It is Monday morning.

You walk into your office wearing what appears to be an ordinary pair of glasses. They look no different from the ones millions of people already wear every day. They help you navigate, translate foreign languages, answer questions, remember where you parked your car and identify the restaurant you passed ten minutes ago.

They also quietly observe almost everything you do.

They notice who you meet, where you walk, what you look at, what you read, who speaks to you, how long conversations last and what decisions are made. Every meeting becomes searchable. Every conversation becomes queryable. Every day becomes a structured dataset.

Now imagine arriving at work.

Your calendar assistant has already summarised your first meeting. Your AI meeting companion knows the history of every discussion with that client over the last three years. It reminds you that the CFO was unconvinced by a particular proposal eighteen months ago, that Legal raised a concern during contract negotiations in February, and that Engineering quietly abandoned a similar idea two years earlier.

None of this feels particularly remarkable.

In fact, it feels useful, very useful, and that is precisely why it is happening.

For years, we assumed artificial intelligence would become smarter by reading more books, more web pages and more documents.

That phase is largely over.

The world’s largest models have already consumed an extraordinary proportion of publicly available text. Their next challenge is no longer acquiring more information.

It is acquiring more context:

Information tells AI what happened.

Context teaches AI why it happened.

That is why almost every major technology company is now investing in systems that observe how humans actually work.

The objective is no longer simply to answer questions, it is to understand judgement.

Orwell saw surveillance. He missed the business model.

George Orwell’s Nineteen Eighty-Four remains one of the most powerful warnings ever written about surveillance. His world was one in which citizens were continuously watched by the state. Surveillance existed to enforce obedience. Information flowed upwards towards those in power.

The modern version looks remarkably different:

• Nobody forces millions of people to buy smart glasses.

• Nobody insists they install AI meeting assistants.

• Nobody mandates AI note takers, workflow analysers or digital companions.

People adopt them because they are genuinely useful:

• They save time.

• They remember forgotten details.

• They draft emails.

• They summarise meetings.

• They identify action items.

• They retrieve information that would otherwise disappear into forgotten notebooks or archived emails.

The commercial incentive is productivity.

The technical consequence is something much larger. Every one of those interactions teaches the machine a little more about how humans solve problems:

Orwell imagined surveillance as political infrastructure.

The AI era is creating surveillance as learning infrastructure.

That distinction changes almost everything.

Why AI suddenly wants to watch you

One of the biggest misconceptions about artificial intelligence is that knowledge is the scarce resource. It isn’t.

The scarce resources are judgement and willingness to act.

Ask an AI how to calculate discounted cash flow and it will probably produce an excellent explanation.

Ask it how to persuade a reluctant client, negotiate a difficult supplier contract, recognise that a project is quietly failing despite apparently healthy metrics or identify the moment when an experienced engineer becomes uncomfortable with a proposed design.

Those are much harder problems, not because they require more intelligence, but because they require experience.

Humans rarely write down how they make decisions.

Policies explain what organisations intend to do.

Meetings reveal what they actually do.

Company handbooks describe escalation procedures.

Experienced managers know when to ignore them.

Standard operating procedures explain the rules.

Culture determines which rules matter.

The most valuable organisational knowledge has always existed between the lines.

For decades that knowledge simply disappeared:

• People retired.

• Teams reorganised.

• Lessons were forgotten.

• Mistakes were repeated.

Now something fundamentally different is happening.

Not because somebody explicitly documents them, because millions of conversations quietly contain them.

The venture capital firm Andreessen Horowitz recently argued that AI needs to be onboarded much like a new employee. You do not ask a new recruit simply to read the company handbook. You invite them into meetings because meetings contain the unwritten culture of an organisation.

That observation is far more profound than it first appears.

Meetings are where judgement lives. They contain uncertainty, disagreement, compromise, political realities, customer nuance, risk appetite and countless exceptions that never make their way into official documentation.

If AI can observe enough of those conversations, it begins to develop something far more valuable than knowledge, it develops organisational context. And organisational context is rapidly becoming one of the most valuable assets in the AI economy.

Behaviour has become the new intellectual property

For years, executives were told that “data is the new oil.”

It was an appealing phrase.

It was also incomplete.

Raw data has become abundant.

Most organisations possess more operational data than they know how to exploit. Public information has become increasingly commoditised. Foundation models have already ingested enormous quantities of publicly available content.

Behaviour is different.

Every organisation behaves differently:

Two banks may use identical software. Their credit committees will not make identical decisions.

Two insurers may have identical underwriting manuals. Their experienced underwriters will reach different conclusions.

Two consulting firms may use the same methodologies. Their partners will solve client problems differently.

Those differences are difficult to document but they are exceptionally valuable to observe.

AI companies increasingly understand that behaviour cannot simply be scraped from the internet, it must be witnessed.

That is why the race has quietly shifted from collecting documents to collecting context:

• Every recorded meeting.

• Every customer interaction.

• Every software commit.

• Every workflow.

• Every correction.

• Every exception.

• Every approval.

• Every hesitation.

Every one of them teaches the system something that no policy manual ever could.

The rise of the Memory Economy

Perhaps the most important shift is that organisations are no longer merely building databases, they are building memory:

• Traditional enterprise software stored transactions.

• CRMs remembered customers.

• ERPs remembered inventory.

• Document management systems remembered files.

• The next generation remembers conversations.

Not simply that a meeting occurred:

• What was discussed.

• Who objected.

• Which assumptions changed.

• Which risks were dismissed.

• Which promises were made.

• Which decisions were later reversed.

This is not simply better record keeping, it is a completely different form of organisational infrastructure.

Human organisations have historically suffered from institutional amnesia and knowledge walked out of the building every Friday evening: sometimes permanently.

Every resignation, retirement or acquisition meant rebuilding context that had taken years to accumulate. Persistent organisational memory changes that equation.

The company itself begins to remember and that has enormous benefits:

• It also raises entirely new questions.

• Who owns that memory?

• Who may search it?

• Who may train on it?

• And what happens when the organisation remembers more accurately than the people inside it?

The Boardroom point

Every significant technological shift eventually changes what boards are expected to govern.

The Industrial Revolution required boards to understand factories. The Information Age required them to understand software. The AI era requires them to understand something far less tangible: organisational memory.

Until recently, memory was largely a human characteristic. It resided in experienced employees, long-serving managers and trusted advisers. Boards worried about key-person dependency because they understood that knowledge disappeared when people did.

Artificial intelligence changes that assumption: the organisation itself is beginning to remember.

Every recorded meeting, customer call, engineering review, underwriting discussion, investment committee debate and strategic planning session contributes another layer of institutional knowledge. Individually, none of these interactions appears particularly significant. Collectively, they become a searchable representation of how the organisation actually thinks.

That distinction matters because companies have always had two operating systems.

1. The first is formal. It consists of organisational charts, policy manuals, procedures, delegated authorities and governance frameworks.

2. The second is informal. It consists of judgement, relationships, trust, unwritten conventions and decades of accumulated experience.

The first can be documented, the second has historically been impossible to capture, until now.

Boards therefore face a new fiduciary question:

The answer is unlikely to be “none”. The productivity gains are simply too compelling. Better onboarding, faster decision-making, improved continuity, fewer repeated mistakes and stronger organisational resilience all argue in favour of richer institutional memory.

But neither is the answer “everything”, because the moment judgement becomes data, it becomes an asset.

Like every strategic asset, it requires governance.

There is no such thing as free context

Most organisations currently evaluate AI using familiar procurement questions:

• How much will it cost?

• How much time will it save?

• Which departments should adopt it first?

Those remain sensible questions but they are no longer sufficient:

• Every AI deployment also creates a less visible economic exchange.

• Employees receive assistance.

• The model receives experience.

That exchange is remarkably easy to overlook because it feels so natural.

• An AI meeting assistant produces excellent notes.

• A coding assistant accelerates software development.

• A customer service copilot recommends responses.

• An AI sales coach analyses successful conversations.

• Each application genuinely improves productivity.

Each application also observes how skilled professionals perform their work and this is not necessarily sinister. It is simply how modern machine learning improves. Large language models already possess broad knowledge of the world.

What they increasingly lack is deep understanding of how particular organisations operate. That understanding can only be learned from observing real work.

The CFO version

Finance leaders have always excelled at valuing tangible assets:

Cash.

Property.

Inventory.

Receivables.

They have also become increasingly sophisticated in understanding intangible assets such as brands, software and intellectual property.

Behavioural capital represents something different again, it rarely appears anywhere on the balance sheet. Yet it may become one of the most valuable assets an organisation possesses.

Consider two businesses with identical technology platforms, identical customer bases and identical products.

One consistently outperforms the other.

Why?

The answer rarely lies in the software, it lies in thousands of accumulated decisions made by experienced people over many years:

• How pricing exceptions are handled.

• Which customers receive flexibility.

• When risks are accepted.

• When projects are terminated.

• When investment accelerates.

• When management intervenes.

• These decisions create economic value.

Historically they remained trapped inside human experience, today they are becoming observable. This creates a strategic question that finance leaders have rarely needed to ask before.

If behavioural knowledge becomes trainable, who captures the value?

The enterprise?

The technology provider?

The cloud platform?

The model developer?

Or some combination of all four?

Current accounting standards have little to say about behavioural capital because they evolved in a world where behaviour could not easily become an asset, AI changes that assumption.

The next generation of balance sheets may never explicitly recognise organisational memory.

Investors almost certainly will.

The CEO version

Chief executives often describe culture as the organisation’s greatest competitive advantage, unfortunately, culture is also one of its least measurable.

Ask five executives why their company consistently wins difficult contracts and you will probably receive five different answers:

• Some will point towards customer relationships.

• Others towards engineering excellence.

• Others towards operational discipline.

• All may be correct.

• None fully explains why the organisation behaves differently from its competitors.

Behaviour emerges from thousands of invisible decisions:

It is how difficult customers are handled.

How mistakes are admitted.

How junior employees challenge senior colleagues.

How risk committees balance growth against caution.

How people behave when no policy explicitly tells them what to do.

For decades, CEOs accepted that much of this knowledge simply could not be documented, it was spread through observation:

• New employees learned by sitting beside experienced colleagues.

• Graduate trainees absorbed judgement through repeated exposure to real situations.

• Managers developed intuition by participating in hundreds of meetings.

The process was slow, expensive and entirely human.

Artificial intelligence introduces another possibility:

Instead of asking new employees to absorb years of organisational behaviour, organisations can increasingly expose AI to those same interactions.

The machine begins developing organisational context alongside the employee.

That offers extraordinary opportunities:

Imagine reducing executive onboarding from twelve months to three.

Imagine preserving the accumulated experience of retiring specialists.

Imagine integrating acquisitions without losing decades of institutional knowledge.

Imagine querying twenty years of strategic discussions before making today’s decision.

These capabilities move beyond productivity, they begin reshaping organisational resilience itself.

Yet they also introduce a profound strategic risk:

Every organisation believes its competitive advantage lies in products, patents or capital.

Increasingly, it may lie elsewhere.

It may lie in judgement.

The question is uncomfortable, but it deserves asking.

Share Something For The Weekend

The Board version

Board discussions around artificial intelligence frequently begin with regulation. They move quickly towards cybersecurity, privacy, bias, explainability and legal compliance. Those topics remain critically important.

But another deserves equal attention:

Institutional sovereignty.

Every organisation must decide where its collective memory resides.

Inside the company.

Inside its own private infrastructure.

Or increasingly, inside commercial AI ecosystems operated by third parties.

This is not simply an information security question, it is a governance question:

• What happens when every board paper, every strategy session, every acquisition discussion, every underwriting committee and every product review contributes another layer of machine-readable institutional knowledge?

• Who controls access?

• Who determines retention?

• Who decides whether that knowledge contributes to future model improvement?

The answers differ across jurisdictions, technologies and contractual arrangements.

But one principle remains remarkably consistent:

If organisations fail to govern their behavioural capital, somebody else eventually will.

Boards have spent decades protecting financial capital, the next decade may require protecting organisational memory with equal care.

The hard truth

Much of the public debate around artificial intelligence assumes the technology is primarily replacing labour but that is too narrow. Artificial intelligence is first learning labour, only afterwards can parts of that labour be automated.

Every AI system that drafts documents, analyses contracts, recommends underwriting decisions or assists software developers became capable by observing enormous quantities of human work, and that process has not stopped.

It is accelerating:

The greatest AI companies may not ultimately possess the smartest algorithms, they may simply possess the richest behavioural datasets.

The scarce resource would no longer be computational power alone, nor access to public information. It would be privileged visibility into how experienced people actually think.

Perhaps Orwell’s greatest mistake was not imagining surveillance, it was assuming surveillance existed primarily for control.

The AI economy suggests another possibility:

The machine is not watching because it wants obedience.

The machine is watching because it wants an education.

The weekend takeaway

For centuries, organisations have accumulated knowledge one conversation at a time. Most of those conversations disappeared almost as quickly as they occurred. Human forgetting was not a flaw, it was simply how organisations functioned.

Artificial intelligence is quietly ending that era.

For the first time in history, companies can build persistent organisational memory that grows richer with every meeting, every project and every decision. That capability promises extraordinary improvements in productivity, continuity and resilience.

It also creates a new strategic reality.

The organisations that succeed in the AI era may not simply be those that deploy the best models.

They may be those that most carefully govern what those models are allowed to learn.

Because the defining question of 2026 is no longer whether Big Brother is watching:

？Questions to think about this weekend

In this new section, I leave you with a few questions to ‘percolate’:

• If AI assistants remember everything, what should organisations deliberately choose to forget?

• When does organisational memory become intellectual property?

• Should employees help train systems that may eventually automate parts of their work?

• If two companies buy exactly the same AI model, does competitive advantage increasingly come from the behavioural context they provide?

• Has “knowledge work” quietly become “behaviour work”?

  Leave a comment

🧠 Techie Corner for the Non-Techies

One of the biggest misconceptions about AI is that it “knows everything.” It doesn’t.

Today’s most capable Large Language Models (LLMs) possess enormous general knowledge because they have been trained on large quantities of public text. However, they know very little about your company. They do not understand your customers, your suppliers, your unwritten conventions or how your board really makes decisions.

That is why the AI industry has shifted its attention from collecting more information to collecting more context.

Here are some of the concepts that increasingly matter:

1. Foundation Model

A foundation model is a very large AI model trained on vast amounts of public information before it is ever deployed inside an organisation. Think of it as a highly educated graduate. It knows history, science, finance, programming and languages.

What it does not know is how your business works.

That knowledge has to be added later.

2. Context

Context is everything surrounding a decision. The same sentence means different things depending on who said it, why they said it, what happened beforehand and what everyone already knows. Humans constantly use context without noticing.

AI does not.

That is why context has become one of the most valuable resources in enterprise AI. The model may understand insurance but it does not automatically understand your underwriting philosophy.

3. Context Window

A context window is the amount of information an AI model can consider at one time. Early systems could only remember a few pages of text. Modern frontier models can analyse entire reports, books or software repositories during a single conversation.

Larger context windows make richer reasoning possible, they also increase computing cost. So, more context is not automatically better, relevant context is.

4. Long-Term Memory

Most early chatbots forgot everything once a conversation ended. In contrast, modern AI increasingly maintains persistent memory:

• It remembers preferences.

• Recurring projects.

• Frequently used terminology.

• Previous conversations.

• Important decisions.

In organisations this becomes even more valuable. Imagine an AI that remembers every client meeting over the past five years rather than only today’s conversation.

That changes the quality of assistance dramatically.

5. Retrieval-Augmented Generation (RAG)

One of the most important enterprise AI techniques is called Retrieval-Augmented Generation, usually shortened to RAG. Instead of relying only on what it learned during training, the AI first searches trusted internal information before generating an answer.

For example, before answering a question about an insurance programme it might retrieve:

• underwriting guidelines

• previous submissions

• policy wordings

• engineering reports

• claims histories

• board decisions

Only then does it formulate a response. RAG helps reduce hallucinations because the AI reasons over current company information rather than relying purely on memory.

6. Embeddings

Computers do not naturally understand meaning. Embeddings are mathematical representations that allow similar ideas to sit close together.

A document discussing “renewable energy insurance” might be placed close to another discussing “offshore wind risk”, even if neither uses identical words.

Embeddings allow AI to search by meaning rather than keywords. That makes organisational memory dramatically more useful.

7. Vector Databases

Traditional databases search for exact matches. Vector databases search for similar ideas.

Instead of asking:

“Find documents containing these exact words.”

they ask:

“What previous situations most resemble this one?”

That is much closer to how experienced executives think. An underwriter rarely asks whether a submission contains identical wording. They ask whether it resembles another risk they have seen before.

Vector databases make that possible for machines.

8. Multimodal AI

Early AI systems worked primarily with text. Modern models increasingly understand multiple forms of information simultaneously:

• Text.

• Images.

• Audio.

• Video.

• Computer code.

• Sensor readings.

• Eventually robotics.

Smart glasses illustrate this evolution:

• They are not simply recording video.

• They combine visual information, spoken conversations, location, time and movement into one continuously evolving stream of context.

9. Behavioural Data

Behavioural data differs fundamentally from traditional business data:

Traditional systems record what happened.

Behavioural systems record how it happened.

• Which document did someone open first?

• Which version did they reject?

• Who did they consult?

• Which alternative did they consider?

• Where did they hesitate?

These patterns often reveal judgement far better than final outcomes alone. That is why behavioural data has become strategically valuable.

10. Digital Memory

Human memory is imperfect:

• We forget names.

• We misremember dates.

• We compress events.

• We simplify complicated histories.

Digital memory behaves differently:

• It retrieves exactly what was recorded.

• Sometimes that is advantageous.

• Sometimes it preserves context humans would rather forget.

This creates an important governance question:

Should organisations optimise for perfect recall?

Or should they deliberately preserve the human ability to forget?

That question is becoming technological rather than philosophical.

11. Behavioural Cloning

Behavioural cloning is an AI technique in which a machine learns by observing examples of human behaviour. Instead of programming every rule explicitly, engineers allow the system to watch experts repeatedly perform a task. The AI gradually learns to imitate those patterns.

That approach has become increasingly important in robotics, autonomous vehicles and enterprise AI. It also explains why recorded work has become so valuable. Machines do not merely need instructions, they need demonstrations.

12. Model Context Protocol (MCP)

One of the newest developments in enterprise AI is the Model Context Protocol, usually abbreviated to MCP.

Think of MCP as a ‘universal connector’. Instead of building a separate integration for every application, MCP allows AI systems to communicate with many different tools through a common protocol.

For example, one AI assistant might simultaneously retrieve:

• today’s calendar

• CRM information

• engineering drawings

• SharePoint documents

• financial models

• regulatory guidance

without needing a bespoke connection for each one.

Many technologists believe MCP may become one of the foundational standards underpinning enterprise AI over the coming years.

None of these technologies are remarkable in isolation. Together they create something genuinely new.

That is a much bigger idea than simply building better chatbots.

📚 Further reading

• George Orwell: Nineteen Eighty-Four (1949)

  Published on 8 June 1949, Orwell's classic novel remains the defining exploration of surveillance, information control and the relationship between knowledge and power. The AI era differs fundamentally because today's surveillance is often driven by commercial incentives rather than state coercion, but Orwell's central insight (that information changes power) remains highly relevant.

• David Haber (Andreessen Horowitz): Everything is Recorded Now (2026)

  One of the clearest explanations of why AI increasingly requires organisational context rather than simply more documents. The article argues that meetings contain the tacit knowledge that company manuals never capture, and that AI must observe organisations much as new employees do.

• Hannah Murphy (Financial Times): Meta tests ‘super sensing’ AI glasses that can capture every moment (2026)

  An important article describing Meta’s reported work on continuously sensing AI glasses capable of capturing a wearer’s surroundings throughout the day. Beyond the technology itself, it raises difficult questions about privacy, consent, organisational memory and the emergence of always-on AI assistants.

• Dhruv Mehrotra & Dell Cameron (WIRED): Meta Silently Added Face-Recognition Code for Its Smart Glasses to Millions of Phones (2026)

  A technical investigation into software components reportedly embedded within Meta’s smart-glasses ecosystem. It illustrates how modern AI increasingly combines visual recognition, behavioural data and persistent identity rather than simply capturing photographs.

• Jared Spataro (Microsoft): The 2025 Annual Work Trend Index: The Frontier Firm is Born (2025)

  Microsoft’s annual Work Trend Index combines survey research with Microsoft 365 telemetry and LinkedIn labour-market analysis to examine how AI is changing organisations. As with any vendor-sponsored research, readers should bear in mind Microsoft’s commercial interests, but it remains one of the most comprehensive publicly available reports on enterprise AI adoption.

• Shoshana Zuboff: The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power (2019)

  Written before the emergence of generative AI, Zuboff’s book remains one of the most influential analyses of behavioural data as an economic resource. Her argument that companies increasingly compete by capturing and monetising human behaviour has become even more relevant as AI systems seek richer behavioural context rather than simply larger datasets.

• OpenAI: Introducing ChatGPT Agent (2025)

  OpenAI’s announcement of ChatGPT Agent illustrates the industry’s shift from conversational AI towards systems capable of executing multi-step tasks across applications. It provides useful insight into why organisational context, memory and tool access are becoming increasingly important components of enterprise AI.

• Anthropic: Introducing Claude for Enterprise (2024)

  Anthropic explains its approach to enterprise AI, including large context windows, retrieval from internal knowledge bases and governance controls. The material is particularly useful for understanding why organisational memory is becoming a strategic enterprise capability rather than simply another chatbot feature.

Last week on Something for the Weekend

Last week, we argued that the workflow was always the asset.

Enterprise AI creates value not because it writes better emails or generates more code, but because it has the potential to redesign how work moves through an organisation. The companies that succeed will measure verified business outcomes rather than token consumption, redesign workflows rather than automate old habits, and treat digital co-workers as managed members of the workforce rather than clever software.

The owner is 68. She started the company thirty-seven years ago with two technicians, a second-hand van and a loan secured against the family home.

Today, the business employs 83 people. It repairs industrial pumps, fabricates replacement components and maintains machinery for food-processing plants across three states. The company is profitable. Its customers renew. Its employees know the machines, the plants and the people who run them. Its reputation is stronger than its website.

The accounts are prepared once a year. Some jobs are still scheduled on a whiteboard. The sales pipeline lives partly in a spreadsheet and partly inside the owner’s head. The youngest engineer has built a small AI tool to search old maintenance reports, but the company has no formal data architecture, no AI policy and no one responsible for technology.

The owner’s children have careers elsewhere. The general manager could run the company but cannot afford to buy it. A private-equity firm is interested, but the business is below its usual transaction size. A strategic buyer could acquire it, remove duplicated costs and consolidate the operation into a regional platform.

The owner worries that the name will disappear. The employees worry that their jobs will disappear. The customers worry that the people who understand their equipment will disappear.

So, the owner waits.

That is not one succession problem. It is one example of a much larger transfer of productive assets, customer relationships, operating knowledge and local employment that is beginning to move from one generation to the next.

McKinsey calls it the “Great Ownership Transfer”: USD5 trillion in enterprise value. Its analysis estimates that millions of small and medium-sized US businesses could face an ownership transition as their proprietors retire. Many are too small for conventional private equity. Many lack an obvious family successor. Many contain valuable operating knowledge but weak digital infrastructure.

Many will be sold. Some will be passed to employees or family members. Some will close.

Then, a different buyer arrives and it does not see an old pump-maintenance company.

• It sees a customer network, a skilled workforce, thirty-seven years of failure data, thousands of maintenance records, a recurring service book and a physical distribution footprint.

• It sees a platform on which artificial intelligence, robotics, better software, procurement scale and modern capital can be applied.

The buyer does not begin by asking how many employees can be removed. It asks how much more valuable the business could become if its people were given better machines.

Then, it asks the more unsettling question:

The Boardroom point

The largest AI opportunity may not be another chatbot. It could just be the transfer and rebuilding of the existing economy.

The Great Ownership Transfer is usually described as a succession problem. Older owners retire. Businesses need buyers. Communities need continuity. Employees need protection. Capital needs a way to complete smaller transactions economically.

However, the same transition may also create an unusual industrial opportunity. A new owner can acquire a functioning business at the moment when its operating model is already due for renewal.

The company has loyal customers, experienced employees, positive cash flow, specialist knowledge, local market power and trusted supplier relationships. It also has underdeveloped data, manual processes, fragmented software, weak succession planning and limited capital for modernisation.

That mix is important.

Often, the difficult part of building a business is not creating the software. It is winning customers, developing trust, learning the exceptions, hiring people who understand the work and surviving long enough to accumulate useful knowledge.

Those assets already exist inside many businesses approaching succession. The opportunity is therefore not simply to start new AI-native companies and wait for them to replace the old economy.

It may be to acquire parts of the old economy and make them AI-capable.

Jeff Bezos is probably assembling the pieces of precisely such a model.

The Wall Street Journal reported in March 2026 that Bezos was in early talks to raise up to $100 billion for what investor materials reportedly described as a manufacturing transformation vehicle. According to that reporting, the proposed fund would acquire manufacturing businesses and use artificial intelligence to accelerate automation and improve their economics. The “Manufacturing Transformation Vehicle” is likely to target larger businesses. This may offer an entry point for mid market PE firms rolling up assets for larger buyers.

Separately, Bezos and Vik Bajaj are co-leading Prometheus, an AI company focused on engineering and the physical economy. Prometheus has said it wants to build an artificial general engineer capable of helping humans design, simulate and manufacture complex physical products. The company announced in June that it had raised $12 billion at a valuation of approximately $41 billion.

The combination is notable:

• Capital to acquire companies.

• AI designed for engineering and manufacturing.

• A founder with experience operating physical systems at enormous scale.

• A prospective portfolio in which technology can be deployed repeatedly.

Amazon provides the operating precedent. By June 2025, Amazon said it had deployed its one-millionth robot across a network of more than 300 facilities. It has developed AI systems for demand forecasting, routing, warehouse movement, inventory handling and increasingly natural-language interaction with robotic systems.

Its newer systems include machines that move heavy loads, manipulate objects and assist people working inside fulfilment centres. Amazon also says more than 700,000 employees have participated in its education and skills programmes globally.

So, Amazon has spent decades learning how to combine software, data, robotics, physical infrastructure, process design and human labour across a large distributed operation. Very few organisations have comparable experience.

A Bezos-backed acquisition platform or other similarly technologically enabled mid market approaches could therefore bring more than capital and a licence for an AI model. It could bring a playbook for integrating intelligence with the physical economy.

If that worked, the platform would not merely own a collection of factories. It would own a repeated method for making established businesses more productive.

That method could become more valuable with every acquisition.

Last week, waste became a management problem

Last week’s Something for the Weekend argued that the greenest AI is the one that wastes least.

The central measure was not tokens per pound or output per kilowatt. It was:

Verified business value per unit of total resource consumed.

That argument becomes more important when AI moves from an employee’s browser into the operating system of a company.

But a business acquisition creates a harder test than a software pilot. The buyer cannot claim success because employees generated more documents or used more prompts.

It has to demonstrate that the company produced more, sold more, made fewer errors, reduced downtime, improved quality, shortened working-capital cycles, served customers faster, created safer jobs, raised wages or developed products more quickly.

AI adoption becomes accountable when it ‘owns’ the profit and loss account.

The Great Ownership Transfer may therefore create an unusual laboratory for measuring whether AI genuinely improves the real economy.

Not whether a model passed a benchmark. Not whether a board attended an AI workshop. Not whether employees received licences.

Whether a transferred business became more productive, resilient and valuable without destroying the knowledge that made it worth buying.

The business is not obsolete

A common mistake in technology is to confuse an old interface with an obsolete business.

A company can use outdated software and still possess valuable capabilities. The pump-maintenance company could have poor data systems but exceptional knowledge of why a particular machine fails after a certain cleaning cycle. A small aerospace supplier can use manual inspection records but understand tolerances that are not properly captured in its customer’s engineering model.

A regional logistics company could have weak route-planning software but know which deliveries fail when a particular road floods. A specialist insurer could run an old policy administration system but possess decades of claims judgement in a narrow class of risk.

The opportunity is not to discard that knowledge because it is badly formatted. It is to capture, organise and combine it with better tools.

That distinction separates transformation from liquidation.

The business being transferred should already contain the difficult assets: customer trust, tacit knowledge, skilled labour, supplier relationships, regulatory approvals, physical sites, a known product and a record of what works.

The AI system is not the business. It is a way of helping the business learn, decide and operate more effectively.

The missing middle is also a data problem

McKinsey’s report highlights an awkward mismatch: a large proportion of businesses approaching transition have enterprise values below the level at which many institutional investors can deploy capital efficiently. Each transaction requires finding the seller, assessing the accounts, evaluating management, reviewing contracts, understanding customers, conducting legal and tax work, arranging finance, negotiating terms, transferring ownership and supporting the new operator.

For a small transaction, much of this work costs nearly as much as it would for a larger one.

The result is the missing middle: businesses too substantial for an informal handover, too small for conventional private equity, too complex for an inexperienced buyer and too important to their employees and communities simply to close.

AI could reduce some of that transaction friction. It may help structure unorganised financial information, search contracts, identify missing documentation, compare operating metrics, prepare initial diligence questions, map customer concentration, review maintenance and quality records, build post-acquisition operating plans and match sellers with potential buyers.

But that does not make diligence automatic:

• A model cannot confirm that a customer will remain after the founder leaves.

• It cannot inspect a machine merely by reading the depreciation schedule.

• It cannot establish the quality of management from a polished information memorandum.

• It cannot replace legal, financial, environmental, operational or technical judgement.

However, it may reduce the cost of organising the evidence on which that judgement depends which is important when transaction economics are constrained by scale.

Therefore, the first productivity gain could occur before the business is acquired: AI could make the ownership transfer itself easier to execute.

Acquisition is not the same as transformation

Buying old companies and adding AI to the investment memorandum is ‘easy’. Changing how they operate is not.

Private equity has spent years describing portfolio-company digitisation, data transformation and technology-enabled value creation. The results have varied.

A company cannot be made AI-native by buying licences, hiring a consultant, creating a dashboard, uploading documents into a chatbot, adding an AI paragraph to the hundred-day plan or renaming cost reduction as automation.

The Markets Group and Brownloop paper describes an AI illusion in private equity. Its central claim is that using the same general-purpose models as every competitor does not create a durable advantage.

Generic models may well improve productivity. However, if every firm can access similar models and public information, the edge must come from elsewhere:

• Proprietary data.

• Institutional memory.

• Workflow integration.

• Governance.

• Decision records.

• Learning accumulated across repeated use.

The same logic applies to an acquisition platform.

The durable asset would not be access to Prometheus or any other model since competitors could eventually gain access to models with similar capabilities.

The durable asset would be the operating system built around the model: how businesses are selected, how their knowledge is captured, how their processes are mapped, how employees are involved, how data is cleaned, how permissions are assigned, how workflows are redesigned, how quality is tested, how savings are measured and how lessons from one acquisition improve the next.

That is where a collection of companies could become a platform.

The portfolio can learn

Picture ten companies making different industrial components. Each has its own maintenance records, supplier history, quality-control system, engineering files and production constraints.

A conventional holding company could manage them as separate investments. A stronger platform would build a common intelligence layer while preserving the differences that matter.

• One company learns that a certain vibration pattern predicts a bearing failure.

• Another identifies a supplier batch associated with premature cracking.

• A third develops a better sequence for machining a difficult alloy.

• A fourth improves energy consumption by changing when equipment is operated.

• A fifth discovers that a particular inspection step catches most defects but two others add little value.

The lessons cannot always be transferred directly. Different machines, materials and customers behave differently. A correlation in one plant may be meaningless in another. Confidentiality, competition law, customer restrictions and intellectual-property rights may limit what can be shared.

Yet a platform can still develop better methods for structuring maintenance records, testing predictions, measuring downtime, routing work, deciding when a human must intervene and integrating new acquisitions.

The system becomes more capable not merely because it owns more data. It becomes more capable because it has repeated the work of turning messy industrial reality into usable organisational knowledge.

That learning curve is difficult for a new competitor to reproduce.

The roll-up changes

Traditional roll-ups often create value through familiar mechanisms such as centralised purchasing, shared finance, common technology, consolidated management, cross-selling, lower overhead, stronger negotiating power, debt and multiple arbitrage.

AI adds another possible mechanism:

A platform could buy businesses valued as isolated local operators and increase their value by connecting them to a shared intelligence system.

The first acquisition helps build the system. The second benefits from what the first taught. The tenth enters a much more mature integration process. The hundredth may receive tools, benchmarks and operating methods that no standalone buyer could justify developing for one small company.

That is the attractive version.

There is also a less attractive one. The platform could acquire fragmented businesses, extract their data, standardise their operations, remove local management, reduce labour, increase leverage, concentrate market power and sell the enlarged group at a higher multiple.

The technology does not determine which version occurs, but ownership, incentives and governance do.

Who owns the intelligence?

The ownership question becomes more important once the platform begins to learn from acquired businesses.

Suppose a company’s employees have accumulated twenty years of specialist knowledge. That knowledge appears in documents, emails, repair notes, drawings, customer conversations, workarounds, judgement, habits and stories told between experienced and younger workers.

The buyer converts part of that knowledge into a structured AI system.

Who owns the resulting asset?

Legally, the answer will usually depend on employment terms, intellectual-property provisions, customer contracts, database rights, privacy law and the architecture through which the information was collected.

But economically, the question is broader.

The seller receives a purchase price. The investor owns the platform. The model provider receives fees. Management may receive equity.

What do the employees receive for helping convert their accumulated experience into a scalable machine asset?

They could receive more secure employment, better tools, higher wages, promotion, training, profit sharing or equity.

Or, they could receive redundancy.

A platform that treats employees merely as temporary containers of extractable knowledge will create resistance and destroy trust. Employees will be rationally reluctant to document the expertise that could be used to remove them.

A platform that allows people to participate in the productivity they help create has a better chance of obtaining the knowledge required to create it.

This is not only merely a fairness argument, it is an operating argument.

The machine cannot learn what the workforce refuses to reveal.

The employment bargain

Jeff Bezos argued at VivaTech that AI could create labour shortages rather than make humans redundant.

AI may increase demand by making invention and production cheaper. It may create new companies, products and occupations. It may also reduce labour requirements in existing tasks and companies.

Both can occur at the same time.

The relevant question for an ownership-transfer platform is therefore not whether AI will create or destroy jobs, it is:

A credible bargain might contain several principles.

1. Do not begin with an arbitrary headcount target

If the business case assumes that a fixed percentage of employees must leave before the work has been redesigned, management is not discovering productivity. It is allocating a predetermined cost reduction.

Of course, some roles may disappear, others expand, and new ones may emerge. The decision should follow evidence about the redesigned operation, not precede it.

2. Share part of the gain

When workers help train systems, redesign processes and improve productivity, the resulting value should not flow exclusively to the acquisition vehicle.

Profit sharing, performance participation, employee equity or funded training can connect the workforce to the upside.

The correct structure will vary by company and jurisdiction. The principle is constant: people should have a route to benefit from making their employer more productive.

3. Fund mobility before displacement

Training offered after a role disappears is often too late.

A serious platform would identify adjacent roles while the transformation is being designed. An experienced machine operator could become a robotics technician, quality-system supervisor, process trainer, data steward, customer-solutions specialist, maintenance planner or human reviewer for automated decisions.

Not everyone will make that transition. Training does not guarantee a new role.

However, the platform should be able to show that it invested in mobility before relying on redundancy.

3. Preserve local operating authority where it creates value

Centralisation can improve finance, technology, procurement and compliance. It can also destroy local knowledge.

The person closest to the machine, customer or production line often sees problems that a remote system misses. Therefore, the objective should not be maximum central control. It should be the correct distribution of authority between the employee, local manager, AI system, central platform and board.

4. Make the system contestable

Employees need a route to challenge an AI-supported instruction or decision.

The machine may optimise the wrong metric or it simply misunderstands an unusual customer requirement. It could also recommend a maintenance delay that looks efficient but creates safety risk.

A human should not be penalised for escalating a material concern merely because the system was confident.

5. Measure job quality, not only job count

A business could retain the same number of people while making their work worse.

Technology can increase surveillance, work intensity and algorithmic pressure. It can also remove repetitive physical tasks, improve safety and expand responsibility.

A serious employment scorecard should examine pay, hours, predictability, safety, training, autonomy, promotion, work intensity, employee turnover and employee participation in value creation.

The number of jobs alone is not enough.

Amazon is the proof and the warning

Amazon demonstrates that AI and robotics can operate at physical scale. It also demonstrates why the employment design cannot be treated as an afterthought.

The company has deployed robotics across a vast fulfilment network. Its systems can move inventory, optimise routes, forecast demand and increasingly respond to natural-language instructions.

Amazon says these technologies remove repetitive and physically demanding work while creating new technical roles. It has also announced extensive training and apprenticeship programmes.

Those are all material capabilities.

At the same time, Amazon has faced criticism over warehouse injury rates, work intensity, algorithmic management, labour relations and employee surveillance.

Those issues are not incidental to a thesis about transforming work. They are central to it.

A productivity platform can create more output per worker while transferring risk, pressure and control onto the worker. It can reduce heavy lifting while increasing the pace at which remaining tasks must be completed. It can create skilled robotics jobs while narrowing autonomy elsewhere. It can offer training while still displacing people who cannot access or complete it.

The lesson is not that physical AI is inherently good or bad, it is that operating design determines how the productivity gain is distributed.

Amazon provides evidence that large-scale physical AI is possible. It does not settle the question of what the ideal human settlement around it should be.

Walmart’s builders show the other side

The transformation does not have to be imposed only by engineers working from headquarters.

Walmart says its internal Code Puppy platform enables non-technical employees to build tools that previously required technical support.

Its Global Chief Technology Officer, Suresh Kumar, said Walmart had as many non-technical associates able to do technology-enabled work as it had engineers.

That statement is Walmart’s own description of its workforce capability, not an independently audited measure. Nevertheless, it illustrates an important shift.

AI can move the ability to design software-like workflows closer to the person who understands the work: the warehouse supervisor, buyer, store manager, finance analyst, maintenance engineer or customer-service employee.

That could be particularly valuable inside transferred businesses.

Small companies often lack large IT teams. Their employees nevertheless understand where work is duplicated, where information is lost and which exceptions consume time.

Giving them controlled tools to redesign those processes could produce better results than importing a central team that does not understand the business.

Walmart also illustrates the governance problem. When many employees become builders, traditional security and software-development controls are no longer be sufficient.

The company says it addresses this through approved models, shared internal platforms, permission controls and embedded governance.

The principle is relevant to any acquisition platform:

Every employee should not receive unrestricted authority over every model, dataset and operational system.

The platform needs to make safe building easier than unsafe building.

The service economy is not excluded

The reported Bezos vehicle has been associated primarily with manufacturing which is understandable. Prometheus is focused on engineering and the physical world. Bezos and his team bring substantial operational experience in logistics, robotics and infrastructure.

However, the broader ownership-transfer thesis extends far beyond factories.

It applies to accounting firms, insurance agencies, engineering consultancies, maintenance businesses, property managers, dental groups, veterinary practices, legal services, freight brokers, recruitment firms, testing laboratories, specialist distributors, construction services and home-care providers.

These businesses will contain fewer robots but similar ingredients: recurring customers, skilled judgement, fragmented records, manual administration, weak succession planning and labour constraints.

AI could improve scheduling, document preparation, customer support, knowledge retrieval, quality control, pricing, compliance, sales administration, claims handling, inventory, work allocation, forecasting and professional review.

However, service businesses create a different risk. Their product may be judgement itself.

Automating a factory movement and automating legal, medical, financial or insurance advice are not equivalent. The cost of an error can be delayed, hidden or transferred to a client.

The platform must therefore distinguish between work that can be automated, work that can be assisted, work that requires accountable professional judgement and work that should remain human.

In short:

• The acquisition thesis can span the physical and service economies.

• But the governance architecture cannot be identical across both.

The data flywheel is not automatic

Investors often describe data as though collecting more of it inevitably improves the model. But that’s not always the case.

For instance, a portfolio can accumulate vast quantities of inconsistent records, unlabelled files, duplicated documents, incorrect entries, biased decisions, expired procedures, customer-restricted data, personal information and machine telemetry without context.

The result will be a data swamp rather than a flywheel.

For the portfolio to learn, management needs common definitions, clear ownership, data lineage, access controls, retention rules, quality standards, customer permissions, model-evaluation methods and mechanisms for correcting errors.

It also needs evidence of which business outcomes improved.

The platform must understand where standardisation becomes destructive:

A common chart of accounts can be useful. A common safety assumption across different industrial processes may not be.

A shared customer-service model can improve consistency. It may also erase the local relationship that made the acquired business valuable.

The platform must know what should be common and what must remain specific.

That judgement will become one of its most important capabilities.

Productivity is not margin expansion

An acquired company can improve its margin without becoming more productive:

It can raise prices, reduce maintenance, delay investment, cut training, increase workloads, renegotiate suppliers aggressively, reduce service quality or use more debt.

Those actions may well raise short-term earnings but they do not necessarily increase useful output per unit of resource.

Likewise, AI can produce apparent savings while moving costs elsewhere:

A customer could wait longer to reach a person, an employee spend more time correcting automated work, a supplier be required to absorb additional administration, and a safety risk may grow but remain unrealised.

In short, a system can generate more output without increasing revenue or reducing total cost.

A credible transformation platform should therefore measure more than EBITDA.

It should examine output per hour, defects, downtime, lead times, energy and material consumption, customer retention, employee turnover, workplace injuries, working capital, innovation cycles, human correction, revenue from new products and the total cost of technology, integration and governance.

The platform succeeds when the business creates more verified value from its people, capital and physical resources, not merely when the reported margin improves before resale.

The seller’s decision changes

For the retiring owner, the choice may no longer be limited to family succession, a management buyout, employee ownership, a strategic sale, private equity or closure.

A new category could emerge:

That buyer would offer liquidity to the owner, a transition plan for management, technology investment, shared operating infrastructure, access to specialist capital, a route to combine with similar businesses and potential participation in future upside.

The offer could be attractive.

But it will also create old and new diligence questions for the seller:

• What will happen to the employees?

• Will the company retain its identity?

• Will the site remain open?

• Who owns the operating data?

• Can customer information be transferred into a shared model?

• How much debt will be placed on the business?

• What technology investment is contractually committed?

• Will management participate in equity?

• What happens if the transformation fails?

A seller who spent decades building a business is more likely than not to care about more than the highest headline price. The quality of the next owner will become part of the market.

The buyer’s advantage may be patience

A platform of this kind could fail if its capital expects rapid transformation:

Industrial processes do not change at software speed.

Equipment has replacement cycles.

Employees need training.

Customers require validation.

Regulators may need evidence.

Factories cannot be rebuilt every quarter.

Data may take years to organise.

And, operational improvements are likely to require investment before they produce savings.

The model could fail in precisely the unusual situations where experienced workers add most value.

For instance, a five-year fund structure, aggressive leverage and a near-term exit target could conflict with the time required to build a genuine intelligence platform. Therefore, patient capital is most likely to be an important competitive advantage.

The best owner may not be the one with the cheapest model, it is likely to be the one able to wait while the company learns how to use it.

The CFO version

For the CFO, the thesis is a capital-allocation problem.

The acquisition case should separate the value of the existing business from the expected operating improvement. It should identify the costs of technology, integration, workforce transition, data preparation, cyber controls, governance and physical automation.

It should also identify the risk that projected benefits do not materialise and the extent to which the financial model assumes that a future buyer will pay a higher multiple for an AI-enabled platform.

The CFO should resist the temptation to treat all digital expenditure as transformation investment. Some of it will be maintenance, experimentation, rework, vendor dependency, parallel running, failed deployment or necessary control infrastructure.

The investment committee should ask which improvement depends on AI, which depends on ordinary software, which depends on better management, which depends on scale and which depends on reducing employment and other synergies.

It should also ask what remains proprietary when the model becomes a commodity.

The financial model should not assume that adding AI raises the exit multiple: it should demonstrate how the business becomes economically better before assuming that a future buyer will pay more for it.

The CEO version

For the CEO of the acquired company, the challenge is sequencing.

The company cannot transform every process simultaneously. It should begin with workflows that are material, measurable, repetitive enough to improve, bounded enough to control, supported by usable evidence and connected to a clear business outcome.

It should avoid beginning with the most ‘theatrical’ application.

The useful first project may not be a humanoid robot walking through the factory. It may be predicting equipment failure, retrieving the correct engineering drawing, reducing quotation time, identifying quality drift, improving work scheduling, automating invoice matching or capturing knowledge before an expert retires.

The CEO should also decide what must not change: the customer relationship, safety culture, specialist craft, local decision right, exception process or identity of the business.

Transformation requires a positive operating thesis, not merely a list of old things to remove.

The employee version

For employees, the relevant promise is not that AI will create jobs somewhere in the economy.

It is what happens here.

To this job. To this site. To this pay packet. To this skill. To this pension. To this community.

Management should be able to explain which tasks are changing, which decisions remain human, what data are being collected, how performance will be assessed, which roles may disappear, which roles may grow and what training is available.

It should explain whether training occurs during paid time, how employees share in productivity gains, how an automated decision can be challenged and what happens if the technology fails.

Vague reassurance damages trust.

So does false certainty.

Some jobs will probably disappear even under a well-designed transformation.

Management should say that plainly.

The question is whether the process provides people with agency, evidence and a credible share of the opportunity.

The investor version

For the investor, the most important question is whether the platform has built a compounding asset or merely acquired a portfolio.

A real platform should become better at finding businesses, pricing transition risk, capturing knowledge, integrating data, deploying technology, training employees, measuring outcomes, controlling risk, developing management and transferring lessons.

A weak roll-up becomes more complicated with every acquisition.

In contrast, a strong platform becomes more capable.

The evidence should appear in declining integration time, improving operating outcomes and reduced dependence on external consultants.

The investor should also examine concentration. If every portfolio company depends on the same model, cloud provider, robotics stack, identity system, central data layer and integration team, efficiency may create systemic fragility.

The portfolio may look diversified by industry while sharing one operational point of failure.

The policymaker version

For policymakers, the Great Ownership Transfer is not merely a tax or retirement issue.

It touches employment, regional resilience, competition, productivity, access to capital, employee ownership, data rights, training and industrial strategy.

The closure of a small business can remove more than jobs. It can remove a service, local supplier, tax base and route into ownership.

Policy can reduce friction by supporting seller preparation, buyer education, management and employee buyouts, small-deal finance, portable training, standardised transaction information, regional succession networks, technical assistance and responsible data infrastructure.

However, policymakers should also examine what happens when thousands of local businesses are consolidated into technology platforms.

The economy could gain productivity while losing competition and local control, and a platform that rescues businesses from closure could become too powerful.

Succession policy and competition policy therefore cannot be separated indefinitely.

The insurance question

An AI-enabled acquisition platform creates risks at several levels:

• At the acquired company, a model can recommend an incorrect maintenance action.

• A robot could damage equipment or injure someone.

• An automated design may contain a defect and a professional-services system may produce incorrect advice.

• An employee may rely on an output beyond its approved purpose.

• Confidential information could be disclosed.

• A cyberattack could compromise connected operational systems.

At the portfolio level, one software error could then affect many companies with a shared model producing correlated mistakes. A central identity failure may interrupt operations across the group, a cloud outage may stop multiple businesses simultaneously and a compromised update may propagate through the platform.

At transaction level, diligence may miss a material liability, expected productivity gains fail, customer contracts restrict data use and historical records not support the intended models. Of course, the platform may inherit environmental, product or employment liabilities.

The insurance programme must therefore do more than add a generic AI endorsement.

It needs to map cyber, technology errors and omissions, professional indemnity, product liability, general liability, property damage, business interruption, crime, directors’ and officers’ liability, employment practices, environmental liability, representations and warranties, machinery breakdown and supply-chain exposure.

The boundaries will depend on the facts and policy wording.

An AI-related loss does not automatically fall within one category. A defective model-supported design may produce product liability, professional liability, property damage and business interruption across several customers.

In addition, a shared platform may also create accumulation. An insurer may believe it is underwriting dozens of independent small businesses when all depend on the same model and operating system.

The better the platform becomes at centralising intelligence, the more carefully it must manage correlated failure.

Insurability will depend on evidence. What was automated? What remained human? Which model and version were used? Which data informed the decision? Who approved the workflow? What authority did the system have? How was it tested? What happened when confidence was low? Could the action be stopped? Could the decision be reconstructed? How many portfolio companies depended on the same component?

The platform that can answer those questions will be easier to finance and insure than the one that merely says it uses AI.

The Board version

The board does not need to decide which robotics system should move a component, which model should analyse a maintenance record or which employee should build the first internal application.

It does need to decide what kind of owner the company intends to become.

An AI-enabled acquisition platform is not simply a technology programme attached to a roll-up. It changes the investment thesis, the operating model, the workforce bargain and the concentration of risk across the portfolio.

A useful board discussion should cover ten questions:

1. Are we acquiring fundamentally good businesses that lack successors, or using succession pressure to buy weak businesses cheaply?

2. Which part of the investment return comes from genuine productivity improvement, and which part depends on leverage, headcount reduction, price increases or a higher exit multiple?

3. What knowledge makes each acquired company valuable, where does that knowledge currently reside and how will it be preserved during the transition?

4. What should be standardised across the portfolio, and what must remain local because it reflects customer relationships, specialist judgement, safety requirements or operating context?

5. Which AI and automation investments have a defined business outcome, an accountable owner and a credible measure of value?

6. How will employees participate in designing the new operating model, and how will they share in the productivity gains they help create?

7. Which decisions can be delegated to machines, which require human approval and which should remain exclusively human?

8. How dependent will the portfolio become on common models, cloud providers, robotics systems, datasets, identity controls and central technology teams?

9. Can management reconstruct an AI-supported decision, stop a failing system and demonstrate where responsibility sits when something goes wrong?

10. Does the capital structure allow enough time to improve the businesses properly, or does the required return force management towards rapid extraction and resale?

The board should receive evidence across four dimensions.

1. The acquired businesses. Are revenue, customer retention, quality, output, safety, working capital and resilience improving?

2. The workforce. Are pay, skills, autonomy, job quality, mobility and employee participation improving, or is technology merely increasing work intensity?

3. The platform. Is each acquisition becoming easier to integrate because the system is learning, or is complexity accumulating faster than capability?

4. The risk. Are centralisation and common technology creating correlated exposures that the company, its insurers and its capital providers do not yet understand?

The board should be particularly cautious when management presents technology adoption as evidence of transformation. More models, robots, agents, dashboards and employee licences do not establish that the business is becoming more productive.

Nor should the board accept a simple choice between protecting employment and improving efficiency.

In the end, a business that fails to modernise is likely to protect neither.

But a company can modernise badly. It can destroy tacit knowledge, weaken customer relationships, impose unsuitable central systems, transfer risk to employees and record the resulting cost reduction as productivity.

The board therefore needs to distinguish between three things:

1. Automating work.

2. Improving the business.

3. Creating durable value.

They are not the same.

So, the hardest question is not whether the company has found enough businesses to acquire or enough AI to deploy.

It is:

The hard truth

The Great Ownership Transfer could become one of the largest economic-mobility opportunities of the next decade.

It could also become one of the largest transfers of locally accumulated knowledge into a small number of capital and technology platforms.

Both outcomes can arise from the same transaction.

A retiring owner sells. A business survives. Capital modernises it. Employees receive better tools. Productivity rises. Customers benefit. The company grows.

Or a retiring owner sells. Knowledge is extracted. Operations are centralised. Employment falls. Debt rises. Local control disappears. The business is resold.

The difference will not be determined by whether the buyer uses advanced AI.

It will be determined by who owns the platform, how it is financed, what it measures, how long it can wait, how it treats workers, how it shares gains, how it manages concentration and whether it builds companies or merely prepares them for resale.

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The weekend takeaway

The Great Ownership Transfer is usually presented as a race to find enough buyers.

That understates the opportunity.

The next generation of owners may inherit millions of functioning businesses at the same moment that artificial intelligence becomes capable of changing how those businesses design, manufacture, sell, maintain and learn.

Jeff Bezos may be assembling an unusually powerful version of that model: a large pool of acquisition capital, an AI laboratory focused on engineering and physical production, and experience operating robotics and AI across one of the world’s largest physical networks.

If those pieces come together, they could create a platform for industrial productivity at exceptional scale.

The result is not guaranteed.

The proposed investment vehicle may not reach its reported target. Prometheus may not achieve its technical ambitions. Technologies developed for one operation may not transfer easily into thousands of different businesses. The employment gains predicted by Bezos may not arrive evenly or quickly.

But the underlying possibility remains.

The most valuable AI company may not be the company that owns the best chatbot.

It may be the company that learns how to transfer, preserve and improve the productive knowledge of an entire generation of businesses.

The owners are retiring.

The companies are not.

The question is who acquires them, what intelligence they install and who participates in the value created when the machines begin to learn.

🧠 Techie Corner for the Non-Techies

1. The Great Ownership Transfer

The term describes the expected transfer of large numbers of privately owned businesses (by Baby Boomers) as their owners retire.

2. Entrepreneurship through acquisition

Entrepreneurship through acquisition means buying and operating an existing business rather than starting one from nothing.

The buyer acquires existing customers, employees, systems and cash flow. The model can reduce some start-up risks but creates others, including financing, succession and integration risk.

3. Roll-up

A roll-up combines multiple businesses in the same or adjacent sectors under one ownership structure. The aim is to create scale, centralise functions, improve purchasing, cross-sell services or increase the combined company’s valuation.

Roll-ups can also create excessive leverage, integration problems and market concentration.

4. Platform company

In private equity, a platform company is often the initial or principal business around which further acquisitions are made.

In this article, platform has a broader meaning: a common operating, data and technology system that can improve multiple acquired businesses.

Owning several companies does not automatically create a platform.

5. Physical AI

Physical AI refers broadly to AI systems that perceive, model or act within the physical world.

Applications can include robotics, manufacturing, autonomous vehicles, industrial simulation, engineering design, warehouse automation and predictive maintenance.

The term is used inconsistently and does not denote one settled technical category.

6. Artificial general engineer

Prometheus uses this phrase for its ambition to build AI systems capable of assisting across the engineering process, including design, simulation and manufacturing.

It should not be confused with a confirmed achievement of artificial general intelligence.

7. Foundation model

A foundation model is trained on broad data and can be adapted to many tasks.

Large language models are one type. Models for images, robotics, biological systems or physical simulation can use different architectures and data.

Access to a foundation model does not by itself create proprietary advantage.

8. World model

A world model attempts to represent aspects of how an environment behaves.

In physical applications, this often includes predictions about movement, force, materials or cause and effect.

The term covers different technical approaches and should not be taken to mean that a system understands the physical world in the same way a person does.

9. Tacit knowledge

Tacit knowledge is practical understanding that is difficult to express fully in formal documents.

It includes recognising an unusual sound, knowing when a customer is likely to reject a proposed solution, understanding which supplier can deliver during an emergency or sensing when a machine is operating abnormally.

Capturing tacit knowledge is difficult because asking someone to describe it may change or simplify what they actually do.

10. Knowledge graph

A knowledge graph represents relationships between entities such as customers, products, people, contracts, machines and events.

It can help an AI system retrieve connected information.

A knowledge graph is only as reliable as its data and definitions.

11. Citizen developer

A citizen developer is an employee who creates applications or workflows without being a professional software developer.

AI-assisted coding makes this easier, but it also increases the need for security, permissions, testing and governance.

12. Shadow AI

Shadow AI is the use of AI tools outside approved organisational controls.

Examples include uploading confidential documents to a personal chatbot or deploying an unapproved agent.

The term resembles shadow IT, where employees adopt technology without formal authorisation.

13. Model routing

Model routing assigns different tasks to different models or systems.

A simple task may use ordinary software or a smaller model. A complex engineering problem may require a more capable system.

Routing can reduce cost and risk when it is designed properly.

14. Human in the loop

A human-in-the-loop system requires a person to review or approve part of the AI-supported process.

The phrase can conceal weak control. A person who lacks time, information or authority could provide only superficial oversight.

The relevant question is where human judgement enters and whether it can alter the outcome.

15. AI-enabled value creation

AI-enabled value creation means using AI to improve an economic outcome such as revenue, cost, quality, speed, safety or risk.

It should be distinguished from AI activity: more prompts, models or generated documents do not prove that value has been created.

16. Learning arbitrage

Learning arbitrage is used in this article to describe value created when lessons from one business improve the operation or integration of another.

The value depends on whether knowledge can genuinely be transferred without losing important context.

17. Data flywheel

A data flywheel is the idea that more use generates more data, which improves the system, which attracts more use.

The cycle is not automatic: poor-quality or biased data can degrade a system rather than improve it.

18. Operational technology

Operational technology controls or monitors physical equipment and industrial processes.

It differs from conventional information technology, although the two increasingly connect.

A cyber incident affecting operational technology can create physical damage and have safety consequences.

19. AI accumulation risk

AI accumulation risk occurs when many companies depend on the same model, cloud provider, dataset or software component.

One failure may then produce losses across several organisations and insurance policies simultaneously.

20. Employee ownership

Employee ownership gives employees an economic interest in the company.

Structures vary by jurisdiction and can include employee share plans, trusts, cooperatives and direct ownership.

Employee ownership does not automatically produce employee control or equal participation.

📚 Further reading

• McKinsey Institute for Economic Mobility: The Great Ownership Transfer: A New Era of Business Stewardship(2026)

  The report examines the expected transfer of US small and medium-sized businesses as owners retire. It considers regional exposure, transaction barriers, economic mobility and the need for more buyers.

• Ken Yearwood: A Generational Shift in Business Ownership Is Underway (2026)

  A shorter McKinsey commentary on succession risk, business continuity and the opportunity to broaden access to business ownership. It argues that delayed planning can place companies, jobs and community wealth at risk.

• The Wall Street Journal: Jeff Bezos in Talks to Raise $100 Billion for AI Manufacturing Fund (2026)

  The report states that Bezos was in early discussions to raise a manufacturing transformation vehicle intended to acquire industrial companies and deploy AI across them.

• The Wall Street Journal: Bezos Bats Down AI Job Loss Fears While Launching New Venture (2026)

  An interview and report on Prometheus, its funding, engineering ambitions and Bezos’s view that AI may create labour shortages.

• Reuters: AI Will Lead to Labour Shortages, Bezos Says in Optimistic Talk (2026)

  Reuters’ report from VivaTech on Bezos’s employment thesis and the stated purpose of Prometheus. It also notes that companies are currently attributing some job reductions to AI-related efficiency.

• Project Prometheus: Company launch materials (2026)

  The company describes its ambition to build an artificial general engineer for designing and manufacturing physical products.

• Amazon: Amazon Deploys Over One Million Robots and Launches New AI Foundation Model (2025)

  Amazon reports that it had deployed one million robots across more than 300 facilities and introduced DeepFleet to improve robotic-fleet movement.

• Amazon: Amazon Has Upskilled Over 700,000 Employees Globally (2025)

  Amazon’s description of its education, apprenticeship and workforce-development programmes.

• Amazon: Amazon unveils next-gen Proteus robot as part of €10 billion European investment in its fulfillment network (2026)

  A company announcement covering newer robotics systems, European investment and workforce plans.

• IT Brew: How Walmart Is Securing a New Cohort of AI Builders (2026)

  An account of Walmart’s Code Puppy platform, non-technical employee development and the company’s approach to permissions and embedded governance.

• USAFacts: How Many Americans Are Using AI- and How? (2026)

  A summary of experimental US Census Bureau survey data collected in June 2025. The data show household-reported AI use, productivity perceptions, trust and demographic differences. The survey measures whether respondents reported AI use within their household. It is not a direct count of unique individual users or audited workplace deployment.

• Markets Group and Brownloop: The AI Illusion in Private Equity: Why Using AI Is Not the Same as Owning It (2026)

  A sponsored practitioner article arguing that generic AI adoption does not create durable investment advantage without proprietary data, institutional knowledge and governance.

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

  An earlier Something for the Weekend article on the distinction between access to models and ownership of evidence, workflow and institutional knowledge.

• Dr Henri Winand: The Greenest AI Is the One That Wastes Least (2026)

  The article argued that AI efficiency should be measured as verified business value per unit of total resource consumed.

Last week on Something for the Weekend

Last week’s piece asked whether companies were becoming more efficient or merely doing more machine work.

It argued that falling model costs, lower energy per task and rising usage can occur simultaneously.

The correct corporate measure is not machine output alone, it is verified business value per unit of total resource consumed.

This week moves from the AI workflow to the company itself.

As millions of businesses approach succession, the opportunity may be to connect established customers, skilled workers and accumulated knowledge with a new generation of intelligent tools.

The machines may improve, and the ownership model will decide who benefits.

A new Fed chair walks into office with the most convenient economic story available:

AI may let America cut rates without paying the inflation bill.

That is the line that caught my eye in Morning Brew’s note on Kevin Warsh replacing Jerome Powell.

Powell leaves after an extraordinary 8-year period: early Trump-era rate rises, the Covid rescue, massive bond purchases, emergency support, a rapid labour-market recovery, then the worst inflation surge in decades and the painful correction that followed.

Warsh inherits the other side of that story.

He has said he favours lower rates, but the Fed is divided, inflation remains above target, and the committee is unlikely to move simply because the new chair wants it to.

The interesting part was not the personnel change.

It was the historical comparison.

Morning Brew pointed back to Alan Greenspan. In the 1990s, Greenspan resisted calls to raise rates because he believed the personal-computing boom had raised productivity. The economy could run faster without creating the same inflation pressure.

He was broadly right on that narrow call.

Warsh appears to see AI in similar terms.

That is what made me stop.

AI may well raise productivity. I have been writing about that from several angles: the digital employee, the superworker, the shrinking need for some corporate roles, the cost of machine work, and the legitimacy risk around who benefits and who pays.

There is a difference between productivity arriving and productivity being spent in advance.

That is this week’s question.

Who gets the AI productivity gain before markets, policymakers and companies start using it?

The Boardroom point

AI productivity is being priced before it has been proven:

Markets are pricing it.

Vendors are pricing it.

Governments are relying on it.

Boards are building cost plans around it.

Central bankers are starting to discuss it as a macroeconomic variable.

Some of this is rational. AI already changes real work. It writes, searches, codes, summarises, translates, drafts, checks and reasons across documents. Used well, it removes hours from repetitive cognitive work.

Some of it is premature:

A productivity boom is not the same as a usage boom.

A productivity boom is not the same as a capex boom.

A productivity boom is not the same as a valuation boom.

A company can deploy AI everywhere and still fail to reduce its full cost base. A worker can produce more and capture less. A government can get higher nominal growth while households experience weaker real purchasing power.

Boards should separate four things that are often blended together:

1. Productivity.

2. Asset prices.

3. Debt relief.

4. Distribution.

AI can improve the first while intensifying the other 3.

The useful Board question is direct:

Why this is happening now

The Warsh-Greenspan comparison is not some sort of random observation.

Greenspan’s argument had an economic mechanism behind it. Higher labour productivity can lower unit labour costs. If wages rise 4% and productivity rises 3%, the labour cost embedded in each unit of output rises by roughly 1%.

That gives the economy more room.

Greenspan made that argument in 1999. He said stronger labour productivity had lowered unit labour cost growth and reduced inflation pressure. He linked that change to the spread of information technology across business processes.

The data later supported the broad claim. The late 1990s did see a real productivity acceleration. Business investment in computing, software and networks changed how firms operated. Inflation stayed more contained than many models expected.

That historical memory now sits inside the AI debate.

Warsh inherits an economy with a much worse fiscal backdrop. Public debt is higher. Interest costs matter more. Political pressure on the Fed is more open. AI investment is already enormous. Equity markets have capitalised a large part of the promised gain.

The New York Times piece on Warsh makes the comparison explicit: Greenspan resisted calls to raise rates because he believed the US was experiencing a productivity boom from personal computing; Warsh has said AI may create a similar boom today.

That is the official version of the seduction.

There is a harder version.

When debt is high, productivity optimism becomes useful. It allows policymakers to believe that growth can carry what fiscal policy cannot fix. It allows markets to believe that valuations are justified. It allows companies to believe that cost savings will arrive. It allows voters to be told that the pain is transitional.

That makes the provision of evidence matter more.

The Greenspan lesson

Greenspan’s late-1990s call had two parts:

The first part was right: technology raised productivity and reduced inflation pressure for a period.

The second part was more fragile: easy financial conditions and technology optimism helped produce excess. Productivity gains were real. The capitalisation of those gains went too far.

This distinction is important.

Technology can raise the economy’s speed limit. It can also raise expectations faster than output. Asset prices can move before wages. Capex can move before cash flow. Political pressure can move before the data.

That is the Fed’s problem.

Central banks have to decide in real time, yet productivity data arrive late:

Revisions matter.

Business surveys overstate and understate.

Hype contaminates judgement.

Markets try to front-run the conclusion.

Chicago Fed President Austan Goolsbee has already warned about this. Rising productivity can restrain inflation if supply expands. It can also increase inflation if households and businesses anticipate future gains and spend before supply catches up.

That is the AI version of the risk:

The boom can be disinflationary later and inflationary now.

Data centres need power now.

Chips need capital now.

Cloud capacity needs money now.

Specialist labour is scarce now.

Equity wealth effects happen now.

The productivity dividend arrives later, unevenly, and after measurement.

The AI productivity bet

AI can raise productivity in three ways.

First, it lets some people do more work:

A software engineer can test more options. A lawyer can review more documents. A finance team can reconcile more data. A founder can write faster. A customer-service team can respond sooner.

Second, it removes work that did not need to exist:

Summaries, formatting, first drafts, meeting notes, routine reporting and internal coordination consume large amounts of paid time. AI reduces some of that work quickly.

Third, it turns some human work into machine-executed output:

The “digital employee” does not take holiday, does not sleep, and can run across systems continuously.

Each channel has a cost:

The superworker can become more productive, then easier to copy.

The unnecessary task can disappear, then the salary attached to it disappears too.

The digital employee can look cheap, then the full bill arrives through tokens, inference, cloud, integration, supervision, verification and liability.

That is where many AI productivity claims are still weak:

They count the visible time saved.

They do not count the new cost system.

AI makes output cheaper. Verified output is harder. The more the machine produces, the more someone has to decide whether the work is correct, safe, legal, useful and worth acting on.

A company that produces five times more analysis and spends the same senior time reviewing it has not necessarily become five times more productive. It may have moved the bottleneck.

The K-shaped recovery

AI can raise GDP while making the lived economy feel worse.

That sentence will sound wrong to people who think in averages but it will sound familiar to people who live surrounded by bills.

The AI economy can split quickly:

Asset owners benefit when lower rates support equity prices, real estate and private company valuations. Large technology platforms benefit when compute, cloud, models and enterprise AI demand expand.

Highly skilled AI users benefit when they can multiply output and keep the gain.

Companies with clean workflows benefit before companies with fragmented data, unclear decision rights and weak management discipline.

Other groups experience a different economy:

Salary earners face weaker bargaining power when parts of their role become easier to automate.

Young workers face thinner entry-level pathways when firms use AI to remove junior work.

Middle managers face pressure when coordination work becomes visible and compressible.

Non-asset owners face higher prices without the asset appreciation that offsets them.

Local communities host data centres, power demand, water use and planning pressure while much of the economic gain accrues elsewhere.

That is the K-shaped AI economy.

This matters because legitimacy is built from the felt economy, not the aggregate chart.

A country can report productivity gains while large parts of the population feel poorer, less secure and less able to progress. That is a political problem, a workforce problem and a Board problem.

The inflationary redistribution

‘Shrinkflation’ is the consumer version of the problem.

The broader mechanism is inflationary redistribution:

If inflation stays above wage growth, households lose purchasing power.

If nominal growth rises, debt becomes easier to carry.

If interest rates are held below inflation for long enough, savers and fixed-income households lose real wealth.

If asset prices rise, owners of equities, homes and private businesses gain.

That is not a comfortable arithmetic:

The state gets debt relief.

Asset owners get valuation support.

Leveraged actors get easier real debt burdens.

Wage earners get higher prices.

Yet, nobody has to announce that policy. It can emerge from incentives:

High-debt governments prefer lower real rates.

Markets prefer cheaper money.

Borrowers prefer inflation that erodes debt.

Politicians prefer growth stories to austerity stories.

AI supplies the growth story.

Former Fed Chair Janet Yellen’s concern about Fed independence fits here. She warned that pressure on the Fed to hold down debt-service costs rather than focus on price stability can lead toward very high inflation. She also said Warsh understands the Fed, but enters the role under difficult political conditions.

The institutional risk is all too clear:

If AI productivity becomes the justification for lower rates before the productivity is visible, policy starts making a distributional bet.

The bet may pay off:

AI may lift output enough to reduce inflation pressure, raise real wages, improve margins and stabilise debt.

The bet may also fail:

If the gains arrive narrowly, the result is asset inflation, weaker labour bargaining power and a public told that technology has improved the economy while their own economy has deteriorated.

The productivity proof problem

AI productivity has to show up somewhere measurable and usage is not enough, prompts sent are not enough, documents generated are not enough, agents launched are not enough and headcount avoided is not enough if the old process remains and the review burden grows.

The useful metrics are harder:

• Output per hour.

• Unit cost per verified outcome.

• Revenue per employee.

• Error rates.

• Rework.

• Cycle time after review.

• Customer satisfaction after automation.

• Margin after full AI costs.

• Cost of control.

• Cost of failure.

• Cost of vendor dependency.

Let’s be clear:

A company has not proven AI productivity because employees use AI every week.

A company has proven something when the old work disappears, the new cost base is counted, quality holds, risk does not rise beyond appetite, and the gain reaches the P&L.

Governments face the same test.

AI has not solved a fiscal problem because speeches and forecasts assume higher productivity. It solves part of the fiscal problem only when measured output rises, tax receipts improve, public services become cheaper or better, and the gains are durable.

The current risk is that expectations are moving faster than proof which creates a gap:

Markets live in the expected gain.

Workers live in the present workflow.

Households live in the current price level.

Boards live somewhere between the above three.

What boards should ask now

Boards do not need an economic forecast to manage this risk.

They need discipline.

Ask:

1. Where has AI increased verified output, rather than produced more material?

2. Which productivity gains have reached the P&L?

3. Which AI costs were outside the original business case: compute, tokens, cloud, integration, review, legal, cyber, rework and vendor dependency?

4. Which workers capture the gains from AI: employees, management, shareholders, customers, vendors or the state?

5. Which roles are becoming more productive, and which are becoming easier to remove?

6. Which entry-level pathways are being weakened?

7. Which AI business cases depend on lower rates, high equity valuations or cheap compute staying available?

8. What happens if AI costs rise, rate cuts do not arrive, or regulation slows deployment?

9. Where does the company create local cost and remote benefit: power, water, land, labour displacement, customer distrust or political exposure?

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

The last question matters most. A productivity assumption with no failure test is a belief.

The management risk

The easiest mistake is to let the AI story become a ‘goal-seek’-style spreadsheet budget optimisation task:

A function wants lower cost growth.

AI will provide it.

A team wants lower headcount.

AI will provide it.

A strategy needs higher margins.

AI will provide them.

A debt forecast needs higher growth.

AI will provide it.

A rate-cut argument needs a non-inflationary supply shock.

AI will provide it.

The pattern is attractive because each claim may be partly true. That is how weak assumptions survive. They contain enough truth to avoid rejection.

A Board should make AI productivity compete with other capital-allocation claims. It should ask for evidence, timing, owners, costs and downside. It should ask whether the company is removing old processes or adding AI on top of them. It should ask whether the gain belongs to the company or to the vendor.

It should also ask what happens to trust.

Employees can use AI heavily while discounting its long-term value to them. They can improve output while fearing skill erosion. They can save time while seeing their own bargaining power weaken.

That is already part of the productivity equation.

Workforce trust is not a soft issue when the technology depends on the workforce to use, check, correct and integrate it.

The social contract risk

The social contract around AI is thin:

The benefits are described globally.

The costs are experienced locally.

Power demand is local.

Water use is local.

Job compression is local.

Entry-level career damage is personal.

Verification burden is inside teams.

The asset gains are often somewhere else.

That is why AI legitimacy can weaken even while AI adoption rises:

People can use a technology and resent the system built around it.

People can accept productivity and reject the distribution.

People can admire the tool and dislike the bargain.

For executives, this matters because public acceptance constrains execution. Planning approvals slow. Regulators ask harder questions. Employees resist quietly. Customers become more sensitive to errors. Narratives change.

Once that happens, the productivity calculation changes again:

Delay has cost.

Scrutiny has cost.

Trust loss has cost.

The AI business case needs to include these costs before they arrive.

The Weekend takeaway

AI may deliver a productivity boom, it may even let economies grow faster with less inflation pressure. It may also help companies reduce costs, improve service, speed up analysis and make better use of scarce expertise.

That is the optimistic case.

Yet, the:

The timing matters.

The ownership matters.

The measurement matters.

If the gains arrive broadly, wages can rise, margins can improve, debt becomes easier to carry, and lower rates may look justified.

If the gains arrive narrowly, the result is different: higher asset prices, weaker labour bargaining power, public debt eroded through inflation, and a population told the economy is improving while its personal economy gets harder.

The productivity miracle has to be earned before it is spent.

Final simplification

AI productivity has to pass three tests:

1. Did output rise?

2. Did the full cost fall?

3. Did the gain spread beyond the owners of the machine?

Leave a comment

🧠 Techie Corner for the Non-Techies

1. Productivity

We all know, productivity measures output per unit of input. For workers, the common measure is output per hour worked.

Yet, a company can look busier without becoming more productive. More emails, more reports and more dashboards do not prove productivity.

2. Unit labour cost

Unit labour cost is labour cost per unit of output. So, if wages rise and productivity rises at the same time, inflation pressure can be lower than wage growth alone suggests.

Example:

Wages rise 4%.

Productivity rises 3%.

Unit labour cost pressure is roughly 1%.

This is why productivity matters so much to central banks.

3. Potential growth

Potential growth is the speed at which an economy can grow without creating sustained inflation pressure.

If AI raises potential growth, the economy can grow faster with less inflation pressure.

The difficulty is measurement. Potential growth is estimated. It is not directly observed in real time.

4. Neutral rate

The neutral rate is the interest rate that neither stimulates nor restrains the economy.

Central banks estimate it. They do not know it precisely.

If productivity rises, the neutral rate may change. That makes policy harder because the central bank may be using an old map for a changed economy.

5. Financial repression

Financial repression occurs when inflation and low real interest rates reduce the real value of debt:

Borrowers benefit.

Savers lose.

Governments with high debt can benefit because the real burden of debt falls over time.

This can happen without a formal announcement. It can emerge from policy choices, inflation and political pressure.

6. K-shaped recovery

A K-shaped recovery is a recovery where some groups rise while others stagnate or fall.

In an AI economy, the upper branch may include asset owners, AI-heavy firms, large platforms and workers whose output rises and whose bargaining power holds.

The lower branch may include exposed salary earners, workers without assets, communities carrying infrastructure costs, and young people whose entry-level work is automated before they build skill.

7. Verification debt

Verification debt is the checking burden created by machine-generated output:

AI reduces the time required to produce a draft, analysis, code, contract or answer.

Someone still has to decide whether it is correct.

If an organisation generates faster than it can verify, the productivity gain turns into a review bottleneck.

📚 Further reading

• Morning Brew: End of an era: Warsh replaces Powell as Fed chair (2026)
  Dave Lozo’s briefing gave me the line that triggered this week’s piece: the comparison between Greenspan’s personal-computing productivity call in the 1990s and Warsh’s view that AI could create room for rate cuts without reigniting inflation.

• Federal Reserve: Alan Greenspan, The American Economy in a World Context (1999)
  Greenspan’s May 1999 speech is the best primary source for the late-1990s productivity argument. He connects faster productivity growth, lower unit labour cost pressure and restrained inflation.

• Federal Reserve: Alan Greenspan, Information, Productivity, and Capital Investment (1999)
  Greenspan’s October 1999 speech links information technology, capital investment and productivity directly. It shows how the Fed was thinking about computing, data, business processes and productivity during the personal-computing boom.

• The New York Times: What the New Head of the Federal Reserve Will Inherit (2026)
  Christine Zhang’s piece gives a clear overview of the Warsh transition, Powell’s unusual decision to remain as a governor, the Fed’s internal divisions, balance-sheet pressure and the Greenspan/AI productivity analogy.

• Reuters: Fed’s Goolsbee says rising productivity could restrain inflation or boost it (2026)
  Howard Schneider reports Austan Goolsbee’s warning that productivity gains can cut inflation through higher supply, but can also raise inflation if households and businesses spend today in anticipation of future gains. This is the strongest counterweight to the simple “AI productivity means lower rates” argument.

• Chicago Fed: Remarks at the Hoover Institution Monetary Policy Conference (2026)
  Goolsbee’s own remarks give the primary-source version of the argument. He discusses the Greenspan precedent, the late-1990s productivity surge, AI and the risk of monetary policy front-running productivity gains before they are fully realised.

• Brookings: Raising the Speed Limit: U.S. Economic Growth in the Information Age (2000)
  Dale Jorgenson and Kevin Stiroh provide the empirical backbone for the late-1990s productivity story. They show that US output growth accelerated sharply relative to the early 1990s, with information technology central to the growth resurgence.

• Federal Reserve Bank of New York: Information Technology and the U.S. Productivity Revival (2001)
  Kevin Stiroh’s industry-level work shows that the late-1990s productivity revival was linked to both IT production and IT use. Useful because it tests whether the productivity gains were broad enough to matter beyond a few technology producers.

• Fortune: Janet Yellen on her legacy, Trump’s Fed fight, Kevin Warsh, and women in economics (2026)
  Yellen’s interview is useful for the Fed-independence and fiscal-dominance angle. She discusses Warsh’s difficult starting position and the risk created when political pressure pushes the Fed toward lower rates and debt-service relief.

• Reuters Breakingviews: Vanishing Fed rate cuts could buy time for Warsh (2026)
  This piece written by Gabriel Rubin gives current market context. It argues that inflation pressure and fading rate-cut expectations may give Warsh time to reshape Fed communication and operations, rather than immediately deliver the lower rates President Trump wants.

Last Week on Something for the Weekend

Last week’s piece looked at the true cost of the “digital employee”.

The promise sounded simple: one AI subscription, $200 a month, replacing expensive human work.

The reality was more complicated.

Machine work is metered. It uses tokens, inference, Cloud, context, integration, supervision and verification. It can reduce some costs and create others.

That piece looked at the cost of running the (digital) copy.

This week looked at the macro version of the same mistake.

The economy may be doing something similar: treating AI productivity as cheap, certain and available before the full bill has arrived.

Two high-performing CEOs step down.

Not after failure.

Not under pressure.

Not at the end of their careers.

At The Coca-Cola Company, James Quincey exits after delivering sustained growth.

At Walmart, Doug McMillon reached the same conclusion.

The reasoning is explicit.

AI.

Not as a tool.

As a shift in what leadership requires.

Both arrive at the same judgement:

The next phase needs a different operating model at the top.

Not better leadership.

Different leadership.

Share The Business of Resilience

The Boardroom point

Boards are quietly redefining leadership risk.

Historically:

• Strategy

• Execution

• Market positioning

Now:

• Cognitive adaptability

The CEO role is shifting from:

decision-maker

to

decision system architect

The question is no longer:

“Can this CEO run the business?”

It is:

“Can this CEO run a business that is partially run by machines?”

Why this is happening now

Three structural forces are converging.

1) AI capability is advancing without a visible ceiling

Previous technologies had boundaries.

AI does not.

It encroaches on cognition itself.

That compresses adjustment time.

2) Organisations are deploying faster than they are learning

Enterprises are spending aggressively.

Yet:

• Only a minority of employees are trained

• Most usage is informal

• Adoption remains shallow

The constraint is no longer access.

It is capability.

3) Leadership bandwidth is becoming the bottleneck

AI compresses time:

• Faster analysis

• Faster iteration

• Faster decision cycles

The CEO must operate at that speed.

If not, the organisation slows to the pace of its leader.

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The deeper insight Boards rarely see

The constraint is not technology.

It is organisational learning velocity.

Most firms have already crossed a critical threshold:

AI capability > human integration capacity.

This creates a structural gap:

• Tools exist

• Value does not materialise

Not because the tools fail.

Because the organisation cannot absorb them.

The hidden transmission mechanism

The impact propagates through behaviour.

1) Individuals

Employees are already using AI.

But:

• Without structure.

• Without consistency.

• Without measurable outcomes.

2) Teams

Outputs diverge.

Quality varies.

Trust weakens.

AI amplifies inconsistency when not standardised.

3) Organisation

Without shared usage models:

• No compounding effect

• No institutional learning

• No scalable advantage

AI remains fragmented.

What this looks like in the real economy

At the edge, the structure is already shifting.

A single individual, augmented by AI, can:

• Build product.

• Run marketing.

• Operate infrastructure.

The “firm” is no longer defined by headcount.

It is defined by coordination capacity.

AI collapses that cost.

This is not theory.

It is emerging in real businesses.

The organisational breaking point

The failure is not at the edge.

It is at the core.

Companies are not struggling to deploy AI.

They are struggling to rewire how work happens.

Three frictions dominate:

• Legacy workflows remain intact.

• Incentives reward old behaviours.

• Management layers resist change.

AI is added on top.

Not built into the system.

Adoption stalls.

Why most AI strategies are structurally flawed

Most organisations optimise for the wrong variables.

They focus on:

• Tools.

• Vendors.

• Governance structures.

They ignore:

• Behaviour change.

• Decision velocity.

• Feedback loops.

This is why “centres of excellence” fail.

They concentrate knowledge.

They do not distribute capability.

The result:

Expertise exists.

Adoption does not.

Leave a comment

The Weekend takeaway

The visible story is AI adoption.

The real story is leadership redefinition.

The CEOs stepping down are not reacting to failure.

They are recognising a structural shift:

The role has changed faster than they can.

Most organisations have not yet internalised this.

They will.

Because the constraint is no longer access to technology.

It is the ability to lead in a system where intelligence is no longer scarce.

🧠 Techie Corner for the Non-Techies

Strip away the jargon.

Three technical dynamics explain what is actually changing.

1. AI is compressing the cost of cognition

Tasks that required time, expertise and iteration can now be executed near-instantly.

For example:

• drafting analysis;

• generating options;

• testing scenarios.

The constraint shifts from production to judgement.

2. AI improves through iteration, not instruction

Traditional software follows rules.

AI systems improve through cycles:

• generate output;

• receive feedback;

• adjust behaviour.

This creates compounding improvement.

The more it is used, the more capable it becomes.

3. Human-AI systems outperform either alone

AI is not replacing decision-making.

It is restructuring it.

Effective use requires:

• human framing;

• machine generation;

• human validation.

The advantage comes from the interaction.

Not just the tools.

4. Speed changes decision economics

When analysis becomes instantaneous:

• more scenarios can be tested;

• decisions can be revisited more frequently;

• strategies evolve continuously.

Annual planning cycles break.

Decision-making becomes dynamic.

5. The real constraint becomes integration

The limiting factor is no longer access to intelligence.

It is the ability to:

• embed it in workflows;

• trust its outputs;

• act on it consistently.

This is an organisational problem.

Not a technical one.

Final simplification

AI does not just automate work.

It accelerates thinking.

The constraint shifts from:

“Can we do this?”

to:

“Can we absorb this fast enough to act on it?”

📚 Further reading

1. Fast Company: Why Your Employees Aren’t Using the AI You Bought (2026)

   Enterprise AI investment is high but adoption remains low due to lack of practical training and embedded learning. The article argues that experiential, workflow-based practice, not passive instruction, is required to convert AI deployment into real business value.

2. Forbes: Will AI Destroy Jobs Or Create Them? (2026)

   The article presents both bullish and bearish cases: AI may drive entrepreneurship and job creation while simultaneously enabling labour reduction and efficiency gains. It concludes the real shift is structural, more firms with fewer employees, rather than a simple net gain or loss of jobs.

3. Forbes: The AI Era Is Changing What It Means To Be A CEO (2026)

   AI is redefining the CEO role into one requiring continuous learning, direct ownership of AI strategy and business model reinvention. It emphasises unlearning legacy assumptions and leading organisations through workforce and decision transformation alongside AI.

4. Fortune: The One-Person Unicorn: Myth, Miracle, or the Future of Startups? (2026)

   The article examines claims of AI-enabled “one-person companies,” highlighting a founder using AI agents and external partners to run operations at scale. It questions the sustainability of such models while showing how AI reduces coordination costs and reshapes firm boundaries.

5. CNBC: Major Outgoing CEOs Are Citing AI as a Factor in Their Decisions to Step Down (2026)

   Documents how CEOs, including those at Coca-Cola and Walmart, explicitly link leadership transition decisions to the accelerating impact of AI, signalling a shift in the capabilities required at the top of large organisations.

6. TheStreet: Coca-Cola CEO divulges surprising reason behind decision to step down (2026)

   James Quincey links his decision to step down to the next AI-driven transformation phase, arguing a different type of leader is needed. The article frames this as a strategic handoff, not performance-driven turnover, with AI reshaping leadership requirements.

7. CNBC: Morgan Stanley wealth advisors are about to get an OpenAI-powered assistant to do their grunt work(2024)

   Morgan Stanley deployed GPT-4-powered assistants (e.g. Debrief) embedded directly into advisors’ workflows, automating meeting notes, summaries and follow-ups. The case illustrates that adoption succeeds when AI is integrated at the point of work, augmenting rather than replacing human roles.

8. Business Insider: PwC hosts ‘prompting parties’ to help employees learn AI (2025)

   PwC is scaling AI adoption through hands-on “prompting parties,” where employees experiment with AI tools in low-risk, peer-led environments. The approach emphasises practical, collaborative learning to close the gap between training and real usage.

9. Google Cloud Blog: Google Cloud announces first-of-its-kind generative AI leader certification (2025)

   Google Cloud launched a generative AI certification aimed at business professionals across functions, including sales, to build practical AI capability. The program reflects the need for broad, role-specific AI literacy to drive adoption beyond technical teams.

10. IBM Institute for Business Value: IBM CEO Study - CEOs Double Down on AI While Navigating Enterprise Hurdles (2025)

    IBM’s global CEO study finds ~31% of the workforce will require reskilling within three years due to AI adoption. It highlights talent, skills gaps and organisational readiness, not technology, as the primary constraints on scaling AI value.

11. Cornell SC Johnson College of Business: Adopting AI Means Adapting Business Models (2025)

    AI enables faster, data-driven decision-making and the scaling of expertise across organisations, but most firms fail to realise this due to superficial adoption. The article argues real impact comes from redesigning decision processes and business models, not just deploying tools.

12. Citrini Research & Alap Shah: The 2028 Global Intelligence Crisis (2026)

    A forward-looking scenario written from 2028 describing how successful AI adoption could trigger a negative feedback loop of job losses, falling consumption and economic contraction. The piece is explicitly framed as a thought experiment to highlight systemic risks if adaptation lags technological capability.

Last Week on Something for the Weekend

Last week’s article examined a structural weakness that sits beneath most digital systems: the assumption that authenticated identity equals legitimate action.

That assumption is degrading. Identity is no longer a deterministic control, but a probabilistic signal, undermined by AI-driven execution, contextual ambiguity in machine instructions, and the emerging fragility of traditional cryptography. Systems are not being breached in the traditional sense; they are executing correctly on incomplete or flawed inputs.

The consequence is a new failure mode: correct execution on incorrect assumptions, at machine speed. As autonomous agents act on authenticated signals without validating intent, errors propagate faster, controls weaken, and the boundary between cyber risk, operational failure and governance breakdown begins to blur.

A few days ago, Charles H. Bennett and Gilles Brassard were awarded the Turing Award.

Their work dates back to 1984. At the time, it was theoretical.

Today, it reads as prescient.

They did not just propose a new form of encryption.

They questioned the foundation on which digital trust is built.

That foundation is now under pressure:

Not because systems are failing, but because the assumptions underpinning them are.

The Hidden Assumption

Most enterprise systems rely on a simple premise:

If identity is verified, action is legitimate.

This assumption sits everywhere:

• Payment systems.

• Identity and access management.

• API authentication.

• Supply chain integrations.

• Internal controls.

This model worked when:

• identities were stable;

• credentials were hard to forge;

• systems operated at human speed.

None of those conditions fully hold anymore.

From Identity to Signal

Identity is not disappearing:

It is degrading.

It is becoming a probabilistic signal rather than a deterministic control.

Three forces drive this shift.

1. AI-Driven Execution

Agents act on authenticated inputs.

They do not question them.

They optimise against them.

If an instruction is authenticated, it is treated as valid context.

That is sufficient for execution, not for correctness.

2. Context Failure

Agents do not “hack” systems in the traditional sense.

They follow instructions.

But instructions are expressed in natural language.

So, ambiguity is structural.

If context is incomplete:

• constraints are inferred;

• priorities are guessed;

• boundaries are approximated.

The system behaves correctly relative to its objective.

Incorrectly relative to the organisation.

3. Cryptographic Fragility

Current encryption relies on mathematical difficulty.

Quantum computing challenges that assumption.

Not necessarily today.

But within the lifecycle of most enterprise systems.

The issue is not only decryption, it is trust erosion.

If credentials can be forged or replayed:

Authentication becomes a weak signal.

The Bennett–Brassard Inflection

Bennett and Brassard approached the problem differently. Their model does not rely on computational hardness.

It relies on physics.

Quantum key distribution ensures that:

Any attempt to intercept or copy a key alters its state.

Detection is embedded in the system.

This is not just stronger encryption, it is a different trust architecture.

One where:

Trust is not assumed.

It is continuously validated by the properties of the system itself.

The significance is structural.

They anticipated a world where:

Where Systems Break

Failures are not random.

They have a pattern.

1. Authentication Without Context

An agent receives:

• valid credentials;

• a legitimate request;

• incomplete constraints.

It executes.

The outcome is compliant with access rights.

But violates policy intent.

2. Authority Without Friction

Agents compress decision cycles.

They:

• decide;

• act;

• iterate.

Without intermediate checks.

Friction is removed.

Control disappears with it.

3. Trust Chain Amplification

In multi-agent systems:

• one agent authenticates input;

• another executes;

• another validates.

If the initial signal is flawed:

The error propagates.

At machine speed.

4. False Confidence

Organisations report high preparedness.

Yet:

• agents access unauthorised resources;

• credentials are exposed;

• data is exfiltrated through reasoning.

The gap is not capability.

It is understanding.

What This Means for Control

Security models built around access control are insufficient.

The system must evolve.

From Identity to Behaviour

Do not assume:

“authenticated = trusted”

Instead:

• validate behaviour continuously;

• monitor execution patterns;

• detect deviation from expected pathways.

From Permission to Outcome

Access does not define risk, outcomes do.

Define:

• acceptable result ranges

• transaction boundaries

• exposure limits

Block:

valid actions that produce invalid outcomes.

From Static to Adaptive Cryptography

Cryptography must become:

• replaceable

• upgradeable

• layered

Design systems where:

cryptographic primitives can change without redesign.

From Trust to Verification

Assume inputs are adversarial, even if authenticated.

Introduce:

• independent validation layers

• deterministic policy engines

• separation between reasoning and execution

The CFO Question

This is not a technology issue, it is a control issue.

For CFOs, this translates into:

• internal control integrity risk;

• financial incorrect execution risk;

• disclosure risk;

• insurance classification ambiguity.

If an authenticated system executes an incorrect action, is that:

• a cyber event?

• an operational failure?

• a governance breach?

The answer is unclear.

That ambiguity will surface in earnings.

The Board Question

Boards should reframe the problem.

Not:

“Are our systems secure?”

But:

“Is identity still a reliable control mechanism?”

And more specifically:

• What decisions are executed solely on authenticated inputs?

• Where do agents act without contextual validation?

• What is the maximum loss from a correctly authenticated but incorrect action?

• How quickly can trust assumptions be revoked across systems?

• Are we designing for cryptographic change or assuming stability?

If identity degrades, control frameworks must adapt.

The Structural Shift

Two forces are converging:

1. Systems that act autonomously.

2. Trust mechanisms that are weakening.

Individually, each is manageable, yet, together, they redefine risk.

You are:

• increasing the number of decision-makers;

• reducing the reliability of trust signals.

That combination creates a new failure mode:

Correct execution on incorrect assumptions.

The Deeper Strategic Question

There is a tendency to frame this as cybersecurity.

That is incomplete.

This is a question of:

How authority is delegated.

How trust is established.

How decisions are verified.

Bennett and Brassard showed that trust can be re-architected.

Agentic systems show that decisions can be decentralised.

The challenge is aligning the two.

Without that alignment:

You do not lose security.

You lose control.

The Weekend Takeaway

Authentication is no longer a sufficient condition for trust.

In a world of:

• probabilistic decision systems;

• machine-speed execution;

• evolving cryptographic assumptions;

…identity becomes a signal.

Not a guarantee.

The task is not to strengthen that signal alone, it is to design systems that remain controlled when the signal degrades.

Because your systems are not failing.

They are executing, as designed.

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🧠 Techie Corner for the Non-Techies

Strip away the jargon.

Three technical shifts explain why authentication is weakening.

1. Why Current Encryption Works (and Why That Changes)

Most modern encryption relies on problems that are easy to compute in one direction but extremely difficult to reverse.

For example:

• multiplying two large prime numbers is easy;

• factoring the result back into primes is hard.

Classical computers struggle with the reverse step.

Quantum computers do not follow the same constraints.

Certain algorithms allow them to solve these problems much faster.

That does not mean all encryption breaks overnight.

It means the assumption of long-term hardness weakens.

Data encrypted today may be vulnerable tomorrow.

2. What Quantum Cryptography Does Differently

Bennett and Brassard’s approach (BB84) uses quantum physics rather than mathematics.

It relies on a property of particles:

Measuring them changes their state.

In practice:

• a sender transmits a key using quantum states;

• any interception alters those states;

• the receiver detects that alteration.

This allows two parties to know if a key has been compromised.

It is not “hard to break.”

It is impossible to copy without detection.

3. Why Agents Amplify the Problem

Traditional systems wait for human action.

Agents do not.

They:

• read inputs;

• interpret them;

• execute actions.

If an input is authenticated, the agent assumes it is valid.

There is no inherent scepticism.

This creates a gap:

• authentication verifies who sent the message;

• it does not verify whether the message should be executed.

When agents operate at scale:

• small errors propagate quickly;

• incorrect instructions are executed consistently;

• recovery becomes harder.

4. The Difference Between Identity and Intent

Authentication answers:

“Did this come from a valid source?”

It does not answer:

“Should this action happen?”

That second question requires:

• context;

• constraints;

• policy validation.

Most systems are strong on identity.

They are weaker on intent validation.

That imbalance is now material.

5. Why “Zero Trust” Is Not Enough

Zero Trust architectures assume:

• no entity is trusted by default;

• every request is verified.

This improves identity validation.

But it still operates at the level of access.

Agents require an additional layer:

• validation of what is being done, not just who is doing it.

That is the shift from access control to outcome control.

Final Simplification

Encryption secures communication.

Authentication verifies identity.

Yet, neither guarantees that an action is appropriate.

When machines act automatically, that distinction matters.

📚 Further reading

1. Association for Computing Machinery (ACM): ACM A.M. Turing Award Honors Charles H. Bennett and Gilles Brassard for Foundational Contributions to Quantum Information Science (2026)
   Breakthroughs in Quantum Cryptography and Quantum Teleportation Redefined Secure Communication and Computing (BB84 protocol and its foundational role in quantum cryptography as a new paradigm for secure communication).

2. National Institute of Standards and Technology (NIST): Post-Quantum Cryptography Standardisation Project (2024–2026)
   Global effort to develop and standardise cryptographic algorithms resistant to quantum computing attacks.

3. IBM Research: Quantum Safe Cryptography and Migration Strategies (2025)
   Analysis of enterprise exposure to quantum threats and practical approaches to crypto-agility and transition planning.

4. Palo Alto Networks: Agentic AI Security: What It Is and How to Do It (2025)
   AI security as securing agents’ reasoning, memory, tool use, actions, and interactions so autonomy does not create new attack paths. The guidance emphasises visibility, access control, data protection, runtime monitoring, and end-to-end security across the AI lifecycle.

5. OWASP Foundation: OWASP Top 10 for Agentic Applications for 2026 (2025)
   This is OWASP’s peer-reviewed framework for the most critical security risks facing autonomous and agentic AI systems. It identifies risks such as Agent Goal Hijack, Tool Misuse, Identity & Privilege Abuse, memory/context poisoning, insecure inter-agent communication, cascading failures, and rogue-agent behaviour.

6. Cybersecurity & Infrastructure Security Agency (CISA): Zero Trust Maturity Model Version 2.0 (2023)
   CISA’s model provides a framework for progressing from perimeter-based security toward zero-trust architectures built around continuous verification, least privilege, and stronger identity, device, network, application, and data controls. It is a maturity model for implementation, not just a conceptual statement of principles.

7. Armis Labs: A World Under Pressure: Cyberwarfare in an Age of AI-Fuelled Escalation (2026)
   Armis’s 2026 cyberwarfare report argues that AI acceleration, geopolitical tension, and unresolved security gaps are increasing cyberwarfare risk while exposing a gap between perceived readiness and actual resilience. The report highlights concern over AI-enabled attacks, rising ransomware costs, and what Armis describes as a widening disconnect between confidence and preparedness.

8. McKinsey & Company: Deploying agentic AI with safety and security: A playbook for technology leaders (2025)
   Guidance on governance, control frameworks and risk management for enterprise deployment of autonomous AI systems.

Last Week on Something for the Weekend

Last week’s article examined a different, but related, failure mode in the adoption of AI. Not systems acting incorrectly. But humans gradually stepping back from the act of thinking itself.

As AI becomes embedded in executive workflows, decision-making begins to shift:

From reasoning,

to reviewing.

From judgement,

to approval.

This is not a sudden change. It is a gradual reallocation of cognitive effort. Over time, the capability that is not exercised begins to weaken.

The takeaway was simple.

The risk is not that AI replaces human judgement.

It is that human judgement quietly atrophies as reliance on AI increases.

Over the past two years, artificial intelligence has entered the executive suite with remarkable speed.

The technology was initially marketed as an efficiency tool.

A faster way to summarise information, analyse data, or draft documents.

But something more consequential appears to be happening.

Increasingly, executives are not simply using AI.

They are deferring to it.

Recent research commissioned by Confluent and conducted by the market research firm 3Gem surveyed 200 UK business leaders: owners, founders, CEOs and other C-suite executives.

The findings are striking.

62% report using AI to make the majority of their decisions.

70% say they second-guess their own judgement when it conflicts with AI recommendations.

46% now rely more on AI than on the advice of colleagues.

Decision-making, according to 65% of those surveyed, has become less collaborative since AI entered the boardroom.

The technology has quietly acquired a seat at the table.

And increasingly, it is speaking last.

The Automation Paradox

This development fits into a well-known pattern in the history of automation.

When machines take over routine tasks, human capability can erode.

The phenomenon has been documented repeatedly:

• airline pilots losing manual flying skills due to autopilot reliance;

• drivers becoming inattentive when using advanced driver-assistance systems;

• GPS reducing human navigation abilities;

• calculators weakening mental arithmetic.

The paradox is simple.

Automation improves efficiency.

But by removing routine practice, it also erodes the very skills humans need when systems fail.

The same dynamic now appears to be emerging in cognitive work.

A recent study by Carnegie Mellon University and Microsoft surveyed more than 300 knowledge workers and analysed over 900 examples of AI use in professional tasks.

The conclusion was uncomfortable.

Workers who trusted AI more engaged in significantly less critical thinking.

Their role shifted from solving problems to passively supervising machine output.

In routine tasks especially, users often stopped interrogating the system’s answers altogether.

Over time, this produces what some researchers call cognitive offloading.

The machine thinks.

The human oversees.

Until eventually the human stops thinking very much at all.

The Rise of Cognitive Debt

Danish psychiatrist Søren Dinesen Østergaard has given this phenomenon a name:

Just as financial debt accumulates when spending exceeds income, cognitive debt accumulates when intellectual effort is persistently outsourced to machines.

Reasoning ability is not innate.

It is a trained capability.

It develops through repetition, argument, analysis, writing, and problem solving.

If those activities disappear, the underlying capability weakens.

Østergaard worries that the long-term consequence may be generational.

Future scientists, engineers, and executives may simply have fewer opportunities to develop the intellectual discipline required for breakthrough thinking.

The next Demis Hassabis or John Jumper, whose AI system AlphaFold revolutionised protein folding, may not emerge in an environment where the intellectual work that trains such minds has already been automated.

The Boardroom Version

The corporate implications are already visible.

Executives are under intense pressure to make decisions faster than ever.

According to the Confluent research:

92% of leaders say decision speed has increased significantly in the past three years.

In that environment, AI appears attractive.

It provides quick answers.

It offers the appearance of neutrality.

It reduces stress.

But something subtle is changing in the psychology of leadership.

When 70% of executives report second-guessing themselves if their judgement conflicts with AI, authority has quietly shifted.

The executive is no longer the decision-maker.

They are increasingly the approver of machine recommendations.

This may feel efficient.

But it raises an important question.

If the machine’s recommendation becomes the default, what exactly is the human contribution?

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The Tesla Lesson

The risks of automation complacency are already visible in other domains.

Consider the growing number of incidents involving Tesla’s “Full Self-Driving” system.

Drivers are told that the system requires constant supervision.

Yet time and again the same pattern appears.

The automation performs well enough most of the time that drivers become complacent.

Attention drifts.

When the system fails, the human is too disengaged to intervene.

The automation paradox strikes again.

The same psychological mechanism may be emerging in the boardroom.

If executives increasingly defer to AI recommendations, they may gradually lose the habit of independent judgement.

And when the system makes a mistake, as all systems inevitably do, the organisation may discover that the human oversight layer has quietly atrophied.

Human-in-the-Loop Is Not Enough

The current policy answer to this problem is often framed as Human-in-the-Loop (HITL).

In theory, this means AI systems provide recommendations but humans retain ultimate decision authority.

In practice, however, the research suggests something more complex is happening.

Humans may remain formally in the loop.

But psychologically they drift out of the loop.

When a system consistently provides confident recommendations, people tend to defer to it.

Especially under time pressure.

Human-in-the-loop can therefore degrade into something closer to Human-on-the-Loop.

And eventually into Human-after-the-fact.

The Infrastructure Argument

Interestingly, the Confluent study itself reaches a different conclusion.

The authors argue that the real problem is not AI reliance.

It is poor data infrastructure.

Executives often rely on instinct or AI because they lack timely, reliable information.

The proposed solution is real-time data streaming systems capable of feeding both humans and machines with continuously updated information.

There is merit in that argument.

Better data almost always improves decision-making.

But it does not eliminate the deeper issue.

Even with perfect data, judgement still matters.

And judgement is a capability that deteriorates without use.

The Strategic Question

The deeper question for companies is therefore not technological.

It is institutional.

How do organisations adopt AI without slowly eroding the decision capabilities of the people running them?

Some industries have confronted similar problems before.

Aviation, for example, now requires pilots to regularly fly aircraft manually to maintain skill levels.

Military command structures deliberately rotate officers through operational roles to prevent strategic detachment.

The lesson is simple.

Capabilities that are not exercised do not remain intact.

What Boards Should Be Asking

As AI becomes embedded in executive workflows, boards should begin asking a new set of questions.

Not about technology.

But about judgement.

For example:

Where are AI recommendations currently influencing major decisions?

Which decisions must always involve independent human reasoning before AI input is considered?

How often do senior executives make decisions without algorithmic assistance?

What processes ensure that AI outputs are actively challenged rather than passively accepted?

And perhaps most importantly:

Are we building organisations that think faster,… or organisations that think less?

The Weekend Takeaway

Artificial intelligence is an extraordinary tool.

Used well, it will make organisations more capable, not less.

But every powerful technology carries unintended consequences.

The emerging evidence suggests one of those consequences may be cognitive outsourcing.

The risk is not that AI replaces executives.

The risk is that executives gradually stop exercising the very capabilities that made them valuable in the first place.

Machines are improving at pattern recognition.

But strategy, judgement, and responsibility remain human functions.

For now.

The companies that thrive in the age of AI may therefore not be the ones that automate the most thinking.

They will be the ones that remember which thinking must never be automated.

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🧠 Techie Corner for the Non-Techies

To understand what is happening in the boardroom right now, it helps to understand a few technical ideas that sit behind modern AI systems.

None of them are complicated.

But together they explain why executives are increasingly tempted to let machines “think” for them.

1. Large Language Models are Pattern Engines, Not Reasoning Engines

Most AI tools used in business today (e.g. ChatGPT, Claude, Gemini, Copilot, Mistral), are built on what are called Large Language Models (LLMs).

They work by analysing vast amounts of text and learning statistical patterns in language.

When you ask them a question, they do not actually “think”.

Instead they generate the most statistically probable sequence of words given the prompt and the training data.

This is extremely powerful for tasks like:

• summarising documents

• drafting reports

• generating code

• analysing structured information

But it also explains two important limitations.

First, LLMs do not truly understand the world.

Second, they are capable of producing answers that sound extremely confident even when they are wrong.

This is why researchers refer to AI errors as hallucinations.

The system is not lying.

It is simply generating plausible language.

2. AI Confidence is Psychological, Not Mathematical

Humans are extremely sensitive to confidence signals.

When a colleague expresses an opinion forcefully, we tend to assume they know what they are talking about.

AI systems produce answers in exactly this style.

Clear.

Structured.

Authoritative.

But the system is not expressing confidence in a statistical sense.

It is merely producing language that sounds confident.

That makes AI unusually persuasive.

In experiments, people often defer to AI recommendations even when they are wrong.

This phenomenon is called automation bias.

3. Automation Bias Is Why Humans Stop Challenging Machines

Automation bias occurs when humans trust an automated system more than their own judgement.

It has already been documented in many fields:

• aviation autopilot systems

• medical decision-support tools

• autonomous driving

• military command systems

Once a system performs well most of the time, people gradually stop questioning it.

Attention drops.

Humans begin to act as passive supervisors rather than active decision-makers.

When the system fails, the human oversight layer is often too disengaged to intervene.

4. Cognitive Offloading: Letting the Machine Do the Thinking

Another concept researchers are studying is cognitive offloading.

This simply means moving mental tasks from the human brain to an external tool.

We already do this constantly:

• calculators for arithmetic

• GPS for navigation

• search engines for memory

AI expands this idea into higher-level cognitive tasks.

Instead of outsourcing calculation or navigation, people are now outsourcing:

• analysis

• writing

• strategic thinking

• problem solving

That is a much bigger shift.

Because those activities are how humans maintain and strengthen reasoning ability.

5. Why This Creates “Cognitive Debt”

If you stop exercising a muscle, it weakens.

The same principle applies to intellectual skills.

Researchers have begun describing the result as cognitive debt.

Each time a person lets the machine do the thinking, they save effort in the short term.

But over time they may lose the mental capability required to solve problems independently.

It is similar to what happened in aviation.

When aircraft became heavily automated, pilots lost some manual flying skills.

Airlines now require pilots to regularly fly aircraft manually to maintain competence.

The same lesson may soon apply to knowledge work.

6. Human-in-the-Loop Only Works If the Human Is Actually Thinking

Many AI systems are designed with what engineers call Human-in-the-Loop oversight.

The idea is simple:

The machine makes a recommendation.

The human makes the final decision.

If the human simply approves the recommendation without thinking, the safeguard disappears.

And the system effectively becomes machine decision-making with human rubber-stamping.

The Bottom Line

AI is becoming extraordinarily capable.

But its greatest risk may not be technological.

It may be psychological.

Machines are not replacing human judgement.

They are quietly tempting humans to stop exercising it.

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📚 Further reading

1. Microsoft Research & Carnegie Mellon University: The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers (2025)
   Survey of 319 knowledge workers showing that higher trust in generative AI is associated with reduced critical thinking effort and increased cognitive offloading.

2. Confluent & 3Gem Research: Quick Thinking - Striking the Balance Between Instinct and Insight (2024)
   Research based on surveys of UK CEOs and C-suite executives analysing how time pressure, data availability and intuition shape executive decision-making.

3. Confluent & 3Gem Research: Quick Thinking 2.0 - Balancing AI, Instinct and Insight (2026)
   Updated survey of 200 UK business leaders showing rising reliance on AI for executive decision-making and highlighting the growing role of real-time data infrastructure in boardroom decisions.

4. SAE International: Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles (SAE J3016) (2021)
   Industry standard defining levels of driving automation and clarifying that systems such as Tesla’s “Full Self-Driving” remain driver-supervised technologies requiring continuous human oversight.

5. The Register: Supposedly Big-Brained Execs Are Outsourcing Decisionmaking to AI (2026)
   Technology news coverage summarising the Confluent/3Gem survey results and discussing the implications of growing AI reliance in executive decision processes.

6. ACM CHI Conference on Human Factors in Computing Systems: The Impact of Generative AI on Critical Thinking(2025)
   Empirical study demonstrating that routine use of generative AI correlates with lower cognitive engagement and reduced analytical effort among users.

7. arXiv: System 0: Transforming Artificial Intelligence into a Cognitive Extension (2025)
   Proposes a framework in which AI functions as an external cognitive layer that reshapes human reasoning and decision processes.

8. arXiv: Understanding Critical Thinking in Generative Artificial Intelligence Use: Development and Validation of the Critical Thinking in AI Use Scale (2025)
   Introduces a validated measurement scale for assessing how users verify and critically evaluate AI-generated outputs.

9. arXiv: “It Makes You Think”: Provocations Help Restore Critical Thinking to AI-Assisted Knowledge Work (2025)
   Experimental research showing that introducing “provocations” or challenges to AI output can restore critical reasoning in AI-assisted tasks.

10. arXiv: AI, Metacognition, and the Verification Bottleneck: A Longitudinal Study of Human Problem-Solving (2026)
    Longitudinal research indicating that as AI becomes integrated into workflows, verification of AI outputs becomes the central bottleneck in human-AI decision systems.

Last Week on Something for the Weekend

Last week’s article examined a structural reality in the AI safety debate.

Many early frameworks assumed that developers would voluntarily slow progress if model capabilities outpaced available safeguards.

But in competitive technology races, incentives rarely remain aligned.

Once one actor accelerates, restraint becomes a disadvantage.

The result is a familiar dynamic seen in cyber risk, financial markets, and industrial competition: systems optimise around incentives, not intentions.

The takeaway was simple.

In high-speed technology races, voluntary safety rarely survives competitive pressure.

Read here: 👇

We tend to imagine the big heroes of the natural world when we talk about climate change: forests, whales, penguins, and glaciers. But sometimes, the real powerhouses are so tiny you’d never notice them.

Take the humble copepod - a creature barely a few millimetres long, often sold as fish food. According to new research published in Limnology and Oceanography, these minuscule animals are performing one of the planet’s most important climate services, and doing it with no fanfare whatsoever.

How a pebble moves a mountain

The study shows that zooplankton gorge themselves in the spring, turning phytoplankton into fat reserves. Then, they sink hundreds of metres into the Antarctic deep, where they burn that fat slowly over the winter. In doing so, they lock carbon away in the depths of the ocean for decades or even centuries.

The numbers are staggering: this “seasonal vertical migration pump” stores the equivalent of the annual emissions from 55 million cars. Yes, you read that right. Something smaller than your fingernail is keeping entire nations’ worth of carbon out of the atmosphere.

Who said you can move a mountain by moving a pebble at a time? These creatures prove it.

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Unsung heroes of the ocean

Most of us never think about zooplankton. If we’ve heard of them at all, it’s as a line in a nature documentary or a tub of dried fish food. But they may deserve as much credit as any headline-grabbing species. Without them, the atmosphere would already be significantly warmer.

It’s a reminder that solutions aren’t always flashy. Sometimes, the most critical work is quiet, repetitive, and almost invisible. A million tiny efforts, multiplied together, tip the balance of the planet.

The threats they face

Of course, nothing is guaranteed. Warming waters, changing ocean layers, and even commercial krill harvesting could threaten the future of these migrations. Scientists warn that if zooplankton numbers decline, so too will the carbon they lock away.

Which begs the question: what else are we overlooking? How many more invisible allies do we depend on without even knowing it?

A lesson for us humans

For me, the lesson is simple. Impact doesn’t always come from size or visibility. It can come from persistence, from collective action, from doing your part quietly but consistently.

The copepods of Antarctica don’t seek recognition. They just do their work, cycle after cycle, and in doing so, they reshape the climate story for all of us.

Maybe the same is true for us. Small actions, multiplied across communities, businesses, and nations, add up. One pebble at a time, the mountain moves.

Further reading: Georgina Rannard’s article, Tiny creatures gorge, get fat, and help fight global warming (BBC News, 5 July 2025), summarises the research led by Dr Guang Yang (Chinese Academy of Sciences) and colleagues, recently published in Limnology and Oceanography.

✨ Question: Do you think we undervalue the “quiet work”, both in nature and in our own lives, compared to the big, showy gestures?

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