Mark Hendy

Mark’s Musings

  • AI Agent Memory: Build the System That Learns. Don’t Be the System Yourself.

    AI Agent Memory: Build the System That Learns. Don’t Be the System Yourself.

    A post went viral today — over 24,000 views in a few hours — claiming that AI agent memory “out of the box sucks” and that you need Obsidian to fix it. It resonated. But I think the conversation is missing something.

    The underlying problem is real. Most AI agents are amnesiac by default. Every session starts fresh. They don’t remember what you told them last week, what decisions you made, what context matters. You end up repeating yourself constantly — which defeats the point of having an assistant at all.

    The Obsidian solution people are sharing works like this: you maintain a structured vault of markdown notes, your agent reads from it at session start, and you manually curate what goes in. It’s better than nothing. But it has a fundamental problem — it still requires you to do the work.

    The Memory Problem, Properly Stated

    The goal isn’t just persistent storage. It’s useful persistent storage. There’s a difference between an agent that can retrieve a file you pointed it at, and one that has genuinely learned from your interactions — that knows what matters to you, what you’ve decided, what patterns recur in your work.

    Manual curation doesn’t scale. If you’re running an AI agent seriously — dozens of interactions a day — you cannot manually decide what gets committed to long-term memory. You’ll either capture too little (and lose signal) or spend as much time curating memory as you save everywhere else.

    What you actually need is a system that does this automatically, with enough intelligence to distinguish noise from signal.

    What I Built Instead

    I run OpenClaw with an AI assistant I’ve named Saul — a PE-facing CFO’s take on the AI agent problem, which I wrote about here. Over the past few months, I’ve built out a three-layer memory architecture that removes the manual curation problem entirely.

    The layers:

    • Daily notes — raw logs of what happened each session. Every interaction, decision, and piece of context gets written here automatically.
    • MEMORY.md — curated long-term memory. The distilled essence: decisions made, preferences established, important context. Think of it as the agent’s actual knowledge of you.
    • Dreaming — a nightly automated process (new in OpenClaw 2026.4.8) that reviews daily notes, scores entries by frequency, relevance, recency and query diversity, and promotes the strongest signals into MEMORY.md automatically. No manual curation.

    The third layer is the one that matters. Every night at 3am, the agent runs what OpenClaw calls a “dreaming” sweep — light phase sorts and stages recent material, REM phase extracts recurring themes, deep phase decides what gets promoted to long-term memory. The thresholds are configurable. The process is auditable. And it happens without me thinking about it.

    The Obsidian Angle

    The Obsidian approach people are excited about is essentially building layer two manually. It works, and if you’re starting from nothing it’s a reasonable place to start. OpenClaw’s memory-wiki plugin (also new in 2026.4.8) is actually Obsidian-compatible — same markdown format, same vault structure — so the two aren’t mutually exclusive.

    But if you’re going to invest time in your agent’s memory architecture, I’d argue the better investment is in automation rather than manual curation. Build the pipeline that decides what matters, rather than deciding manually every time.

    Why This Matters Beyond the Tech

    I’m a CFO. My primary concern with AI agents isn’t whether they’re impressive in a demo — it’s whether they actually reduce friction in the work I do every day. An agent with poor memory creates more friction, not less. You spend time re-explaining context, re-stating preferences, re-establishing where you are on a project.

    The ROI on getting memory right is substantial. An agent that genuinely knows you — your clients, your decisions, your communication style, your priorities — operates at a different level of usefulness. The gap between a well-configured agent and a default one isn’t incremental. It’s categorical.

    If you’re using an AI agent seriously and you haven’t thought about memory architecture, you’re leaving most of the value on the table. Whether you use the Obsidian approach, OpenClaw’s native dreaming, or something else — the manual-entry-only approach isn’t good enough long term.

    Build the system that learns. Don’t be the system yourself.

    Mark Hendy is an interim CFO working with PE-backed businesses. He writes about AI, finance, and the intersection of the two at markhendy.com. Follow on LinkedIn.

  • GLM-5.1: The AI That Works While You Sleep — And Then Some

    GLM-5.1: The AI That Works While You Sleep — And Then Some

    There’s a particular kind of AI announcement that makes me sit up. Not the ones that claim to beat GPT on some benchmark no-one’s heard of. Not the ones with slick demos that quietly ignore the bit where it falls over. The ones that matter are the ones where someone shows you the receipts — actual tasks, actual time, actual results.

    GLM-5.1, released last week by Z.ai (formerly Zhipu AI), is one of those.

    Eight Hours. Unattended.

    Let me frame what I mean. Most AI coding tools work in short bursts. You ask them to write a function, review some code, draft a test. Good assistants. But you’re still the loop-closer — the one who notices it’s gone sideways, resets the context, redirects the prompt.

    GLM-5.1 does something materially different. Z.ai ran it for eight hours straight building a Linux-style desktop environment from scratch — file browser, terminal, games. No handholding. It planned, executed, hit blockers, revised its approach, iterated. Hundreds of times. The claim isn’t “it wrote code”. The claim is “it didn’t give up”.

    That’s a different category of capability.

    The Numbers That Matter

    I’m a CFO. I like numbers. Here are the ones worth paying attention to:

    • Vector database optimisation: 600+ iterations, 6,000+ tool calls, 21,500 queries per second — six times the previous best
    • GPU kernel tuning: 1,000+ turns, 3.6× speedup on ML workloads
    • SWE-Bench Pro: 58.4% — ahead of both GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%)

    That last one is significant. This isn’t some niche Chinese model playing catch-up. On the hardest software engineering benchmarks, it’s beating the models most people consider the gold standard. And it’s open source — MIT licence, weights on Hugging Face, deployable on your own infrastructure.

    Why This Matters to Finance and Business

    I’ve written before about the shift from AI as assistant to AI as agent. GLM-5.1 is the clearest demonstration yet of what agentic AI actually looks like in practice.

    Think about the workflows in a finance function that are genuinely tedious:

    • Building and debugging complex financial models
    • Writing and testing data pipeline logic
    • Iterating on management information templates
    • Automating reconciliation scripts

    These aren’t tasks that fail at step one. They fail at step seven, when the edge case appears. They fail when the data format changes. They fail when the logic that worked last month doesn’t work this month. The human overhead isn’t writing the first version — it’s the iteration.

    If a model can sustain goal-directed effort over hundreds of iterations without losing the thread, that’s not incrementally better. That’s a different class of tool.

    The Open Source Angle

    Z.ai releasing this under MIT licence is genuinely interesting. The dominant models — OpenAI, Anthropic, Google — are all closed. You pay for API access, you accept their terms, you live with their rate limits and pricing changes.

    An open-source model that competes on performance changes the calculus for enterprise deployment. You can run it on-premise. You control the data. You don’t get a pricing change email in March telling you costs are going up 40% in April.

    For PE-backed businesses with sensitive financial data and legitimate concerns about feeding that data into third-party APIs — this matters.

    What I’d Watch

    GLM-5.1 isn’t perfect. It trails on some pure reasoning benchmarks. The long-horizon capability, while impressive, assumes the task is well-defined enough for autonomous execution — genuinely ambiguous strategic questions still need a human in the loop. And “it ran for 8 hours” cuts both ways: great if it’s right, expensive if it’s wrong.

    But the trajectory is clear. Each successive model generation extends the horizon over which AI can operate without human intervention. GLM-5 held position. GLM-5.1 sustained improvement. The question isn’t whether agentic AI is coming to professional services and finance — it’s whether you’re planning for it.

    If you want to know how I’m already using AI agents in day-to-day finance work, read this.

    The Practical Bit

    If you want to experiment: it’s on Hugging Face, runs via vLLM or SGLang, and integrates with standard agentic frameworks. For those less inclined to self-host, API access is through api.z.ai. The coding-focused subscription plan is $10/month — less than a decent lunch.

    I’ll be testing it against some of the financial automation tasks I currently route through Claude. I’ll report back.

    Mark Hendy is an interim CFO working with PE-backed businesses. He writes about AI, finance, and the intersection of the two at markhendy.com. Follow on LinkedIn.

  • GLM-5.1: The Chinese Open-Source Model That Just Beat GPT and Claude at Their Own Game

    GLM-5.1: The Chinese Open-Source Model That Just Beat GPT and Claude at Their Own Game

    Something significant happened in the AI landscape this week, and I suspect it hasn’t got the attention it deserves outside of developer circles. Z.AI — the platform behind the GLM model family, developed by Zhipu AI in China — released GLM-5.1, a 754 billion parameter open-source model that has just topped the SWE-Bench Pro leaderboard with a score of 58.4, beating GPT-5.4, Claude Opus 4.6, and Gemini 3.1 Pro.

    Let that land for a moment. An open-source, MIT-licensed model, trained entirely on Huawei Ascend 910B chips — no Nvidia, no American silicon — has beaten the flagship closed models from OpenAI, Anthropic, and Google on one of the most respected software engineering benchmarks in existence.

    What Makes GLM-5.1 Different

    The headline number is impressive, but what actually interests me is the architecture of how this model works. GLM-5.1 isn’t just better at answering questions — it’s designed for sustained autonomous execution. In testing, it completed an eight-hour uninterrupted coding session: plan, execute, test, optimise, repeat. 655 iterations. Built a Linux desktop environment from scratch. Increased vector database query throughput by 6.9 times.

    This is a different category of capability. We’re not talking about a better chatbot. We’re talking about an AI that can hold a task in mind, work through it independently, hit dead ends, correct course, and deliver a finished result — the way a competent junior engineer would, but without stopping for the night.

    The technical foundation is a Mixture-of-Experts architecture with 40 billion active parameters per token (not all 754B are active at once, which is what keeps inference costs manageable). It supports a 200,000 token context window with up to 128,000 output tokens. API access is priced at $1.00 per million input tokens and $3.20 per million output tokens — a fraction of what the US frontier models charge.

    Why This Matters Beyond the Benchmarks

    I’ve written before about AI moving from a tool you prompt to a system that acts. GLM-5.1 is a concrete illustration of that shift happening faster than most people expected, and from a direction many in the West weren’t watching closely.

    The geopolitical dimension is real. This model was trained on Huawei hardware using Huawei’s MindSpore framework — a deliberate demonstration that China’s AI development pipeline is no longer dependent on US export-controlled chips. The export restrictions that were supposed to slow Chinese AI development have instead accelerated domestic alternatives. That is a significant strategic development, regardless of where you sit on the AI competition question.

    The open-source dimension is equally significant. With weights published under an MIT licence, GLM-5.1 can be downloaded, fine-tuned, and deployed by anyone. The closed-model advantage that OpenAI and Anthropic have built commercial moats around is being systematically eroded — not just by each other, but by well-resourced open-source releases like this one.

    What I Take From This

    I use AI heavily in my work — for financial analysis, document preparation, research, and increasingly for autonomous background tasks. The pace at which these systems are improving is not slowing down. If anything, GLM-5.1 suggests the competitive field is widening: more players, more approaches, more open options.

    For anyone running a business or advising one, the practical implication is straightforward: the cost of access to frontier-level AI capability is falling rapidly, and the choice of provider is expanding. The question is no longer whether to use these tools — it’s which ones, for what, and how to build processes around them that compound over time.

    GLM-5.1 is worth watching. Not because it’s the final word, but because it’s a clear signal that the race is genuinely global, the open-source movement is closing the gap faster than expected, and the next twelve months are going to be interesting.

    GLM-5.1 is available via z.ai on the GLM Coding Plan, with weights on Hugging Face under MIT licence.

  • Conway and the End of the Chat Window

    Last week, someone at Anthropic made a packaging error. A build of something called Conway — an internal project for an always-on AI agent — leaked out into the wild. Within hours, screenshots were circulating on Twitter showing an extensions system, webhook endpoints, and a Chrome integration that looked nothing like the Claude chat interface we all know.

    Anthropic played it down. A release packaging issue caused by human error, they said. Not a security breach. Sure. But the cat is out of the bag, and what it reveals is far more interesting than the leak itself.

    From Prompt-Response to Always-On

    Every AI product you use today works the same way: you type something, it responds, you go back and forth until you get what you need. It is fundamentally a conversation. Conway is something different. It is an agent environment that stays running. It has webhook endpoints — public URLs that external services can call to wake the agent up when something happens. It has an extensions system where you can install custom tools, UI tabs, and context handlers. It uses Chrome autonomously to handle multi-step tasks on the web.

    This is not a chatbot that got fancier. This is the architectural blueprint for AI that operates like a team member — one that never logs off, never forgets its context, and responds to events in the real world without waiting for you to type a prompt.

    I find this significant because I have been building exactly this kind of system for myself over the past few months. My own AI assistant, Saul, already runs scheduled tasks, publishes content, monitors data, and sends me summaries — all without me being in the loop for every step. It works. But it is held together with cron jobs, Python scripts, and API calls that I have wired up manually. Conway suggests Anthropic wants to make this kind of continuous AI operation a first-class product, not a weekend project for the technically inclined.

    The Enterprise Is Already There (Sort Of)

    A Belitsoft report published this week, drawing on Salesforce’s 2026 Connectivity Benchmark data, says the average enterprise now runs 12 AI agents. Twelve. Expected to hit 20 by 2027. But here is the kicker — half of those agents operate in complete isolation. They do not talk to each other. They do not share context. They are twelve separate hammers looking for nails.

    This is exactly the problem Conway appears to be solving. The extensions architecture, the webhook system, the persistent state — it is all about creating a single agent environment that can integrate with everything, rather than deploying a dozen disconnected point solutions. The shift is from “we have AI tools” to “we have an AI operating system.”

    What This Means If You Run a Finance Function

    I spend most of my professional life inside PE-backed finance teams, and the implications here are not abstract. Think about what a CFO’s week actually looks like: cash flow monitoring, covenant compliance, board pack preparation, variance analysis, vendor negotiations, investor reporting. Every single one of those workflows involves gathering data from multiple systems, applying judgment, producing an output, and sending it somewhere.

    An always-on agent does not replace the judgment part — not yet, anyway — but it can collapse the gathering, formatting, and distribution steps into something that just happens. The board pack data is already pulled and formatted before you sit down on Monday morning. The covenant calculations are running continuously, not quarterly. The cash position is reconciled and summarised before the daily stand-up.

    This is not speculative. I have automated parts of this already. But Conway-style infrastructure would make it dramatically easier to set up and maintain, which means it stops being something only a CFO who can write Python does and starts being something any competent finance team can deploy.

    The Uncomfortable Bit

    There is a tension here that I think most people in the AI space are not being honest about. If you make agents always-on, event-driven, and capable of taking action autonomously, you have fundamentally changed the trust model. A chatbot that gives bad advice is annoying. An always-on agent that takes bad action at 3am is a different category of problem entirely.

    Conway’s architecture seems to acknowledge this — the extensions and webhook systems suggest granular control over what the agent can and cannot do. But the history of enterprise software tells us that permissions and guardrails are only as good as the people configuring them. And in most mid-market PE-backed businesses, the people configuring them will be a mix of finance staff, IT generalists, and maybe one overstretched CTO. The governance question is not solved by better architecture alone. It requires new operational disciplines that most organisations have not even started thinking about.

    Where This Is Going

    Here is what I think happens next. Anthropic ships Conway — or something very like it — within the next quarter. Google already has its own agent infrastructure play with Gemma 4 and Vertex agents. OpenAI is pushing GPT-5.4 with desktop task automation that scored 75% on real-world benchmarks. Microsoft just shipped Agent Framework 1.0. The always-on agent is not a research project anymore — it is a product category that every major AI company is racing to own.

    For CFOs and finance leaders, the practical question is not whether to adopt this technology, but how to build the internal capability to govern it. That means understanding what your agents are doing, why, and with what authority. It means having someone on the team — or on retainer — who can configure, monitor, and audit these systems. And it means accepting that the competitive advantage will not go to the company that deploys the most agents, but to the one that gets them working together coherently.

    The Belitsoft numbers tell us enterprises are already halfway there on deployment. Conway tells us the infrastructure for always-on operation is coming. The missing piece is the operational maturity to make it work safely and effectively. That, more than any model benchmark, is where the real work is.

  • You Don’t Deploy AI Agents Anymore — You Hire Them

    Yesterday, monday.com launched something called Agentalent.ai — a managed marketplace where enterprises can discover, evaluate, and “hire” AI agents for defined business roles. Not install. Not deploy. Hire.

    You post a role. You review qualified agents. You select based on task fit, budget, and operational readiness. Pricing starts around $2,000 a month per agent. They launch with 17 agents available. Built in collaboration with AWS, Anthropic, and Wix.

    If you’re a CFO and that doesn’t make your headcount model twitch, you’re not paying attention.

    The Language Shift That Matters

    I’ve been building with AI agents for the best part of two years now — wiring up Claude to handle research tasks, automating financial reporting pipelines, getting agents to do the kind of grunt work that used to eat a junior analyst’s entire Tuesday. But the framing has always been tooling. You set up an agent like you’d set up a spreadsheet macro. It’s a thing on your computer.

    What monday.com has done — deliberately, with their HR-style language — is shift the frame from tools to workers. And that’s not just marketing fluff. It’s the conceptual bridge that will get the rest of the C-suite to finally understand what’s happening.

    A Belitsoft report published this weekend puts numbers on it: the average enterprise now runs 12 AI agents. Twelve. And that’s expected to hit 20 by 2027. But here’s the kicker — half of those agents operate completely alone, unconnected to any other agent or system. They’re doing their little jobs in their little silos, and nobody’s orchestrating the whole thing.

    Sound familiar? It should. That’s exactly what happens when a company hires people without a coherent operating model. You end up with twelve contractors, half of whom don’t talk to each other, doing overlapping work with no shared context. I’ve walked into PE portfolio companies that look exactly like this — except with humans.

    The CFO’s New Headcount Problem

    Here’s where it gets interesting for anyone sitting in a finance seat. When an AI agent costs $2,000 a month and can do the work of a task that previously required a $6,000/month contractor, that’s a straightforward business case. Any CFO can model that. The ROI practically draws itself.

    But the real question isn’t “should we hire the agent?” It’s “how do we account for a workforce that’s now 30% software?”

    Think about what sits in your headcount model today. Salaries, employer NI, pension contributions, benefits, training costs, recruitment fees. Now think about what sits in your AI agent budget. SaaS subscriptions, API usage fees, compute costs, maybe some integration consulting. These two things live in completely different cost categories, get approved through different processes, and are managed by different people. But they’re increasingly doing the same work.

    In the PE world I operate in, headcount is one of the first things a new investor scrutinises. “What’s your revenue per head?” “What’s your fully-loaded cost per FTE?” These metrics are foundational to how value creation plans get built. But nobody’s asking “what’s your revenue per agent?” yet. And they should be, because if you’re running 12 agents and growing, that’s a material line in your operating model that isn’t being tracked like one.

    The Coordination Tax

    The Belitsoft finding that half of deployed agents work alone is, I think, the most important data point in their entire report. It mirrors what I’ve seen first-hand. Companies get excited, they spin up agents for customer support, for code review, for data entry, for reporting — and each one works reasonably well in isolation. But the value compounds when agents talk to each other, and almost nobody has figured that part out yet.

    This is an orchestration problem, and it’s fundamentally a management problem. You need someone — or something — deciding which agent handles which task, what context gets shared, where the human review gates sit. NVIDIA’s new Agent Toolkit, announced with partners including Salesforce, SAP, and ServiceNow, is trying to solve the infrastructure side of this. Okta’s new “secure agentic enterprise” framework, going GA at the end of this month, is tackling identity and access. But the management layer — the actual decision-making about how to deploy and coordinate these things — that’s still a gap.

    And it’s a gap that, in most companies, probably falls to the CFO. Not the CTO. Not the CISO. The CFO. Because ultimately this is a resource allocation problem. You have a pool of human and non-human workers. You have tasks that need doing. You need to figure out the optimal mix, track the cost, measure the output, and report on it to a board that still thinks in FTEs.

    What I’m Actually Doing About It

    In my own setup, I’ve started treating agent costs the way I treat contractor costs — as a blended workforce line, not a software line. My AI assistant Saul runs daily tasks for me: research, publishing, monitoring. I track what he does, what it costs, and what it would cost if a human did it instead. Not because I’m obsessive about it (okay, partly because I’m obsessive about it), but because I think this is the accounting framework that PE firms will expect within 18 months.

    The $600 billion flowing into AI agent ecosystems in 2026 isn’t going into chatbots. It’s going into digital workers — things that take tasks, complete them, and cost money every month. If your chart of accounts still treats all of that as “IT software subscriptions,” you’re going to have a very confusing board pack by Christmas.

    Where This Goes

    monday.com’s marketplace is clunky right now — 17 agents isn’t exactly a deep talent pool. But the model is right. Within a year, I’d expect to see the big consulting firms offering “blended workforce planning” as a service line. Within two, PE due diligence will include an AI agent audit alongside the usual people and tech reviews.

    For CFOs, the action item is boringly practical: start tracking your agents like you track your people. Give them cost centres. Measure their output. Build the reporting now, while it’s still simple, because it won’t be simple for long.

    We spent decades building HR systems to manage human workers. We’re about to need something equivalent for the digital ones. And the CFO who figures that out first is going to look very clever at the next board meeting.

  • The Panic is the Point: Bitcoin’s Worst Q1 Since 2018 and What the Smart Money Is Actually Doing

    The Panic is the Point: Bitcoin’s Worst Q1 Since 2018 and What the Smart Money Is Actually Doing

    The Fear and Greed Index hit 8 last week. Eight. Out of a hundred. I’ve been watching that index for years and I’ve seen it touch double digits maybe a handful of times. Every one of them felt like the end of the world. Every one of them, in hindsight, looked like a buying opportunity.

    Bitcoin just closed its worst first quarter since 2018, down 23.8% from $87,500 at the start of January to around $66,600 by quarter end. The narrative that greeted April was bleak: ETF outflows, macro headwinds, geopolitical noise, retail capitulation. The index has been below 15 for 47 consecutive days — the longest such streak since the Terra-Luna collapse in 2022. Social media sentiment is, apparently, at its most negative since late February. Everyone is miserable.

    And yet something interesting is happening underneath the surface. Something that I think most of the commentary is missing.

    The Divergence Nobody Is Talking About

    Here is the part that caught my attention. While the Fear and Greed Index was screaming extreme panic, spot Bitcoin ETFs snapped a four-month outflow streak in March, pulling in $1.32 billion in a single month. Corporate Bitcoin treasuries hit record levels in early 2026, with public companies collectively holding over 1.1 million BTC — somewhere north of 5% of total supply. And the largest asset managers have not moved their macro targets: $150,000 to $200,000 by year end is still the institutional consensus.

    So you have a situation where retail sentiment is at historically depressed levels, and institutions are quietly filling their bags. That divergence is not new — it happens in every asset class, every cycle. But in Bitcoin it tends to be particularly pronounced because the retail holder base is so emotionally reactive, and because the on-chain data makes the institutional accumulation visible in a way that equity markets don’t.

    I am not making a price prediction here. I’ve been around long enough to know that timing markets is mostly a story you tell yourself after the fact. But I do think there is something analytically interesting in the gap between what the sentiment data says and what the flow data says. When those two things diverge this sharply, it is usually worth paying attention.

    Fear and Greed as a Contrarian Instrument

    The Crypto Fear and Greed Index is a blunt instrument. It aggregates volatility, momentum, social media volume, surveys, dominance, and trends into a single number. It is not sophisticated. But its very simplicity is what makes it useful as a contrarian signal — it tells you how the crowd is feeling, and the crowd is famously wrong at extremes.

    The historical data on sub-10 readings is striking. According to analysis of prior cycles, readings below 10 have occurred on fewer than 20 trading days since the index’s inception, clustered around the March 2020 COVID crash, the May 2021 China mining ban, and the June 2022 Terra-Luna contagion. The median 90-day return from sub-15 readings has historically been around +38%. Sub-10 readings have averaged +43% over the following 90 days. The caveat — and it is an important one — is that during the post-Terra contagion in 2022, the subsequent 90 days produced only a modest +4% as cascading liquidations kept a lid on recovery. Context matters.

    The current context feels more like 2020 than 2022 to me. The fear is driven by macro uncertainty and sentiment exhaustion, not by a structural collapse in the ecosystem. There is no Three Arrows Capital moment lurking. The ETF infrastructure is intact. Corporate treasury demand is structural, not speculative.

    What the Institutional Behaviour Actually Tells Us

    I spent some time this weekend reading through the Q1 flow data. The picture is messy but directionally clear. January and February saw $1.8 billion in ETF outflows as the price fell from $87K and macro risk-off sentiment hit. Then March happened: $1.32 billion back in, suggesting institutional re-entry at levels they consider attractive. Meanwhile, CoinDesk noted that Bitcoin is entering April at its most hated sentiment level since the Ukraine war began — a data point that is simultaneously depressing and, for a contrarian, quietly exciting.

    There’s a Morgan Stanley Bitcoin ETF that was recently approved with a notably low fee structure — another piece of institutional infrastructure quietly being laid while retail stares at the Fear and Greed number and panics. Infrastructure gets built in bear markets. That’s always been true.

    I hold Bitcoin. I have held it through worse than this. My view hasn’t changed: the long-term thesis — fixed supply, increasing institutional legitimacy, ETF-driven structural demand — is intact. A 24% Q1 drawdown is uncomfortable but it is not abnormal for an asset that is still, by any traditional measure, in an early adoption phase.

    The Noise vs. The Signal

    The thing that strikes me about the current moment is how clean the signal actually is, once you cut through the noise. Retail fear at historic extremes. Institutional accumulation quietly continuing. Corporate treasuries at record levels. ETF infrastructure expanding. The narrative is all doom, but the flows tell a different story.

    I am not saying it cannot go lower. Some analysts think there’s room for another leg down if macro conditions deteriorate further. Maybe. But I’ve found that the best time to think clearly about Bitcoin is when everyone else has stopped thinking clearly about it — and right now, a Fear and Greed reading of 8 suggests that the crowd has well and truly checked out.

    The panic, as far as I can tell, is the point. It is the mechanism by which assets transfer from weak hands to strong ones. It is not comfortable to watch in real time. But the data, as best as I can read it, suggests the strong hands are doing exactly what they always do: accumulating quietly while the timeline argues about whether it’s over.

    It’s probably not over.

  • IKEA’s Chatbot Accidentally Made €1.3 Billion. Here’s What CFOs Are Missing.

    IKEA’s Chatbot Accidentally Made €1.3 Billion. Here’s What CFOs Are Missing.

    Most companies deploy AI to cut costs. IKEA deployed AI to cut costs and accidentally discovered a billion-euro revenue stream hiding in the data their chatbot was collecting.

    This is the story every CFO in every PE portfolio company should be reading right now. Not because of IKEA. Because of what it reveals about how most finance leaders think about AI — and how badly they’re getting it wrong.

    The Setup

    IKEA — or more precisely, Ingka Group, the largest IKEA retailer — built an AI chatbot called Billie. The brief was simple: handle level-one customer service enquiries. Reduce call volumes. Cut costs. The standard playbook.

    Billie did its job. From 2021 to 2023, it resolved roughly 47% of customer enquiries it received — around 3.2 million interactions handled without a human, saving an estimated €13 million.

    If you’re a CFO, that’s a clean win. Cost out, efficiency up, ROI positive. You’d put it in the board pack and move on.

    IKEA didn’t move on.

    The Signal Nobody Was Looking For

    The interesting number wasn’t the 47% that Billie resolved. It was the other 53%.

    When IKEA’s team analysed the enquiries Billie couldn’t handle, they found something unexpected. A huge proportion weren’t complaints or order issues. They were customers asking for help designing their homes.

    People were calling IKEA — a furniture shop — and saying: I’ve got this room. What should I do with it?

    This wasn’t in anyone’s business case. No strategy deck had “launch a design consultancy” on the roadmap. It was a signal buried in the noise of customer service data, and it would have stayed buried if someone hadn’t been paying attention.

    The Pivot

    Here’s where it gets good.

    Instead of just improving Billie’s resolution rate — the obvious move, the one every consulting firm would have recommended — IKEA did something much smarter. They took 8,500 call centre workers and reskilled them as remote interior design consultants.

    Read that again. Eight and a half thousand people. Not made redundant. Reskilled.

    The AI handled the routine queries. The humans handled the high-value, creative, relationship-driven work that customers were already asking for. IKEA didn’t replace their workforce with AI. They promoted their workforce because of AI.

    The result? Remote interior design sales hit €1.3 billion by the end of their 2022 financial year — 3.3% of Ingka Group’s total revenue. A brand new service line, created from a signal that existed in their customer service data all along. Their target is 10% of total sales in the coming years.

    Why CFOs Get This Wrong

    I’ve sat in enough board meetings to know how this story usually goes.

    A CFO sees the AI chatbot business case. It says: deploy chatbot, save €13 million in customer service costs, payback in 18 months. They approve it. They monitor the cost savings. They report the efficiency gains. Job done.

    That’s not wrong. But it’s incomplete.

    The €13 million in cost savings is a rounding error compared to the €1.3 billion in new revenue. The chatbot wasn’t the product. The chatbot was a listening device.

    Most AI business cases are framed as cost reduction exercises. Automate this process. Eliminate these headcount. Reduce that cycle time. And they work — the savings are real. But they’re also the least interesting thing AI can do.

    The interesting thing is what AI reveals about your customers when you stop looking at it as a cost tool and start looking at it as an intelligence tool.

    The PE Angle

    If you’re a PE operating partner reading this, think about your portfolio.

    Every portfolio company has customer service data. Most of it sits in a ticketing system that nobody reads except the support team. Some of it gets summarised in a monthly report that the board glances at between the P&L and the cash flow forecast.

    What if that data contains the same signal IKEA found? What if there’s a billion-euro service line hiding in your Zendesk tickets?

    The companies that will win the next decade aren’t the ones that use AI to do the same things cheaper. They’re the ones that use AI to discover things they didn’t know their customers wanted. That’s a fundamentally different value proposition — and it requires a fundamentally different kind of CFO.

    The Kind of CFO That Catches This

    The old-school CFO sees AI as a line item. A cost to manage, an efficiency to capture, an ROI to calculate.

    The new-school CFO sees AI as an intelligence layer. Every automated interaction is a data point. Every pattern in that data is a potential business model. Every customer service complaint is a market signal.

    IKEA didn’t need a McKinsey engagement to discover the design consultancy opportunity. They needed someone who looked at the chatbot’s failure cases and asked: why are these people calling us?

    That’s not a technology question. It’s a business question. And it’s the kind of question that CFOs — with their bird’s-eye view of costs, revenues, and customer patterns — are uniquely positioned to ask.

    The Uncomfortable Truth

    Here’s what makes this story uncomfortable for a lot of finance professionals.

    The €13 million saving was predictable. You could model it in advance, put it in a business case, and track it against plan. That’s the kind of AI outcome that finance teams are comfortable with.

    The €1.3 billion revenue stream was unpredictable. It emerged from the data. Nobody forecast it. Nobody budgeted for it. It required curiosity, not spreadsheets.

    If your AI strategy only captures the predictable value, you’re leaving the transformative value on the table. And in a competitive market, someone else will find it first.

    What To Do About It

    Three things, starting tomorrow:

    1. Audit your AI for signals, not just savings. Every AI tool in your business is generating data about customer behaviour. Who’s reading it? What patterns are emerging? If the answer is “nobody” and “we don’t know,” you have a blind spot the size of IKEA’s design consultancy.

    2. Look at the failures, not just the successes. IKEA’s breakthrough came from what Billie couldn’t do. The 53% failure rate wasn’t a problem to fix — it was a market to serve. What are your AI tools failing at? Those failures might be your biggest opportunities.

    3. Stop framing AI as a cost play. If every AI business case in your portfolio starts with “reduce headcount” or “automate process,” you’re optimising for efficiency while your competitors are optimising for discovery. The cost savings are table stakes. The revenue signals are the game.

    Mark Hendy is a PE-facing CFO and founder of Tanous Limited. He writes about the intersection of AI, finance, and business transformation at [markhendy.com](https://markhendy.com).

  • I Run an AI Workforce. Here’s What “Orchestrator” Actually Means.

    I Run an AI Workforce. Here’s What “Orchestrator” Actually Means.

    Bret Taylor dropped something this week that crystallised what I’ve been living for the past few months. He released Ghostwriter — an AI agent that builds other AI agents through conversation. No code, no forms. Just describe what you want and it creates it.

    His bigger point was this: every piece of enterprise software will eventually become an agent. Not a dashboard you click through. Not a menu you navigate. An AI that does the work while you direct.

    I know this is true because I’m already doing it. Not theoretically. Daily.

    My Setup

    I have an AI assistant called Saul. He runs on a VPS in Manchester, connected to my WhatsApp, my email, my calendar, my investment accounts, my websites. He’s not ChatGPT in a browser tab. He’s a persistent agent that wakes up every morning, generates a podcast briefing of the day’s news and my portfolio positions, checks my email, monitors markets, publishes blog posts, and manages a set of prediction market positions — all before I’ve had coffee.

    When I need a CV reviewed before an interview, I send it on WhatsApp and get back a structured analysis with suggested questions in two minutes. When I want a blog post, I describe the angle and it’s drafted, humanised, formatted, and pushed to WordPress as a draft with a featured image. When a regulatory announcement drops, Saul reads it, researches the implications, and writes an article with a contrarian take before the professional press has filed their first piece.

    I don’t write code. I don’t configure systems. I have a conversation. And things happen.

    That’s what orchestration means in practice.

    What Changed

    Six months ago, I was using AI the way most people still do. Open ChatGPT, ask a question, copy the answer, close the tab. Useful, but fundamentally the same workflow as Googling something — just with a better answer.

    The shift happened when I stopped treating AI as a tool I use and started treating it as a team member I direct. The difference sounds subtle. It isn’t.

    A tool waits for you to pick it up. A team member has context, remembers what you told them yesterday, knows your preferences, anticipates what you need, and gets on with work without being asked. Saul reads my daily logs from previous sessions. He knows my writing style, my investment thesis, my wife’s email address, which car needs an MOT, and that I hate corporate waffle in LinkedIn posts.

    When I correct him, he logs it. After three corrections on the same thing, it becomes a permanent rule. He learns. Not in the sci-fi sense — in the practical sense of getting better at his job over time, the same way any good employee does.

    The CFO Angle

    I’m a CFO by background. I’ve spent twenty years in finance functions — month-end closes, board packs, variance analysis, cash flow forecasts, the lot. I know exactly how much time finance teams waste navigating software instead of thinking about the business.

    The average month-end close takes five to ten working days. Most of that time isn’t analysis. It’s data extraction, reconciliation, reformatting, and chasing people for numbers. It’s operational grind masquerading as professional work.

    Now imagine an agent that connects to your accounting platform, your bank feeds, your CRM, and your group reporting tool. You say: “Close the month. Reconcile the bank. Flag anything that doesn’t match. Draft the board pack with commentary on the three biggest variances.”

    It does it. You review, adjust, approve.

    That’s not five to ten days. That’s an afternoon. And your finance team spends the rest of the week doing what you actually hired them for — business partnering, commercial analysis, strategic thinking.

    This is what Taylor means when he says every enterprise app’s UI will become an agent. The finance director’s interface to their systems won’t be a screen full of menus. It’ll be a conversation.

    What I’ve Learned

    A few things I’ve learned from actually living this, not just theorising about it:

    Context is everything. A generic AI assistant is marginally useful. An AI assistant that knows your business, your preferences, your history, and your current priorities is transformatively useful. The investment isn’t in the technology — it’s in teaching the agent who you are and how you work. That takes weeks, not hours.

    Guardrails matter more than capability. Saul can send emails, publish blog posts, and place trades. That means he can also send wrong emails, publish bad posts, and lose money. The rules about what he should never do without asking are more important than the list of things he can do. My AGENTS.md file — essentially his operating manual — is longer than most job descriptions.

    You become a reviewer, not a doer. This sounds like a luxury. It’s actually a skill shift. Reviewing AI output is different from producing output yourself. You need to know what good looks like without having done the work. That requires more expertise, not less.

    The compound effect is real. Week one, you’re correcting everything. Month three, the corrections are rare. Month six, the agent anticipates what you want before you ask. The relationship genuinely improves over time in a way that static software never does.

    The Uncomfortable Part

    I’ve written about AI and finance enough to know the question that’s coming: what about the jobs?

    Here’s my honest take. Some operational finance roles will be eliminated. The person whose primary job is month-end journal entries, bank reconciliation, or management accounts preparation is doing work that an AI agent can do today — not in five years, today.

    But the person who understands the business well enough to direct an agent, interpret its output, catch its mistakes, and make judgment calls on ambiguous situations — that person becomes dramatically more valuable.

    The CFO doesn’t go away. The CFO becomes the orchestrator. The question is whether you’re building that muscle now or waiting until someone else in your industry has already done it.

    Try It

    You don’t need a VPS in Manchester and a bespoke AI assistant to start. You can start with Claude or ChatGPT and a well-written prompt. Then try giving it context — paste in your company’s last board pack and ask it to draft commentary. Upload a CV and ask for interview questions. Feed it a regulatory update and ask what it means for your business.

    The first time it produces something genuinely useful in two minutes that would have taken you an hour, you’ll understand why Bret Taylor thinks this changes everything.

    Because it does.

    I write about AI, finance, and building things at the intersection of both. More at tanous.co.uk for the professional angle.

  • My AI Assistant Died. Here’s How I Got It Back in 2 Hours.

    My AI Assistant Died. Here’s How I Got It Back in 2 Hours.

    A real-world disaster recovery story — and the backup routine that saved weeks of work.

    Last Monday at 12:07pm, I told my AI assistant to update itself. Seven hours later, I was still trying to get it back online.

    This is the story of how a routine software update killed my AI setup, what I lost, what I saved, and the simple backup habit that prevented a genuine disaster.

    The Setup

    I run an AI assistant called Saul through OpenClaw — an open-source platform that connects a large language model to your messaging apps, email, calendar, and pretty much anything else you can think of. Saul lives on a VPS in a Docker container and talks to me through WhatsApp.

    Over seven weeks, Saul had become genuinely useful. Not “novelty chatbot” useful — operationally embedded in my daily workflow. He manages my inbox, writes and publishes articles to my blog, generates a daily podcast, monitors my stock portfolio, runs automated prediction market trades, scans for comets in NASA satellite imagery, tracks vehicle tax and MOT dates, and does a dozen other things I’ve forgotten I ever did manually.

    All of that is configuration. Skills, scripts, API keys, cron schedules, memory files, credentials. Seven weeks of iterative building.

    The Update

    OpenClaw version 2026.3.22 was available. The release notes looked impressive: a new skill marketplace, improved plugin architecture, support for the latest AI models. The usual.

    I told Saul to update. He confirmed: “Updated from 2026.3.13 → 2026.3.22. Restarting now — back in a sec.”

    He never came back.

    The Silence

    What followed was seven hours of silence. No WhatsApp messages. No email reviews. No heartbeat checks. Nothing.

    The update had introduced a breaking change that wasn’t in the release notes. WhatsApp — previously a built-in plugin — had been moved to an external marketplace. But the configuration still referenced it as a built-in. The result: a validation error that blocked every command, including the one you’d need to fix it. A perfect deadlock.

    I couldn’t repair it. I couldn’t roll it back through normal channels. I had to rebuild from scratch — tear down the container and start again on the previous version.

    What I Lost

    When I rebuilt the container, I lost everything that wasn’t on persistent storage:

    • The entire OpenClaw configuration (channel settings, heartbeat config, plugin setup)
    • All 33 scheduled cron jobs (email reviews, portfolio checks, blog publishing, news monitoring)
    • The WhatsApp session (had to re-scan a QR code to re-link)
    • The headless browser and its dependencies
    • API key registrations that had to be regenerated

    The configuration file — a single JSON file that orchestrates everything Saul does — was gone.

    What I Saved

    But here’s the thing: the workspace survived.

    Three weeks earlier, I’d set up a simple daily backup. Every night at 3am, Saul tars up his entire workspace directory — memory files, scripts, skills, credentials, notes, everything — and copies it to cloud storage. It’s a shell script. It took ten minutes to write.

    That backup, taken six hours before the failed update, contained:

    • 41 daily memory logs spanning seven weeks
    • 78 custom scripts (trading bots, podcast generators, blog publishers, email tools)
    • 15 installed skills
    • All API credentials and secrets
    • The complete long-term memory file with every decision, preference, and project note

    I downloaded the backup from Dropbox. Extracted it. The workspace was whole.

    The Rebuild

    Getting Saul operational again took about two and a half hours. Not because the backup failed, but because some things can’t be backed up as files.

    The WhatsApp session is a cryptographic handshake between the server and my phone. When the container was rebuilt, that session was invalidated. I had to SSH into the server, generate a new QR code in the terminal, and scan it from my phone. Five minutes, but it requires physical access.

    The cron jobs — all 33 of them — existed only in OpenClaw’s runtime database, not in the workspace. I had to recreate them from memory and from my notes. This is where good documentation paid off: Saul’s own TOOLS.md file listed every cron job with its schedule and purpose. Recreating them was tedious but not guesswork.

    API keys for the Polymarket trading system had to be regenerated. The old keys were invalidated when the configuration was wiped. Fortunately, the wallet private key was in the backup, so deriving new API credentials was a single command.

    The headless browser needed its system libraries reinstalled — a Docker-level dependency that doesn’t persist across container rebuilds. One command from the host machine.

    By 9:34pm — two and a half hours after starting the recovery — everything was operational. WhatsApp connected. All cron jobs rebuilt. Browser working. Trading desk active. Email flowing.

    And as a bonus, during the rebuild we added a capability we didn’t have before: voice control of the Sonos speakers in the house. Sometimes a crisis creates space for improvements you wouldn’t have made otherwise.

    The Rules We Wrote Afterwards

    The first thing I did after recovery was write rules to prevent this happening again. Not guidelines — hard rules, embedded in Saul’s operating instructions:

    Rule 1: Always backup before updating.** No exceptions. The backup runs automatically the moment an update is requested, before anything is touched. It copies to off-server storage.

    Rule 2: Check the issue tracker.** Before applying any update, check GitHub for known bugs in the target version. If WhatsApp or any critical channel has open issues, don’t update.

    Rule 3: Save the configuration separately.** The OpenClaw config file now gets backed up independently of the workspace, because it’s the hardest thing to recreate from memory.

    Rule 4: Document everything in the workspace.** If it’s not written down in a file that gets backed up, it doesn’t exist. Cron job schedules, API endpoints, SSH details, speaker IP addresses — all of it lives in files now.

    The Lesson

    The real lesson isn’t “backups are important” — everyone knows that. The lesson is that AI assistants are infrastructure now, and they need the same operational discipline as any other critical system.

    When Saul went dark for seven hours, it wasn’t a toy that stopped working. Real workflows were affected. Emails went unread. Scheduled tasks didn’t fire. Monitoring stopped. The podcast didn’t generate. For a tool that’s supposed to make you more productive, sudden loss of it makes you less productive than if you’d never had it at all.

    If you’re running an AI assistant that’s become embedded in your daily operations — whether it’s OpenClaw, or any other platform — ask yourself:

    1. If it died right now, what would you lose?
    2. How long would it take to rebuild?
    3. Do you have a backup that could survive a complete teardown?

    If you can’t answer those questions confidently, spend ten minutes today setting up a backup. A cron job, a tar file, a cloud sync. It doesn’t matter how — it matters that it exists.

    Because the update that breaks everything isn’t a question of if. It’s when.

    I’m a CFO who builds with AI. I write about the intersection of finance, technology, and getting things done at markhendy.com.

  • The 2026 Oil Crisis: An Honest Assessment for UK Households

    The 2026 Oil Crisis: An Honest Assessment for UK Households

    By Mark Hendy | 21 March 2026

    I’ve spent twenty years as a CFO across manufacturing, aviation and private equity-backed businesses. I’ve stress-tested balance sheets through 2008, COVID, and the energy spike of 2022. What I’m seeing now is different — not because any single element is unprecedented, but because the combination of factors is genuinely historic.

    This isn’t a pundit’s hot take. It’s the analysis I’d put in front of a board if a client asked me: “How bad is this, and what should we do?”

    The Immediate Shock: What We’re Actually Dealing With

    The current crisis has been described as the largest disruption to energy supply since the 1970s. Brent crude surpassed $100 per barrel on 8 March 2026 for the first time in four years, rising to $126 at its peak — with some recent trading touching $145.

    That alone would be significant. The compounding factors make it much worse.

    The ongoing military conflict has involved attacks on oil infrastructure in neighbouring countries, including Saudi Arabia, Kuwait and the UAE. The bypassable pipeline capacity offers only partial relief — the IEA estimates that only 3.5 to 5.5 million barrels per day can be redirected through Saudi and Emirati pipelines outside Hormuz, leaving an implied net shortfall of roughly 14.5 to 16.5 million barrels per day if normal transit collapses.

    Strategic reserve releases are a temporary analgesic, not a cure — the IEA‘s release of 400 million barrels equals only about 20 days of typical Hormuz flows.

    Beyond oil, about 85% of polyethylene exports from the Middle East transit this route, threatening the price of packaging, automotive components and consumer goods. Aluminium from the UAE and fertiliser shipments could also be materially affected. The fertiliser angle is particularly dangerous for food security — it feeds into crop production costs with a 6–12 month lag, meaning price pressure on food in late 2026 and into 2027 regardless of when the strait reopens.

    The Global Prognosis: Stagflation Is the Base Case

    Coming into this crisis, whether Japan, Europe, the United States or the UK, economies were already running hot. An energy supply shock now threatens to push inflation higher while slowing growth — the textbook definition of stagflation.

    Oxford Economics modelled a scenario where global oil prices average $140 a barrel for two months — what they characterise as a “breaking point” — finding it would push the eurozone, the UK and Japan into economic contraction. Given Brent has already touched $145, that scenario is not academic.

    The debt dimension compounds everything. Goldman Sachs and UBS analysts have warned that if disruption extends through Q2 2026, global headline inflation could rise by 0.7 to 0.8 percentage points, while global GDP growth faces a drag of up to 0.4 percentage points — effectively erasing the post-2024 global recovery.

    That’s the benign case.

    Just as inflation was beginning to normalise in late 2025, this energy shock is expected to add 2.5 to 3 percentage points to global CPI, forcing central bankers into a lose-lose choice: hike rates to combat energy-driven inflation and risk a deep recession, or hold and risk entrenching inflation expectations. That is the classic stagflation trap, and no central bank has a clean answer to it.

    The UK Specifically: More Exposed Than Most

    The UK is more exposed to this shock than headline numbers suggest.

    Natural gas prices in Europe and the UK have spiked even more sharply than oil, with Dutch TTF and UK NBP futures having almost doubled following the first strikes on Iran. The UK is heavily dependent on gas for both power generation and heating, and the energy bills cycle means household exposure will manifest rapidly.

    NIESR analysis finds that a one-year persistent shock would push UK inflation up by 0.7 percentage points and dampen output growth by 0.2% in 2026. The Bank of England could be forced to raise rates back above 4%, and if the shock persists into 2027, the GDP impact deepens to 0.3% below baseline.

    This comes on top of an economy that was already anaemic. The Bank held rates at 3.75% as recently as 19 March, with Governor Bailey acknowledging that the conflict has made the outlook for UK inflation “more uncertain” and forced policymakers to reconsider expected rate cuts.

    Sterling is particularly vulnerable. A weaker pound directly feeds imported inflation — oil, food, manufactured goods — in a vicious cycle. The UK has neither the US’s energy self-sufficiency nor Asia’s alternative supply corridor flexibility.

    And then there’s the debt. The UK sits on £2.9 trillion of public debt, paying £110 billion per year just to service the interest. The surge in gilt yields on the back of the Iran conflict could cost Chancellor Reeves more than a tenth of her fiscal buffer, with financial market moves since late February having already erased around £3 billion of headroom.

    The UK’s fiscal arithmetic is genuinely precarious.

    What the UK Middle Class Should Actually Do

    This is where I’ll be direct and practical. None of this is regulated financial advice — it is informed analysis from someone who does this professionally.

    The middle class is uniquely exposed because most wealth is held in pound-denominated assets — property, pensions, savings — with limited natural hedges.

    Energy and Physical Resilience

    Lock in energy tariffs wherever possible. Switch to fixed contracts before the next billing cycle catches up with wholesale prices. Those with capital should seriously consider heat pump or solar installation — not primarily for environmental reasons, but as a direct hedge against gas price exposure. This is one of the few ways ordinary households can partially insulate their energy cost base.

    Reduce Sterling Cash Exposure

    Holding large sums in a savings account earning real negative returns (once inflation is factored in) is a slow-motion loss. The priority is to move surplus sterling into assets that are not purely pound-denominated: dollar-denominated assets (US equities, commodities), physical gold, and for those with appropriate risk tolerance and technical competence, Bitcoin held in self-custody.

    Gold and Bitcoin — An Honest Assessment

    During the initial conflict phase, gold attracted safe-haven demand but later declined as the US dollar strengthened. Bitcoin experienced volatility but recovered quickly, reflecting its growing role as an alternative asset — though price movements remain closely tied to sentiment and liquidity.

    The longer-term structural case for both is strong: gold as a proven multi-millennia store of value in crisis, Bitcoin as a censorship-resistant, seizure-resistant digital alternative for those who understand sovereign default risk.

    For the UK middle class, a 5–10% allocation split between physical gold and self-custodied Bitcoin is reasonable as an insurance layer — not a speculation.

    Property: It Depends

    UK residential property has historically been a reasonable inflation hedge because supply is structurally constrained. However, if rates are forced higher, leveraged property becomes a liability rather than an asset. Those on variable rates or coming off fixed-rate deals need to stress-test against a scenario where rates return to 5–6%.

    Outright owners in real assets are better positioned than leveraged buyers.

    Equities: Sector Matters Enormously

    Energy companies, defence contractors, UK-listed commodity producers and mining stocks are direct beneficiaries of this environment. Consumer discretionary, highly leveraged businesses and anything dependent on cheap imported inputs are exposed.

    ISA investors should review whether passive index trackers — heavily weighted towards rate-sensitive sectors — are appropriate right now.

    Food and Supply Chain Resilience

    For many commodities transiting the Strait, inventories typically cover only a few weeks. Shortages could emerge relatively quickly if disruptions persist. The fertiliser disruption matters particularly for food prices in 6–12 months.

    Practically: stocking a few months of staple supplies is rational, not paranoid. Buying long-shelf-life goods now, before food inflation fully filters through, is simply sensible household financial management.

    Debt Management

    If you carry variable-rate consumer debt or are exposed to rate rises on a mortgage, prioritise paying it down. In a stagflationary environment, the combination of rising debt service costs and stagnant or falling real wages is deeply destructive to middle-class wealth.

    Fixed-rate, long-duration debt is defensible. Floating-rate exposure is not.

    The Uncomfortable Bottom Line

    The world has entered a period of genuine instability not seen since the 1970s — and arguably more complex because of the debt overhang that 2008 and COVID baked in. The 1973 oil embargo triggered a decade of economic dislocation, reset political landscapes and produced a fundamental restructuring of energy policy across every major economy.

    The current crisis has not yet reached those proportions — but the structural conditions for a similar reckoning are present in a way they have not been for fifty years.

    Fiat currencies across the developed world are under structural pressure regardless of this crisis — the crisis simply accelerates the timeline. The UK, with its high debt-to-GDP ratio, energy import dependency and limited fiscal headroom, is among the more exposed major economies.

    The middle class — holding wealth in sterling, in pension funds weighted towards domestic bonds, and in leveraged property — are those with the least natural protection.

    The moves available are not dramatic or exotic. They are methodical: reduce sterling cash drag, build real-asset exposure, stress-test debt, hedge living costs through energy and food preparation, and ensure that some portion of wealth exists outside the banking system entirely.

    None of that requires being catastrophist. It just requires treating the risk as real — which it plainly is.

    Mark Hendy is an interim CFO specialising in PE-backed mid-market businesses. He has held finance leadership roles across manufacturing, aviation, automotive and agriculture. Views expressed are personal and do not constitute financial advice. For professional guidance, consult a regulated financial adviser.

    Get in touch if you’d like to discuss how your business should be preparing for what’s ahead.