For years, the OpenAI-versus-Anthropic rivalry played out in model benchmarks and enterprise contracts. In 2026, it’s playing out on Wall Street. Both companies have confidentially filed IPO paperwork with the SEC — but reporting suggests Anthropic is on track to reach the public markets first, and potentially at a larger valuation. Here’s how the two AI leaders actually compare, number for number.

Key Takeaways

• Both Anthropic and OpenAI have confidentially filed for an IPO with the SEC, but Anthropic’s listing is reportedly targeted for September or October 2026, ahead of OpenAI’s, which is seen as more likely in 2027.
• Anthropic’s revenue run rate reportedly reached $65 billion by end of July 2026, versus OpenAI’s most recently reported run rate of roughly $40 billion.
• Anthropic’s last private valuation was $965 billion (May 2026 Series H); reported IPO valuation target is ~$2 trillion.
• Morgan Stanley, Goldman Sachs, and JPMorgan are reportedly leading Anthropic’s offering — the same trio that anchored the SpaceX IPO.
• The two companies may not calculate revenue the same way, which complicates a clean apples-to-apples comparison.
• Neither company has confirmed final valuation, share pricing, or exact listing date.

The Race to Wall Street: Timeline Comparison

Revenue Growth: Anthropic’s Steeper Curve

The headline gap between the two companies isn’t just the absolute revenue number — it’s the shape of the growth curve. Anthropic’s run rate moved from roughly $9 billion at the end of 2025 to $65 billion by the end of July 2026, a sevenfold increase in about seven months. OpenAI’s run rate, by contrast, has roughly doubled over a comparable period, from about $20 billion to $40 billion, according to figures shared internally by OpenAI co-founder Greg Brockman.

Both trajectories are, by any historical standard for software companies, extraordinary. But Anthropic’s pace of acceleration is the steeper one right now, and it’s the reason bankers are willing to entertain a valuation approaching $2 trillion despite the company’s last private mark sitting at less than half that figure just months earlier.

One caveat matters here: the two companies may not measure revenue the same way. Run-rate methodology, what counts as recognized revenue, and treatment of enterprise contracts versus consumer subscriptions can all vary. A side-by-side comparison should be read directionally, not as a precise scientific measurement.

Why Anthropic Might Get There First

Several structural factors point toward Anthropic reaching Wall Street ahead of OpenAI:

1. Filing timeline. Anthropic’s confidential S-1 was filed June 1, 2026, giving it a multi-month head start in the SEC review process relative to OpenAI’s reported filing.
2. Underwriter readiness. Morgan Stanley and Goldman Sachs are reportedly close to finalizing lead roles, with Citigroup and Barclays also expected to join the syndicate — a sign of advanced deal preparation.
3. Capital structure prep. Anthropic is finalizing a reported $15 billion pre-IPO credit facility, a step companies typically take shortly before a public listing to shore up balance sheet flexibility.
4. Corporate structure decisions. Anthropic is reportedly considering super-voting shares for co-founder Dario Amodei and other founders — the kind of governance decision typically finalized in the run-up to a roadshow.

Valuation Multiples: Which Company Is Priced More Aggressively?

Using Anthropic’s reported figures, a $2 trillion valuation implies:

• ~30x trailing 2026 run rate ($65B)
• ~17–20x projected full-year 2026 revenue ($100–120B)
• ~10x projected 2028 revenue ($190–200B)

OpenAI’s IPO valuation target has not been reported with the same specificity, making a direct multiple comparison premature. What can be said is that Anthropic’s reported multiple sits below software comparables like Palantir (53x revenue) and Cloudflare (41.6x revenue), suggesting bankers are not pricing Anthropic at the most extreme end of current AI/SaaS valuations — even at $2 trillion.

Investor Positioning: How Institutional Money Is Splitting Its Bets

Institutional investors exposed to both companies through earlier private funding rounds are unlikely to view this as a binary, winner-take-all outcome. The broader enterprise AI software market has shown room for multiple scaled players — Anthropic leaning into coding and agentic enterprise workloads, OpenAI maintaining a broader consumer and developer platform footprint. For investors building exposure through AI-focused ETFs or diversified tech portfolios, the more relevant question may not be “which company wins” but how much combined market cap the sector can support once both companies are public.

What Could Change the Order

• Regulatory review delays. SEC review timelines are not guaranteed; either company’s IPO could slip.
• Market conditions. U.S. IPOs had raised $160.6 billion through August 19, 2026, closing in on the 2021 record of $195.2 billion — a hot market that could cool and affect timing for either company.
• A surprise OpenAI acceleration. If OpenAI’s board decides to move up its own filing timeline in response to Anthropic’s progress, the “who’s first” narrative could shift quickly.

FAQ

Is Anthropic definitely going public before OpenAI?

It’s the most likely outcome based on current reporting — Anthropic filed confidentially in June 2026 and is targeting a fall listing, while OpenAI’s IPO is seen as more likely in 2027 — but neither timeline is confirmed or guaranteed.

Which company has higher revenue: OpenAI or Anthropic?

As of the most recent reporting, Anthropic’s revenue run rate (~$65 billion) is reported higher than OpenAI’s (~$40 billion), though methodology differences mean this isn’t a perfectly apples-to-apples comparison.

Will OpenAI and Anthropic use the same underwriters?

Anthropic is reportedly working with Morgan Stanley, Goldman Sachs, and JPMorgan. OpenAI’s underwriting syndicate has not been confirmed in current reporting.

Should investors buy both companies once they’re public?

That depends on individual risk tolerance, portfolio construction, and valuation at the time of listing. Diversifying across AI infrastructure and enterprise software exposure — rather than concentrating in a single name — is a common approach financial advisors suggest during high-profile IPO waves.

Anthropic’s path toward a public listing has put a single number under a microscope: $2 trillion. That’s the valuation investors reportedly expect the Claude creator to target when it lists on Nasdaq, according to the Financial Times — a figure that would more than double its last private valuation of $965 billion, set just months earlier. The question every institutional and retail investor is asking is whether that number reflects genuine fundamentals or momentum-driven excess.

Key Takeaways

• Anthropic’s revenue run rate went from $9 billion to $65 billion in roughly seven months — one of the fastest scaling curves ever recorded for a company of this size.
• At $2 trillion, the IPO valuation implies roughly 10x Anthropic’s projected 2028 revenue of $190–200 billion, but over 30x its 2026 revenue of an estimated $100–120 billion.
• By comparison, Palantir trades near 53x revenue and Cloudflare near 41.6x — meaning Anthropic’s multiple isn’t the most extreme in the software sector.
• The company reported a net loss of nearly $42 billion in 2025, though it reached positive adjusted operating income in Q2 2026.
• A $15 billion pre-IPO credit facility and heavy compute spending commitments are central to the bear case.
• The $2 trillion figure is a market expectation reported via investors and bankers — Anthropic itself has not confirmed a target valuation.

The Bull Case: Growth at a Scale Nobody Has Seen Before

Start with the headline number. Anthropic’s annualized revenue run rate — a snapshot metric that extrapolates a recent period of sales into a full-year figure — moved as follows, according to Bloomberg’s reporting sourced to people familiar with the company’s finances:

That’s a sevenfold increase in a single year. Preliminary Q2 2026 revenue reportedly exceeded $11.5 billion — more than 14 times what the company generated in the same quarter of 2025, and more than double Q1’s $4.73 billion. Few software or infrastructure companies in history have compounded at that pace at this scale.

Bulls argue that Anthropic’s coding-focused Claude models have become deeply embedded in enterprise software workflows, giving the company durable, expanding B2B SaaS-style revenue rather than one-off consumer spending. One investor told the Financial Times that 800% annual growth justifies a multiple north of 30x revenue on a trailing basis.

The Bear Case: A Run Rate Is Not Revenue

Skeptics point to a more mundane but important technical distinction: a run rate is not audited, trailing revenue. It takes a short window — sometimes as narrow as a single hot month — and multiplies it across twelve months as though that pace holds steady. Anthropic’s Q2 2026 revenue of $11.5 billion works out to roughly a $46 billion annualized pace on its own; the $65 billion figure implies July alone ran meaningfully hotter than the quarter that preceded it.

Add to that:

• A reported net loss of approximately $42 billion in 2025, roughly five times the $8.3 billion loss the year before
• Continued heavy compute infrastructure spending, including a multi-year arrangement with SpaceX potentially worth tens of billions of dollars
• No audited prospectus yet in public form — all current figures come from investor briefings and reporting, not SEC-reviewed financial statements

How the Multiple Actually Stacks Up

Here’s where the valuation debate gets genuinely interesting rather than just directional. Bankers are reportedly using a two-year forward horizon rather than the standard one-year “NTM” (next-twelve-months) multiple, arguing that Anthropic’s near-term revenue understates its real trajectory.

This is the crux of the bull argument: on a two-year-forward basis, $2 trillion doesn’t look unreasonable relative to comparable high-growth software and AI infrastructure names. On a trailing basis, it looks aggressive but not unprecedented for a company growing revenue sevenfold annually.

What Could Break the Thesis

1. Growth deceleration. If the run rate stalls anywhere near current levels rather than compounding toward $100–120 billion by December, the forward multiples used to justify $2 trillion collapse quickly.
2. Margin durability. Positive adjusted operating income in Q2 2026 is an encouraging signal, but “adjusted” figures typically exclude stock compensation and other costs that show up in GAAP net losses.
3. Customer concentration and competitive pressure. OpenAI’s run rate, reported around $40 billion, shows the enterprise AI market can support more than one scaled winner — but also that pricing power isn’t guaranteed to either party long-term.
4. Compute cost inflation. The $15 billion pre-IPO credit facility signals how capital-intensive scaling a frontier AI lab remains, even with fast-growing revenue.

The Verdict: Priced for Perfection, Not Necessarily Overpriced

Calling Anthropic “overvalued” or “undervalued” at $2 trillion depends almost entirely on which multiple you anchor to and whether you trust the 2028 revenue projection underpinning the banker math. On a trailing basis, the valuation assumes near-flawless execution of an already extraordinary growth trajectory. On a forward basis, it looks more defensible against the current wave of high-growth enterprise AI and SaaS multiples.

For investors evaluating enterprise AI software and B2B SaaS exposure more broadly, Anthropic’s pricing will likely become the reference point the way Snowflake’s IPO once set the bar for cloud data multiples — for better or worse.

FAQ

What does Anthropic’s $65 billion revenue run rate actually mean?

It’s an annualized projection based on a recent, short period of sales (reportedly the end of July 2026), not audited trailing twelve-month revenue. It shows the pace of growth, not confirmed full-year income.

Is a $2 trillion valuation reasonable for Anthropic?

It depends on the time horizon. Against 2028 revenue projections of $190–200 billion, the implied multiple (~10x) looks comparable to or cheaper than Nvidia. Against 2026 revenue, the multiple is closer to 17–30x, more aggressive but within range of high-growth SaaS comparables like Palantir and Cloudflare.

How does Anthropic’s valuation compare to OpenAI’s?

OpenAI’s most recently reported revenue run rate sits around $40 billion, below Anthropic’s reported $65 billion, though the two companies may measure revenue differently and OpenAI’s IPO timeline is reportedly further out, into 2027.

Has Anthropic confirmed the $2 trillion figure?

No. It originates from investors and bankers cited by the Financial Times, not from Anthropic’s own public guidance.

Atreides Management CIO Gavin Baker argues the AI market has the story backwards: rather than an oversupply bubble, he sees a severe and persistent compute shortage that could keep token costs elevated — and by some estimates rising as much as 10x — through 2028. His firm’s own internal AI spending grew roughly 100x from March to August 2026 while continuing to double monthly, a data point he’s used publicly to illustrate how fast real-world demand is actually accelerating beneath a stock market that sold off sharply in July and August.

Gavin Baker’s AI Thesis at a Glance

Sources: Invest Like the Best podcast (“Watts and Wafers,” May 2026), a16z Podcast (late August 2026), Sohn New York Conference (2026), and Baker’s public statements via X, as reported by Yahoo Finance, BigGo Finance, and HedgeFundAlpha — all within the 90-day recency window except the May 2026 podcast episodes, cited for foundational framework context.

Deep Dive: The Contrarian Case for Undersupply, Not Oversupply

The Core Argument: “Can You Name One Data Point That’s Getting Worse?”

Baker has framed his entire thesis around a simple diagnostic question he says he puts to every AI company he speaks with: can they identify a single quantitative business metric that deteriorated in July or August 2026? By his own account, he could not find anyone who said yes — even as public AI stocks fell 40–60% from their highs during the same window. That divergence between falling share prices and, in his telling, uniformly strong underlying business metrics is the foundation of his contrarian call: the market drawdown reflects sentiment and positioning, not a change in the fundamental demand picture.

Two Physical Constraints: Watts and Wafers

Baker’s framework centers on two hard physical bottlenecks he believes will govern the next phase of AI infrastructure buildout, independent of capital availability or corporate willingness to spend: electricity (“watts”) and semiconductor manufacturing capacity (“wafers”). On power, his view is that the near-term shortage begins to ease in 2027 and 2028 as new energy sources come online, with orbital compute — solar-powered data centers in space — offering a longer-term structural solution he believes could eventually make some terrestrial data center capacity optional. On wafers, he points to TSMC’s capacity allocation decisions as potentially the single most important variable determining how fast the broader AI buildout can proceed, distinguishing the current cycle from the dot-com bubble on the grounds that physical manufacturing capacity, not speculative capital, is the binding constraint this time.

The Compute Payback Math That Underpins His Bullishness

Central to Baker’s argument is a specific unit-economics claim: citing data from neocloud providers Nebius and CoreWeave, he estimates the payback period for a gigawatt of AI compute capacity at roughly 9 to 10 months — an unusually fast capital-recovery timeline for large-scale infrastructure investment. He extends this into a broader monetization framework: a lab allocating, say, 8 of 10 gigawatts of available power to revenue-generating inference, at a monetization rate around $60 billion per gigawatt annually, could generate roughly $480 billion in revenue — implying a roughly one-year payback on a revenue basis for that capacity. Baker’s own frame acknowledges this creates genuine structural volatility unique to this technology cycle: a single research breakthrough could prompt a lab to reallocate that same power toward training rather than inference, cutting the implied revenue dramatically overnight in a way that had no clear analogue in the prior internet infrastructure buildout.

Demand Diffusion Has Barely Started, By His Count

Baker’s demand-side argument rests on a stark diffusion gap: he estimates fewer than 10 million people globally are currently heavy paying users of AI products, against a backdrop of roughly 1.5 billion knowledge workers worldwide who represent the theoretical addressable market. He also points to a real-world cost signal as evidence of undersupply rather than oversupply: prices for older-generation GPUs, he notes, were still rising through 2026 — a pattern he says few people anticipated as recently as 2024 or 2025, and one that is difficult to reconcile with a narrative of excess capacity sitting idle.

The “Bottleneck Trade” Is Evolving, Not Disappearing

Baker has also described what he calls the “bottleneck trade” — concentrated positioning in companies that control scarce resources across the AI supply chain, including TSMC wafer capacity, power generation, cooling systems, optics, and networking equipment — as a trade that is “winding down” in its original form as some physical chokepoints ease, even as he maintains that compute broadly remains severely undersupplied relative to underlying demand. This is a more nuanced position than a blanket “shortage forever” call: specific bottlenecks (certain equipment categories) may be resolving even as the aggregate compute-versus-demand gap persists.

Where the Application Layer Fits — Or Doesn’t

Perhaps Baker’s most pointed critique is reserved for the application layer of the AI stack rather than infrastructure. He has argued that even prominent AI-native application companies have net-destroyed economic value at the application layer, potentially in the trillions of dollars in aggregate, as competitive pressure and thin differentiation erode margins faster than revenue scales. His conclusion is that durable value in this cycle accrues disproportionately to owners of scarce infrastructure and compute — chips, power, and specialized silicon — rather than to companies building products on top of frontier models, a view that shapes Atreides’ own concentrated positioning in infrastructure names over application-layer bets.

The Important Caveat Investors Should Weigh

Every element of this thesis comes from a fund manager who is, by his own extensive public disclosure, long most of the positions his framework favors — infrastructure, memory, and private silicon names. That doesn’t invalidate the analytical framework, but it does mean the specific conclusions (which sectors will outperform, which trades are “washed out”) reflect a vested interest and should be treated as claims to pressure-test against independent data rather than a neutral forecast.

Actionable Takeaways for Investors

1. Distinguish stock-price drawdowns from business fundamentals before reacting to AI-sector selloffs. Baker’s framework suggests checking a handful of hard operating metrics (revenue growth, capacity utilization, backlog) for AI-exposed holdings before assuming a share-price decline reflects deteriorating fundamentals.
2. Track GPU secondary-market pricing as a real-time demand signal. Persistent or rising prices for older-generation GPUs is one of the more falsifiable, checkable claims in this thesis — it’s public market data, not a private assertion.
3. Watch TSMC capacity allocation announcements and energy-project timelines as the two key physical catalysts. Per this framework, easing in either wafer capacity or power availability — expected to begin in 2027–2028 on the power side — would be the leading indicator of the shortage narrative shifting toward resolution.
4. Separate infrastructure exposure from application-layer exposure when sizing AI-related positions. Baker’s value-destruction critique of the application layer is a useful lens for distinguishing picks-and-shovels exposure from higher-risk, thinner-margin application bets, regardless of whether you share his specific stock calls.
5. Weight any single fund manager’s thesis by its own disclosed bias. Use Baker’s framework as one analytical lens among several — his specific security-level calls carry the same conflict-of-interest caveat as any concentrated long-only manager discussing his own book.

Frequently Asked Questions

Does Gavin Baker think there is an AI bubble? No — Baker has explicitly argued the opposite of the prevailing bubble narrative, contending that the AI industry faces a severe, largely self-inflicted compute shortage rather than oversupply, based on his inability to find deteriorating business metrics among AI companies even during a sharp July–August 2026 stock selloff.

How long does Gavin Baker think the AI compute shortage will last? Baker’s framework points to the shortage easing on the power (“watts”) side starting in 2027 and 2028 as new energy sources come online, though he separately suggests token costs could keep rising — potentially by as much as 10x — through 2028 given the scale of the demand-supply gap he describes.

What is Atreides Management and who is Gavin Baker? Gavin Baker is the founding partner and CIO of Atreides Management, a fund he launched in 2019 after running Fidelity’s roughly $17 billion OTC Portfolio for eight years; Atreides holds concentrated positions across AI infrastructure, memory, and private semiconductor companies.

What is the “bottleneck trade” in AI investing? The bottleneck trade refers to concentrated investment positioning in companies that control physically scarce resources across the AI supply chain — including semiconductor wafer capacity, power generation, cooling, optics, and networking equipment — a trade Baker says is evolving as certain specific chokepoints ease even as the aggregate compute shortage persists.