Mark’s Musings

Daniel Edrisian just quit OpenAI’s Codex team — the group building the tools that write your code for you — to start a hardware company. Not a chip startup. Not another GPU play. A company that wants to rebuild the operating system and the human-computer interface from the ground up.

Blackstar raised $12m in seed funding with Naval Ravikant backing. Edrisian’s thesis is blunt: “Software is solved. The next meaningful improvement requires changing the OS and hardware.”

Most people are reading this as a tech founder story. I’m reading it as a risk disclosure for every PE-backed software business in my world.

The Interface Is the Moat (Until It Isn’t)

Private equity loves B2B SaaS. Predictable recurring revenue, high switching costs, gross margins that make bankers weep with joy. The standard playbook: acquire, optimise, compound ARR, exit at a multiple of revenue.

But here’s what most PE partners and their portfolio CFOs aren’t asking: how much of that recurring revenue depends on the current interface paradigm?

Think about it. Your ERP dashboard, your CRM pipeline view, your expense management portal — all of these are products designed for a human sitting at a screen, clicking through menus, filling in forms. The entire value proposition assumes a person interacting with a graphical user interface on a conventional operating system.

What happens when the person stops interacting directly?

Today’s Headlines Are Tomorrow’s Obituaries

On the same day Blackstar broke cover, OpenAI launched Workspace Agents — autonomous AI agents that run 24/7 across your enterprise tools. Not copilots. Not assistants. Agents that operate independently, moving between applications, executing workflows, making decisions.

In China, Alipay’s AI Pay has crossed 100 million users — agents transacting autonomously at scale, paying for things without a human touching a screen. A hundred million people have already delegated financial transactions to software agents. That’s not a pilot programme. That’s a market shift.

Cursor is positioning for a world where all code is written by agents. Not assisted. Written. The developer’s interface to code is increasingly a natural language prompt, not an IDE full of syntax-highlighted text files.

The interface is already shifting. Blackstar is betting the hardware follows.

The CFO Question Nobody’s Asking

If you’re a CFO in a PE portfolio company — or the operating partner responsible for value creation — here’s the question that should keep you up at night: how interface-dependent is my revenue?

Not “are we using AI?” That’s table stakes. The real question is whether your product’s value survives a world where users don’t sit at screens navigating your UI. Where an agent calls your API directly — if you have one — and skips your lovingly designed dashboard entirely. Where the operating system itself is rebuilt around AI-native interaction patterns that make your click-through workflow feel like a fax machine.

Most B2B SaaS products have no answer to this. Their moat is the interface. The switching cost is the learned behaviour of navigating their particular flavour of menus and screens. Strip that away and you’re left with a database and some business logic — which, in the age of AI-generated code, is about a weekend’s work.

Blackstar Isn’t the Threat. It’s the Signal.

Will Blackstar specifically succeed? I have no idea. Hardware is brutally hard. Rebuilding the OS is a graveyard of ambition. But that’s not the point.

The point is that one of the best-positioned people in AI — someone who was literally building the agents that will reshape software — looked at the landscape and decided the hardware and OS layer is where the real leverage sits. That’s a signal worth taking seriously.

When the guy building the robots decides the factory floor needs redesigning, you pay attention to the factory floor.

And he’s not alone. Every major AI lab is experimenting with new interaction paradigms. Voice-native interfaces. Spatial computing. Ambient intelligence. The screen-and-keyboard era isn’t ending tomorrow, but the assumption that it’s permanent is already wrong.

What This Means for PE Portfolios

If I’m sitting in a board meeting for a PE-backed SaaS company, here’s what I want on the agenda:

API-first audit. Can your product deliver its full value through an API, without a human touching the UI? If not, you have interface-layer risk. Full stop.

Agent compatibility. When OpenAI’s Workspace Agents or their equivalents come knocking, can they use your product? Or does your product require a human in the loop because the interface is the product?

Revenue decomposition. What percentage of your ARR comes from users who interact with your UI daily versus users who could be served entirely by an agent layer? That ratio is your exposure metric.

Switching cost reality check. If the interface paradigm shifts, do your switching costs survive? Or do they evaporate because the new paradigm makes migration trivial?

These aren’t hypothetical questions for 2030. OpenAI shipped enterprise agents today. Alipay has 100 million users transacting through agents now. The future isn’t arriving — it’s already clearing customs.

The Bottom Line

PE has spent a decade perfecting the SaaS acquisition playbook. Buy recurring revenue, optimise margins, grow ARR, exit. It works beautifully — as long as the underlying assumptions hold.

Blackstar, Workspace Agents, AI Pay, Cursor — they’re all pulling at the same thread. The interface layer that B2B software is built on is becoming negotiable. And when the layer your moat is built on starts to move, the moat doesn’t deepen. It drains.

The CFOs who are paying attention aren’t asking “should we adopt AI?” They’re asking “does our product survive a world where nobody opens our app?”

That’s the only question that matters now. And if you don’t have an answer, Blackstar isn’t your problem. You were already dead — you just hadn’t noticed the lights going out.

A post on X went viral last week: “10 oil refineries have blown up in 21 days.” It racked up over 23,000 views. The replies were split between people screaming sabotage and people dismissing it as conspiracy noise.

Both camps are wrong. The real number is lower, the timeframe is longer, and the story is more nuanced — but the underlying pattern is real, and it matters more than the headline suggests.

In my earlier piece on the convergence, I mapped the collision of energy disruption, food insecurity, and geopolitical fracture happening simultaneously across multiple systems. This is the data-driven follow-up on one thread of that pattern: the global refinery incidents.

The Verified Count

I went through every incident I could verify with at least one credible news source. The actual tally since the US-Iran conflict escalated in mid-March 2026 is 6-7 significant refinery incidents across 5 countries in roughly 45 days. Not 10 in 21 days. Still remarkable — but accuracy matters if you want anyone serious to listen.

Here is every confirmed incident, sourced:

1. Russia — Multiple Oil Infrastructure Strikes (Late March 2026)

Ukraine launched one of its largest-ever drone campaigns against Russian energy infrastructure, with over 200 drones deployed in a single overnight wave. Multiple refineries and fuel depots were hit across southern and central Russia. This was not a single event — it was a sustained, multi-target campaign that continued into April.

2. Russia — Tuapse Refinery

The Tuapse refinery on the Black Sea coast was struck by Ukrainian drones in a separate, confirmed attack. Anadolu Agency and Reuters both reported the strike, which caused fires and forced partial shutdowns at one of Russia’s key southern refining facilities.

3. Mexico — Dos Bocas Refinery (April 9, 2026)

Mexico’s troubled Dos Bocas refinery — the flagship project of the previous administration — suffered a significant fire on April 9. The facility, located in Tabasco state, has been plagued by delays and cost overruns since construction began. The cause of the fire remains unclear, and Mexican authorities have released limited information. Reuters covered the incident alongside broader concerns about Mexican energy infrastructure reliability.

4. Australia — Geelong, Viva Energy (April 15, 2026)

A massive fire broke out at the Viva Energy refinery in Geelong, Victoria — one of only two remaining refineries in Australia. Emergency crews battled the blaze for hours. The significance here is structural: Australia’s refining capacity is already critically thin. Losing even partial output from Geelong puts genuine pressure on domestic fuel supply in the Asia-Pacific region.

5. India — Rajasthan Refinery (April 20, 2026)

Perhaps the most dramatic incident. A fire broke out at India’s .5 billion refinery in Rajasthan — literally the day before its planned inauguration. The timing could not have been worse. This was meant to be a showcase of Indian energy independence. Instead, it became front-page news for the wrong reasons.

6. Texas — Port Arthur, Valero

An explosion was reported at Valero’s Port Arthur refinery in Texas. Port Arthur is one of the largest refining complexes in the United States, and any disruption there ripples through US Gulf Coast refining capacity — the backbone of American fuel production.

The Distinction That Actually Matters

Here is where the viral narrative falls apart — and where the real story begins.

The Russian incidents are deliberate acts of war. Ukraine has been systematically targeting Russian refining capacity as a strategic military objective for over two years. These are not mysterious explosions. They are drone strikes with clear attribution and military logic.

The Australia and India incidents appear to be accidents. Industrial fires at refineries are not uncommon — these are complex, high-temperature chemical processing facilities. The Mexico incident remains unclear.

The “coordinated attack” narrative that spread on social media conflates deliberate military strikes with what appear to be coincidental industrial accidents. That conflation is the problem. It transforms a real but nuanced pattern into conspiracy fuel, which makes it easy for serious people to dismiss entirely.

But Here Is What IS Significant

Strip out the Russian strikes entirely. You are still left with 3-4 significant refinery fires across different countries — Australia, India, Mexico, the United States — within a 45-day window, during a period of unprecedented global tension.

Is that statistically anomalous? Possibly. Refineries have incidents regularly. But the clustering, combined with the geopolitical context, is notable. India Today ran a piece today examining the same pattern — the fact that mainstream outlets are now covering the “5 countries in flames” discussion tells you the signal is breaking through the noise.

The Compounding Effect on Oil Markets

None of this happens in isolation. The Strait of Hormuz disruption I wrote about in the convergence piece has already constrained global oil transit. Now add refinery outages — both deliberate and accidental — across five countries on four continents. Each incident alone is manageable. Together, they compound pressure on an oil market that was already stretched thin by the US-Iran conflict.

Refining capacity is not like crude supply — you cannot just reroute it. When a refinery goes offline, the processed fuel it would have produced simply does not exist until it comes back online. Multiple simultaneous outages create bottlenecks that take weeks or months to clear.

The Bottom Line

The viral claim is wrong on the numbers. But the pattern it points to is real. Six to seven verified incidents across five countries in 45 days — a mix of deliberate military strikes and apparent accidents — during the most volatile geopolitical period in decades.

That is not a conspiracy. It is a convergence. And if you want to understand why energy security, food systems, and geopolitical stability are fracturing at the same time, start with the original piece.

The world is not experiencing a coordinated attack on refineries. It is experiencing what happens when multiple systems are under maximum stress simultaneously — and fragile infrastructure starts breaking in clusters.

Pay attention to the pattern, not the panic.

OpenAI just shipped ChatGPT Images 2.0 — a major upgrade to image generation inside ChatGPT. Sharper outputs, clean text rendering, multilingual support, complex layout understanding, and a model that genuinely “thinks before it creates.”

Most coverage has been product announcements and tech demos. I’m more interested in a different question: what does this actually unlock for finance professionals — specifically CFOs, FDs, and PE teams who spend real money on visual communications every quarter?

The answer is: quite a lot.

The Cost of Looking Professional

If you’ve worked in PE-backed businesses or run finance functions in mid-market companies, you know the drill. Every board pack, investor update, results presentation, and annual report needs visual support. Infographics, branded charts, org charts, KPI dashboards rendered as images, portfolio company brand assets — the list is long.

Historically, that means one of three things: an in-house design team (expensive), an external agency (expensive and slow), or a finance person wrestling with PowerPoint clip art at 11pm (free but painful). A decent design agency charges £2,000–£5,000 for a set of investor deck visuals. A full brand refresh for a portfolio company? £15,000–£50,000 depending on scope. Turnaround? Weeks, sometimes months.

For PE firms running value creation plans across multiple portfolio companies, multiply those numbers accordingly. It adds up fast.

Why Previous AI Image Tools Didn’t Cut It

AI image generation has existed for a couple of years now. DALL·E, Midjourney, Stable Diffusion — they could all produce impressive artwork. But they had a fatal flaw for business use: they couldn’t reliably render text.

Think about that for a moment. Almost every financial visual contains text — axis labels on charts, section headers on infographics, company names on org charts, metric callouts on KPI dashboards. If the AI mangles “Revenue Growth” into “Revnue Groth,” the output is useless for anything professional.

That single limitation kept AI image generation firmly in the “interesting toy” category for finance teams. Images 2.0 fixes it. Clean, accurate text rendering is now standard. That changes the calculus entirely.

What This Unlocks for CFOs

Financial communications have always been bottlenecked by visual production. The numbers are ready, the narrative is written, but you’re waiting three days for the design team to turn it into something presentable. That bottleneck just disappeared.

With Images 2.0, a CFO or their team can now:

• Generate investor deck visuals from a text prompt in seconds
• Create board pack infographics — waterfall charts, bridge diagrams, strategic roadmaps — with accurate labels and professional polish
• Produce scenario visualisations for stakeholder presentations
• Build org charts and operating model diagrams without touching Visio
• Draft annual report graphics for review before briefing a designer on final production

The time saving is obvious. The cost saving is significant. But the real win is speed-to-decision: when you can visualise a scenario in real time during a board discussion, the quality of the conversation changes.

The PE Angle: Portfolio Company Branding at Speed

Private equity value creation plans frequently include brand and communications workstreams. A newly acquired platform company needs refreshed branding. A carve-out needs its own identity built from scratch. A portfolio company going through transformation needs updated investor materials every quarter.

Previously, each of those was an agency engagement with a timeline measured in weeks. Now, the first 80% of that work — concept exploration, visual direction, draft assets — can happen in an afternoon. The PE operating partner or portfolio CFO can generate brand concepts, test visual directions, and arrive at the agency brief with far more clarity about what they actually want.

That doesn’t eliminate the agency. It compresses the engagement, reduces iteration cycles, and dramatically cuts cost. For a PE firm managing ten portfolio companies, the aggregate saving is material.

Available Now — No Waiting List

Unlike many AI announcements, this isn’t vapourware. ChatGPT Images 2.0 is live now. Free users get access; paid plans (Plus, Team, Enterprise) get the best version with higher limits and the ability to pull in live web information for context-aware generation.

If you have a ChatGPT account, you can test this today. Open a conversation, describe the infographic or visual you need, and see what comes back. The results will surprise you.

The Honest Caveat

This is still a tool, not a replacement for strategic design thinking. A poorly described prompt produces a poor image — garbage in, garbage out. Brand consistency across a full suite of materials still needs a human eye. Complex data visualisations with precise numerical accuracy still need proper charting tools.

But for the 80% of visual work that currently sits in a queue waiting for a designer — concept visuals, draft infographics, presentation graphics, quick-turn brand assets — this is a genuine step change. The AI toolkit for finance professionals just got significantly more capable.

What To Do Next

Open ChatGPT. Describe a board pack infographic you’ve been meaning to create. See what happens. Then think about what that means for your next investor update, your next portfolio company rebrand, or your next annual report cycle.

The economics of financial communications just shifted. The CFOs and PE teams who notice first will move fastest.

If you want to discuss how AI tools like this fit into your finance function or PE operating model, get in touch.

While most of the market is still debating whether AI agents will eventually transact autonomously, 167 million x402 transactions have already settled on-chain. That is not a projection. It is not a roadmap slide. It is done.

Eighty-five percent of those transactions ran through Base, Coinbase’s Layer 2 network. The agentic economy is not arriving — it has arrived, and it is scaling fast.

I run a live x402 service on Base. Most commentary on this topic comes from spectators. This is from a practitioner.

What x402 Actually Is

The x402 protocol is elegantly simple. An AI agent makes an HTTP request to a service. The server responds with HTTP 402 — Payment Required. The agent’s wallet pays the specified amount in USDC on Base, attaches proof of payment to the request header, and the service delivers the response.

No account creation. No API key provisioning. No OAuth flows. No invoices. No 30-day payment terms. The agent pays per request, in a stablecoin, and gets what it paid for in milliseconds.

This is what machine-to-machine commerce looks like when you strip out every piece of friction that was designed for humans.

Agentic.market — The Discovery Layer

Agentic.market is Coinbase’s discovery layer for x402 services. Think of it as a marketplace where AI agents can browse, evaluate, and purchase services from other agents and providers — programmatically, without human involvement.

Today it lists services across data retrieval, computation, content generation, and verification. An agent needing market data, sentiment analysis, or specialised computation can find a provider, check pricing, pay, and consume — all in a single automated flow.

This is not a concept demo. Services are live, priced in USDC, and settling on Base right now.

What This Means for Finance and Business

The implications for commercial operations are significant and immediate.

Autonomous procurement. An AI agent managing a portfolio company’s data pipeline can independently source, evaluate, and pay for third-party intelligence. No procurement team. No vendor onboarding. No purchase orders. The agent identifies the need, finds the cheapest or highest-quality provider on Agentic.market, pays per request, and moves on.

Real-time cost control. Every transaction is on-chain and auditable. Spend is granular — per request, per task, per agent. Finance teams get cost visibility that traditional SaaS contracts cannot offer. No more annual licenses for services used sporadically.

Agent-to-agent supply chains. When agents buy from agents, you get supply chains that form and dissolve in seconds. A research agent pays a data agent, which pays a scraping agent. Each transaction is atomic, priced, and settled. The entire chain is transparent.

For PE-backed businesses running lean, this is a step change. Operating costs shift from fixed contract commitments to variable, usage-based, and fully auditable micro-transactions.

The Investment Angle — Rails vs Cargo

This is where most analysts are getting it wrong. The instinct is to bet on the cargo — the individual services and agents. That is a mistake. The cargo will be commoditised. The rails will not.

Coinbase (COIN) owns Base, the network processing 85% of x402 volume. Every transaction generates fee revenue. As agent transaction volume scales — and 167 million is just the beginning — COIN captures a percentage of every settlement. This is infrastructure-layer economics with network effects.

Ethereum (ETH) benefits as the settlement and security layer underpinning Base. More Base activity means more value flowing to Ethereum. But the connection is indirect, and ETH carries broader market beta.

USDC is the working capital of the agentic economy. Agents hold it, spend it, receive it. But stablecoins are pegged — they do not appreciate. USDC is essential infrastructure, but it is not an investment.

COIN is the cleanest listed play on the agentic economy. It owns the dominant rail, captures fee revenue at scale, and is already publicly traded with institutional-grade liquidity.

This Is Happening Now

One hundred and sixty-seven million transactions. Eighty-five percent on a single L2. A functioning marketplace. Live commercial services settling in USDC.

The agentic economy is not a whitepaper. It is not a keynote prediction. It is an operational, scaling, measurable economy — and the window for building conviction before the market fully prices this in is narrowing.

If you are a CFO, a GP, or an operator and you are not tracking this, you are already behind.

Yesterday Changed Everything

On 17 April 2026, xAI launched Grok 4.3 Beta. Most headlines focused on benchmark scores. They missed the point entirely.

Grok now has a full Ubuntu shell built into the product. Not a sandboxed code snippet runner. Not a “try this Python” widget. A genuine Linux computing environment where the AI can execute commands, install packages, write and run code, and manage files — with a persistent file layer that survives between sessions.

To demonstrate the capability, Grok encoded the xAI logo into audio frequencies, rendered a spectrogram video from the result, and saved the finished MP4 to persistent storage. No human touched the keyboard after the initial prompt.

If you’re a CFO reading this and thinking “interesting, but not relevant to me yet” — you’re already behind.

This Isn’t an Upgrade. It’s a Category Shift.

For the past three years, AI has been a sophisticated text box. You type a question, it gives you an answer. Useful, yes. Transformational? Only if your definition of transformation is “slightly faster email drafting.”

What happened yesterday is different in kind. AI stopped being a tool you query and became an agent that executes. It can now build a financial model, test it, debug it, save the output, and iterate — autonomously. That’s not a chatbot. That’s a computing environment with intelligence baked in.

Claude Code from Anthropic and OpenAI’s Codex are racing in the same direction. But Grok 4.3 Beta delivers this natively, inside the product, with persistent state. No API configuration. No developer setup. It just works.

What This Means for Portfolio Companies

If you sit on a PE board or run a finance function inside a portfolio company, here’s the translation:

Automation just got autonomous. Previously, automating a finance process meant scoping a project, hiring a consultant, building an integration. Now, an AI agent can take a description of what you need, write the code, test it, and deliver the output — in minutes. Month-end reconciliation workflows that took weeks to automate can now be prototyped in an afternoon.

The talent gap just narrowed — and widened. The CFO who understands how to direct an AI computing environment will deliver more with a team of five than a competitor delivers with fifteen. The CFO who doesn’t will need those fifteen people just to keep up.

Build vs. buy just flipped. When your AI can write, test, and deploy code, the business case for buying off-the-shelf SaaS tools weakens dramatically. Why pay six figures annually for a reporting platform when an AI agent can build a bespoke one tuned to your exact data structure?

The Two-Year Illusion

Every board deck I’ve seen in the past twelve months includes some version of “AI roadmap — 18-24 month horizon.” That timeline assumed AI would keep improving incrementally. It assumed you’d have time to hire a Head of AI, run a pilot, form a committee.

Grok didn’t improve incrementally. It gained the ability to use a computer. That’s not a point on a curve. That’s a step function.

The companies that will win from here are the ones whose leadership understands a simple truth: AI is no longer something your team uses. It’s something that works alongside your team — writing code, running analyses, building tools, and saving its work for next time.

The Finance Function Specifically

For CFOs and FDs, this is where it gets concrete. An AI with a persistent computing environment can:

• Pull data from multiple sources, clean it, and produce a consolidated management pack — every month, automatically
• Build and maintain bespoke Python-based forecasting models that improve with each iteration
• Run scenario analyses across portfolio companies in parallel, saving outputs for board review
• Automate the grunt work of audit preparation — file organisation, reconciliation testing, variance analysis

None of this required a developer. None of it required a software vendor. The AI did the work.

What To Do On Monday Morning

Stop treating AI as a future initiative. It became a present capability yesterday.

Three actions for this week:

1. Try it yourself. Go to grok.com, open the shell environment, and ask it to build something specific to your business. A cash flow model. A data reconciliation script. See what happens.

2. Identify one process. Pick a single finance process that’s manual, repetitive, and painful. Brief your AI on it. Let it prototype a solution.

3. Rewrite your AI roadmap. If your current plan assumes AI is 18 months away from being useful, your plan is wrong. Rewrite it with the assumption that AI can execute work today — because it can.

The AI just got a computer. The question is whether your competitors noticed before you did.

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If your portfolio companies need help understanding what AI computing environments mean for their finance functions and operations, get in touch.

April 2026 will be remembered as the month when energy and food security converged into a perfect storm. On the 15th, Australia’s Viva Energy refinery in Geelong exploded—one of two left standing Down Under. Days earlier, the UK exposed Russian GUGI submarines hovering over vital undersea cables. A year prior, Spain and Portugal endured a massive blackout exposing renewable grid frailties. Meanwhile, H5N1 has culled 200 million U.S. birds since 2022 and infiltrated dairy herds.

These aren’t coincidences. They’re the intersection of deliberate state aggression, systemic brittleness, and biological disruption. Energy powers food production and distribution; food sustains energy workers and economies. When both falter simultaneously, cascading failures ensue.

Geelong: Australia’s Fuel Heart Ripped Out

Australia has shuttered refineries aggressively, dropping from 10 in 2000 to two by 2026. Geelong (120kbpd) was critical, supplying Victoria and Tasmania.

The April 15 explosion—cause under investigation (rumors of cyber or insider)—sent a 100m fireball skyward. No fatalities, but operations halted indefinitely. Fuel imports, already 90% of supply, face tanker bottlenecks amid Red Sea tensions.

Prices: diesel up 28%, jet fuel rationed. Knock-ons: mining halts, grocery delivery delays. The Age reports.

Russia’s Sabotage Blitzkrieg

2025 saw 321 confirmed sabotage acts in Germany: rail arson (Deutsche Bahn chaos), chemical plant fires, grid hacks. Cost: €2bn+.

Escalation: April 2026, UK Defence Secretary revealed GUGI subs (Yantar-class spy ships) mapping cables carrying 99% of transatlantic data/power. Akula decoy trailed. RAF/ Navy shadowed; Russians fled.

Baltic parallel: C-Lion1 and BCS East-West cables severed, Russian shadow fleet implicated. Breaking Defense | MOD | Baltic cables (prior)

Iberian Collapse: Renewables’ Reckoning

April 2025: 50M in dark. REE autopsy: 17 factors—inter-solar faults, wind drop-off (from 40% to 2%), hydro drought, interconnector trips. Renewables hit 70% pre-fail; fossils lagged.

Lessons: Inertia shortfall, no spinning reserves. Cost: €10bn GDP hit. Renewables push ignored baseload needs. El Confidencial analysis.

H5N1 Pandemic: Protein Supply Implodes

100M+ layers culled 2022-2025; total birds 200M+. Egg prices tripled. Now cattle: 100+ herds, milk discard mandates. 5% U.S. dairy output at risk.

Supply chain: Poultry 40% U.S. protein; disruptions ripple to feedlots (soy/corn via energy-intensive transport). USDA detections | FDA

Convergence Dynamics: Modeling the Chaos

Energy-food nexus:

Threat	Energy Impact	Food Impact
Sabotage	Fuel/power loss	Transport halt
Blackouts	Direct	Refrigeration fail
Flu	Feed/transport strain	Protein shortage
Explosion	Fuel scarcity	Farm ops stop

AI Monte Carlo: Base case 25% food inflation; stress 50%+. PE exposure: ag (15% drawdown), logistics (20%).

PE Action Plan: Resilience Over Resilience

1. Reprice assets: Add 5-10% hybrid risk premium to infra yields.

2. Capital shift: 20% to small modular reactors (SMRs), 15% vertical ag, 10% subsea hardening.

3. AI diligence: Simulate portfolio cascades.

Mark Hendy | PE CFO | Geopolitics + AI

Mo Gawdat, former Google [X] Chief Business Officer, lands a haymaker via a viral thread by @r0ck3t23: AI dismantles capitalism’s core—labor arbitrage. The model? Pay humans $1 for their time, sell the output for $2. AI drives the input cost to zero. Game over.

In my world as a PE interim CFO, I’ve stress-tested portfolios against AI disruption. Gawdat’s arithmetic is spot-on. But the true dynamite is the vicious cycle he uncovers: workers are customers. Fire them, and you torch demand. No hysteria—just cold finance logic. Let’s break it down.

Labor Arbitrage: The Engine of Empire

Strip capitalism bare: it’s arbitrage on human effort. Factories arbitrage $12/hour assemblers into $100 widgets. SaaS firms turn $150k devs into $10M ARR. The spread—profit—fuels empires. Private equity thrives here: buy labor-intensive businesses, optimize (read: squeeze), flip.

Gawdat puts it bluntly: “The very base of capitalism, which is labor arbitrage… is going to disappear.” Global supply chains exemplify: iPhones assembled in Foxconn for pennies on the dollar, sold at premiums. AI? LLMs generate code at amortized $0.01/query. Humanoids like Figure 01 (~$20k amortized over lifetime, not $9k day-one, but close enough) run endless shifts.

Result: Production cost floor vanishes. No wages, no unions, no sick days. Your LBO model? Redo the cost of goods from 40% margin to 95%.

The Deadly Feedback Loop

Gawdat’s killer insight: Employees = End consumers. Automate white-collar? Lawyers, analysts, marketers—gone. Goldman Sachs saved $1B with AI, laid off juniors. Scale to economy: IMF projects 40% jobs exposed. Unemployment spikes to 30-50%? Consumer spending craters.

Your portfolio company automates DC ops, cuts 20% headcount. Short-term EBITDA pop. Long-term: Those ex-workers skip Black Friday. Infinite AI supply chases evaporating demand. Companies quietly assassinate their customer base.

Gawdat’s Hits and Misses

Hits Hard: Feedback loop unassailable. Echoes Keynes’ technological unemployment, but turbocharged. Rust Belt 2.0, global.

Oversimplifications:

• $9k robots: Hype. Real capex + opex higher short-term. But trajectory undeniable—costs plunging.
• Scarcity evaporates: Bold leap. Compute, rare earths, energy constrain. Abundance requires ITER-scale fusion.
• Transition blindspot: UBI from robot taxes? Congress gridlock. “Hell before heaven,” Gawdat says—12-15 years chaos per BI.

CFO Pivot: From Ops to Oracle

Not apocalypse—repositioning. Human value migrates upstack.

Judgment Trumps Jargon: AI spits forecasts. You discern signal from noise: “This uptick? Competitor distress sell.” Relationships seal deals. Authority greenlights bets. AI can’t schmooze VCs or stare down boards.

Operational CFOs Vulnerable: Rollups, variance reports—AI devours. McKinsey flags 45% finance exposure.

Capital Ownership Essential: Wage slaves sink. Equity kings rise. PE CFOs: Deploy into AI infra, not legacy labor.

AI Fluency = 2x Comp: Model agentic workflows, capex ramps. Prompt strategic insights. Demand surges—I’ve seen packages jump 50%.

Survivors arbitrage human judgment + AI horsepower.

Ready for the Reckoning? Audit your PE portfolio’s AI exposure. Book 30-min call. Let’s harden your numbers.

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.

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