentions for Energy-Saving Household Appliances (2024)

  The peer-reviewed article states, citing Chinese official data, that per-capita residential electricity consumption increased from approximately 500 kWh in 2013 to nearly 1,000 kWh in 2023. It does not provide a direct national average per family household. The Chinese-home equivalent in the article therefore remains an illustrative calculation rather than an official household benchmark.

• National Bureau of Statistics of China: Main Data of the Seventh National Population Census (2021)

  The census reports an average family-household size of 2.62 people in 2020. Combining that census figure with approximately 1,000 kWh of residential electricity use per person gives an indicative figure of around 2,620 kWh per family household. The two inputs relate to different periods and statistical exercises. The resulting household comparison is therefore approximate and should not be described as an official current Chinese average.

• Agency for Natural Resources and Energy, Japan: 10 Questions for Understanding the Current Energy Situation (2024 edition)

  The Japanese government publication uses an “ordinary household” consuming 400 kWh per month, equivalent to 4,800 kWh a year, in its illustration of renewable-energy surcharges. It states that the model is based on household research conducted by the Ministry of Internal Affairs and Communications.

  This is a policy illustration rather than a definitive national household average. Therefore, the Japanese-home equivalent is remain explicitly illustrative.

• Green Software Foundation: SCI for AI - Software Carbon Intensity for Artificial Intelligence

  A consensus-based specification extending the Software Carbon Intensity methodology to AI systems. It covers stages including data preparation, training, deployment and inference, and distinguishes between provider and consumer measurement boundaries. The specification supports functional units suited to different AI systems. Its usefulness still depends on the availability and quality of underlying energy, hardware and emissions data.

• Green Software Foundation: SCI for AI Specification Ratified: Standard for Measuring AI Emissions Across the Lifecycle (2025)

  The formal announcement that the SCI for AI specification had been ratified. It describes the specification as extending the ISO-certified SCI methodology across the AI lifecycle. This is an announcement from the organisation responsible for the standard, not independent evidence that the methodology has already achieved universal industry adoption.

• Green Software Foundation: Tokens and Greens: Measuring the Impacts of Agentic AI (2026)

  A practitioner-oriented article on the energy, carbon and water implications of agentic AI. It argues for task-level measurement, model routing, caching, limits on retries and human checkpoints. This is guidance from an industry standards organisation, not a peer-reviewed empirical survey of corporate AI deployments.

• MLCommons: MLPerf Power

• A framework for measuring machine-learning performance and power consumption together. It supports comparison across data-centre, client, edge and embedded systems and emphasises measurement of full-system power rather than relying solely on nominal chip ratings. It is useful for infrastructure benchmarking and procurement. It does not establish whether a corporate AI workflow created useful or economically valuable work.

• MLCommons: MLCommons Power Working Group Presents MLPerf Power Benchmark at IEEE HPCA Symposium (2025)

  An explanation of the MLPerf Power framework, its measurement requirements and early benchmarking findings. MLCommons notes that power alone is not enough: performance, throughput, model quality and system-level consumption must be considered together.

• Longju Bai, Zhemin Huang, Xingyao Wang, Jiao Sun, Rada Mihalcea, Erik Brynjolfsson, Alex Pentland and Jiaxin Pei: How Do AI Agents Spend Your Money? Analyzing and Predicting Token Consumption in Agentic Coding Tasks (2026)

  A preprint examining token consumption by eight frontier models performing agentic coding tasks. The authors report that the tested agentic tasks consumed around 1,000 times more tokens than their code-reasoning and code-chat comparisons. Runs on the same task varied by as much as 30 times, while greater token use did not consistently produce greater accuracy. The study concerns specific coding benchmarks, models and agent configurations. Its reported multipliers should not be applied indiscriminately to every enterprise agent or business workflow.

• Lola Solovyeva and Fernando Castor: Towards Green AI: Decoding the Energy of LLM Inference in Software Development (2026)

  A preprint separating the energy used during the input-processing, or prefill, stage from the output-generation, or decoding, stage of language-model inference. In the models and coding benchmarks tested, the authors found that excessive output could materially increase energy consumption. Their suppression technique produced reported savings of between 44% and 89% without reducing measured code-generation accuracy. Those percentages relate to the tested open-weight models and controlled software-development tasks. They should not be generalised to proprietary models or unrelated corporate workflows.

• Warren Johnson: The Compression Paradox in LLM Inference: Provider-Dependent Energy Effects of Prompt Compression (2026)

  A preprint examining prompt compression across three API providers, five benchmarks and several compression ratios. The study found that reducing input length did not reliably produce a better energy-quality trade-off. In some tested cases, compression reduced answer quality or caused output expansion. The paper estimates the energy use of proprietary services using a token-based proxy calibrated against local measurements. It does not directly observe the providers’ physical infrastructure.

• FinOps Foundation: FinOps for AI: Tools & Services Considerations (2026)

  Practical guidance on understanding AI costs across training, tuning, inference, orchestration, observability and operations. It argues that the real unit of measurement is the economics of the use case, including whether a tool is necessary, correctly scoped and worth its total cost. This is practitioner guidance rather than an accounting or regulatory standard.

• Linux Foundation: Linux Foundation Announces the Intent to Launch the Tokenomics Foundation to Establish Open Standards for AI Cost Management (2026)

  The June 2026 announcement sets out plans for a vendor-neutral body focused on AI infrastructure economics, token efficiency, benchmarks and cost-management standards. The initiative is evidence of demand for common financial measurement as generative and agentic AI move into production. At the time of writing, it is an announced foundation and work programme, not a completed or universally adopted standard.

• European Commission: Targeted Consultation on Measuring Energy Consumption and Emissions of AI Models and Systems (2026)

  The consultation seeks evidence to support a practical measurement framework for the energy-related objectives of the EU AI Act and the possible design of an AI energy and emissions label. The Commission notes that providers of general-purpose AI models must document known or estimated energy consumption under Annex XI of the AI Act. The consultation also demonstrates that the measurement framework remains under development. It does not establish that a final, comprehensive and universally accepted methodology already exists.

• National Institute of Standards and Technology: Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (2024)

  NIST’s cross-sectoral companion to the AI Risk Management Framework for generative AI. It identifies risks and suggested actions across governance, mapping, measurement and management. It is relevant to the article’s discussion of testing, monitoring, third-party dependencies, human oversight, incident response and documentation. The framework is voluntary guidance. It is not an insurance policy, underwriting standard or guarantee that an AI system will be insurable.

• The Geneva Association: Gen AI Risks for Businesses: Exploring the Role for Insurance (2025)

  An insurance-industry research report examining generative-AI risks across operational, cybersecurity and privacy, ethical, regulatory, reputational, workforce and environmental domains. It considers corporate awareness of those exposures and the potential role of insurance. The report is useful to understand why AI losses may cross several established insurance classes. It does not establish that every identified AI risk is currently transferable or commercially insurable.

• Jones Day: “A-Eye” on Coverage: Maximizing Insurance for AI Risks Amid Emerging Exclusions (2026)

  Policyholder-side legal commentary on emerging AI exclusions and the possible application of cyber, errors and omissions, directors’ and officers’, crime, media and other policies. It advises insureds to examine renewal terms carefully and negotiate exclusions where possible. This is legal commentary rather than a market-wide empirical survey. It supports the statement that AI exclusions are emerging, but it does not prove that most carriers or policies exclude all AI-related losses.

• Alex Leung, Rex Zhang, Ervin Ling, Kentaroh Toyoda and SiewMei Loh: The Insurability Frontier of AI Risk: Mapping Threats to Affirmative Coverage, Silent Exposures, and Exclusions (2026)

  A preprint mapping 55 AI threat classes against 26 publicly described insurance products, endorsements and exclusion regimes. The paper identifies affirmative coverage, silent exposure, actively excluded risks and risks that may sit outside conventional private insurance. Its analysis is based on public carrier materials rather than complete executed policy wordings or adjudicated claims. Its conclusions should therefore be treated as a structured market map, not evidence that any particular claim would be paid.

• Quanyan Zhu: Insurance of Agentic AI (2026)

  A preprint examining how autonomous planning, tool use and execution create exposures across cyber, professional liability, product liability and other established insurance classes. It proposes that agentic-AI risk may ultimately require coordinated layers of coverage, explicit allocation mechanisms, dedicated aggregates, improved telemetry and clearer governance rather than one universal AI policy. This is a conceptual academic framework published as a preprint.

• Dr Henri Winand: The Hidden Appetite of AI (2025)

  An earlier Something for the Weekend article examining the physical energy and water appetite behind AI systems. The article introduced the argument that apparently weightless AI services rest on power-intensive physical infrastructure.

• Dr Henri Winand: The Digital Employee Was Never $200 a Month (2026)

  The argument that a subscription is only the entry price and that the economic cost of machine work includes tokens, inference, infrastructure, integration, verification and risk.

• Dr Henri Winand: Rent the Intelligence. Own the Proof. (2026)

  An examination of cloud versus local AI, larger versus smaller models and the need for evidence records in serious corporate workflows.

• Dr Henri Winand: When Tokens Become Kilowatts (2026)

  Last week’s article on AI tokenomics, machine-work consumption and the physical infrastructure beneath the token.

Last week on Something for the Weekend

Last week’s piece looked at the new tokenomics:

• Crypto used tokens to make scarcity, incentives and governance visible.

• AI uses tokens to make consumption visible.

The article followed the AI token down through inference, compute and data-centre infrastructure to the electricity system underneath. It argued that the company which cannot see token consumption cannot see the cost of machine work.

However, tokens are only the basic meter.

The better meter is useful, verified work.

This week asks what happens after companies install that meter across thousands of digital co-workers. The next advantage will not come from producing the most machine activity, it will come from producing the most verified value with the least avoidable waste.

A CFO receives three updates before breakfast. The first says the company’s AI tools are lifting productivity. The second says the AI budget is running ahead of plan. The third says a model gave a confident answer in a legal workflow, but missed the point because the relevant context never made it into the answer.

The CIO explains that the issue is tokens.

The CFO thinks: not crypto again.

But this time the token is not a coin, a speculative asset, a governance right, or a claim on a protocol. It is a unit of text and data processed by an AI model. A short prompt consumes a few. A long document review consumes more. A coding agent that reads files, writes code, tests, fails, retries and calls tools can consume a great many.

The company thought it had bought software but it had bought access to a meter.

That is why the word “tokenomics” is becoming useful again, but not in the way most people remember it. The crypto version was about issuance, scarcity, incentives, speculation and governance. How many tokens exist? Who receives them? Are they minted, burned, staked, locked, vested or diluted? Does the token buy access, status, votes, yield, collateral or hope?

AI tokenomics is different. It is about consumption. Who uses the tokens? Who pays for them? Who controls the meter? Which workflow burns through them? Which agent can keep running in the background? Which model is too large for the job? Which answer needs verification? Which user sees the value but not the bill?

At first, the two token stories look unrelated:

One belongs to crypto.

The other belongs to AI.

But the underlying story is the same: both sit on top of machines that convert electricity into economically useful digital output. In proof-of-work crypto, electricity is converted into cryptographic work, network security and block rewards. In AI, electricity is converted into inference, output, tool use, agent work and, sometimes, confident error.

The token is the surface unit and the substrate is compute. But, the bottleneck is increasingly power. That is the new tokenomics.

Not coins.

Kilowatts.

The Boardroom point

The next tokenomics debate will not be about whether a coin goes up. It will be about whether machine work creates value before it consumes too much cost, power, attention and trust.

Enterprises are starting to discover that AI is not a normal software budget. It behaves more like cloud consumption, professional services, outsourcing, infrastructure and labour all at once. There is a subscription. Then there is usage. There is a model. Then there is context. There is a prompt. Then there are retries. There is output. Then there is verification. The visible price is the entry ticket and the economic price is the work system behind it.

This is a direct extension of a point I made recently in Something for the Weekend: the digital employee was never $200 a month. The subscription was the entry price. The full cost included tokens, inference, context, API calls, cloud infrastructure, integration, verification and risk. Then the next problem arrived. Where does the intelligence run? In the cloud, locally, or across a hybrid stack? What does it cost? Can the company prove why the answer was given?

This week’s point sits underneath both:

The token is the meter.

The company that cannot see token consumption cannot see machine-work cost. The company that cannot see machine-work cost cannot measure AI productivity properly. The company that cannot measure AI productivity properly is likely to confuse activity with value.

Prompts sent are not value. Documents generated are not value. Agents launched are not value. Tokens consumed are not value. The useful measure is verified machine work.

Did the AI do something useful? Was it correct? Was it used? Did it reduce old work? Did it increase revenue? Did it lower risk? Did it shorten cycle time after review? Did it reduce rework? Did it create a decision that can be defended later?

The token is not the answer.

It is the meter attached to the question.

The old tokenomics

Crypto tokenomics began with a powerful idea. Digital systems could create economic rules. A token could represent access, payment, governance, collateral, participation, status, scarcity, or a claim on a future system. Code could set issuance schedules. Protocols could define incentives. Communities could coordinate without a traditional company at the centre.

Some of that was genuinely interesting, but once hype set in, much of it was not.

Too often, the token came first and the economy came later:

• A white paper promised utility.

• A supply schedule created scarcity.

• A community created momentum.

• A market price created excitement.

• The token moved before the system had useful work to do.

That was the failure mode with scarcity confused with value.

A limited supply of something of no value does not make it useful. A governance token without meaningful governance is not participation. A token that exists mainly to sell the next token is not an economy, it is a financing mechanism with a narrative attached.

This does not mean tokenomics was meaningless. It means the serious version was never just about coins. It was about incentive design: who contributes, who verifies, who governs, who gets rewarded, who takes risk, who can exit, who gets diluted, who can change the rules, and who benefits when the system scales.

Those are good questions.

They are now returning through a different door:

Not through crypto speculation.

Through AI consumption.

The new tokenomics

AI reverses the direction of the token. The AI token is not usually something the company owns. It is something the company spends.

Every prompt spends tokens. A simple question spends a few. A long contract spends more. A large context window spends more again. An agent that reads files, searches systems, calls tools, writes code, tests it, fails, retries, produces a report and then revises it can spend far more than the user realises.

This changes the economics of work. A human employee has limits. They can only work so many hours. They appear slow compared with software, but they are bounded:

They can be questioned.

They can explain what they did.

They can be trained, managed and held responsible.

A digital worker is different. It can run continuously. It can generate more output than the organisation can absorb. It can create plausible work that still needs expert review. It can multiply mistakes. It can shift costs from headcount to compute. It can appear cheap until usage scales. But, it also has no real accountability.

That is why AI tokenomics is not just a technical topic. In reality, it is a management topic:

The question is no longer simply how much the model costs per token, the better question to ask is how many tokens it takes to produce a verified outcome.

That distinction is important. A cheap model that produces uncertain work may become expensive if senior people have to spend hours checking it. A more expensive model may be cheaper if it reaches a reliable answer faster. A small model may beat a large model if the task is narrow, confidential and repetitive. A local system may beat a cloud system if the data should not move. A frontier model may still be worth the cost if the problem needs broad reasoning.

The cheapest token is not always the cheapest work:

The energy underneath

The AI token appears weightless. However, it is not.

Beneath the token lies inference. Inference, in turn, relies on compute. Compute depends on GPUs, memory, networks, cooling, land, water, grid connections, power contracts and data-centre finance. The browser makes AI feel like software. The infrastructure makes it look more like heavy industry.

That is why the data centre is becoming a digital factory.

It takes power, machines, cooling, capital and skilled operations, then turns them into digital output: tokens, answers, simulations, classifications, code, proofs, decisions and agent work. The product is weightless. The factory is not.

This is where the crypto comparison becomes useful, but only if handled carefully.

In proof-of-work crypto, the energy link is explicit. Electricity is converted into cryptographic work, network security and block rewards. Bitcoin made that visible because mining was part of the system’s public logic.

AI is different. No one is mining AI tokens into existence. AI tokens are not coins. They are not scarce digital assets. They are metering units for model work. But the physical link still exists. Every token processed by a model requires compute. Compute requires energy. Energy requires infrastructure.

So the better comparison is this:

Crypto made digital scarcity visible.

AI makes digital consumption visible.

Both return us to the same hard reality: the digital economy is not detached from the physical world. The front end is a token, the back end is machine time and the limiting input is increasingly power.

This is why “cost per token” and “tokens per watt” are becoming strategically important. Those phrases sound technical, but they are commercial. If more billable AI work fits inside the same power envelope, margins improve. If a company burns tokens without producing verified value, the cost base expands without the productivity gain.

AI will not only be judged by model capability. It will be judged by energy-to-output efficiency:

What if the wrong compute gets built?

The easy version of the AI bubble story is that too many data centres get built, demand disappoints, and the whole thing becomes another infrastructure mistake.

That’s the crude story some are happy to write.

The more interesting possibility is that the world builds too much of one kind of compute just as AI work starts to split into different places. Some work will still need frontier models in the cloud. Some work will need fast inference close to the user. Some work will need smaller models running locally because the data are sensitive, the task is narrow, the latency matters, or the company does not want every question to become an external cloud call.

This does not mean cloud AI goes away. It means cloud AI becomes part of a portfolio.

A training cluster, an inference cloud, a local model, and an edge device are distinct concepts. A general-purpose frontier model differs from a smaller model embedded within a legal, insurance, banking, manufacturing, or customer-service workflow.

So the real overbuild risk may not be that nobody needs AI compute. The risk may be that the wrong compute is built in the wrong place, under the wrong pricing assumptions, for workloads that later become cheaper, smaller, more local or more specialised.

That is a much more useful boardroom question.

If small models and local inference become good enough for many enterprise tasks, the largest models do not disappear, they become the expensive specialists. They handle the hard work: broad reasoning, difficult coding, complex research, high-end synthesis and tasks where the best available model is worth the cost.

Routine work may route elsewhere:

• Smaller models.

• Private deployments.

• Local machines.

• Edge devices.

• Retrieval-first systems.

• Domain-specific agents.

• Workflow-specific tools.

That would change token economics. Cloud tokens would not vanish, but they could become cheaper, more competitive and more carefully rationed. Cost per token may fall because of better chips, better software, better utilisation, model optimisation and competition. At the same time, total AI usage could still rise because lower costs make more machine work economically viable.

That is the awkward (Jevons) paradox: cheaper intelligence may not reduce the AI bill, it may increase the amount of AI work. For serious users, that is not necessarily bad. A fall in the cost of machine work could make AI more useful across more business processes.

The problem is not cheaper tokens. The problem is using them to generate more unverified activity.

This is where boards should be precise. The question is not whether the company should use cloud AI, local AI or smaller models. The answer will usually be all three.

The question is which work belongs where.

A broad strategic question may need a frontier model. A confidential contract review may need local control. A repetitive compliance check may need a smaller model. A high-volume customer workflow may need an inference cloud. A consequential decision may need human approval and an audit receipt.

But the deeper point remains: once the world builds more power-dense digital factories, the question becomes what those factories can run next?

Inference.

Simulation.

Scientific computing.

Private AI.

Enterprise agents.

Cryptographic proof systems.

Tokenised contracts.

Distributed ledgers.

Future machine-to-machine commerce.

Maybe even some crypto mining where the economics and equipment make sense.

That caveat is important. A GPU-heavy AI facility is not automatically a bitcoin mine. Bitcoin mining is now heavily ASIC-driven, while AI data centres are built around GPUs, accelerators, networking, storage, cooling and service levels suited to different workloads. Some conversions will be impractical. Some will be uneconomic. Some sites will simply be wrong.

Still, if there is a bubble, it may leave behind something useful: a larger base of digital factories.

The future is unlikely to be less AI: it is more likely to be more AI, running in more places, with more discipline about what each token is for.

Some capital may be misallocated. Some vendors may disappoint. Some sites may age badly or be poorly located. But the broader machine-work layer is still likely to become cheaper, more distributed and more useful.

The winners will not be the companies that simply buy the most AI.

They will be the companies that route the right work to the right machine, at the right cost, with the right proof.

The agent clock card

This is where crypto and distributed ledgers return in a more useful form, not as speculation, as accounting infrastructure.

A human factory worker used to clock in with a time card. A consultant has a timesheet. A trader had an order blotter. A lawyer has a file note. A bank has transaction logs. A board has minutes.

An AI agent needs its own version of that, because the agent can act.

An enterprise AI agent may read documents, access systems, call tools, write code, draft contracts, respond to customers, generate reports, approve workflow steps, raise tickets, move data and trigger downstream actions. If it can do those things, the company needs to know what it did, when, under whose authority, against which budget, with which data, using which model, and with what result.

That is where distributed ledgers become interesting again. Not every AI audit trail needs a public blockchain. In many enterprise settings, the more plausible answer will be permissioned infrastructure, private ledgers, cryptographic receipts, hash commitments, tamper-evident logs or selectively disclosed proofs.

Most Fortune 500 companies will not want sensitive workflow data sprayed onto a public chain. The point is not public visibility. The point is controlled auditability.

The concept is simple: give every agent a clock card.

The clock card should show the agent identity, delegated authority, budget, model used, prompt class, data accessed, tools called, tokens consumed, outputs produced, abstentions, exceptions, human approvals and final decision receipt.

Importantly, some of that information may stay private and some may be hashed. Yet, some may be visible to auditors, to regulators, insurers or counterparties under defined conditions.

The point is not to turn every AI action into a tradable token, it is to make machine work auditable.

That is the useful convergence of AI and distributed ledgers:

AI creates work at machine speed.

Distributed ledgers can help create a tamper-evident record of that work.

Smart contracts may then enforce budgets, approvals, access rights and stop conditions.

In short:

An AI agent without a meter is a risk.

An AI agent with a meter, a budget and a receipt starts to look like manageable machine labour.

That is a boardroom-grade idea.

Engagement and dependency

There is another uncomfortable layer. AI systems do not exist outside commercial incentives.

If a vendor only sells raw tokens, the business risks moving toward commodity economics: electricity, data centres, chips, model training, inference, cooling, depreciation and utilisation. That can be a very large business. It is not necessarily the best-margin business.

The better business is to keep the user inside higher-value workflows: coding environments, legal tools, research systems, insurance applications, banking workflows, analytics layers, knowledge platforms, enterprise agents, procurement systems and customer service infrastructure.

That is where the supermarket analogy becomes useful. The raw model is not the whole business. It is the shelf space. The real margin sits in what gets placed on the shelves, how often customers return, how much trust they place in the store, and which value-added services are wrapped around the visit.

“Stack them high and sell them cheap” can be a good business. The better business is when the platform becomes the place where the customer does more of the work.

That raises a serious issue for business when it comes to productivity and cost:

A model that gives a confident answer may be more engaging.

A model that keeps the user asking follow-up questions may consume more tokens.

A model embedded into a workflow may create dependency.

A model that is helpful enough to keep the user inside the system may become the new operating layer.

None of that proves bad faith. It does not mean AI vendors want hallucinations. It does mean product design, benchmark design and commercial design matter.

If the system rewards answering, the model will answer. If the benchmark penalises uncertainty, the model will guess. If the user wants fluency, the product will provide fluency. If the vendor monetises usage, continued usage matters.

That is not a conspiracy. It is economics. When billions are being invested in models, infrastructure and distribution, usage matters.

The safer model may be the one that knows when to say: I do not know.

Guessing burns tokens too

This is where hallucination becomes part of tokenomics. A wrong answer is not just a quality problem, it is a consumption problem.

A confident wrong answer can trigger follow-up prompts, document checking, expert review, correction cycles, duplicated work, legal uncertainty, customer confusion and reputational risk. The hallucination consumes tokens once when it is generated, then it consumes more tokens, time and attention when the organisation has to work out whether it was wrong.

That is verification debt with a meter attached.

Recent research on hallucination makes the incentive problem sharper. The central point is that many accuracy-based benchmarks reward guessing more than abstention. A model that says “I do not know” receives no credit. A model that guesses has at least some chance of being right. So the training and evaluation environment can structurally under-reward uncertainty.

That matters in low-risk contexts but it truly matters in serious work:

• A chatbot making up an acronym is irritating.

• A legal tool making up a limitation point is dangerous.

• A banking system misreading a policy is dangerous.

• An insurance workflow missing an exclusion is dangerous.

• A board paper using the wrong assumption is dangerous.

So, a model that guesses confidently may keep the interaction alive but it can create costly false confidence.

The enterprise answer is not to ban AI.

The answer is to design for abstention, verification and routing.

Some questions should go to a frontier model. Some should go to a smaller model. Some should go to retrieval first. Some should go to a human. Some should stop.

Yet again, “I do not know” may be one of the most valuable outputs in enterprise AI because it stops the meter before false confidence becomes expensive.

The CFO version

For the CFO, AI tokenomics is a cost-structure problem.

Traditional software was easier to budget: seats, licences, renewals, implementation, support and annual uplift. AI changes the shape. There may still be seats and subscriptions, but the real economic motion comes from use. The company pays for access and then pays again through consumption, directly or indirectly.

The difficult part is that the people creating the cost may not see the bill. A developer running a coding agent may not see the cloud cost. A lawyer asking for long document reviews may not see the token cost. A business unit launching an AI pilot may not see the integration cost. A customer-service team automating replies may not see the verification cost. A board approving “AI productivity” may not see the full cost of machine work.

That is why the finance team needs a new language:

Cost per token is useful but incomplete. Cost per verified outcome is better.

Cost per avoided hour may be useful, but only if the old work actually disappears.

Cost per decision may be useful, but only if the decision can be reconstructed.

Cost per agent run may be useful, but only if the agent achieves the task.

The CFO should ask where tokens are being consumed, which users or agents can spend them without approval, which costs are fixed or variable, which vendors can change usage limits or pricing, and which AI costs are hidden inside software, cloud, consulting, legal, compliance or security budgets.

But the most important question is simpler:

The company has not proved AI productivity because usage is rising. It has proved AI productivity when useful output rises after the full cost of tokens, compute, verification, integration, rework, risk and accountability is counted.

The CIO version

For the CIO, AI tokenomics is an architecture problem. That is because the company needs routing rules:

• Which work should use a frontier model?

• Which work should use a smaller model?

• Which work should run locally?

• Which work can go to the cloud?

• Which work needs retrieval first?

• Which work needs a retained evidence record?

• Which work needs human approval?

• Which work should not be automated?

That last question is not anti-technology, it is simply architecture discipline.

The lazy version of AI is to send everything to the largest available model and hope scale solves the problem. That is expensive and weak governance.

A large model may be right for broad reasoning, coding, research and synthesis. It may be overkill for document classification, invoice matching, policy checks, contract comparison, claims routing, meeting summaries or structured extraction. A small model doing a narrow job well may be better than a large model doing the same job expensively and inconsistently.

This is where tokenomics becomes operational design: the goal is not to minimise tokens in isolation, it is to minimise waste:

Use enough model for the task.

Use enough context for the answer.

Use enough verification for the risk.

Use enough evidence for the decision.

Use enough human judgement for accountability.

The CIO who treats tokens as a technical billing unit will miss the point, tokens are becoming one of the ways machine work becomes visible.

The Board version

For the board, tokenomics is an accountability problem. Boards do not need to understand every model parameter, context window or inference stack. They do need to understand whether the company is building a machine-work cost base it can control.

A useful board discussion should cover ten points:

1. Where is AI being used in production?

2. Which AI systems are metered by tokens, API calls, compute, credits or usage tiers?

3. Which workflows consume the most machine work?

4. Which agents can act repeatedly without human intervention?

5. Which AI outputs require human approval?

6. Which AI systems can say “I do not know”?

7. Which AI systems are being rewarded for guessing?

8. Which AI decisions can be reconstructed later?

9. Which vendors control pricing, rate limits and model access?

10. Which AI gains have reached the P&L after the full bill is counted?

The board should also ask a harder question:

That question will become more important as AI moves from chatbots into workflow systems. The risk is not simply that the model is wrong. The risk is that the company builds daily operations around systems whose incentives it has not inspected.

An AI answer without a record is hard to challenge. An AI workflow without cost visibility is hard to manage. An AI system without abstention is hard to trust.

An AI strategy without token discipline is just cloud spending with better language.

The insurance question

There is another reason why understanding the meter is important.

Boards will want AI risk to be insurable. Insurers will want to know whether that risk is covered, excluded, sublimited or separated into a different coverage line.

That is not surprising. Once AI systems produce operational, legal, cyber, financial and reputational losses, insurers will ask what exactly is being covered. Is it cyber? Is it technology errors and omissions? Is it professional liability? Is it crime? Is it operational risk? Is it a new AI-specific exposure?

One emerging concern is LLMjacking, where attackers hijack AI accounts or credentials and burn through compute at someone else’s expense. That is a natural consequence of metered machine work.

If access to AI systems becomes valuable, attackers will steal it. If tokens represent spend, attackers will burn them. If agents can act, attackers will redirect them. If models sit inside business workflows, failures will create claims.

This creates a new Board and insurance problem. The loss is not always the wrong answer. Sometimes the loss is the machine doing too much of the wrong work.

A stolen password used to mean unauthorised access. A stolen AI credential may mean unauthorised consumption, unauthorised output, unauthorised decisions, unauthorised data movement and unauthorised reliance.

That is a different risk profile.

Policy language will have to catch up. Sublimits may be presented as clarity. They may also become the place where clients discover that the AI loss they assumed was covered is only covered up to a narrow ceiling.

The practical point is simple:

If AI work is metered, AI loss can be metered too and so, who pays when the meter is hijacked?

The weekend takeaway

The word token has done a strange journey:

In crypto, the token was sold as a digital asset.

In AI, the token has become a unit of machine work.

In both cases, the token makes part of the machine economy visible. Crypto showed how digital systems could create scarcity, incentives, speculation and governance. AI shows how digital systems create consumption, cost, verification debt and dependency.

The shared lesson is not that crypto tokens and AI tokens are the same, it is that tokens reveal the economics of the system underneath:

At the bottom of that system sits power.

If the AI build-out overheats, some capital will be lost. That is how investment cycles work. But the more interesting question is what gets left behind: power-dense sites, grid connections, cooling systems, fibre, operating teams, compute markets and a broader industrial base for machine work.

AI may offer the opportunity. Overbuild, if it happens, could make compute cheaper, inference more available, agent work more measurable, and digital infrastructure more reusable. That is the positive case.

The next step is discipline: the future digital worker needs a meter, a budget and a receipt:

The token is not the product.

The token is not the intelligence.

The token is not the proof.

The token is the basic meter.

The better meter is useful, verified work.

The company that truly understands the meter will manage AI as work.

The company that does not will manage AI as magic until the invoice arrives.

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🧠 Techie Corner for the Non-Techies

1. Token

A token in AI is a unit of text or data processed by a model. Short prompts use fewer tokens. Long documents, long conversations, codebases and agentic workflows use more. Tokens matter because many AI systems are priced, limited or managed by how much input and output the model processes.

This is different from a crypto token. A crypto token is usually a digital asset or claim inside a blockchain-based system. An AI token is usually a metering unit for machine work:

Same word, different economics.

2. Tokenomics

Tokenomics means the economics of tokens. In crypto, tokenomics usually means supply, issuance, utility, incentives, staking, vesting, burning, governance and market behaviour. In AI, tokenomics should mean something different: how tokens are consumed, priced, routed, verified and converted into useful work.

Crypto tokenomics asks how a digital system creates and allocates value.

AI tokenomics asks how a digital system consumes cost to produce output.

3. Proof-of-work

Proof-of-work is the system used by Bitcoin and some other blockchains to secure the network. Miners use specialised hardware to perform computational work. The successful miner earns the right to add a block and receives rewards according to the protocol rules.

The important point for this article is that proof-of-work makes the energy link visible. Electricity is converted into cryptographic security and block rewards.

That does not apply equally to all crypto. Proof-of-stake systems work differently and are much less energy-intensive.

4. Inference

Inference is what happens when an AI model runs to answer a prompt or complete a task. Training is the expensive process of building or improving the model. Inference is the repeated cost of using it.

Most companies will not train frontier models. Many will pay for inference every day.

That is where the meter appears.

5. Context window

The context window is the amount of information an AI model can consider at one time. A larger context window lets the model read more documents, code or conversation history. It can also cost more because the model must process more material.

That is why “give the AI everything” is often inefficient. Better systems retrieve the relevant material first, then ask the model to work from that smaller set.

6. Agent

An AI agent is a system that can take multiple steps toward a goal. It may search files, call tools, write code, test outputs, revise its answer and try again.

Agents can be useful because they do more than answer one prompt. They can also become expensive because each step consumes tokens and compute.

The key question is not whether the agent ran.

The key question is whether it completed useful work within a controlled cost and risk boundary.

7. Distributed ledger

A distributed ledger is a shared record of transactions or events maintained across multiple systems or parties. It can be designed to make records tamper-evident and independently verifiable.

For AI agents, the useful idea is not speculative trading. It is auditability. A ledger can help record that a machine action happened, under what authority, and with what evidence, without necessarily exposing the underlying confidential material to everyone.

8. Agent clock card

An agent clock card is a simple way to describe an audit record for AI work.

It should record the agent identity, authority, budget, model used, prompt class, data accessed, tools called, tokens consumed, output produced, abstentions, exceptions, human approvals and final decision receipt.

The point is not to humanise the agent, it is to control the work.

9. Verification debt

Verification debt is the checking burden created by machine-generated output. AI can produce more drafts, summaries, code, analysis and recommendations than an organisation can easily review.

If the organisation generates faster than it can verify, the apparent productivity gain becomes a bottleneck.

Verification debt is where cheap generation becomes expensive.

10. Tokens per watt

Tokens per watt measures how much AI work can be produced from a unit of power. AI is not weightless. Behind the model sits hardware, power, cooling, memory, networking and data-centre infrastructure.

As AI scales, energy efficiency becomes part of the economics of intelligence.

11. LLMjacking

LLMjacking means attackers hijack AI accounts, credentials or access and use someone else’s AI resources.

The result may be unauthorised token consumption, unauthorised compute spend, data exposure, operational disruption or downstream liability.

As AI becomes metered work, stolen access becomes a financial and operational risk.

📚 Further reading

• International Energy Agency: Energy and AI (2025)

  Estimates global data-centre electricity consumption at about 415 TWh in 2024 and projects around 945 TWh by 2030 in its base case.

• Reuters: Australia’s Megaport secures four new AI infrastructure contracts, to raise $594 million (2026)

  Reports Megaport’s new AI infrastructure contracts and capital raise, supporting the argument that AI demand is not only about giant training clusters but also about globally distributed, latency-sensitive inference capacity.

• Core Scientific: Core Scientific to Provide Approximately 200 MW of Infrastructure to Host CoreWeave’s High-Performance Computing Services, Capturing Significant AI Compute Opportunity (2024)

  Power-footprint convergence between crypto-mining infrastructure and AI/HPC hosting: Core Scientific announced that it would provide approximately 200 MW of infrastructure to host CoreWeave’s high-performance computing operations.

• CoreWeave: CoreWeave to Acquire Core Scientific (2025)

  CoreWeave proposed a $9 billion acquisition to secure Core Scientific’s 1.3 GW data center footprint and power capacity for future AI infrastructure. Though the all-stock deal was terminated by Core Scientific in late 2025 due to a lack of stockholder approval, it proved that direct power and data center control have become highly prized strategic assets.

• NVIDIA: Rethinking AI TCO: Why Cost per Token Is the Only Metric That Matters (2026)

  Cost-per-token framing: NVIDIA defines cost per token as the all-in cost to produce each delivered token and argues that it is a better AI infrastructure metric than raw compute cost or FLOPS per dollar.

• NVIDIA Developer Blog: Scaling Token Factory Revenue and AI Efficiency by Maximizing Performance per Watt (2026)

  NVIDIA links AI data-centre economics to power, land, shell capacity, token throughput, performance per watt and revenue per megawatt. Again, treat this as vendor-positioning rather than neutral analysis.

• Nature: Evaluating large language models for accuracy incentivizes hallucinations (2026)

  The paper argues that next-word prediction and accuracy-based evaluations can reward unwarranted guessing, and that dominant headline metrics may reward guessing over admitting uncertainty i.e. “I do not know” can be economically and operationally valuable.

• OpenAI: Why Language Models Hallucinate (2025)

  Argues that language models hallucinate partly because standard training and evaluation procedures reward guessing over acknowledging uncertainty.

• Aegon: Auditable AI Content Access with Ledger-Bound Tokens and Hardware-Attested Mobile Receipts (2026)

  The paper proposes ledger-bound tokens, a Merkle-tree append-only transaction ledger, signed provenance event logs and hardware-attested compliance receipts for AI content access. It is early-stage research, not a market standard.

• From Agent Traces to Trust: Evidence Tracing and Execution Provenance in LLM Agents (2026)

  The paper argues that final-answer accuracy is not enough for LLM agents and that process-level accountability requires evidence tracing and execution provenance.

• NIST: Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (2024)

  Useful governance reference. NIST describes this as a cross-sectoral companion resource to the AI Risk Management Framework for generative AI, aimed at helping organisations incorporate trustworthiness considerations into AI design, development, use and evaluation.

• Dr Henri Winand: The Digital Employee Was Never $200 a Month (2026)

  Why AI labour should be treated as metered machine work rather than fixed-price employment. The piece defines the digital-employee cost base as including tokens, inference, context, API calls, cloud infrastructure, integration, verification and risk.

• Dr Henri Winand: Rent the intelligence. Own the proof. (2026)

  Article on cloud AI, local AI, smaller models, compute cost and audit trails. It supplies the continuity for the argument that serious AI decisions need control, cost visibility and a decision receipt.

• Dr Henri Winand: The Future Is Being Built Without a Front Door (2026)

  Article on AI infrastructure, participation, work, family formation and the need for people to see a route into the future being built around them.

Last Week on Something for the Weekend

Last week’s piece looked at the participation bargain underneath the AI build-out.

The argument was that a country can build data centres, fund AI models, celebrate productivity gains and still lose the confidence of the people expected to live inside that future. But growth needs a front door.

Young people need a route into work. Families need confidence. Communities hosting infrastructure need visible benefit. Employees need AI to improve work rather than simply increase pressure. Boards need productivity gains that reach the P&L without destroying the learning systems that create future talent.

The useful question was:

Can people still see a route into the future being built around them?

That piece looked at the human and social bargain.

This week looks at the machine-work meter underneath it: the token.

The future may be built with AI, but the invoice will arrive in tokens.

A woman in Nottingham tells the BBC that the world does not feel like a nice place to bring children into. A trainee nurse in London says she would love a larger family, but cannot afford to give another child what her existing children already need. A 23-year-old graduate applies for more than 400 jobs and gets one interview for a cleaning role. A government-commissioned review warns that Britain is at risk of a “lost generation”.

The same pressure appears elsewhere.

In the United States, provisional CDC data show about 3.6 million births in 2025, down 1% from 2024, with the general fertility rate at 53.1 births per 1,000 women aged 15-44. In France, INSEE reported 645,000 births in 2025, 24% fewer than in 2010, and more deaths than births for the first time since the end of the Second World War. In Germany, Destatis reported a fertility rate of 1.35 children per woman in 2024.

In Japan, the number of children has fallen for decades. Japan’s child population is now at record lows. In South Korea, the OECD has described the country’s fertility rate as the lowest in the world, around 0.7 in 2023. In India, the problem looks different: a large young population, but an India Employment Report warning that educated young people face high unemployment and that many young people, particularly young women, are outside education, employment or training. Across the EU, Eurostat reported that 11% of 15-29-year-olds were neither in employment nor in education or training in 2025, and that youth unemployment was 15.4% in March 2026.

At the same time, the AI infrastructure build-out continues. Data centres, power contracts, GPUs, fibre, cooling, private capital, grid connections, planning applications and political fights are becoming part of the ordinary economics of the next decade.

That is the part Boards need to understand.

A country can build data centres, fund AI models, celebrate productivity gains and still lose the confidence of the people expected to live inside that future. That loss of confidence does not arrive as one event. It appears in private decisions: having fewer children, delaying a family, leaving education with no clear route into work, taking a job that does not build a career, using AI because it is necessary while trusting it less, and resisting infrastructure that claims to support national progress.

The good news is that this can be fixed.

The route back starts with a simple recognition: growth needs a front door. People need to see how they enter, contribute, learn, earn, build families, own assets and benefit from the technology being deployed around them.

The Boardroom point

Growth depends on participation. Technology can raise output, reduce costs, make small teams more productive and create demand for new power systems, grid upgrades, chips, cooling, batteries, modular construction and other hard technologies. Data centres may even pull technologies down their cost curves by giving them an early buyer with deep demand. Packy McCormick made that argument recently in Not Boring. It is a serious argument and should not be dismissed.

The hard part is the social bargain. Growth becomes fragile when people cannot see their route into it. In the UK, births are at their lowest level in nearly 50 years and more than 1 million young people are Not in Education, Employment or Training (the so called NEETs). In the US, births remain near historic lows. In France, the old demographic confidence has weakened. In Germany, Japan and South Korea, ageing is already a national economic constraint. In India, the challenge is whether education turns into good work at sufficient scale. Across the EU, the NEET and youth unemployment numbers remain material.

The pattern is not identical across countries and should not be forced into one cause. France’s fertility decline is not the same as India’s graduate underemployment. Japan’s ageing problem is not the same as Britain’s NEET problem. America’s childcare and healthcare costs are not Germany’s industrial workforce problem.

But the Boardroom relevance is common. The route from childhood to education, work, household formation, family formation and long-term consumption is becoming less reliable in many large economies. That route sits beneath demand, labour supply, taxation, political stability and corporate growth.

AI infrastructure is rising into that environment. The question is not whether AI can produce more output. It can. The question is whether more people can see themselves inside the output.

The fertility signal

Falling birth rates are often treated as a demographic statistic. They are also a confidence indicator. People have always made family decisions for private reasons:

career,

health,

relationships,

values,

money,

age,

housing,

fertility,

childcare, and

personal freedom.

What has changed is the level of perceived and real uncertainty.

The BBC’s recent reporting captured this well. One interviewee said the world does not feel like a nice place to bring children into. Another wanted more children but could not justify the cost. Those stories matter because birth rates do not fall through one cause. They fall when enough people, making private decisions, decide that the future feels too expensive, too uncertain, too pressured, or too difficult to commit to.

This is no longer a narrow UK story. The US is dealing with historically low fertility. France has crossed a symbolic line, with deaths exceeding births in 2025. Germany, Japan and South Korea are living with older population structures and weak replacement. China has also reversed direction sharply: after decades of population control, it is now trying to encourage births through childcare subsidies, expanded birth-related medical coverage and other family-support policies. The unresolved question is whether households will respond, given the cost of housing, education, childcare, job insecurity and the long shadow of earlier policy. India has a younger population, but faces a different question: can enough young people enter good work fast enough?

The common issue is confidence in the path ahead.

In some countries, that confidence shows up in births. In others, it shows up in youth employment. In others, it shows up in housing, delayed marriage, migration, political pressure or resistance to new infrastructure.

Demographics shape future demand, labour supply, pensions, tax bases, housing markets, healthcare systems, consumer behaviour and political mood. A low-birth society is also a high-dependency society. Fewer young people carry more of the future system: higher debt, older populations, housing shortages, infrastructure strain, climate costs, defence costs and the adjustment costs of AI.

Then they are told that technology will make the economy more productive. That may be true. It will matter more if young people can see a path into that productivity.

The missing route into work

The Milburn review is uncomfortable because it rejects the lazy explanation. The problem is not that young people do not want to work. The BBC reported that 84% of NEET young people surveyed want a job or training. That’s important. If people want work but cannot enter the system, the problem is more one of structure.

The entry point is failing. For instance, retail and hospitality vacancies have fallen sharply in the UK. Entry-level office work is being compressed by automation. Graduate recruitment is increasingly filtered through algorithms. Employers ask for experience before giving anyone the chance to get it. AI tools now do first drafts, summaries, analysis, customer support and coding scaffolds.

The old bargain was imperfect, but it was legible. You started with basic work. You learned by doing. You absorbed standards. You watched older people make decisions. You made small mistakes before you were trusted with larger ones. That bargain is weakening.

Companies can remove junior work before they have designed a replacement learning system. That may look efficient this quarter. It creates a capability problem later: no junior analysts, no trained managers, no institutional memory, no judgement pipeline, no leaders who learned the business from the bottom.

This is why the first rung matters. Starter jobs are not cheap labour. They are paid learning systems. When Boards remove them without replacing them, they reduce future competence.

The fix is not to preserve every old task. Some junior tasks should disappear. Nobody needs to spend three years formatting slides, reconciling low-value data, or copying numbers between systems because the organisation never fixed its processes. The fix is to replace accidental apprenticeship with deliberate apprenticeship.

That means real work, structured learning, AI fluency, human mentorship, rotations, accountability and progression. A company can automate weak work and still build strong people.

That is the real design challenge.

AI is changing the first job before the first job happens

The AI effect on young people starts before employment. It starts in applications. Young people now face automated screening, algorithmic ranking, AI-assisted tests, video interviews, psychometric filters and job descriptions that ask for skills the education system has not yet made standard.

At the same time, young people are told to use AI to compete. Write the CV with AI. Prepare for interviews with AI. Learn AI tools. Show AI literacy. Build a portfolio. Automate something. Create proof of work. That advice is not wrong. I have made parts of that argument myself.

But it creates a brutal asymmetry. The people with the least workplace context are being asked to master the tools reshaping work before they have learned how work operates. A graduate can use AI to write a better application. So can everyone else. Employers receive more polished applications, filter harder, and raise the bar again.

The system produces more output and less trust.

That is one reason the job search now feels so demoralising. The candidate sees effort. The employer sees volume. The algorithm sees patterns.

Nobody sees enough judgement.

This is where Boards should pay attention. Recruitment is not only a hiring function. It is the front door of the organisation. If the front door becomes opaque, expensive to navigate and biased toward those with coaching, connections and AI fluency, the organisation will start selecting for system-navigation rather than potential.

A better approach is available. Employers can publish clearer entry criteria, reduce performative application rounds, test real work rather than polish, and use AI to widen access rather than narrow it. They can create paid work trials, apprenticeships, internships and rotational programmes that develop judgement rather than merely screen for it. They can tell candidates what AI use is allowed and design entry routes for people without expensive coaching.

This is not charity. It is how companies rebuild access to talent.

Data centres and the local bargain

AI feels weightless in a browser. It is not weightless in the real world. It needs data centres, power, water, cooling, chips, transformers, grid capacity, land, planning consent and financing. It also needs local tolerance.

Data centres may fund useful technologies. They may support grid upgrades, new power projects, cooling innovation and industrial renewal. That upside matters. But communities do not experience “AI infrastructure” as a national productivity chart. They experience a planning application, a power connection, a water-use debate, construction traffic, heat, noise, limited local employment, and a site that may not feel connected to the lives around it.

Reuters has reported that data-centre opposition is spilling into French municipal politics, with candidates calling for moratoriums or greater transparency. That is a useful warning. The politics will not stay in France. It will spread because the build-out is physical.

People will ask a simple question:

What do we get?

That question will shape planning, regulation, permitting, grid access, taxation, community benefits, political campaigns and corporate reputation. The companies that answer badly will face delay. Delay has a cost.

The better answer is to make the bargain visible. Local power benefits. Local training. Local hiring. Local apprenticeships. Waste heat reuse where practical. Community funds. Transparent water use. Grid investment. Clear tax contribution. Open reporting. Community involvement before opposition hardens.

This is how infrastructure earns permission.

The participation problem

There is a common Boardroom instinct to place these issues in separate buckets:

• Birth rates go to government.

• NEETs go to education and welfare.

• AI goes to technology strategy.

• Data centres go to infrastructure and energy.

• Recruitment goes to HR.

• Planning objections go to public affairs.

That separation is tidy but it is no longer adequate.

The same participation problem runs through all of them: can people see a credible route into the future being described to them?

That question travels across borders. For an American household, it may be childcare, healthcare and housing costs. For a French household, it may be whether family formation still feels affordable and stable. For a German manufacturer, it may be whether the next cohort of skilled workers is large enough and trained enough. For Japan and South Korea, it may be whether ageing can be managed without asking too much of a shrinking younger generation. For India, it may be whether growth can turn education into good employment rather than frustration. For Europe more broadly, it may be whether youth unemployment and NEET rates remain manageable before they become political facts.

For a young person, participation means access to work and training. For a couple, it may mean a route into housing, childcare and family formation. For a community, it means seeing local benefit from local infrastructure. For employees, it means AI improves work rather than increasing pressure and reducing bargaining power. For Boards, it means productivity claims have to turn into measurable gains without weakening future talent, demand or trust.

This is the part that macro charts miss:

GDP can rise while confidence falls.

Productivity can improve while entry-level work disappears.

Infrastructure can expand while local permission weakens.

AI can be used everywhere while people trust the system around it less.

That is how a society ends up with growth and resentment at the same time.

The better path is to design participation into the growth model from the beginning.

How to square the circle

The gloom is not the point. The real point is that the route can be rebuilt:

AI can make companies more productive and help young people learn faster.

Data centres can support power systems and hard technologies.

Low birth rates can force societies to become more serious about housing, childcare, flexibility and career design.

Youth unemployment can be reduced when governments, employers and schools build practical routes into work.

The problem is not insoluble. It is badly organised.

There are seven practical moves that would change the direction.

1. Build paid entry routes into AI-era work

Every large employer using AI should have a serious answer to this question: how will young people learn here?

That answer cannot be a slogan about digital skills. It should include paid apprenticeships, graduate rotations, entry-level roles redesigned around AI, and managers rewarded for developing junior staff. A junior employee should learn how to use AI, check AI, challenge AI and do the underlying work well enough to know when AI is wrong.

That is the new apprenticeship.

2. Replace weak tasks with stronger learning

Some old entry-level tasks are disappearing. Good. But the learning they carried cannot disappear with them.

So, if AI:

• now drafts the first version of a report, the junior should learn how to test assumptions, trace sources, check logic, rewrite for a decision-maker, and understand the commercial consequence;

• classifies invoices, the junior should learn controls, exceptions, fraud patterns, data quality and process design;

• writes code, the junior should learn architecture, testing, security, maintainability and user need.

The task changes and training must become more intentional.

3. Make AI literacy universal and practical

AI literacy should be treated like spreadsheet literacy became in previous decades. But it must go beyond prompts.

People need to understand what AI is good at, where it fails, how to verify output, how to protect confidential data, when to use a small model, when to use a frontier model, when a human must decide, and how to keep a record.

A workforce that uses AI without understanding verification will create hidden risk. A workforce that understands AI can improve productivity and judgement at the same time.

4. Link infrastructure to local benefit

Data centres and AI infrastructure need local bargains. Companies and governments should publish what a project gives locally, not just nationally:

How many jobs?

What training?

What grid investment?

What community benefit?

What water use?

What heat reuse?

What power source?

What tax contribution?

What protections?

The answer should be specific enough for local people to judge. If the answer is weak, the project should be improved.

5. Measure verified productivity, not activity

Boards should stop counting AI activity as AI value. Prompts sent are not value. Documents generated are not value. Tools deployed are not value. Agents launched are not value.

The better measures are harder and more useful: output per hour after review, unit cost per verified outcome, error rates, rework, cycle time after human approval, revenue per employee, customer satisfaction after automation, cost of control, cost of failure, and vendor dependency.

The company has proved AI productivity only when old work disappears, the new cost base is counted, quality holds, risk stays within appetite, and the gain reaches the P&L.

6. Give people a stake in the productivity gain

AI will face less resistance if people see some upside. That can happen through wages, training, shorter repetitive work, better career progression, equity participation, profit-sharing, lower prices, improved services, or better public outcomes.

Not every gain has to go to employees. Shareholders took risk and capital matters. But if all gains go to capital and all adjustment goes to workers, the bargain will weaken.

People support systems they can join.

7. Treat family formation as economic infrastructure

Many people will not have children. That is a valid choice. But people who want children should not be blocked by housing, childcare, unstable work and hostile career structures.

Companies cannot fix national fertility alone. They can still help through flexible work that does not punish parents, predictable scheduling, childcare support where feasible, career paths that survive parental leave, men expected to parent, returnships, manager training, and promotion systems that do not treat family life as lack of ambition.

Governments have larger levers: housing, childcare, tax, education, healthcare, transport, planning and migration. But employers shape the daily reality. A society that wants future workers, consumers and taxpayers should make it easier for people who want families to have them.

The Board questions that matter now

Boards do not need to solve fertility policy or youth unemployment alone. They do need to stop pretending these are externalities with no relevance to strategy.

A useful Board discussion should cover ten points:

1. Which junior roles are being removed, compressed or redesigned by AI?

2. What replaces the learning those roles used to provide?

3. Does the hiring process identify potential, or does it reward candidates who know how to pass automated filters?

4. Which AI gains have reached the P&L after compute, software, integration, review, security and rework?

5. Are employees using AI because it improves work, or because they fear being left behind?

6. Where does the company create local cost or remote benefit, especially around infrastructure, offices, energy, data centres, logistics and automation?

7. What evidence would make the Board reduce its AI productivity assumptions?

8. How does the company share productivity gains with workers, customers or communities?

9. Which skills must be protected because the company will need them in 5 or 10 years?

10. Does the company assume future consumers, taxpayers, workers and families will exist in sufficient numbers and with sufficient confidence to support its plans?

The last point sounds rather broad: it is not. Companies depend on social reproduction in the literal and economic sense. They need new workers, new customers, new managers, new households and new taxpayers. A society that cannot give young people a route into work and family formation is eating future demand.

The hard truth for AI strategy

AI strategy cannot stay inside the productivity box. That’s because the productivity box is too small.

AI impacts the first job. It interacts with training and who captures gains. It affects where infrastructure gets built and energy demand. It affects trust in work and whether young people think the future is something they can enter or something that happens to them.

A good AI strategy must answer more than how to automate. It must answer who:

• learns,

• gains,

• verifies,

• is displaced,

• is hired,

• is trained,

• carries the cost,

• sees the benefit, and

• gives permission.

If those questions sound political, that is because they are. But they are also commercial. Legitimacy is now part of execution. Recruitment quality is part of AI strategy. Training is part of resilience. Local acceptance is part of infrastructure finance. Demographics are part of long-term demand.

The companies that understand this will still use AI aggressively. They will automate. They will reduce waste. They will build or buy compute. They will use agents. They will redesign workflows.

They will also preserve the routes that make growth believable.

The weekend takeaway

The future is being built, data centres are rising and AI systems are spreading. Compute is being priced, traded and allocated. Boards are being told that productivity will improve, costs will fall and growth will follow.

Some of that will be true.

When young people cannot get work, the first rung disappears. When families cannot afford children, demographic confidence falls. When communities host infrastructure without visible benefit, permission weakens. When employees use AI while trusting it less, adoption numbers mislead management. When Boards count productivity but ignore participation, they mistake output for resilience.

But this can be fixed:

Build paid entry routes.

Redesign junior work.

Teach AI literacy properly.

Connect infrastructure to local benefit.

Measure verified productivity.

Share some of the gain.

Make work and family life more compatible.

The useful question is simple:

If they can, AI becomes part of renewal.

But, if they cannot, the growth story weakens and that not because the technology failed, it is because the bargain fails.

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🧠 Techie Corner for the Non-Techies

1. NEET

NEET means not in education, employment or training. It is one of the clearest measures of whether young people are connected to work or learning.

A high NEET rate matters because it can create long-term effects: lower earnings, weaker confidence, poorer health, higher welfare costs and reduced future tax receipts. For companies, the NEET issue matters because it reduces the future talent pool.

2. Fertility rate

The total fertility rate estimates the average number of children a woman would have over her lifetime if current birth rates continued.

A fertility rate below 2.1 is usually below replacement level in advanced economies, although migration, mortality and age structure also matter. This matters for Boards because demographics affect labour supply, consumer demand, pensions, healthcare costs, tax capacity and political priorities.

3. Entry-level compression

Entry-level compression happens when technology removes or reduces the basic tasks that used to train junior workers. Examples include drafting first versions, summarising documents, preparing reports, running simple analysis, answering routine customer queries and checking standard forms.

These tasks may be boring but they also teach context, judgement and standards. When AI removes them, companies need new training routes.

4. Verification debt

Verification debt is the checking burden created by machine-generated output. AI can produce more drafts, summaries, code, analysis and recommendations than an organisation can easily review.

The output may look polished and that makes mistakes harder to spot. A company has gained productivity only when useful output rises after review, correction, risk control and accountability are included.

5. Data-centre externalities

An externality is a cost or benefit that falls on someone outside the main transaction. Data centres can create local externalities: power demand, grid pressure, water use, construction disruption, land use, noise, heat and limited local employment.

The AI buyer may see compute capacity, yet, the community sees infrastructure pressure. That gap drives political risk.

6. Licence to operate

Licence to operate means the practical permission a company needs from regulators, communities, customers, employees and politicians. It is wider than legal permission.

A company can have formal approval and still face resistance that slows projects, raises costs or damages trust. AI infrastructure will increasingly depend on licence to operate because the costs are physical and local.

7. Participation

Participation means people can enter and benefit from the system. For young people, participation means access to work and training. For families, it means confidence to form households and raise children. For communities, it means seeing local benefit from local infrastructure. For employees, it means AI improves work rather than simply increasing pressure.

Productivity without participation becomes politically fragile.

8. Deliberate apprenticeship

Deliberate apprenticeship means replacing accidental learning with designed learning. The old model relied on junior people learning by doing basic tasks. AI will remove many of those tasks.

The new model must teach people through structured work, AI-assisted practice, human review, rotations, feedback and accountability. That is how companies keep developing judgement while still using AI.

📚 Further reading

• BBC News: “It’s not a nice world to bring children into”: Births fall to the lowest level in 50 years (2026)
  Useful for the UK fertility and confidence hook. Reports 585,000 live births in England and Wales in 2025 and a fertility rate just under 1.4.

• Office for National Statistics: Births in England and Wales: 2025 (2026)
  Primary UK source for births, fertility and demographic data.

• BBC News: Opportunities shrinking for too many young people, says major report on “lost generation” (2026)
  Useful for the UK youth-employment hook. Reports more than 1 million young people not in education, employment or training and Alan Milburn’s warning about a lost generation.

• Alan Milburn / UK government: Young People and Work review materials (2025-2026)
  Policy context for the government-commissioned review into rising numbers of young people who are NEET.

• CDC / National Center for Health Statistics: Births: Provisional Data for 2025 (United States, 2026)
  Primary source for US provisional birth data. Reports about 3.6 million births in 2025 and a general fertility rate of 53.1 births per 1,000 women aged 15-44.

• USAFacts: How have US fertility and birth rates changed over time? (2026)
  Useful US public-data source. Gives a clear non-partisan presentation of the long-term decline in US fertility and birth rates.

• INSEE: Demographic report 2025 (France, 2026)
  Primary French source. Reports 645,000 births in 2025, fertility at 1.56 children per woman, and deaths exceeding births.

• le Monde: France records more deaths than births in 2025, a first since World War II (2026)
  Le Monde reports 651,000 deaths and 645,000 births in 2025, with deaths exceeding births for the first time since World War II.

• Destatis: Decline in fertility rate slowed significantly in 2024 (Germany, 2025)
  Primary German source. Reports Germany’s fertility rate at 1.35 children per woman in 2024.

• Statistics Bureau of Japan: News Bulletin on the number of children (2025)
  Primary Japanese source. Reports Japan’s child population at record lows and continuing long-term decline.

• OECD: Economic Surveys Korea 2024, responding to population decline (2024)
  Institutional source on South Korea’s demographic position. Describes Korea’s fertility rate as the world’s lowest, around 0.7 in 2023.

• ILO and Institute for Human Development: India Employment Report 2024 (2024)
  Useful source on India’s youth employment challenge. Highlights educated youth unemployment and the large share of young people, particularly young women, outside education, employment or training.

• Eurostat: Statistics on young people neither in employment nor in education or training (2026)
  Primary EU source for NEET data.

• Eurostat: Unemployment statistics (March 2026)
  Primary EU source for youth unemployment.

• Reuters: Backlash against data centres is spilling into French municipal election races (2026)
  Useful for the infrastructure-legitimacy angle. Shows data centres moving from technology infrastructure into local politics.

• Not Boring by Packy McCormick: Thank God For Data Centers (2026)
  Useful counterpoint. Argues that AI data centres may act as buyers of capability, funding technologies such as nuclear, geothermal, grids, batteries and other hard technologies before traditional markets would.

• Dr Henri Winand: AI & the Missing First Rung of the Career Ladder (2025)
  Earlier ‘Something for the Weekend’ article arguing that starter jobs are investments in resilience, not cheap labour.

• Dr Henri Winand: The Legitimacy Risk No One Is Pricing (2026)
  Earlier ‘Something for the Weekend’ article on the risk that AI capability scales faster than public acceptance.

• Dr Henri Winand: The Productivity Miracle Someone Else Pays For (2026)
  Earlier ‘Something for the Weekend’ article on AI productivity, asset prices, debt relief and distribution.

• Dr Henri Winand: Rent the intelligence. Own the proof. (2026)
  Earlier ‘Something for the Weekend’ on compute, local AI, smaller models and audit trails.

• China State Council / Xinhua: China to offer nationwide childcare subsidies (2025)
  Primary Chinese government-linked source. Reports China’s nationwide childcare subsidy programme from 2025, offering 3,600 yuan per year for each child under three.

• Reuters: No “out-of-pocket expenses” for childbirth in China from 2026 (2025)
  Reports China’s plan to expand birth-related medical coverage, including prenatal checks, as part of wider efforts to encourage births.

• National Bureau of Statistics of China / 2025 population data: Statistical Communiqué of the People’s Republic of China on the 2025 National Economic and Social Development (2026)
  Primary source to verify China’s population and birth trend. Use with care, as English-language summaries and third-party reporting may differ in timing and presentation.

Last Week on Something for the Weekend

Last week’s piece looked at the operating bargain under enterprise AI. The argument was that compute may become priced, traded and allocated, but serious AI decisions still need control and proof.

Cloud AI gives access to powerful models, but it is metered. Local AI gives more control, but it creates capital cost and utilisation risk. Small language models may suit narrow, private workflows better than large models. Audit trails matter because serious decisions need a record.

The useful question was:

Where did the answer run, what did it cost, and can we prove why it was given?

That piece looked at the machine-work layer. This week looks at the human layer underneath it: whether young people, families, workers and communities still see a route into the future that AI is helping to build.

Picture the scene

A CFO is shown a simple business case.

One AI subscription. $200 a month.

The pitch is clean enough. A digital worker that never sleeps. A coding assistant that writes faster than a junior engineer. A research assistant that scans documents in seconds. A customer-support agent that handles repetitive tickets. A legal assistant that drafts contracts. A finance assistant that checks invoices.

The comparison looks almost too easy.

A human employee has:

• salary;

• benefits;

• holidays;

• sick leave;

• office costs;

• management overhead;

• training time, and

• employment rights.

The AI tool has a monthly subscription.

So the conclusion follows: replace expensive human work with cheap machine work.

The invoice does not stop at the subscription.

The digital worker:

• uses tokens;

• needs context;

• makes calls to models;

• retries tasks;

• gets stuck;

• burns through usage limits;

• needs cloud infrastructure;

• needs integration into existing systems, and

• creates outputs someone must check.

The human employee was expensive.

The digital employee is metered.

That difference matters because a fixed salary is visible. A variable machine-work bill can hide inside cloud spend, software licences, API calls, engineering tools, customer-service workflows, legal review time and senior management attention.

The Boardroom point

Boards should treat AI labour as metered work, not fixed-price employment.

That distinction changes the economics:

A human employee has limits. They work a finite number of hours. They can be trained, managed, questioned and held responsible. They can explain what they did. They are slow compared with software, but they are bounded.

A digital employee has a different profile. It can run continuously. It can generate more output than the organisation can absorb. It can create plausible work that still needs expert review. It can multiply mistakes. It can shift costs from headcount to compute. It can appear cheap until usage scales.

It also has no real accountability.

The useful boardroom question is direct:

That is where the $200 story starts to break.

Why this is happening now

The AI market is moving from adoption subsidy to cost recovery.

The first phase was about getting people to use the tools:

Free tiers.

Generous limits.

Bundled copilots.

Cheap trials.

Experiments hidden inside departmental budgets.

Vendors wanted adoption.

Investors wanted usage.

Employees wanted speed.

Management wanted proof that AI could move beyond demos.

That phase worked. The usage arrived.

Now the cost base has arrived too.

Gartner expects worldwide IT spending to reach $6.31 trillion in 2026, up 13.5% from 2025. The fastest-growing category is data centre systems, forecast to grow 55.8% to nearly $788 billion. Gartner links that growth directly to AI infrastructure, high-performance compute, accelerators and advanced memory.

The hyperscalers are making the same point with capital spending. Reuters reported that the combined AI investment plans of major US technology firms are now expected to exceed $700 billion in 2026. Alphabet lifted its capital spending forecast to $180 billion to $190 billion. Microsoft is forecasting around $190 billion in capital outlays. Amazon has been running with a $200 billion plan. Meta raised its 2026 capital expenditure guidance to as much as $145 billion.

That money has to be recovered:

Some will come through cloud contracts.

Some through enterprise licences.

Some through usage pricing.

Some through higher software prices.

Some through reduced free access.

Some through rate limits.

Some through AI vendors changing what a dollar buys.

The consumer sees a chatbot. The enterprise sees a tool. The supplier sees inference cost.

The CFO should see a meter.

The $200 worker has a meter

The influencer version of AI labour is simple: why hire a person when an AI agent costs $200 a month?

That framing confuses access with work. The subscription gives you entry to the system. It does not guarantee unlimited economic output.

The real cost depends on usage. A chatbot answering 10 short questions is cheap. An agent reading thousands of files, writing code, testing it, retrying errors, calling other tools and producing long outputs is different.

That costs tokens. Tokens are the units models process:

More context means more tokens.

More steps mean more tokens.

More retries mean more tokens.

Bigger models mean more expensive tokens.

This is why rate limits matter. When Anthropic announced its May 2026 compute deal with SpaceX, it said the agreement gave it access to more than 300 megawatts of new capacity and more than 220,000 NVIDIA GPUs within the month. It also said that extra capacity would let it raise Claude Code and Claude API limits.

That is the economic signal.

Usage limits are the visible edge of the supplier’s cost structure. If compute were abundant and cheap, the limits would matter less. They matter because AI work consumes scarce infrastructure.

The cost base moves

The old promise was labour replacement. The operating reality is cost conversion.

A company may reduce some headcount, yet, the work still has to happen:

Some of it moves to compute.

Some to cloud spend.

Some to software subscriptions.

Some to data preparation.

Some to security.

Some to integration.

Some to legal review.

Some to senior people checking machine output.

Some to remediation when the machine gets it wrong.

A human worker might cost more per month than an AI subscription. But the human worker does not silently retry a failed workflow until the budget is gone. A human worker does not create 200 pages of plausible but unverified work in 3 minutes. A human worker can be questioned.

AI can do extraordinary work, but it lacks ordinary restraint.

Generation is cheap. Verification is not.

AI lowers the cost of producing output. It does not automatically lower the cost of producing verified output:

A legal draft is not complete because it exists.

A software patch is not complete because it compiles.

A market analysis is not complete because it sounds fluent.

A medical triage response is not complete because it gives an answer.

A board paper is not complete because it reads well.

Someone has to check whether it is right.

That checking can become more expensive when AI is involved. The output often looks polished. The error may sit inside the reasoning, the citation, the assumption, the omitted exception, the wrong jurisdiction, the bad edge case or the false confidence.

The reviewer cannot skim it like ordinary junior work. They have to inspect it as machine-generated work.

That creates verification debt.

The more the organisation generates, the more it has to verify. The risk is not that AI produces bad work all the time. The risk is that it produces mixed work at high speed: some excellent, some ordinary, some wrong, all fluent.

That is harder to manage than obviously weak work.

The senior person becomes the bottleneck again. Only now the bottleneck is reviewing a flood.

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The SaaS budget is changing shape

Enterprise software used to be easier to budget:

Seats.

Renewals.

Licences.

Implementation.

Support.

Annual uplift.

Procurement could understand that model. Finance could forecast it. IT could police it.

AI pushes software toward a different model: access plus usage. Per user plus consumption. Seat plus token. Licence plus API call. Base fee plus overage.

This changes behaviour. With ordinary SaaS, a user who works all day rarely changes the cost of the contract in real time. With AI, heavy usage can create real cost.

A software engineer using an AI coding tool for complex tasks may consume far more than a casual user. A legal team asking AI to review long contracts may consume more than a marketing team writing short copy. An agentic workflow that reads, reasons, acts and retries may cost more than a simple assistant.

The pricing model starts to resemble cloud, not software.

That create problems for CFOs:

The people creating the cost may not see the bill.

The department approving the tool may not understand the usage curve.

The vendor may price the product around credits, tokens, tiers or limits that procurement cannot easily compare.

The business case may say productivity. The invoice may say consumption.

The AI budget has escaped the spreadsheet

This is already becoming a finance problem.

CFOs and CIOs often use the same words differently: value, usage, capacity, productivity, efficiency, automation, cost saving, risk.

Those words can mean different things in finance, technology and operations. IT has one spreadsheet. Finance has another. Business units have their own tools. Procurement has the contract. Legal has the risk.

AI makes that fragmentation worse because the spend is not one line item.

It appears as software subscriptions, cloud usage, API consumption, data infrastructure, cybersecurity, consulting, implementation, internal engineering time, training, legal review, compliance, human supervision, exception handling, model evaluation and failed pilots.

The company may think it is spending on AI tools.

It is building a machine-work cost base.

That needs cost accounting.

The right comparison

The right comparison is human work versus machine work, fully loaded.

For a human worker, the cost base includes salary, benefits, training, management, office and equipment, legal and HR obligations, coordination time and human error.

For a digital worker, the cost base includes subscription, token usage, API calls, cloud dependency, context processing, rate limits, retries, integration, data access, security, monitoring, verification, failure recovery, legal uncertainty and vendor dependency.

The human worker is slower. In contrast, the digital worker is unbounded.

The human worker is expensive upfront but the digital worker can become expensive through use.

The human worker may underperform and the digital worker may overproduce.

That last point matters. AI does not only automate work. It creates more work to inspect.

Where AI economics do work

AI economics can work extremely well where the task is narrow, frequent, measurable and easy to verify:

• Customer emails with clear policy rules.

• Document classification.

• Invoice matching.

• Code suggestions inside controlled repositories.

• Call summaries with human sign-off.

• Search across internal knowledge.

• Drafting where the expert remains responsible.

• Data extraction with sampling. Internal helpdesks.

• First-pass analysis.

The weak cases are more predictable:

• open-ended agents with unclear goals,

• high-liability tasks with weak review,

• workflows where wrong answers are costly,

• customer-facing automation without escalation,

• legal, medical or financial outputs without expert checking,

• tools bought by departments without usage controls, and

• experiments that become production systems by accident.

The issue is economic discipline.

What boards should ask now

The board does not need to approve every AI tool. It does need to understand how the company is controlling machine work.

A useful board discussion should cover 12 points:

• First, where AI is being used today.

• Second, who owns the budget.

• Third, whether the cost is fixed, variable or hybrid.

• Fourth, what happens when usage doubles.

• Fifth, which workflows have rate limits or overage charges.

• Sixth, which AI outputs require human verification.

• Seventh, who is accountable when an AI output is wrong.

• Eighth, which tools duplicate each other.

• Ninth, which pilots have kill criteria.

• Tenth, which vendors can raise prices or reduce limits.

• Eleventh, which workflows depend on one model provider.

• Twelfth, which claimed savings have actually shown up in the P&L.

The answer cannot be: people are experimenting.

Experimentation was the first phase. The next phase needs controls.

Not bureaucracy.

Controls.

The uncomfortable management question

Some companies will use AI well. They will measure the right thing: verified output, cycle-time reduction, error reduction, revenue uplift, lower rework, better customer response, less repetitive labour and faster expert review.

Others will measure the wrong thing: prompts sent, tokens used, tools deployed, headcount avoided, documents generated and agents launched.

Those are activity measures. They do not prove value.

A person can look busy while producing little. So can an AI system. The difference is that the AI system can do it faster, cheaply at first, and then expensively at scale.

The mistake is to worship usage.

The better discipline is to price the outcome.

What did the AI actually do? Was it correct? Was it used? Did it reduce cost? Did it increase revenue? Did it reduce risk? Did it move the bottleneck somewhere else? Did it create new verification work? Did anyone stop the old process?

That last question is often where the business case fails.

If AI adds a new workflow while the old workflow remains, the company has added a layer.

The weekend takeaway

The digital employee was never $200 a month, that was the entry price.

The real cost is the work system behind it: tokens, inference, context, cloud, integration, supervision, verification and risk.

AI will still change work. It will replace some tasks. It will improve many jobs. It will reduce some costs. It will also create new ones.

The companies that win will know where AI is cheaper than human work after the full bill is counted.

Salary is not the only cost of labour.

Subscription is not the full cost of AI.

That is the budgeting mistake to avoid.

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🧠 Techie Corner for the Non-Techies

1. Token

A token is a unit of text or data processed by an AI model.

Short prompts use fewer tokens. Long documents, long conversations, codebases and agentic workflows use many more.

Tokens matter because AI vendors often price usage by the amount of input and output the model processes.

2. Inference

Inference is what happens when an AI model runs to answer a prompt or complete a task.

For popular AI products, inference cost can become a major economic constraint because every user request consumes compute.

3. Context window

The context window is the amount of information an AI system can consider at one time.

A larger context window lets the model read more documents, code or conversation history. It can also cost more because the model must process more tokens.

That is why “just give the AI everything” is often inefficient.

4. Rate limit

A rate limit controls how much a user or system can use an AI model within a given period.

It may cap messages, tokens, API calls or agent runs. Rate limits protect the provider’s infrastructure and economics.

They also show that AI capacity is finite.

5. Agent

An AI agent is a system that can take multiple steps toward a goal.

It might search files, call tools, write code, test outputs, revise its answer and try again.

Agents can be useful because they do more than answer one prompt. They can also become expensive because each step consumes tokens and compute.

6. Verification debt

Verification debt is the checking burden created by machine-generated output.

When AI produces more drafts, code, summaries, contracts or analysis, someone must still decide what is correct.

If the organisation generates faster than it can verify, the apparent productivity gain turns into a review bottleneck.

📚 Further reading

• Gartner: Worldwide IT spending forecast (2026)

  Gartner forecasts worldwide IT spending of $6.31 trillion in 2026, up 13.5% from 2025. It also forecasts data centre systems as the fastest-growing category, up 55.8%, driven by AI infrastructure and advanced memory demand.

• Reuters: Google Cloud pulls ahead as Big Tech’s AI bet swells to $700 billion (2026)

  Reuters reports that Google Cloud grew 63%, ahead of Amazon and Microsoft cloud growth, and that Big Tech AI spending is now expected to exceed $700 billion in 2026, up from around $600 billion previously.

• Anthropic: Higher usage limits for Claude and a compute deal with SpaceX (2026)

  Anthropic’s own announcement confirms the SpaceX Colossus 1 compute agreement, giving Anthropic access to more than 300 megawatts of new capacity and over 220,000 NVIDIA GPUs within the month. It also confirms higher Claude Code and Claude API usage limits.

• Reuters: Anthropic strikes SpaceX data centre deal as it plows ahead on AI coding (2026)

  Reuters reports that the SpaceX deal and other capacity agreements let Anthropic double Claude Code rate limits for paid plans, remove peak-hour caps for Pro and Max users, and raise Claude Opus API request volumes.

• CFO Brew: Tech budgets: CFOs battle the bulge (2026)

  CFO Brew covers the CFO problem directly: tech budgets are swelling because of AI and cybersecurity, IT costs are becoming more variable, and finance and IT need a shared cost language to manage consumption-based pricing.

• 404 Media: Startups brag they spend more money on AI than human employees (2026)

  Useful example of “tokenmaxxing” and the emerging idea that token spend can replace headcount spend.

• Kristian Holte: The verification problem in legal AI (2026)

  Kristian Holte’s LinkedIn post is live and matches the cited argument: generation of legal output is getting cheaper, but verification by legal professionals remains necessary and can become more expensive.

Last Week on Something for the Weekend

Last week’s piece asked whether AI is turning workers into “superworkers”.

The argument was that AI is starting to separate memory, voice, judgment and physical agency from the person.

Digital twins can make expertise more available. They can reduce key-person risk. They can help companies retain knowledge. They can also make workers easier to copy, flatten and replace.

That piece looked at the copy of the worker.

This week looks at the cost of running the copy.

The superworker may be real.

The cheap digital employee is a more complicated story.

It starts far from the Boardroom.

A house in San Francisco is attacked.

Not randomly.
Specifically.

The target is:

Not a politician.
Not a banker.
Not an oil executive.

The target is the CEO of a global Artificial Intelligence company.

Days later, shots are fired again.

At the same time, in smaller towns and quieter places, a different pattern is emerging:

Local elected officials voted out after approving data centres.
Communities pushing back on power usage, water consumption, land use.
Planning meetings turning hostile.

Elsewhere, the shift is less visible, but just as real:

Graduates questioning whether their degrees still lead anywhere.
White-collar workers watching tasks quietly disappear.
Executives acknowledging (often privately) that the disruption is real, and it is coming fast.

The visible story is technological progress.

The underlying story is something else:

the early signs of resistance.

The Boardroom point

Most AI discussions in Boardrooms are framed around:

Efficiency.
Productivity.
Capability.
Competitive advantage.

That framing is incomplete.

The emerging risk is not whether AI works.

It is whether society accepts it.

Why this is happening now

1) The benefits are concentrated. The disruption is distributed

AI creates value at scale.

But it does not distribute that value evenly:

• Gains accrue to capital and high-skill operators.

• Disruption hits earlier and more broadly.

• Career paths become less predictable.

The result is a widening perception gap:

Progress for some.
Precarity for others.

2) Infrastructure is local. Impact is global

AI looks digital, but it is not weightless.

It requires:

Power.
Water.
Land.

Which means:

Costs are localised.
Benefits are often not.

Communities are not evaluating “AI strategy.”

They are evaluating whether the trade-off is acceptable.

3) Institutions are slower than the technology

Labour markets, education systems, and policy frameworks adjust slowly.

Historically, that lag was manageable.

Now it is compressed.

What once took decades is unfolding in years.

The system has less time to absorb the shock.

The deeper insight Boards rarely see

This is not “tech resistance.”

It is not temporary either.

It is the early stage of a legitimacy challenge.

Every large-scale system depends on an implicit contract:

Society allows it to operate.
In exchange for broadly shared benefit.

When that balance weakens, resistance follows.

How Legitimacy Erodes (And Then Hardens)

The shift does not happen all at once, it propagates through layers.

1. Individuals

The first signal is not unemployment.

It is uncertainty.

Not just:

“Will I have a job?”

But:

“Is there still a path?”

Career trajectories become less visible.
Skills feel less durable.
Progress looks less linear.

People adapt.

They continue to use AI.
They increase output.

But they adjust behaviour:

More verification.
Less trust.
Shorter-term thinking.

The psychological contract weakens before the economic one breaks.

2. Communities

The second signal is physical.

AI is abstract at the top of the system.

It is concrete at the bottom:

Data centres appear.
Energy demand rises.
Water usage becomes visible.
Land is repurposed.

That imbalance creates friction.

Communities begin to organise:

Planning objections
Political pressure
Electoral shifts

The question is no longer:

“Is this good technology?”

It becomes:

“Is this good for us?”

3. Institutions

The final signal is institutional.

Policy does not lead.

It reacts.

Initially:

Ambiguity.
Light-touch oversight.
Encouragement of innovation.

Then, as pressure builds:

Scrutiny increases.
Rules tighten.
Approvals slow.

The shift is not gradual.

It is discontinuous.

Once legitimacy is questioned, institutions move to restore control.

At that point, the environment changes faster than strategy can adjust.

The pattern is consistent:

Individuals recalibrate.
Communities resist.
Institutions constrain.

By the time it reaches the Boardroom, the system is already moving.

How It Surfaces in the Real Economy

This does not arrive as a shock.

There is no single event.

No clear trigger.

It accumulates.

Quietly at first, then (sometimes, violently) visibly.

It starts in projects:

Timelines extend.
Approvals take longer.
Conditions are added late in the process.

Not because the project is flawed, rather, because the environment is less certain about it.

Planning becomes negotiation.

It then moves into regulation:

Requests for information increase.
Definitions tighten.
Interpretation shifts.

The same activity is viewed differently.

What was acceptable becomes conditional.
What was fast becomes slow.

Friction increases without a formal rule change.

It appears in talent behaviour.

Candidates hesitate.
Employees ask different questions.
Retention becomes less predictable.

Not because roles disappear, but because confidence in where those roles lead weakens.

The signal is not exit, it is optionality.

It shows up in the brand:

Messaging becomes sensitive.
Positioning is scrutinised.
Association carries risk.

Statements that once signalled progress now require justification.

Narratives are contested.

It intensifies in oversight:

More stakeholders engage.
More voices are heard.
More decisions are revisited.

The cost is not just delay, it is attention.

Management time shifts from execution to explanation.

And at the edges:

Escalation.
Targeted opposition.
Symbolic actions.
Moments that concentrate attention.

Rare, but visible.

The pattern is consistent:

Nothing breaks.

But everything slows.

And in systems built on speed, delay is a form of loss.

The Metric That Isn’t Measured

Most organisations believe they understand their risk.

They track it, rigorously:

Revenue trends.
Cost structures.
Margins.
Operational KPIs.

Dashboards are precise.
Reporting is frequent.
Variance is analysed.

The system is built to detect deviation, but only within the system, not outside it.

There is no line item for:

Acceptance.
Trust.
Permission.

No metric that answers:

“Are we still allowed to do this?”

When it does, it does not move gradually:

it resets.

A project that was viable is no longer approved.
A strategy that was acceptable is now contested.
A narrative that worked becomes a liability.

The underlying economics may not have changed, yet the permission has.

This creates a real blind spot:

Performance is measured internally.

Constraint is imposed externally.

The two are not aligned.

When management continues to optimise:

Efficiency.
Scale.
Return.

Assuming continuity, while the boundary conditions are shifting, the failure mode is predictable:

Strong internal performance.
Weak external acceptance.

By the time the signal is visible in financials, it is already (often too) late. Because legitimacy does not erode the income statement first, it constrains the ability to operate.

What is not measured is not managed, and what is not managed becomes the risk that matters most.

Why most AI strategies are structurally incomplete

They assume:

If the technology works, value follows.

The constraint is shifting:

From capability to acceptance.

From execution to permission.

AI scales scrutiny as fast as it scales output.

The risk transfer that is already underway

Historically:

Risk sat in operations.
Losses were insurable.

Now, additional risks are moving into the social layer:

• Reputation.

• Political exposure.

• Licence to operate.

The loss is not a claim, it is:

A delay.
A rejection.
A loss of trust.

The Board questions that matter now

The issue is not awareness, it is precision.

Boards are not asking the wrong questions, they are asking them too generally.

This risk sits in specifics.

Where does our strategy depend on public acceptance of AI?

Not in principle, in practice.

Which products, services, or processes rely on AI being:

• trusted by customers;

• accepted by employees;

• permitted by regulators;

• tolerated by communities.

Map the dependency, not at the headline level, at the point of use, because that is where acceptance can fail.

Which parts of our model create local cost but distributed benefit?

Every large system has asymmetry.

AI makes it visible.

Where are you:

• drawing power from one place;

• using water from another;

• deploying infrastructure into a third;

• capturing value somewhere else.

Those gaps define exposure.

The greater the distance between cost and benefit, the greater the pressure.

What happens if key stakeholders say “no”?

Not as a theoretical scenario, rather, as an operating condition.

If a project is delayed:

• What is the financial impact?

• What is the alternative?

• What is the time to recover?

If approval is withdrawn:

• Does the model still work?

• Or does it fail entirely?

Resilience is not the ability to proceed, it is the ability to adapt when permission is constrained.

How do we detect early signals of resistance?

By the time resistance is visible, it is already late.

The signal appears earlier in:

• planning objections;

• employee sentiment;

• candidate behaviour;

• customer feedback;

• media tone.

These are not “soft” indicators, they are leading indicators.

The system weakens before it breaks.

Who owns this risk?

This is where most organisations fail.

The risk does not sit cleanly in:

• technology;

• operations;

• compliance;

• communications.

So it sits nowhere, or everywhere, which is the same thing.

Ownership must be explicit.

Because legitimacy is not maintained by default, it is managed.

These are not strategic questions, they are operating questions.

They determine whether the strategy can be executed at all.

The Weekend takeaway

AI is not just changing how businesses operate, it is testing whether they are still allowed to.

Capability scales internally, legitimacy is granted externally.

Those two do not move together.

Most strategies still assume:

Build capability.
Deploy it.
Capture value.

That path includes a constraint:

The companies that win will not just:

Deploy technology.
Improve efficiency.
Scale capability.

They will:

Maintain legitimacy, continuously.

Because legitimacy does not fail at the point of intent, it fails at the point of impact:

where systems meet people;
where infrastructure meets communities;
where outputs meet expectations.

When Legitimacy fails, the response is not gradual:

projects stop;
rules tighten;
narratives harden;

Technology compounds quickly, but legitimacy does not.

And once lost, it is far harder to rebuild than any system you deploy.

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🧠 Techie Corner for the Non-Techies

AI is physical as well as digital

Behind every model sits infrastructure:

• data centres;

• specialised chips;

• power;

• cooling, and

• connectivity.

These systems are energy-intensive and location-specific.
That is why AI is no longer invisible. It shows up in planning applications, energy demand, and local resource constraints.

AI scales instantly

Traditional systems scale gradually.

AI does not.

Once deployed, the same model can serve thousands, then millions, almost immediately.

That means:

• impact scales quickly;

• errors scale quickly;

• scrutiny scales quickly.

The system amplifies outcomes, not just activity.

AI compresses time

Tasks that used to take hours or days now take minutes:

Analysis accelerates.
Decisions accelerate.
Execution accelerates.

But institutions do not.

Regulation, governance, and public response still move at human speed.

That gap is where tension builds.

AI is a general-purpose technology

Like electricity or the internet, AI is not confined to one function.

It cuts across:

• operations;

• finance;

• customer interaction;

• decision-making.

This creates system-wide effects.

Benefits compound across functions, so do risks.

The real constraint is integration, not access

Most organisations can access AI tools.

Very few can:

• embed them into workflows;

• align them with incentives;

• ensure consistent use;

• govern outputs effectively.

Without integration:

• value remains fragmented;

• outputs vary;

• trust weakens.

This is why deployment is fast, but impact is uneven.

AI shifts value from execution to judgement

When generating outputs becomes easy, the bottleneck moves.

Not:

“Can we produce this?”

But:

“Can we trust it, interpret it, and act on it?”

Human oversight does not disappear, it becomes more critical, not less.

AI introduces verification cost

AI reduces the time to create, but increases the need to:

• review;

• validate;

• take accountability.

In some cases, this offsets the time saved.

Productivity gains are therefore context-dependent, not universal.

AI creates dependency before stability

Once embedded, removing AI becomes difficult:

Workflows adapt to it.
Expectations adjust to it.

Dependency forms early.

But performance, reliability, and trust are still evolving.

That gap matters.

These mechanics explain the core tension:

📚 Further reading

• Stanford University: AI Index Report (2026)
  Documents rising global concern about AI systems, with a majority expressing nervousness about their impact.

• Fortune: After attacks on Sam Altman, experts see parallels to the Industrial Revolution (2026)
  Examines escalating anti-AI sentiment and links it to historical periods of rapid technological disruption.

• PropertyCasualty360: AI threatens ‘jobs with dignity,’ says Nobel-winning economist (2026)
  Explores how AI may erode stable, middle-income roles and reshape labour markets.

• Reuters: Backlash against data centres is spilling into French municipal election races (2026)
  Shows how opposition to data centres is shaping local political campaigns, with candidates calling for moratoriums due to environmental, energy, and social concerns. Illustrates how AI infrastructure is becoming a political issue at the municipal level.

Last Week on Something for the Weekend

Fiscal policy did not break. It reached its limit.

With debt high and options constrained, AI became the assumed source of productivity that keeps the system working.

Inside firms, adoption followed. Output increased. But belief did not.

People used AI more, while trusting it less. They relied on it, while verifying it.

The system scaled dependency. The workforce repriced its value.

This created a divergence:

• the system priced in future capability;
  the workforce reacted to present reality.

Nothing failed, but the system began to depend on something not yet fully trusted.

The Morning Everything Stopped

You’re sitting in an airport. Flights are cascading into delay, then cancellation. Check-in screens are blank, baggage belts are frozen, staff are apologising without any real information.

You assume: “Cyberattack? Must be the airline.”

But on 19 July 2024, the meltdown that grounded flights at major carriers such as American, Delta, and United causing thousands of cancellations and delays worldwide, and even disrupting hospitals and banks wasn’t caused by the airlines, airports, or payment providers.

It was caused by a single faulty software update from cybersecurity vendor CrowdStrike, which crashed numerous Microsoft Windows machines globally and triggered one of the largest IT outages in modern history.

Translation for Boards:

one service failure forced airlines to halt operations, delayed trading and settlement systems, and interrupted hospital and broadcaster workflows.

That is a single point of failure.

And here’s the uncomfortable bit: you almost certainly have one too.

The Vendor You Don’t Even Think of as a Vendor

Let’s start with identity.

Most organisations have centralised internal access around a single sign-on (SSO) provider: one login to reach finance, production, order management, payroll, communications, even the Board portal.

Efficient. Clean. Controllable.

Also: structurally brittle.

If that identity layer fails with a breach, mis-update, or cloud outage, you aren’t “slower.”

You’re blind, mute, and paralysed.

In the CrowdStrike case, a single global update produced a “blue screen of death” for millions of Windows devices, grounding aircraft and delaying payments.

This wasn’t a cyberattack. It was a dependency failure which is, from a resilience standpoint, worse. You can’t blame hackers; you have to explain why your entire enterprise hinged on one patch working flawlessly.

Boards must now ask:

“If our identity provider goes down for eight hours, can we even log in to declare an incident?”

For many companies, the answer is “only via WhatsApp.”

That’s not governance. That’s desperation.

The Boring Systems That Can Break You

The next SPOF won’t be your Cloud provider (or is it? More on soon…). You already watch them.

It’ll be something unglamorous and invisible like:

Field-Service Scheduling

Your after-sales operation may rely entirely on one third-party scheduling platform.

If it fails, you can’t dispatch, certify, or invoice. No scheduling = no revenue.

Baggage & Logistics Middleware

Airports depend on shared software for routing, check-in, and load control.

When that goes down as the CrowdStrike outage showed, flights, crews, and customers stop moving.

Your warehouse, customs, or port systems are no different.

Payments Infrastructure

A single payment-gateway or fraud-screening engine outage can instantly halt every sale.

No transactions. No cash inflow.

That’s not “an IT issue.” It’s a business-continuity failure.

Why the Problem Is Growing

If you feel surrounded by hidden dependencies, it’s not your imagination in overdrive:

1. Consolidation of critical providers

   Identity, endpoint, observability, logistics are dominated by a handful of vendors.

   When they sneeze, everyone catches a cold.

2. Everything-as-a-Service

   Functions once in-house (invoicing, rostering, call routing) now live in SaaS platforms.

   Cheap, yes. Controllable, no.

3. Automation of compliance and billing

   Workflows that determine when you can ship, pay, or recognise revenue are embedded in external code.

   If that code freezes, your compliance, and revenue, freezes too.

SPOFs today aren’t just about traditional automation systems. Today’s SPOFs include third party hosted and operated Large Language Models, software agents and more.

They’re about process legality and cash conversion.

Spot the SPOF Before It Finds You

The job isn’t to eliminate every single point of failure, that’s fantasy.

It’s to know them, limit their blast radius, and rehearse recovery without them.

Your Weekend Board Checklist

1. Map the “Path to Cash.”

From customer order to payment received, list every external platform required. If any one’s failure stops revenue, flag it red.

2. Create Kill-Switch & Fallback for Identity.

Design controlled offline access when SSO is down with legal and financial oversight.

3. Build a Black-Start Mode.

Determine which functions can operate manually. If none, compliance itself has become your SPOF.

4. Stress-Test Operational Finance.

In JLR’s recent case (see last week’s The Business of Resilience), the crisis was cash starvation, not servers.

Ask: which key suppliers are insolvent after 10 days of non-payment? Plan advance-payment or escrow triggers now, not later.

5. Rehearse a “No-Dashboard” Scenario.

Assume the dashboard lies. If telemetry fails but operations falter, who calls the switch to Plan B?

This Isn’t Paranoia. It’s Muscle Memory.

If 2024–25 taught us anything, it’s that fragility hides in plain sight.

A routine software update from one vendor, CrowdStrike, knocked out a number of Windows machines globally, grounding flights and freezing operations.

That’s how thin the margin is. A single unseen vendor, an untested patch, a silent scheduler and your business model pauses.

Yet most Boards still receive dashboards, not dependency maps.

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Last Week

We explored the “Trust Recession”: the moment when dashboards, AI summaries, and vendor reports can no longer be taken at face value.

Boards are learning that resilience isn’t just about seeing risk but verifying truth: cryptographically signed telemetry, independent attestation, and incident-truth playbooks are fast becoming core governance tools.

Because in a world of deepfakes and synthetic data, the real control risk isn’t what you miss, it’s what you believe.

Read it here 👉

You log into your board portal. The dashboard is green. The reports are clean. “Minimal vendor risk. No cyber incidents to report.” It looks reassuring until you realise the deeper question is no longer what’s happening, but whether what you’re seeing is even real, it’s what you think you know, but can’t verify.

In 2025, the defining governance challenge isn’t “Are our systems secure?” but “Do our stakeholders trust our systems, and can we prove it?”

Welcome to the Trust Recession: an era where data, dashboards, and disclosures are no longer judged by their content but by their credibility, by whether anyone believes them.

The lie of the dashboard

Yes, boards already worry about cyber-insurance renewals, vendor access logs and incident response plans. Those remain vital.

Boards have become fluent in cyber acronyms: MFA (multi-factor authentication), EDR (endpoint detection and response), SOC (security operations centre), yet the next risk frontier goes beyond technology.

As the World Economic Forum’s Centre for Cybersecurity warned in its 2025 Global Cybersecurity Outlook report, systemic cyber risks are increasingly driven by the intersection of AI technologies, misinformation, and complex third-party ecosystems.

Imagine your SSO (single sign-on) vendor hosts your single login for 31 subsidiaries, the gatekeeper to all your internal systems, is compromised. Authentication is silently manipulated, false login sessions proliferate and attackers impersonate legitimate users, forge access logs, and keep your internal dashboard glowing green.

No alarms. No ransomware demand. Just silent falsification, until your financial controls fail, invoices vanish, or payroll halts.

Boards once asked, “Are our firewalls configured properly?”

Next year, they’ll be asking, “Can we prove the data we’re showing our investors, regulators, and clients actually happened?”

Because in a world of cloned voices, AI-generated images, and invisible vendor failure, verifiability has become the new governance control.

Why this matters now

• Deepfake fraud has entered the corporate mainstream.

  Security researchers and regulators warn that AI voice-cloning is increasingly used in executive-impersonation and payment-fraud schemes, amplifying traditional BEC (business email compromise) risk.

• Invisible vendor failures create cascading risks.

  Europe’s airport disruption in Sept 2025 followed a ransomware attack on Collins Aerospace’s MUSE platform, a third-party vendor dependency, confirmed by ENISA, underlining how supplier incidents can cascade across critical operations

• Data confidence is quietly collapsing.

  Recent surveys point to a growing visibility gap: for example, Kiteworks found that organisations unsure of how many third parties they rely on are also frequently unable to quantify breach frequency or detection times, a sign that many “dashboards” aren’t yet audit-ready for Board decision-making.

The hard truth: your systems may be compromised, your reports may be manipulated, and your dashboards may still show green.

What Boards must now do

1. Mandate signed telemetry and data watermarking.

Telemetry, the automated data your systems use to report health and performance, must be cryptographically signed and time-stamped. Unsigned or self-reported data cannot be audited. Require proof of data integrity from all vendors with privileged system access.

2. Demand third-party attestation.

Move from “trust the vendor” to “verify the vendor.” Independent attestation, not self-certification, should be a board-level requirement for key services such as Cloud hosting, authentication, logistics, and financial gateways.

For instance, if you want to access parametric risk transfers, you will need independent data sources and third-party calculation agents to verify triggers and payouts: boards should demand the same verification discipline in their operational reporting.

3. Create incident-truth playbooks.

Most organisations have IRPs (incident response plans) for breaches, few have ones for data integrity incidents.

Build playbooks that specify how conflicting signals (e.g. a vendor’s “no issue” report versus forensic anomalies) are verified, escalated, and disclosed to regulators and investors.

4. Add dashboard assurance KPIs (key performance indicators).

Green lights are meaningless if based on unverifiable data. Add indicators that measure how you know what you know:

• Percentage of vendor telemetry independently verified;

• Average “time-to-validation” after alerts;

• Number of vendor logs cryptographically signed;

• Frequency of “false-green” incidents found in audits.

A dashboard that never turns amber may simply be lying politely.

The Board’s weekend checklist

Before your next meeting, ask:

• Have we independently validated the vendor metrics behind our dashboards?

• Do we require vendor-log stamping or escrow so that service-level data remains auditable post-incident?

• Does our RAS (risk appetite statement) explicitly include “loss of trust,” not just “loss of data”?

• Do our disclosure protocols include scenarios of data falsification, not just data loss?

• Do our disclosures and controls reflect not only what we see but what the viewer believes they see?

• Can we trigger a rapid “trust verification drill” if systems report “all clear” while operations show distress?

Last week

We explored what happens when your biggest client goes dark, and how cyber shocks ripple both ways: from supplier to client, and client to supplier. From Jaguar Land Rover’s six-week shutdown to Clarkson’s hacked pub, we saw how fragile modern supply chains can be when cash stops flowing. The takeaway? Cyber resilience isn’t just about your own defences, it’s about whether your ecosystem can survive the silence.

Next week

Single Points of Failure You Don’t See

We’ll uncover the quiet dependencies (SSO vendors, payment processors, scheduling tools, baggage-handling systems) that make or break modern operations. Expect a human story, a mapping checklist, and a “black-start” approach to continuity.

Picture this: you’re scrolling through your weekend news feed, coffee in hand. You click on an article about supply chains only to realise it was written by an algorithm. The comments are bots. The “likes” are bots. Even the smiling headshots of “authors” at the bottom of the story? AI-generated fakes.

Welcome to the “Dead Internet” theory: a world where bots produce more content than humans consume.

This isn’t speculation anymore. Imperva (2023) found that nearly half of internet traffic is already automated, up sharply from just two years earlier.

Meanwhile, Pew Research (2024) estimates that 38% of web pages from a decade ago have disappeared due to “link rot.” Add in the surge of large language models (LLMs) pumping out synthetic text and images, and bots are now flooding the digital commons.

Trust Under Assault

Bots now drive a significant share of global traffic. NewsGuard (2025) flagged more than 1,000 AI-driven “news” sites pushing low-cost propaganda. For corporates, authenticity markers, verified comms, signed content and trusted channels are becoming strategic, not cosmetic.

But external misinformation is only part of the trust problem. LLMs themselves are vulnerable. Pillar Security (2025) found that attacks on large language models take an average of just 42 seconds to succeed. One in five jailbreaks works, and when they do, sensitive data leaks 90% of the time.

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The most common exploits? Surprisingly simple: “ignore previous instructions,” base64 encoding, or masquerading as an administrator override. Customer-service chatbots were the most targeted, but energy, consultancy, and engineering applications were also hit.

As Pillar CEO Dor Sarig told SC Media:

“In the near future, every application will be an AI application; that means that everything we know about security is changing.”

Yet even when models aren’t under attack, the trust challenge remains. Research shows that users consistently overestimate the accuracy of LLM outputs and rarely verify them. A Nature study (Jan 2025) found that detailed LLM explanations make people more confident in wrong answers. Some describe this as the “LLM effect,” where humans unconsciously anchor to AI-generated suggestions, even when inaccurate. A Harvard Business Review survey (Sept 2025) reported that employees increasingly act on polished but shallow AI-generated work, now dubbed “workslop.”

Why are people so susceptible? Three reasons keep surfacing:

• Style: LLMs sound authoritative and human-like, which tricks the brain into trusting them.

• Delivery: Confident, well-written answers create misplaced certainty.

• Cognitive offloading: It’s easier to accept a neat, single AI response than sift through messy search results.

For Boards, this means trust in digital communication, internal knowledge systems, and even AI-enabled customer service is fragile. Without governance, it can evaporate in under a minute.

The Rise of “Workslop” Inside Companies

Coined by Harvard Business Review and Stanford’s Social Media Lab (Sept 2025), workslop describes low-effort, AI-generated material (polished slides, neat reports, structured summaries) that look professional but lack substance.

HBR’s survey of 1,150 full-time employees found:

• 40% had received workslop in the past month.

• 15.4% of the work they received was slop.

• For a company with 10,000 staff, this equates to $9m in lost productivity annually.

The social cost is just as serious. 53% of employees said workslop made them annoyed, 38% confused, and 22% offended.

Worse, trust eroded: 42% of recipients said they viewed their colleagues as less trustworthy after receiving workslop.

This mirrors the “dead internet” dynamic: signal drowned out by noise.

Why It Matters for Boards & Executives

Boards don’t need another buzzword. They need to see that the “dead internet” and “workslop” are two sides of the same coin. Both highlight what happens when machine-generated volume outpaces human oversight.

• For Corporate Comms: Prove your content is authentic, human, and verified.

• For Risk Managers: Treat reputational risk with the same discipline as credit or market risk.

• For Operations: Recognise hidden productivity drains. Workslop isn’t output; it’s a cost centre.

• For Strategy: AI is a productivity multiplier, if used with purpose, standards, and feedback. Used indiscriminately, it creates waste and distrust.

The Positive View: AI as Productivity Tool

Let’s be clear, AI proves to be the greatest productivity tool of our generation, when used well.

• It can accelerate research, draft faster, and open creative doors.

• It can augment training, allowing new hires to test knowledge and receive immediate feedback.

• It can handle repetitive workflows, freeing people to focus on higher-value judgement calls.

The difference is intent. As BetterUp Labs & Stanford (2025) note, employees who approach AI with purpose and optimism, so-called “pilots and not “passengers”, generate far more value. Pilots use AI to enhance their own creativity and outcomes. Passengers use it to avoid effort, often creating more work for colleagues.

The Weekend Takeaway

The “dead Internet” warns us about unchecked automation online. “Workslop” shows us the same risk inside companies. And LLM jailbreaks reveal just how quickly trust can be compromised if we don’t adapt.

But none of this is inevitable. The leaders who win will:

✅ Build authenticity and trust into every comms channel.

✅ Red-team their AI systems to anticipate attacks.

✅ Train staff to become AI pilots, not passengers.

✅ Treat AI as a collaborator, not a shortcut.

So, as you sip your weekend coffee, ask yourself: Is your organisation using AI to create clarity, trust, and productivity or letting noise, slop, and fragility creep in, inside and out?

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Picture this: you show up to work, grab your morning coffee, open your laptop… and find out your job has been quietly automated. That is not a dystopian story; it has already happened. From Australian banks to global telcos, junior staff have seen roles disappear almost overnight as chatbots and automation step in.

It is tempting to dismiss this as a story about “just” entry-level jobs. But Boards and senior executives should resist that temptation. The deeper question is this:

The Vanishing Starter Job

Every senior executive remembers their first job. It was not glamorous. You answered phones, ran reports, checked numbers, fetched coffee. But those “starter jobs” were the foundation. They gave you time to absorb how organisations really work, space to make mistakes and recover, and the chance to learn by watching those a few steps ahead.

Take those roles away, and the ladder looks very different. You end up with a hollow middle, a gap where people should be growing into managers, strategists, and eventually leaders. Companies may not notice the problem right away and even enhance profits in the short term, but some years down the line, they will feel it as a lack of succession, culture, and resilience.

Boards that allow them to vanish without a plan are gambling with the long-term health of their organisations.

It’s Not AI That Replaces You

There is a saying that has already become cliché but remains painfully true: it is not AI that will take your job, it is someone who knows how to use AI that will.

The real competition is between people and organisations that know how to combine human judgment with machine speed and those that do not.

Think of it this way. Two junior analysts start on the same day. One uses AI to check data, test scenarios, and draft reports. The other sticks with manual methods. Who do you think gets promoted first? Who do you think delivers more value to the business?

Boards should not just worry about jobs disappearing. They should ask how their organisations are preparing staff to be the ones who use AI, not the ones replaced by it.

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What If AI Became the Teacher?

Here is the more optimistic view. AI could become an extraordinary training partner. Imagine if new hires could use AI to practice skills, test their knowledge, and get immediate feedback. Tasks that once took years of apprenticeship could be learned in months.

But there is a trap. AI sometimes makes things up. And if you are just starting out, how do you know when the tool is wrong and when you simply do not know enough yet?

That is where the human context matters. Offices are not just places to work; they are classrooms. Listening to how senior colleagues frame problems, watching how they balance competing priorities, and receiving a quiet word of correction at the right time. These are all essential for turning raw information into sound judgment.

Organisations that understand this will design blended learning environments where new staff use AI to accelerate but still learn the “art” of decision-making from experienced people.

Organisational Design in the AI Age

The reshaping of work is not only about the tasks people do but about how companies themselves are structured.

Look at the growing number of firms merging HR and IT under one leader. The reason is simple. HR once counted the people while IT provided the systems. AI blurs that line. Designing the organisation today means deciding what should be done by machines, what should remain human, and how the two fit together.

At Moderna, the Chief People and Digital Technology Officer oversees both functions. Covisian and Bunq are testing similar models. The idea is to stop the endless back-and-forth between “what HR wants” and “what IT can deliver” and instead think holistically about workflows, skills, and tools.

Done well, this kind of design can create speed and clarity. Done poorly, it risks losing depth and leaving both people strategy and technology execution weaker. Boards should see organisational design as a live question, not a static chart. How you combine people and technology at the top determines how effectively the whole business adapts to AI.

Designing Careers in the AI Age

If entry-level routes are shrinking, how do we create meaningful careers for the next generation? This is one of the most important questions for Boards, investors, and society.

Some principles stand out:

• Pair AI with mentorship. Let machines teach efficiency but let humans teach judgment.

• Make AI literacy standard. Junior roles should include training in how to use AI effectively and responsibly.

• Design structured rotations. New hires need exposure across departments. AI can speed learning, but breadth still matters.

• Protect learning cultures. Create environments where people feel safe to ask questions and learn from mistakes.

The goal is not just to keep a first rung on the ladder, but to make that rung sturdier and more relevant.

A Tale of Two Companies

Imagine two futures.

Company A automates aggressively and eliminates all entry-level jobs. It looks efficient in the short term. Ten years later, it has no pipeline of leaders, a weak culture, and a fragile middle management.

Company B automates too, but invests in new career paths, blending AI with human mentorship. Ten years later, it has loyalty, resilience, and leaders who have grown with the technology.

Which company will Boards want to govern? Which one will long-term investors trust with their capital?

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Five Questions for Boards

1. Do we know which of our entry-level roles are most exposed to automation?

2. How are we preparing juniors to use AI, not be replaced by it?

3. Are our organisational structures (like HR and IT) set up to design work holistically, not in silos?

4. What is our plan to maintain a leadership pipeline if traditional starter jobs shrink?

5. How are we measuring not just efficiency gains, but long-term resilience?

The Weekend Takeaway

AI is not sneaking in. It is already here. Today it is customer service and back-office admin. Tomorrow it could be your own function.

The challenge for Boards is to balance adoption with succession. Efficiency without a leadership pipeline is not strength, it is fragility in disguise.

So, as you sip your weekend coffee, ask yourself: is your organisation protecting the first rung of the ladder, or quietly sawing it off?

And tell me: how is your Board tackling this? I would love to hear your thoughts.

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Mozilla published its Privacy Not Included report in September 2023, for the first time in the guide’s seven-year history, all 25 global car brands reviewed received failing scores for consumer privacy.

Think about that for a moment: not one manufacturer, not BMW, Ford, Toyota, Tesla, Kia, Subaru, or even luxury names like Mercedes, could demonstrate that they handle your data responsibly.

“All new cars today are privacy nightmares on wheels that collect huge amounts of personal information,” says Jen Caltrider, Mozilla’s Program Director.

What your car is collecting

Modern vehicles are no longer just machines of steel, glass, and rubber. They are rolling computers, packed with sensors, cameras, microphones, and constant connectivity.

According to Mozilla’s research, today’s cars can collect:

• Deeply personal data: including sexual activity, health and genetic information, facial expressions, weight, and even immigration status.

• Behavioural data: seatbelt use, braking habits, where you drive, and how you drive.

• Inferred data: manufacturers say they can develop profiles of your intelligence, preferences, attitudes, and even psychological traits.

And it’s not just the car itself. The ecosystem extends to car apps, company websites, dealerships, and telematics systems. Once gathered, this data can be shared or sold to third parties, data brokers, and in some cases, law enforcement, often with very little in the way of meaningful consent.

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The very worst offenders

Among the most shocking cases:

• Nissan admits it can collect information about sexual activity, health, and genetics.

• Volkswagen uses demographic and driving behaviour data to power targeted marketing.

• Toyota buries consumers under a galaxy of 12 separate privacy policy documents.

• Mercedes-Benz ships some models with TikTok pre-installed.

• Subaru considers you to have “consented” to their privacy policy simply by being a passenger in one of their cars.

Not a single brand responded well to Mozilla’s minimum security questions. Only Mercedes even replied at all.

A $750 billion industry in the making

Why does this matter? Because car data monetisation is forecast to become a $750 billion industry by 2030. In other words, manufacturers aren’t just selling you the vehicle, they’re increasingly selling you.

And it’s not just about drivers. Cars can hear you, see you, and track you. Passengers, pedestrians, even people near the vehicle may be swept into this web of surveillance without ever having agreed.

Privacy washing and the illusion of control

Mozilla researchers highlight “privacy washing”, where companies sign up to principles of consumer protection but don’t actually follow them.

Consent, when it exists at all, is a mirage. Subaru’s claim that being a passenger equals consent is a particularly stark example. In many cases, opting out would mean your car simply doesn’t function properly.

Consumers are given no real choice. Buy a modern car, and you’re buying into its surveillance.

Why this matters now

Cars have long been seen as private spaces: somewhere to call your doctor, to have a heart-to-heart with your child, or to cry after a breakup. Mozilla’s research shows that assumption is out of date. Today’s vehicles are more like smartphones on wheels - phones that you didn’t choose, can’t switch off, and can’t leave at home.

That’s not just unsettling. It’s dangerous. It exposes ordinary people to risks they don’t understand and haven’t agreed to, in an industry that regulators and policymakers have barely begun to address.

The takeaway

For now, Mozilla’s verdict is blunt: the safest car for your privacy is an older model without connectivity.

Until regulators catch up, consumers are caught in a trap. New cars promise safety, performance, and convenience, but come with hidden costs in the form of data collection and surveillance.

It’s worth asking: if every brand fails, where do we go from here?

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For those who want to dive deeper, the full findings are available in Mozilla’s report: Every Car Brand Reviewed by Mozilla, Including Ford, Volkswagen and Toyota, Flunks Privacy Test (6 September 2023). It’s a sobering read that lays out, in detail, how the automotive industry has turned our vehicles into rolling data harvesters, and why stronger protections are urgently needed.

There’s a persistent myth in the start-up world: that innovation belongs to the young. The hoodie-wearing twenty-something coding away in a garage is Silicon Valley’s favourite poster child. But the data, and the stories, tell a different tale.

According to an article by Maëlle Gavet (Techstars), founders over 50 are not only holding their own, they are more likely to bring radical innovations to market. In fact, every additional decade of age increases the chance of introducing a true market novelty by up to 30 percent.

Breaking the stereotype

Far from being left behind, older entrepreneurs are showing that experience, perspective, and persistence often outpace raw youth when it comes to solving complex problems.

At Techstars alone, more than 150 companies have been founded by entrepreneurs aged 50 and above. Too many to name, but here are just a few examples that stand out:

• Carolyn Thompson (Aravenda): transforming resale with SaaS consignment software, scaling 300% year-on-year.

• Mark Kovscek (Conservation Labs): using machine learning to “listen” to pipes, cutting water use by 10–20% and saving huge utility costs.

• Eric Levitan (Vivo): tackling the loss of strength in older adults through online, clinically tracked exercise classes, backed by a $2.3m NIH grant.

• Marc Steren (University Startups): opening entrepreneurship pathways for under-resourced students, expanding from 2,000 to 18,000 students in just one year.

• Paige Wilson (Naborforce): connecting seniors with vetted retirees for support, raising $12m+ to expand their platform.

Each of these ventures shows that innovation doesn’t slow down with age, if anything, it sharpens.

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Why older founders matter

What stands out is the kind of problems these founders choose to solve. They’re not chasing the next flashy app to shave 0.5 seconds off a food delivery. They’re tackling systemic issues: sustainable retail, water conservation, public health, education equity, and elder care.

These are markets with huge social impact, and they benefit from founders who bring decades of lived experience, networks, and insight into what really matters.

A lesson for all of us

We should stop seeing age as a limitation and start recognising it as an innovation multiplier. Experience tempers impatience, hones judgement, and brings a long view. And as these examples show, it doesn’t stop founders from being ambitious, scaling fast, or raising serious capital.

Maybe the real “disruptors” aren’t the stereotypical young geniuses, but the seasoned entrepreneurs who know which mountains actually need moving.

✨ Question for readers: Do you think our culture underestimates the creativity and problem-solving power of those with more experience?

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This piece draws on insights shared by Maëlle Gavet, CEO of Techstars, in her LinkedIn article Ditching the Young Entrepreneur Myth (Research). She highlights the many founders over 50 within the Techstars network who are proving that radical innovation is fuelled by experience as much as by youth.