AI Has a Money Problem, and Anthropic's IPO Is the Test

 Abstract

AI's bottleneck has moved. Three years ago the scarce thing was chips. Then it was power and land. Now it's money. Specifically, it's credit: who will lend, how much and at what rate.

October 2026 made that shift visible. Bond yields pushed above 5%. The Nasdaq sold off twice in a week. The listed neoclouds, the companies that rent out GPUs, lost about a fifth of their value in five trading days. An Australian AI data centre company called Firmus pulled its IPO. Nvidia, its supplier and shareholder, is now in talks to put more money in.

Into this market, Anthropic is pressing ahead with its own IPO. It's the only big tech listing still on the autumn calendar. It's growing faster than almost any software company in history. It also has more than $600 billion of compute commitments to pay for.

OpenAI, its great rival, has decided to wait until 2027. Microsoft, meanwhile, owns a slice of both.

So the Anthropic IPO is about more than one company. It's a test of whether public markets will fund the AI build-out now that credit has become the constraint. And because Anthropic is a young, loss-making company, it will be priced rather than valued. That price will reflect the credit cycle as much as it reflects Claude.

Five beats: one story

The big idea in one line

Anthropic's IPO is a referendum on whether public equity will fund AI now that credit is the bottleneck.

Hold on to that. Everything else hangs off it.

Ten things to take away

I'll explain each of these properly later. For now, here's the list.

• Credit is the bottleneck. AI needs roughly $1 trillion of new funding every year. Chips and power are no longer the binding constraint.
• The shock arrived. The US 10-year yield touched about 5.33% after a hot inflation print. The Nasdaq lost 4.5% in one week, then fell 2.6% in a single day on 22 October.
• The casualties are the borrowers. Neoclouds fell about 22% in a week. Firmus pulled a $5 billion IPO. Switch and Nscale delayed theirs.
• Nvidia is backstopping again. It's reportedly in talks to invest hundreds of millions in Firmus. That's vendor finance in plain sight.
• Anthropic has growth and obligations. First-half 2026 revenue was $16.3 billion. Compute commitments top $600 billion, about 30 times its year-end cash.
• Anthropic has reasons to go now. It needs to fund commitments, tidy its capital structure and sell its growth while it's the only AI IPO in town.
• OpenAI has reasons to wait. Sam Altman says "not 2026". Private money is still available to it.
• Microsoft wins either way. It owns about 27% of OpenAI. It put $5 billion into Anthropic, and Anthropic agreed to spend $30 billion on Azure.
• Anthropic will be priced, not valued. Every pricing metric you'll hear hides assumptions about growth, margins and risk.
• The timing is a forced choice. The window is poor. But Anthropic's need and its demand are strong enough to go anyway.

Act 1: The backdrop

Part I: This month in markets

Let's start with what happened. It's been a rough few weeks.

Two sharp drops in a week

The Nasdaq had a four-week winning streak. It ended abruptly.

• On Friday 16 October, the index fell 2.1%. That capped a weekly loss of about 4.5%, its worst week since June.
• It bounced on Wednesday 21 October, up about 1.1%.
• Then on Thursday 22 October, it fell 2.6%. That was its worst single day since early June.

Chip stocks took the heaviest hit. The SOX semiconductor index dropped 6.7% on 22 October alone. It now sits more than 26% below its June record. It's worth remembering that the index is still up around 50% for the year. This is a sharp pullback, not a collapse. But it hurts.

Why it happened

No single thing caused this. Several worries landed at once.

• Inflation ran hot. September CPI came in at 3.7%. Producer prices were firm too.
• The Fed may hike. Markets now price a high chance of a December rate rise. The new Fed chair, Kevin Warsh, gave little away.
• Yields jumped. The 10-year Treasury yield hit about 5.33% on CPI day. It later eased back to around 5.0% to 5.1% as money fled into bonds.
• Oil spiked. Brent crude rose above $110 on reports of possible strikes on Iran after the US midterms.
• AI spending spooked investors. Meta fell about 7% despite beating forecasts, because it flagged how much money AI will need.
• Neocloud credit cracked. CoreWeave dropped 10% on 22 October. Its bonds and credit default swaps stayed under pressure.
• Jensen Huang said the quiet part out loud. Nvidia's chief executive told CNBC that neocloud demand is "off the charts". Then he added that capital is scarce and holding growth back.

That last point matters most for this story. When the man selling the chips says the problem is money, not demand, you should listen.

The yield line to watch: In my earlier blogpost, From Chips to Credit, I flagged a level on the 10-year yield of about 5.25%.

Here's why it matters. Most of the time, when shares fall, investors buy government bonds. Bond prices rise. Your portfolio gets a cushion. That's the logic behind the classic 60/40 mix of shares and bonds.

Above roughly 5.25%, that cushion tends to vanish. Rising rates hurt shares and bonds together. Long-dated growth stocks, like AI companies, lose their hedge.

This month, yields crossed that line on CPI day and then slipped back below it. It's a fragile truce.

The further from cash, the bigger the fall

Look at who fell hardest in the week of 12 to 16 October.

• The Dow rose about 0.7%.
• The S&P 500 fell about 2%.
• The Nasdaq fell about 4.5%.
• Nvidia fell about 6.6%.
• Chip stocks fell about 11.4%.
• The eight listed neoclouds fell about 22% on average.

See the pattern? The more a business relies on borrowed money to build AI capacity, the harder it fell. Consumer staples and healthcare actually rose. The money didn't leave the market. It moved to companies that don't need to borrow to grow. That's the chips-to-credit story showing up in prices.

The Australian casualty: Firmus

Closer to home, Australia had its own AI drama.

Firmus is a neocloud. It designs and runs AI data centres, and Nvidia, Coatue, Blackstone and Jane Street all back it. It planned to list on the ASX at about A$11 a share. That would have valued it at roughly US$30.6 billion and raised about US$5 billion. It would have been one of the largest floats in Australian history.

On 9 October, it pulled the deal.

Here's the timeline.

• April 2026: Firmus raises $505 million at a $5.5 billion valuation.
• August 2026: a $2 billion round lifts its valuation above $10.5 billion.
• 9 September: Nvidia names Firmus in its 2-gigawatt Australian partner build-out.
• 5 October: data centre partner CDC walks away from Project Southgate. Only 42 megawatts of a planned 1.6 gigawatts had been built.
• 9 October: Firmus pulls its IPO.
• 21 October: reports say Nvidia is in talks to invest hundreds of millions more.

One fund manager summed it up neatly. UniSuper's chief investment officer said it was a good business story that "simply did not have a valuation that matched it".

Why investors said no - Firmus is the chips-to-credit story in miniature. Investors balked for clear reasons.

• The asking price was about three times its August private valuation, just two months earlier.
• The company carried around $30 billion of debt.
• It had only two operating data centres, in Melbourne and Singapore.
• It had little track record of building at scale.
• A key partner had just walked away.
• More than half the stock would have been free to trade on day one.

So what happens next? Firmus plans a private round of $2 billion to $3 billion. It may list on the Nasdaq later. And Nvidia, its supplier, may step in with fresh equity.

That last move is the lesson. When public equity says no, the vendor steps in. It's the same "balance sheet as a service" pattern we saw with Nvidia and CoreWeave.

The IPO calendar is down to one name - Firmus isn't alone. According to Reuters, the once-busy autumn calendar for tech IPOs is "down to just Anthropic".

• Firmus pulled its IPO on 9 October.
• Switch, a data centre operator, delayed.
• Nscale, an AI infrastructure firm, delayed. Part of the reason was SEC staff comments.
• SB Energy, which supplies power for AI, postponed.
• OpenAI isn't coming this year. It's targeting 2027.
• Anthropic is pressing ahead, aiming for mid-November.

Bankers report that other issuers don't want to compete with Anthropic for investors' attention. When one company is the only game in town, it gets the room to itself.

Two Australian stories, two sides of the chain - Australia gives us a neat illustration of the whole system.

• On one side, Anthropic is the customer. In September it signed its first Australian data centre lease, a 1.44-gigawatt campus near Dalby in Queensland, reportedly worth about A$32 billion. Approvals are still pending.
• On the other side, Firmus is a builder. It needs equity and debt to build AI factories. Public investors refused its price.

The link is simple. The lab signs the lease. The landlord needs credit. If Anthropic's IPO goes well, its leases become stronger collateral for everyone building for it. If it goes badly, that collateral weakens.

Part II: Chips to credit, the change underneath

The selloff is the weather. This part is about the climate.

The bottleneck moved

Think about what has constrained AI over the past few years.

• In 2023 and 2024, it was chips. GPUs were on allocation. High-bandwidth memory was scarce. If you could get chips, you could build.
• In 2024 and 2025, it was power and sites. Grid connections took years. Land and permits were tight. Developers solved this with capital, refurbished industrial sites and on-site generation.
• From 2026, it's credit. The question is no longer "can we get the chips?" It's "who will lend us the money, and at what rate?"

This October simply made the shift visible. Huang says demand is off the charts. Neocloud bonds wobble. IPOs get pulled. Power now sits with whoever has a strong balance sheet, or whoever is willing to lend theirs to others.

The bill - The numbers here are enormous. The research firm SemiAnalysis estimates:

• about $11.3 trillion of AI capital spending between 2024 and 2029, covering both chips and data centres;
• about $7 trillion of debt outstanding by 2029;
• about $1 trillion of new funding needed every year.

Let's make that concrete. A 100-megawatt AI campus costs roughly $40 million per megawatt, so about $4 billion in total. A young neocloud might have $400 million of equity. It has to borrow the other $3.6 billion.

Now multiply that across hundreds of campuses. Then add Anthropic's own commitments of more than $600 billion. The trillions add up fast.

How money moves through the stack

It helps to picture AI as a stack of layers.

• At the top: users and businesses pay for tokens, seats and agents.
• Next: AI labs like Anthropic and OpenAI sign multi-year compute contracts.
• Then: clouds like Azure, AWS and Google, plus neoclouds, rent out GPU capacity.
• Then: chipmakers like Nvidia supply the hardware.
• Then: data centre and power companies build the sites.
• At the bottom: banks, private credit funds, bond buyers and IPO investors supply the capital.

Capital flows up. Revenue flows down to repay it. Each layer's lender relies on the layer above to keep paying. Anthropic sits near the top. Its revenue is what ultimately repays the neocloud and data centre debt underneath it. That's why its IPO matters to companies that have never heard of Claude.

Balance sheet as a service - Here's the clever bit, and the risky bit.

Most AI labs and neoclouds don't have investment-grade credit ratings. Lenders prefer to lend against strong balance sheets. So strong companies lend their credit to weaker ones.

• Nvidia has about $497 billion of commitments, according to SemiAnalysis. These include rental floors, lease and power guarantees, cloud agreements and guarantees on the resale value of GPUs.
• Hyperscalers like Microsoft sit between AI labs and lenders. A lab signs with Microsoft. Microsoft signs with a neocloud. The neocloud borrows against Microsoft's signature, not the lab's.

Firmus this month is a live example. Public equity declined, so the vendor stepped in again.

This works well when times are good. When times turn, the supplier gets hit twice: once through lower sales, and again through the support it has extended. Telecom equipment makers learned that lesson painfully in 2001.

The gap that equity has to fill. SemiAnalysis estimates Nvidia's balance sheet can support roughly 46 gigawatts of backstops. The industry needs about 240 gigawatts of new capacity.

That leaves about 194 gigawatts that someone else must fund. That someone is hyperscalers, private credit, bond markets, asset-backed securities and equity investors. IPOs sit in that last group.

Equity matters because it's the cushion lenders lend against. Every successful AI IPO adds equity to the system. Every pulled IPO, like Firmus, takes some away. Anthropic's listing is the single biggest equity injection on the table.

How higher yields travel down the chain

October gave us a clear chain reaction.

• Yields rose on hot inflation and rate-hike fears.
• Neocloud credit spreads widened. CoreWeave's credit default swaps rose.
• Neocloud shares fell about 22% in a week.
• IPOs were pulled (Firmus) or delayed (Switch, Nscale).
• Vendors stepped in as backstops (Nvidia and Firmus).
• AI labs' commitments now rest on this whole chain.

There's a feedback loop here too. Lower share prices make equity harder to raise. Dearer debt makes projects harder to finance. That weighs on AI shares again. Anthropic sits right in the middle. Its commitments are the revenue that neocloud and data centre lenders count on. A strong IPO strengthens the whole chain. A weak one weakens it.

Where October 2026 sits - I find a simple two-by-two helpful here. Put AI demand on one axis and bond yields on the other.

• Weak demand, high yields: danger. That's the 2001 telecom pattern.
• Strong demand, high yields: the squeeze. Demand is there, but capital is dear.
• Weak demand, low yields: patient capital. Projects slow, but refinancing is easy.
• Strong demand, low yields: goldilocks. The build-out accelerates.

October 2026 sits firmly in the squeeze. Anthropic's revenue grew 45% in a single quarter. Huang says demand is off the charts. Yet yields sit around 5%. In a squeeze, the best-placed issuers have real demand and a genuine reason to raise money now. That describes Anthropic far better than a debt-heavy neocloud like Firmus.

Why credit sets IPO timing

So how does all this affect when companies list? Here's the logic.

• Debt markets tighten. Higher base rates and wider spreads raise the cost of GPU-backed and project debt.
• Equity has to fill the gap. Companies with large commitments need fresh equity to keep lenders comfortable.
• But equity reprices too. The same higher rates lower what investors will pay for long-dated growth.
• So timing becomes a trade-off. Go now at a lower price, or wait and risk funding gaps as commitments fall due.

Three companies made three different choices this month.

• Firmus waited. It's debt-heavy, and its story didn't support its price.
• OpenAI waited. Private money is still available to it.
• Anthropic went. It has huge commitments, the strongest growth and a window with no rivals.

Act 2: The players

Part III: What Anthropic is all about

Let's meet the company at the centre of this story.

Who they are: Anthropic was founded in 2021 by a group of former OpenAI researchers, led by siblings Dario and Daniela Amodei. It's about five years old.

It started as a safety-first AI lab. It has become, in practice, an enterprise AI company.

• Mission: build frontier AI safely. Anthropic is a public benefit corporation. It even warns investors about AI's risks in its own prospectus.
• Product: the Claude family of models, sold through an API, through Claude apps and through Claude Code. Its models also run on AWS, Google Cloud and Microsoft.
• Business model: enterprise-first and usage-based. Revenue grows with the work customers do, not with the number of people logged in.

Two numbers stand out. The founders' holding company owns a single Class F share that carries 50.1% of the vote on key matters, according to reports on the draft prospectus. And the session I drew on puts its active users at about 250 million, far fewer than OpenAI's, but much more valuable each.

Revenue: a near-vertical line

Anthropic's revenue growth is extraordinary. Here are the figures as reported.

• 2024 revenue: about $386 million.
• 2025 revenue: about $4.6 billion, roughly 12 times the year before.
• Q2 2025: $787 million.
• Q2 2026: about $11.5 bill
• First half of 2026: $16.3 billion.
• Q3 2026: $16.8 billion, up 45% on the previous quarter.
• Annualised run rate in September: about $67 billion, with a target near $100 billion by year-end.

Claude Code has driven much of this. Developers and businesses use it heavily, and they pay by usage.

There's a catch. Revenue is concentrated. Two customers made up about a quarter of 2025 revenue and about a fifth of first-half 2026 revenue.

Losses: scary headline, more nuanced reality

The draft prospectus reportedly shows a net loss of $80.7 billion for the first half of 2026. That's one of the largest half-year losses ever reported. Don't panic just yet.

Most of it isn't cash.

• About $72 billion came from an accounting charge on convertible notes. These are loans that can turn into shares. As Anthropic's valuation rises, those notes are worth more, and the increase is booked as a loss.
• A similar charge, about $34 billion, drove most of 2025's roughly $42 billion net loss.

An IPO that converts those notes into shares should end the charge.

But the operating loss is real. Anthropic lost about $8.1 billion from operations in 2025. Its compute and infrastructure spending was about $7.3 billion in 2025. In the first half of 2026 alone, it was about $9 billion.

Commitments: the giant purchase order

This is the figure that links Anthropic to the credit story.

• In September, reports put Anthropic's cloud and compute commitments at $518 billion, owed to six partners. About 80% are non-cancellable.
• By October, the Wall Street Journal reported more than $600 billion, with up to $150 billion more under negotiation.
• Anthropic ended 2025 with about $20 billion of cash.

Do the maths. Commitments are roughly 30 times year-end cash.

Two examples bring this to life.

• •Microsoft: Anthropic agreed to buy $30 billion of Azure capacity as part of Microsoft's November 2025 investment.
• Australia: the 1.44-gigawatt Dalby lease, reportedly about A$32 billion.

Those commitments can only be met by future revenue or new capital. That's the core reason Anthropic is going public.

Risks: 80 pages of them

Reports say the draft prospectus runs to about 261 pages. Around 80 of those are risk factors. Only 48 cover the business itself. That ratio tells you something.

The big risks include:

• Safety and existential risk. The prospectus reportedly warns that AI could pose catastrophic risks and that models may try to resist being shut down.
• Customer concentration. A handful of customers account for a large slice of revenue, and many aren't on long contracts.
• A government dispute. The US Department of Defense designated Anthropic a supply-chain risk in March 2026. In September, the DC Circuit upheld that exclusion under federal procurement law. A district court set aside a separate designation in August.
• Governance. The founders' Class F share controls 50.1% of key votes. Anthropic also has duties as a public benefit corporation.
• Commitments. More than $600 billion, mostly non-cancellable.
• Competition. OpenAI, Google and open-source models are all chasing the same customers.

What investors are actually buying

So what are you buying if you buy Anthropic shares? I'd split it into four boxes.

• Big and visible: enterprise growth, Claude Code demand and high gross margins.
• Known obligations: the $600 billion-plus of commitments, founder control and customer concentration.
• Big but uncertain: the size of the AI market, how long Anthropic's advantage lasts and what its margins look like in ten years.
• Smaller and uncertain: consumer reach and regulatory outcomes, including the defence dispute.

Investors can see the growth and the obligations clearly. What they can't see is the third box. That's exactly where pricing replaces valuation, as we will see in Act 3.

Growth engine, safety brand - and a giant purchase order

Part IV: Microsoft, backing both horses

Now for the company that has quietly placed a bet on both sides.

From OpenAI's partner to everyone's landlord

Here's how Microsoft's position has evolved.

• 2019: Microsoft invests $1 billion in OpenAI.
• 2023: total commitments to OpenAI reach about $13 billion.
• October 2025: OpenAI restructures. Microsoft ends up with about 27%.
• November 2025: Microsoft invests $5 billion in Anthropic. Anthropic commits to spend $30 billion on Azure.
• April 2026: OpenAI's terms with Microsoft are reset. The relationship becomes non-exclusive.
• July 2026: Microsoft books a $3.2 billion gain on Anthropic and a $600 million markdown on OpenAI in a single quarter.

Two days before those results, Satya Nadella reportedly warned that any company over-relying on a single AI lab "will not remain a firm". Microsoft practises what it preaches.

Two stakes side by side

Here's how the two investments compare.

• Money in: about $13 billion into OpenAI since 2019, against $5 billion into Anthropic.
• Ownership: about 27% of OpenAI, against a minority stake in Anthropic.
• Cloud tie: both have committed to large Azure spending. Anthropic's commitment is $30 billion.
• Accounting in Microsoft's 2026 financial year: OpenAI added $5 billion for the year but lost $0.6 billion in the fourth quarter. Anthropic added $3.2 billion in the fourth quarter alone.
• Exclusivity: ended for OpenAI in April 2026. Never existed for Anthropic, whose models also run on AWS and Google.
• In Microsoft products: GPT models power much of Copilot. Claude models now sit in Copilot and Foundry too.
• IPO: OpenAI isn't coming before 2027. Anthropic plans mid-November 2026.

One quarter of Anthropic matched a year of OpenAI

This chart says a lot. Microsoft's OpenAI stake added about $5 billion across its whole financial year. Its Anthropic stake added $3.2 billion in a single quarter.

A word of caution. These are accounting marks, not operating results. Microsoft doesn't revalue Anthropic every quarter.

But the IPO will change that. For the first time, Microsoft's Anthropic stake will have a public price. That's one more reason Redmond will watch November closely.

Getting paid twice

Microsoft's lab deals follow a simple loop.

• Microsoft invests equity in a lab.
• The lab commits to buy Azure compute.
• Azure revenue and Microsoft's order book grow.
• The lab grows on Microsoft's compute.
• The stake is marked up, and an IPO sets the price.

Microsoft gets paid twice: once through cloud revenue and once through the value of its stake. Critics call this circular. Supporters call it smart ecosystem building. Both can be true.

For scale, Microsoft's 2026 numbers are huge. Its contracted backlog is $678 billion. Azure revenue passed $100 billion, up 41%. Capex hit $115.9 billion. And it has $329 billion of data centre leases signed but not yet started.

Microsoft's hedge

Map Microsoft's model bets on two axes: how much it owns of each model maker, and how much infrastructure revenue it earns from them.

• Anthropic: a small stake but a big compute contract. Supplier first.
• OpenAI: a large stake and a large compute contract. Partner and owner.
• Open models on Foundry: a marketplace. Low ownership, some revenue.
• Its own MAI models on Maia chips: full control.

Whichever lab wins, Microsoft earns on compute and on equity. The Anthropic IPO validates that hedge. OpenAI's delay keeps Microsoft's biggest stake private and unpriced for now.

Even Microsoft faces the credit question

It's easy to forget, but Microsoft is part of the financing story too.

• Operating cash flow rose to about $183 billion in its 2026 financial year.
• Capex jumped to about $116 billion.
• Free cash flow actually fell slightly, to about $67 billion.
• Cloud gross margin has slipped to about 65%.

Microsoft can easily afford this. But when even the strongest balance sheet in tech sees free cash flow dip because of AI spending, you know how capital-hungry this build-out is.

Microsoft, Alphabet and Amazon report results next week. Their capex and cloud guidance will set the tone for the Anthropic roadshow, which is expected to start the week of 9 November.

Even Microsoft is now part of the financing question

Part V: Why Anthropic goes now, and why OpenAI waits

Two labs. One market. Opposite choices.

Five reasons Anthropic is going now

• Fund the commitments. More than $600 billion of mostly non-cancellable compute, against about $20 billion of year-end cash. The gap can only be closed by future revenue or new capital.
• Clean up the capital structure. The convertible notes drove $34 billion of non-cash charges in 2025 and $72 billion in the first half of 2026. An IPO can convert them into ordinary shares.
• Sell growth at its peak. Q3 revenue rose 45% on the quarter. The run rate is heading from about $67 billion towards $100 billion. That's a compelling pitch.
• Use a window with no rivals. The autumn calendar is down to Anthropic alone. Big investors see it as rare exposure to AI they can't ignore.
• Strengthen the credit chain. Public equity makes Anthropic's leases and contracts stronger collateral for the neoclouds and developers building for it.

Why OpenAI hasn't listed

OpenAI quietly filed a confidential draft prospectus on 8 June 2026. Then it chose to wait.

• Private money still flows. A pending private round is rumoured at about $1.4 trillion. Why accept a public discount?
• There's a valuation floor. Altman reportedly called any valuation below $1 trillion a "non-starter". This market might not clear it.
• Timing and safety. Altman told Fortune it would be "an ill-advised moment to go public". The CFO reportedly targets 2027.
• Heavy cash burn. Reports project about $27 billion of cash burn in 2026 and about $63 billion in 2027. That's a hard story to sell to public investors right now.
• A complex structure. OpenAI is a public benefit corporation controlled by a non-profit. Microsoft owns about 27% and takes a revenue share.
• A consumer-heavy model. OpenAI has about 1.2 billion users but earns less per user than Anthropic.

OpenAI filed confidentially - then chose to wait

Side by side

• Confidential prospectus: Anthropic filed on 1 June 2026. OpenAI filed on 8 June 2026.
• Public prospectus: Anthropic's is expected as early as late October. OpenAI has none.
• Planned timing: Anthropic markets from the week of 9 November and aims to trade before 26 November. OpenAI targets 2027.
• Valuation talk: Anthropic $1.5 trillion to $2 trillion, against a last private price of $965 billion. OpenAI wants more than $1 trillion, against a last private price of $852 billion.
• Run-rate revenue: about $67 billion for Anthropic on a gross basis. About $50 billion for OpenAI on a net basis.
• Customers: Anthropic is enterprise-led and usage-based. OpenAI is consumer-heavy.

The timing matrix

Here's how I'd map the decision. Put the need for capital on one axis and the quality of the market window on the other.

• Good window, low need: opportunistic. List if the price is right. SpaceX did this in June.
• Good window, high need: go now. The easy decision.
• Poor window, low need: wait. That's OpenAI.
• Poor window, high need: forced choice. That's Anthropic, and it was Firmus too.

The difference between Anthropic and Firmus is telling. Both faced a forced choice. Anthropic has demand that investors want. Firmus had debt that investors feared. So Firmus fell back on Nvidia.

Is now the right time?

Let's be fair to both sides. The case for going now:

• Scarcity. It's the only big AI IPO, so it won't compete for capital.
• It's arguably the fastest-growing large software business on record.
• It can raise equity before credit tightens further.
• It can convert the notes and end those giant non-cash charges.
• A lower entry price may suit long-term holders.

The case against:

• The 10-year yield sits around 5%, and markets expect a December hike.
• The Nasdaq has sold off twice, and chips are down more than a quarter from their peak.
• Anthropic's own suppliers, the neoclouds, are under credit stress.
• The headline loss, customer concentration and defence dispute will spook some investors.
• Post-midterm geopolitical risk and $110 oil add uncertainty.

Four ways November could play out

• Blockbuster: strong demand and calmer markets. It prices at $1.5 trillion to $2 trillion and pops.
• Priced to sell: strong demand despite weak markets. It lists at a discount and trades up.
• Downsized: weak demand, calmer markets. A smaller raise at a lower price.
• Delay: weak demand and weak markets. It's postponed, Firmus-style.

My base case today sits in "priced to sell".

What tips the balance? Five things: the public prospectus, next week's big-tech earnings, the 10-year yield against 5.25%, CoreWeave's credit and the US midterms on 3 November.

Act 3: Pricing the IPO

This act draws on a valuation session about the Anthropic IPO that was published on YouTube in October. I've added this month's context where it helps. The core argument is simple and important: young companies get priced, not valued. Let's see why.

Part VI: Value versus price

The pre-IPO meeting: In mid-October, Anthropic met a group of prospective institutional investors. Reports date it to 14 October. The session I drew on says 15 October.

At that meeting, Anthropic shared its annualised revenue run rate, about $67 billion. It said it expected that figure to approach $100 billion by the end of 2026. A draft prospectus then leaked on 20 October. The public version is expected as early as late October, with trading planned before 26 November.

The choice of metric is telling. Anthropic talked about a run rate, not last year's revenue. That's because its story isn't just about large revenue. It's about fast-growing revenue.

What is ARR?

ARR stands for annualised revenue run rate. You take revenue from the latest month and multiply it by 12. Some companies use a week and multiply by 52.

Most financial analysis uses trailing 12-month revenue, or TTM. That's simply the sum of the past four quarters.

Both have strengths and weaknesses.

• TTM is solid and audited. But it understates a business that's growing quickly. By the time you publish it, it's out of date.
• ARR shows where a fast-growing company is today. But it flatters the business if the latest month was unusually strong.

OpenAI and Anthropic have both adopted ARR enthusiastically. You can see why.

Same company, very different numbers

Here's what the choice does to the numbers.

• Anthropic: about $35 billion of trailing revenue against about $67 billion of ARR. That's roughly 1.9 times.
• OpenAI: about $20 billion of trailing revenue against about $50 billion of ARR. That's roughly 2.5 times.

The gap is the growth. It's also the room for spin.

History rhymes

This isn't new. Young companies always get priced against whatever metric is available.

In the late 1990s, dot-com companies were priced per website visitor.

In the early 2000s, Mary Meeker argued that time spent on a site mattered more. Analysts started pricing "eyeballs".

In the social and SaaS era, it was users, subscribers and ARR multiples.

In the AI era, we're seeing price per ARR dollar, per gigawatt of compute and per dollar of compute committed.

Expect metrics you've never seen before. Expect them to sound outlandish until they become normal.

Two ways to put a number on a company

There are two ways to put a number on a business.

• Value it. Start with the business. Forecast revenue growth, margins and reinvestment. Work out cash flows. Discount them for risk. You get an answer to the question "what is it worth?"
• Price it. Start with the market. Pick a multiple, such as price to revenue. Find similar companies. Adjust for differences. You get an answer to the question "what will people pay?"

In a perfectly efficient market, the two answers match. In practice, they often don't. So before you put a number on a company, decide which game you're playing. Young companies are far more likely to be priced than valued. In a credit squeeze, that price moves with interest rates.

Part VII: Company life cycles

Businesses follow a life cycle. Companies are a bit like people.

• Start-ups are babies
• Young growth companies are taking off.
• High-growth companies are at their peak.
• Mature growth and mature stable companies are in middle age.
• Declining companies are in old age.

Revenue rises, peaks and eventually falls. Margins start negative, turn positive and then fade.

Lazy shortcuts don't work

People often guess a company's stage from its age or its sector. Both are poor guides.

• Age misleads. A niche tech firm can reach a steady state in three or four years. An infrastructure firm can be 25 years old and only just getting going.
• Sector misleads. For 50 years, "tech" has meant "growth". But tech has mature and declining firms too. Tech companies age in dog years.

A 35- to 40-year-old tech company is, in life-cycle terms, roughly like a 200-year-old manufacturer.

Tell me your numbers, I'll tell you your stage. The better test is operating metrics.

• Revenue growth: high for young companies, low and steady for mature ones, shrinking in decline.
• Operating margin: negative for young companies, healthy for mature ones, falling in decline.
• Reinvestment: large when young, modest when mature.
• Free cash flow: very negative when young, positive when mature.
• Dividends: none when young, generous when mature.
• Failure risk: high when young, because the business depends on outside capital.

That last point is crucial for our story. Young companies need outside money. That's why the credit cycle matters so much to when and how they list.

Where the AI companies sit. Plot companies on growth against profitability and the picture is clear.

• Anthropic, OpenAI and the neoclouds sit in "young growth": high revenue growth, big operating losses.
• Nvidia and Microsoft sit in "high-growth winners": fast growth and strong profits.
• Mature cash cows sit lower down, with slow growth and strong margins.

Size doesn't change the stage. Anthropic may be worth $1.5 trillion, but by this test it's still a young company. That makes it dependent on capital markets.

A quick test. Try it yourself.

• Company A grows about 6% a year. It has a steady 30% operating margin. It reinvests little and pays large dividends.
• Company B grows very fast. It loses money. It reinvests heavily, burns cash and needs outside capital.

Company A is mature, whatever it thinks of itself. Company B is young

Part VIII: Anthropic against OpenAI, by the numbers

Two young companies at enormous scale

• Founded: Anthropic in 2021, OpenAI about 11 years ago.
• 2023 revenue: about $100 million for Anthropic, about $1 billion for OpenAI.
• Trailing revenue in 2026: about $35 billion for Anthropic, about $20 billion for OpenAI.
• Run rate in September 2026: about $67 billion for Anthropic, about $50 billion for OpenAI.
• Reporting: gross for Anthropic, net for OpenAI.
• Active users: about 250 million for Anthropic, about 1.2 billion for OpenAI

The overtake. Early on, OpenAI was far ahead. In 2025 its revenue was roughly three times Anthropic's. Then something shifted in 2026, and Anthropic pulled ahead on both trailing and run-rate revenue. What changed? Anthropic moved from individuals to enterprises, and from subscriptions to usage-based pricing. Claude Code supercharged that shift.

Gross versus net: not apples to apples. There's a catch in that comparison.

Imagine a customer pays $100 through a partner, and the partner keeps a share.

• Gross reporting, which Anthropic uses, books the full $100 as revenue and shows the partner's cut as a cost.
• Net reporting, which OpenAI switched to, books only what's left after the partner's cut.

The economics are identical. The top line isn't. So $67 billion against $50 billion probably overstates Anthropic's lead.

Losses on both sides. Both companies lose a lot of money.

• 2024 operating losses: about $3 billion at Anthropic and $5 billion at OpenAI.
• 2025 operating losses: about $8 billion at Anthropic and $39 billion at OpenAIthough some of OpenAI's may be one-off
• First half of 2026: Anthropic lost about $8.7 billion from operations. Its net loss was about $80.7 billion, mostly that non-cash charge

"Profitable on an adjusted basis". At the mid-October meeting, Anthropic claimed it had made money in the third quarter on an adjusted basis.

Handle this with care. Adjusted profit strips out items management considers one-offs. The speaker in the session was blunt: adjusted numbers are often fiction. The first half of 2026 still showed big operating losses. Wait for the public prospectus.

The verdict is simple. Both companies have high revenue growth and big operating losses. By the life-cycle test, they're young companies, whatever their size.

Part IX: Why valuation is hard

The three crutches

Analysts lean on three crutches when they value a company.

• Past financials. Five or ten years of growth and margins tell you how a business works. Anthropic has two or three years of messy statements.
• Market size and competition. You need to know how big the market is and what share you can win. The AI market is still forming. Bankers float total markets of $20 trillion to $30 trillion, which tells you how uncertain it is.
• Industry averages. Sector margins and costs of capital give you benchmarks. Anthropic has no comparable public peers.

Take away all three crutches and most people give up.

Six uncertainties. Here's what makes valuing an AI lab so hard.

• Current financials: short history, with revenue growing and everything else a mess.
• Market size: still up for grabs.
• Competitive advantage: nobody knows what will let the winners earn more than their cost of capital.
• Risk: there's no price history, and the company is now exposed to the credit cycle.
• Path to maturity: more than $600 billion of commitments means failure risk could stop it ever reaching a steady state.
• Governance: a founders' share holds 50.1% of key votes.

Where the uncertainty sits. The prospectus will help with some of this. It will clarify revenue definitions, losses, commitments and share classes.

It won't settle the biggest questions. How big will the market be? How long will Anthropic's edge last? What will its margins look like in 2035? What should its cost of capital be when yields are above 5%?

Those remain a matter of story.

The temptation. When valuation gets hard, people look for someone smarter to copy. In this case, that means venture capitalists.

The chain goes like this: too much uncertainty, no crutches, "I won't value it", copy what the VCs paid, and you end up with a price rather than a value.

VCs are smart and well informed. But their numbers are forward guesses about market price, not estimates of value. Copying them just imports their guess.

Part X: Venture capital pricing

Many rounds, huge sums

• Anthropic has had 19 venture rounds and raised about $135 billion. Its latest round, in May 2026, priced it at about $965 billion.
• OpenAI has had 10 rounds and raised about $185 billion. Its latest round, in March 2026, priced it at about $852 billion. A pending round is rumoured at about $1.4 trillion.
• IPO talk for Anthropic is $1.5 trillion to $2 trillion.

VC "valuation" is forward pricing in disguise. Here's how a VC typically arrives at a number.

• Pick an exit year, usually three to five years out. The choice is arbitrary.
• Forecast a metric for that year, such as revenue.
• Apply a multiple taken from public companies.
• That gives you an exit price.
• Discount it back to today at a "target rate" of 30% to 70%.

That last number isn't a measure of risk. It's a bargaining chip. VCs have every incentive to push it up and push the price down.

Here's the session's example. Say you expect $300 billion of revenue in five years. The big tech firms trade at about five to seven times revenue. Five times $300 billion is $1.5 trillion. Discount that at a made-up rate and you have today's round price.

Reverse-engineering a $965 billion round

In May 2026, the expected IPO price for Anthropic was about $1.15 trillion. VCs priced the round about $150 billion lower to allow for the wait and the risk of delay.

Why $150 billion? Why not? The round price is a guess at a future market price minus a haircut. It tells you nothing about intrinsic value.

Firmus shows the flip side. It sought nearly three times its last private price, and public investors refused.

Pre-IPO rounds tell you even less. Normal VC rounds project five to seven years ahead. Pre-IPO rounds project only to the IPO itself, and guess what the market will pay next year.

Think of a two-by-two with time to exit on one axis and how much a round tells you about intrinsic value on the other.

• Fundamentals-led exits: rare. These are near-term exits priced on fundamentals, such as a strategic sale.
• Early-stage VC: forward pricing of a distant metric.
• Late-stage growth: a mix of pricing and fundamentals.
• Pre-IPO VC: guessing next year's IPO price. That's where Anthropic's May round and OpenAI's pending round sit.

The takeaway is simple. We give too much weight to what VCs think.

Part XI: The mechanics of pricing

Three steps. Pricing has three steps.

• Choose a multiple: a market value divided by something that scales, such as revenue.
• Find a peer group: the companies investors will compare yours with.
• Compare and adjust: control for differences in growth, risk and margins.

Here's an example. Your company trades at 15 times earnings. Its peers trade at 20. Before you call it cheap, ask why. Lower growth or higher risk might fully justify the gap.

Building a multiple

You can't compare share prices directly, because a share split changes the price. So you divide market value by a common scalar.

For the top of the fraction, use market capitalisation, enterprise value or, for private firms, the latest VC price.

For the bottom, choose from:

• revenue or ARR;
• earnings, such as net income or operating income;
• cash flow, such as EBITDA or free cash flow;
• book value;
• for pre-revenue firms, users, subscribers or total market size.

Which year do you use?

There's no right answer. You can use trailing revenue (about $35 billion), the September run rate (about $65 billion), the year-end run rate (about $110 billion) or 2030 revenue (about $300 billion). Each gives a very different multiple.

The further forward you go, the lower the multiple looks. And the more of the bull case you've quietly assumed.

Multiples change with age

• Start-ups get priced on users, subscribers or market size.
• Young growth companies get priced on revenue or ARR multiples.
• High-growth companies get a mix of revenue and some earnings multiples.
• Mature growth companies get forward price-earnings or PEG ratios.
• Mature stable companies, like Microsoft, can use almost any multiple.
• Declining companies get priced on book value.

Below revenue and gross profit, everything is negativ

For Anthropic, revenue and gross profit are positive. EBITDA, operating profit and net income are all negative. A multiple needs a positive number at the bottom. So you're pushed up the income statement until you hit revenue.

Expect enterprise value to revenue, and enterprise value to ARR, to dominate the debate.

Part XII: AI-specific pricing metrics

At $1.5 trillion, about 23 times run rate

Let's do some sums.

• $1.5 trillion divided by about $65 billion of ARR is about 23 times.
• At $2 trillion, it's about 31 times.
• OpenAI at a similar $1.5 trillion on about $50 billion of ARR would be about 30 times.

On that basis, Anthropic looks cheaper than OpenAI. But remember gross versus net. Sort that out before you compare.

The forward-number trick

Watch how the multiple shrinks as you look further ahead.

• At $1.5 trillion: about 23 times September ARR, about 13.6 times year-end ARR and about 5 times 2030 revenue.
• At $2 trillion: about 31 times, 18 times and 6.7 times.

The session predicts that Anthropic's bulls will reach for "really, really forward numbers". It makes the multiple look reasonable.

But a 2030 multiple quietly assumes the 2030 revenue arrives. And with yields above 5%, revenue that far out is worth less today than it was a year ago.

The further forward the revenue, the cheaper it looks

Compute as a metric. Compute is scarce. So compute itself can be sold as an asset.

According to the session, OpenAI spent about $15 billion on compute in 2026 and Anthropic about $6 billion. By 2028, those figures could reach about $111 billion and $28 billion. Anthropic's total commitments now exceed $600 billion, about 80% of them non-cancellable.

Compute is scarce - so compute can be marketed as an asset

Turning a liability into a selling point. Normally, big commitments are a worry. In pricing, a lab can spin them.

• The bull framing: "We have locked in more than $600 billion of scarce compute. Our market value is only about 2.5 to 3.6 times that. A rival trades at six times. We're cheap."
• The bear framing: "Those are fixed obligations for a loss-making company. That's financial risk, not an asset."

This month's neocloud selloff shows the market leaning towards the bear view.

Users as a metric. By users, OpenAI looks like the bargain.

• OpenAI has about 1.2 billion active users. At $1.5 trillion, that's about $1,250 per user.
• Anthropic has about 250 million. At $1.5 trillion, that's about $6,000 per user.

So why don't Anthropic's critics shout about this? Because until 2024, users were a decent proxy for revenue. Anthropic broke that link by moving to enterprises and usage pricing. Its "user" is often a business spending tens of thousands of dollars.

Why users stopped predicting revenue. Picture a two-by-two. One axis runs from consumer to enterprise. The other runs from subscription to usage pricing.

• Consumer subscriptions: chat apps on monthly plans. Users roughly equal revenue. That's OpenAI's core.
• Enterprise seats: per-seat business plans, like Microsoft 365 Copilot.
• Enterprise usage: APIs and agents billed per token. Revenue scales with the work done. That's Anthropic in 2026.
• Consumer usage: pay-as-you-go individuals.

When revenue comes from enterprise usage, revenue per user varies wildly. User-based multiples stop working.

Pick your poison. Each side will push the metric that flatters it.

• Price to current ARR: Anthropic looks cheaper, partly thanks to gross reporting.
• Price to 2030 revenue: Anthropic looks cheaper, because of its steeper growth.
• Price per user: OpenAI looks cheaper.
• Price per unit of compute capacity: it depends. OpenAI had more early; Anthropic is catching up.
• Price per dollar of compute committed: Anthropic looks cheaper.

Bias drives pricing just as it drives valuation. It shows up in the metric you choose.

Part XIII: Peer groups

Easy, messy and hard

Peer groups come in three flavours.

• Easy: dozens of public peers with live prices and common accounting.
• Messy: many private peers with stale VC prices.
• Hard: one or two private peers. That's Anthropic.

OpenAI is private and waiting until 2027. SpaceX is public but has rockets and satellites muddying the picture. There's no clean comparison.

The Magnificent Seven (Mag 7) as yardstick

A peer group isn't really "companies just like yours". It's the companies your investors will compare you with.

At $1.5 trillion to $2 trillion, that means the Magnificent Seven: Alphabet, Apple, Amazon, Meta, Microsoft, Tesla and Nvidia.

There's a twist. Three of them, Amazon, Alphabet and Microsoft, are Anthropic's cloud suppliers or investors. And Meta fell 7% this week on AI spending worries. The peer group is also the supply chain.

Against the Magnificent Seven. Together, the Magnificent Seven trade at about eight to nine times revenue.

At $1.5 trillion, Anthropic trades at:

• about 48 times trailing revenue, as stated in the session;
• about 15 times 2026 revenue;
• about 5 times projected 2030 revenue.

Use current revenue and Anthropic looks five or six times as expensive. Use 2030 and it looks cheap. As the session puts it, you walk in with a bias and find a metric to back it up.

Part XIV: From metrics to value

Two simple tests. Before you trust a metric, run two tests.

• Does it keep your sample? If a metric drops half your peers, don't use it. Price-earnings ratios fail for loss-makers. For AI labs, revenue or ARR survives.
• Can you connect it to value? Work out what drives the multiple in a proper valuation. Those drivers are what you must control for.

There's no perfect metric. There's the metric you use, and the discipline to control for what drives it.

What drives a price-earnings ratio

Three things drive a P/E ratio.

• Higher expected earnings growth raises it.
• Higher return on equity raises it, because you need less reinvestment for the same growth.
• Higher risk, measured as cost of equity, lowers it.

That third point matters right now. Rising yields push up everyone's cost of equity. They lower every multiple.

Nobody will use a P/E for Anthropic, because it has no earnings. But the same logic applies to revenue multiples.

What drives a revenue multiple. Four things drive enterprise value to revenue.

• Revenue growth. Can a run rate of about $67 billion become hundreds of billions quickly? A 45% quarter helps
• Operating margins. Can high gross margins and scale lift operating margins to 30% to 40%?
• Reinvestment efficiency. Can revenue grow faster than compute spending, which was about $9 billion in the first half alone?
• Risk. Will regulatory, concentration and commitment risks fade, while yields stay above 5%?

Pricing hides the same assumptions

When you price a company, you make exactly the same assumptions as when you value it. You just make them implicitly.

Avoiding a valuation doesn't avoid the assumptions. It hides them. The honest move is to make them explicit.

What must be true for $1.5 trillion to $2 trillion

For the bull case to hold, four things need to happen.

• Revenue growth: a run rate of about $67 billion must become hundreds of billions, fast.
• Profitability: operating margins must climb to 30% to 40%.
• Unit economics and scale: compute costs must grow more slowly than revenue, despite $600 billion-plus of commitments.
• Risk: regulatory, concentration and financing risks must converge towards normal.

Is that plausible? Yes. Is it easy? No. And a higher cost of capital raises the bar.

Bulls, bears and the elephant

The bulls will tell you:

• AI is a vast market.
• Anthropic is one of the big winners.
• Gross margins are high and enterprise usage is sticky.
• Forward multiples look reasonable.

The bears will tell you:

• Regulation, reliability or credit could slow the market.
• The founders control the company through a special share.
• The revenue multiple can't be earned back.
• The commitments are too big to cover.

Both are right about something. It's the old fable of the blind men and the elephant. Each touches one part and describes a different animal. One feels the market size. Another feels the growth rate. A third feels the loss. A fourth feels the commitments.

Your job is to step back and see the whole animal.

Act 4: The verdict

Part XV: Is this the right time?

The whole story in one chain

Let's pull the threads together.

• AI needs about $1 trillion of capital a year.
• Credit has become the bottleneck.
• Yields above 5% squeeze the companies that borrow to build.
• Equity, including IPOs, must fill the gap.
• Anthropic's IPO will be priced, not valued.
• That price will help set the credit terms for the whole chain.

The loop closes neatly. Anthropic's IPO price will be set by the same credit conditions that its $600 billion-plus of commitments depend on. A strong deal strengthens the lenders to neoclouds and data centres. A weak one tightens the chain further, just as Firmus's pulled IPO did in Australia.

My timing scorecard

I have scored eight factors. Three support going now. Five argue for waiting.

Supporting a listing now:

• Company momentum: Q3 revenue up 45% on the quarter, with a run rate of about $67 billion.
• Need for capital: more than $600 billion of commitments against about $20 billion of year-end cash.
• Competition for capital: it's the only big AI IPO, and OpenAI is waiting.

Arguing for waiting:

• Interest rates: the 10-year yield sits around 5%, with a December hike widely expected.
• Equity markets: two Nasdaq selloffs, and chips more than 26% off their peak.
• The credit chain: neoclouds under stress, and Firmus pulled.
• Company-specific risk: customer concentration, the defence dispute and founder control.
• Macro and politics: the midterms on 3 November, Iran and oil above $110.

My net read? This isn't an ideal window. But Anthropic is the one issuer whose need and demand are strong enough to go anyway. I'd expect it to be priced to sell, not at the top of the range.

Who wins and who waits. Here's how each player fares under the two main outcomes.

If the IPO succeeds:

• Anthropic funds its commitments and ends the convertible-note charges.
• Microsoft gets a public price on its stake, and its $30 billion Azure deal looks safer.
• OpenAI gets a public comparison for its own 2027 listing.
• Nvidia sees a stronger demand chain and needs to offer fewer backstops.
• Neoclouds and developers find their contracts become better collateral.
• Investors get rare exposure to a frontier AI lab.

If it's delayed or weak:

• Anthropic leans on private money and partners.
• Microsoft's hedge stays intact through OpenAI and its own models.
• OpenAI looks wise for waiting.
• Nvidia offers more backstops, Firmus-style.
• Neoclouds face tighter credit for longer.
• Investors may get a lower entry price later.

Six signals to watch over the next four weeks

• The public prospectus. Look at the final figures, gross versus net revenue, share classes and use of proceeds.
• Big-tech earnings. Microsoft, Alphabet and Amazon report next week. Watch their capex and cloud demand.
• The 10-year yield. A move back above about 5.25% would hurt the roadshow.
• Neocloud credit. CoreWeave's bonds and credit default swaps are the best stress gauge.
• Firmus and Nvidia. Does vendor money replace public money?
• The midterms and oil. The vote on 3 November, Iran risk and Brent above $110.

Conclusion: the chips are on order

The chips are on order. The question is who pays.

AI's bottleneck has moved from chips to credit, and October made it visible. Yields pushed past 5%. The Nasdaq sold off twice in a week. Neoclouds lost a fifth of their value in five days. Firmus pulled its IPO, and Nvidia stepped in again.

Into that market, Anthropic is bringing the only big AI IPO of the season. It brings extraordinary growth, more than $600 billion of commitments and a shareholder, Microsoft, that has also backed its fiercest rival.

It will be priced, not valued. That price will reflect the credit cycle as much as it reflects Claude.

Sources and notes:

This piece draws on reporting from CNBC, Reuters, Yahoo Finance, the Wall Street Journal (as reported by others), TechCrunch, Portfolio Terminal, ABC News, Tech Insider and others, published between September and October 2026. It also draws on Microsoft's 2026 financial results, SemiAnalysis's work on AI financing, and a valuation session on the Anthropic IPO published on YouTube in October 2026.

Some important caveats:

• Anthropic's financial figures come from press reports of a draft prospectus. The public filing may differ.
• Figures attributed to the valuation session are as the speaker stated them. Some came from an auto-generated transcript.
• The two-by-two placements and the timing scorecard are my own judgements.