Briefings for boards, a selective reading list on AI risk, short Quick Reads, essays on risk, uncertainty and AI, and guidance and reports I wrote for the OECD and the Institute of Risk Management. Search it all in your own words, or browse by kind or topic.
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For board chairs and directors: what the prices of four companies assume (Anthropic, SpaceX, Wayve and Amazon), what that means for organisations that build on AI suppliers, and three questions for your next board meeting.
There are many flavours of risk. Some we understand perfectly well. Leave the ice cream out of the freezer overnight and we know what we will find in the morning. We teach our children to look both ways when they cross the road, even on a one-way street (dangers of Lime bikes!) Banks employ specialists to manage the mismatch between assets and liabilities, because they know exactly what happens when they don’t.
But sometimes we simply do not know. The old cartographers had an honesty about how they dealt with this. Where their knowledge ran out, they wrote “Here Be Dragons”. Gradually knowledge pushed the dragons off the map, and we learnt that we could sail around the world without meeting one (unless we went to Komodo).
Read the essayAI-assisted illustration by Richard Anderson using Midjourney and ChatGPT
In an earlier article, I borrowed the warning sometimes attributed to old cartographers: “Here Be Dragons.” John Adams calls risks of this kind virtual: matters where knowledge is lacking, or experts cannot agree. The dragons did not need to exist for the uncertainty beyond the map to influence how people sailed.
Perhaps the modern map should say: “Here Be Aliens.” There is a small joke hiding in the word, but also a serious question. We cannot know everything AI will make possible, or what an organisation might become as it builds AI into its decisions. Like the dragons, our aliens give shape to something we do not yet understand.
Read the essayAI-assisted illustration by Richard Anderson using ChatGPT
In 2000, Lucent and Nortel were lending their customers the money to buy their equipment. Telecoms start-ups borrowed from the kit makers, bought the kit, and the kit makers booked the sales. For a while it looked like demand.
Read the essayAI-assisted illustration by Richard Anderson using ChatGPT
What Has the Board Actually Agreed? · 19 October 2026
A board approves an AI initiative. Someone asks whether it is consistent with the organisation’s risk appetite. The answer is reassuring: the firm has a low appetite for AI risk, human oversight will remain in place, and the usual controls will apply.
Read the essayAI-assisted illustration by Richard Anderson using ChatGPT
Reading Anthropic’s risk factors · 3 November 2026
Anthropic’s prospectus gives 94 pages to its risk factors, written as a list. One of them warns that the risks are interconnected, and that one can set off another with a combined effect greater than the sum of the parts. Read with that in mind, the filing reveals three chains: contractual contagion, a common shock from government, and uncertainty spreading through an industry. Each can defeat controls designed around individual risks, and each could reach the firms that use Anthropic’s models.
Bubble or bust is the AI question of the month. There is a comforting answer to it. Even if it overshoots, something valuable will be left behind, the way the railways survived the mania and the internet survived the crash. That answer assumes tomorrow’s AI will be a larger version of today’s, financed on a path that simply continues. I think the more dangerous assumption is the straight line itself. The assets securing much of today’s AI debt are GPUs and data centres financed on the premise that today’s giant models will still be the dominant architecture when the debt matures. If a cheaper, smaller, more specialised architecture wins instead, the technology succeeds and the collateral fails at the same moment. That is not a demand problem. It is a straight line drawn through a bend.
The industry measures progress along a single line: bigger model, more knowledge, another benchmark passed, another straight extension of the same ladder. Every serious institution I have worked in knows that intelligence does not actually work that way. We do not hand one individual origination, risk judgement and final sign-off and call it efficient. We call it a control failure waiting to happen. Yet the dominant model of artificial intelligence asks one system to propose, reason, calculate and certify its own answer, and calls the final pass self-critique. I think the more interesting question is not how much a model knows. It is whether the architecture around it resembles a governed institution or an unaccountable one.
The division of labour is so embedded in modern life that we barely notice it. Yet the dominant ambition in artificial intelligence is moving in the opposite direction. We have begun to talk about artificial intelligence as though intelligence itself were a single commodity. Intelligence may be less like a ladder and more like an economy. The division of labour allowed us to move beyond the farmer who had to become a warrior. The division of intelligence may allow us to move beyond the idea that one machine must know everything.
Reported figures from Anthropic’s prospectus point to a valuation of about $2 trillion. Rather than arguing about whether that number is right, this essay works backwards from it: what would have to be believed about growth, margins and how long they last for the price to make sense? The answer depends on a long, uninterrupted line of growth, the very line my Straight Line Fallacy essays question. The bend is not a forecast, but it is the possibility the price ignores, and it raises a further question: how much of that risk passes to the new shareholders who buy in at the top of the line?
SpaceX listed in June at $1.77 trillion and is now worth about $2.3 trillion. Running the same reverse valuation I used on Anthropic, my model can account for about a ninth of that. The two cases turn out to be different in kind. Anthropic’s price requires extreme assumptions about a business we can see. SpaceX’s price requires businesses we cannot yet see. At $2.3 trillion a shareholder is no longer paying for the company SpaceX is. The question is how much of the future you are willing to pay for before it arrives.
Wayve, the British self-driving company, raised money in February at $8.6 billion. I ran the same reverse valuation I used on Anthropic and SpaceX. For the first time in this series, the answer is a number I can picture. It suggests that AI can be built in bite-sized chunks, for small beer beside the sums the frontier consumes. What I cannot model is whether people will let go of the wheel.
Price, Patience and Survival · 13 October 2026 · Figures as at 9 October 2026
Amazon is the obvious stress test for what I have written about Anthropic and SpaceX. So I ran the same reverse valuation on Amazon three times: at its IPO in 1997, at the dot-com peak in 1999, and today. In the past the business has grown into its prices. What that took is the part people forget.
Three series written between January and May 2026 on why boards keep being surprised, and what it takes to see risk while there is still time to act on it.
Series 1: Risk Intelligence12 essays, January to March 2026
Across large organisations, risk management activity has grown relentlessly. Boards receive thicker packs, more dashboards, more formal attestations, and more assurance mapping than at any time in memory. Risk committees are busy. Control frameworks are mature.
When organisations talk about trust failure, they usually reach for ethical language. They speak of values, integrity, tone from the top, and individual wrongdoing. Investigations focus on who knew what, who should have acted, and where accountability lies. Training programmes are refreshed.
Artificial intelligence is being presented as the next great solution to risk management. Vendors promise earlier detection, better prediction, continuous monitoring, and objective insight.
Modern risk management is built around a powerful assumption: that the primary task of governance is control. Risks are to be identified, categorised, mitigated, and monitored. Deviations are to be corrected. Assurance is to be obtained.
The Three Lines Model has become one of the most widely adopted organising frameworks in modern governance. Boards recognise it. Regulators reference it. Assurance functions are designed around it.
After every major failure, the regulatory response follows a familiar pattern. Controls are strengthened. Reporting requirements expand. Supervisory expectations are clarified. Assurance thresholds rise. From the outside, this looks like learning. The system appears to be correcting itself.
Most boards believe they have a clear view of their organisation’s risk appetite. There is usually a statement approved annually, often supported by metrics, tolerances, and heat maps. It is discussed seriously, minuted carefully, and referenced in governance papers.
When organisations talk about improving risk culture, the conversation often turns quickly to measurement. Surveys are commissioned. Scores are benchmarked. Heat maps are produced. Results are tracked over time. Culture becomes something that can be quantified, monitored, and reported to the board.
Most organisations still govern risk as if the boundary of the firm were clear. Responsibilities are mapped to internal functions. Controls are designed around owned processes. Assurance focuses on activities that sit within formal organisational lines.
Boards rarely fail because they do not care about risk. Most spend significant time on it. Papers are reviewed, committees are active, and assurance is sought. The problem is not attention. It is orientation.
If you accept the view of risk developed in the preceding essays, one implication follows immediately: risk intelligence is not primarily about frameworks, controls, or reports. It is about whether an organisation can still recognise emerging danger before it becomes undeniable.
Series 2: Risk Intelligence in Practice6 essays, March 2026
The essays in this second series sit alongside, rather than within, the main Risk Intelligence work. They do not attempt to restate its arguments or introduce its architecture. Instead, they begin from a different place.
Assurance occupies a privileged position in modern governance. It comes with formality, professional language, and the implied authority of process. Controls are tested, opinions are formed, and statements are signed. For boards operating in complex and regulated environments, this matters.
Electricity systems across Europe are increasingly powered by low-cost renewable generation. Wind and solar now supply a substantial share of electricity in many markets, including the United Kingdom. Yet electricity prices remain closely tied to the price of natural gas.
Boards are accustomed to working from structured information. The risk pack arrives on time, professionally assembled, and aligned to an agreed framework. It provides consistency across periods, comparability across business units, and a shared basis for discussion.
Modern governance has achieved a great deal. Roles are clearer than they once were. Responsibilities are documented. Escalation paths are defined. Boards can demonstrate oversight in ways that would have been difficult to evidence a generation ago.
At critical moments in board discussion, the language often becomes less technical and more human. After the analysis has been presented and the options explored, someone, usually the chair, will ask a deceptively simple question.
Series 3: Building for Risk Intelligence14 essays, April to May 2026
Over the past six to twelve months, I have written and published a series of essays exploring Risk Intelligence as a discipline. Those pieces were necessarily conceptual.
For as long as organisations have attempted to manage risk formally, they have sought to make it visible. Visibility has been treated as a virtue in itself, and static dashboards have become the dominant expression of that belief.
Risk registers were created to impose order. They offered a way to catalogue threats, assign ownership, and demonstrate that uncertainty was being taken seriously.
Artificial intelligence has entered the risk management domain with remarkable speed and little reflection. Vendors promise earlier detection, deeper insight, and the ability to see patterns humans miss.
Modern organisations are deeply comfortable with programmes. When a problem is recognised, the instinctive response is to design an initiative, define milestones, appoint owners, and declare success once the artefacts are in place. Risk management has followed this pattern for decades.
Boards are accustomed to being offered reassurance. Risk reporting, assurance statements, and control attestations are framed as protections against surprise and failure. Over time, this has shaped expectations about what good governance feels like. It feels orderly, complete, and calm.
Organisations rarely fail because they lack information. They fail because the story they tell themselves about that information no longer holds, and no one is able, or willing, to say so clearly enough.
Risk appetite is often presented as the point at which strategy and risk meet. It is described as a statement of intent, a boundary within which the organisation is prepared to operate, and a guide to decision-making under uncertainty.
Organisations often describe adverse outcomes as surprises. The term is used to signal that an event was unforeseen, that available information did not indicate its likelihood, and that reasonable processes were followed. It carries an implicit defence.
Modern boards are not short of information. Board packs have expanded in volume and sophistication, incorporating financial analysis, risk metrics, performance indicators, and increasingly, predictive insights.
Organisations are often assessed as if they were self-contained. Strategies are evaluated within organisational boundaries, risks are catalogued within internal frameworks, and controls are designed to manage exposures that can be clearly attributed.
Organisations have invested heavily in reducing the time it takes to produce risk information. Reporting cycles have been shortened, dashboards updated more frequently, and data pipelines accelerated. The expectation is straightforward.
For much of its modern history, risk management has been organised around the idea of assurance. The objective has been to demonstrate that risks are identified, understood, and controlled within acceptable bounds.
Over the past few weeks I have been writing a series of essays on Risk Intelligence. They began with familiar artefacts such as dashboards and registers, and moved through signals, narrative, appetite, and the experience of surprise, before arriving at boards, interdependence, and the limits of…
IntroductionI was listening not so long ago to a BBC series by Rory Stewart about ignorance. It resonated greatly with my views and linked to my approach to leadership: seek out uncertainty, look for resonance and leverage difficult learning.
In my recent conversation with Curly Moloney on my podcast we were riffing briefly on the idea of DAOs (Decentralised Autonomous Organisations which operate through blockchain technology) replacing most of the functions of a board, but that such organisations would still need real people for “tiller.
I had the absolute pleasure of speaking to a group of NEDs at a Dynamic Boards meet up on the subject of risk management. Here is the video:
Guidance and reports
Reports and guidance I wrote or co-wrote for the OECD and the Institute of Risk Management. The IRM guidance is free to download, with a short executive summary for board members and a full document for practitioners.
Prepared for the OECDApril 2009Author
Risk Management & Corporate Governance
Written for the OECD’s work on corporate governance and the financial crisis, covering banks in the UK, the United States and France. It argued that boards had become dependent on management for assurance, that part-time non-executive oversight was stretched, and that risk management had to reach beyond the organisation into its extended enterprise. The same pressures return today, as boards come to rely on AI they did not build and cannot easily inspect.
No longer on the OECD website. The OECD cites it in its 2014 review, Risk Management and Corporate Governance.
Written as the UK Corporate Governance Code first asked boards to determine the nature and extent of the risks they are willing to take. Its questions for the boardroom, and its use of John Adams’s categories of risk, including virtual risk, apply directly to decisions about AI today.
On risk that sits outside an organisation’s direct control: in suppliers, outsourcing and networks of partners. Among its questions for boards is whether they understand their reliance on outsourced IT and cloud services. In 2014 that meant the cloud; today it also means AI.