AI

AI Voice Phishing 2026: How Vishing Is Forcing Banks to Adapt

Published

1 hour ago

September 6, 2026

The reliable “tells” that once let a wary consumer spot a scam call — bad grammar, robotic cadence, obvious accent mismatches — have largely disappeared. In 2026, an AI-generated voice can convincingly clone a real person from as little as three to ten seconds of audio, adapt its script in real time under questioning, and pass through a spoofed number that appears to originate from a legitimate bank fraud line. The result is a category of fraud that has moved from a nuisance to a board-level risk, forcing financial institutions to rewrite verification protocols that have gone essentially unchanged for a decade.

Key Takeaways

Financial institutions reported a 32% rise in deepfake-related fraud attempts in 2025, with over 10% of banks reporting individual deepfake vishing losses exceeding $1 million per case.
Fraudsters need as little as 3–10 seconds of audio to clone a voice convincingly, with deepfake audio now achieving over 90% accuracy in mimicking real voices, according to multiple 2026 fraud research compilations.
Vishing now accounts for over 60% of phishing-related incident response engagements, and in more than 80% of voice phishing attacks, attackers use spoofed caller IDs to make calls appear to originate from legitimate numbers.
The 2024 Arup case remains the reference incident for enterprise risk: an employee at the UK engineering firm authorized 15 wire transactions totaling $25.6 million after joining a video call featuring convincing real-time deepfakes of the company’s CFO and several executives.
Verizon’s 2026 Data Breach Investigations Report tracks pretexting (synchronous voice or chat manipulation) at 6% of initial access vectors, with phone-based phishing simulations showing a median click rate roughly 40% higher than email-based simulations.

Why Deepfake Vishing Broke the Old Verification Model

Voice-based identity verification has historically relied on a simple, largely unstated assumption: that a familiar voice, speaking in a familiar and contextually appropriate way, is a reasonably reliable signal of identity. That assumption depended on voice cloning being expensive, technically demanding, and largely confined to research labs and high-budget production environments. That constraint dissolved in 2024 and 2025, as open-source models, real-time inference, and cheap, abundant compute closed the technical gap — reducing the cost of a convincing voice-cloning attack from what industry practitioners describe as a “research lab” undertaking to a “weekend project.”

The critical architectural failure this exposes: any verification process that depends on a human listening to a voice and confirming it “sounds right” can now be defeated by AI, because the voice only needs to be convincing under pressure — not indefinitely, and not against forensic scrutiny, just long enough to complete a transaction.

First-Generation vs. Second-Generation AI Vishing

The evolution of AI voice phishing across 2025 and 2026 illustrates why static defenses have consistently fallen behind:

First-generation (pre-rendered audio): Attackers scripted a short call, generated the audio in advance, and played it through a SIP gateway. Defenders could reliably defeat this by throwing the call off-script — asking an unexpected question, requesting a callback, or changing the topic — because pre-rendered audio could not adapt.
Second-generation (real-time inference, 2025–2026): Real-time inference services now synthesize responses inside the call itself, with end-to-end latency low enough to feel like a normal conversation. The off-script defense that worked reliably against first-generation attacks is substantially weaker against a system that can adapt its responses live.

This progression matters directly for bank security protocol design: verification procedures built around the assumption that unpredictable questioning defeats vishing are now defending against a threat model that no longer exists in its original form.

The Arup Case: What $25.6 Million Bought as a Lesson

The 2024 Arup incident remains the most frequently cited case study in 2026 vishing analysis, and for good reason: it demonstrates the failure mode at enterprise scale. An employee at the UK engineering firm joined what appeared to be a routine video conference featuring the company’s CFO and several senior executives — everyone looked right, and everyone sounded right. The employee authorized 15 separate transactions totaling $25.6 million to Hong Kong bank accounts before the fraud was identified. The case has become the reference point specifically because it defeated not just voice verification but visual verification simultaneously, illustrating that multi-channel deepfake attacks — voice plus video plus contextually accurate scripting — represent the frontier threat model banks and enterprises must now defend against, not single-channel voice calls in isolation.

How Banks Are Rewriting Security Protocols in 2026

Several concrete protocol shifts are emerging across financial institutions in response to this threat environment:

Out-of-band verification as a hard requirement. The consistent recommendation across 2026 fraud research is to verify any high-risk request on a channel the caller does not control — for example, calling back through an independently sourced phone number rather than a number provided during the suspicious call itself, or confirming through a separate app-based channel.
Behavioral and telephony metadata analysis over voice recognition alone. Since caller identity and voice familiarity are no longer sufficient trust signals in high-risk workflows, leading practitioners now emphasize behavioral detection and telephony metadata analysis — call origination patterns, timing anomalies, SIP routing irregularities — as stronger risk signals than voice identity checks.
Mandatory delay windows for high-value transfers. Given that wire recall success rates drop sharply after the first six hours following a fraudulent transfer, banks are increasingly building mandatory cooling-off periods for large or unusual transfers specifically to create a window for after-the-fact verification.
Pre-established fraud team relationships. Practitioner guidance increasingly recommends that businesses establish a relationship with their bank’s fraud team before an incident occurs, since wire recall procedures, session revocation, and credential rotation all move faster when a pre-existing escalation path exists.
No-blame reporting culture. Because deepfake vishing has higher success rates than traditional email phishing due to its emotional-manipulation component, organizations that punish employees for falling victim risk delayed incident discovery; a no-blame reporting culture surfaces incidents in real time rather than days later.

The Data Gap: Where Awareness Training Is Misallocated

A notable finding from 2026 security awareness research is a significant mismatch between actual risk and training prioritization: while 73% of security leaders prioritize phishing reporting training, only 10% prioritize deepfake recognition training specifically — despite 35% of organizations having already experienced a deepfake incident, according to Gartner’s 2025 AI Risk Management Survey. Phone-based phishing simulations show a median click rate roughly 40% higher than email-based simulations, according to Verizon’s 2026 Data Breach Investigations Report, suggesting that voice-channel vulnerability is measurably higher than email-channel vulnerability even as training investment remains skewed toward the latter.

A Practical Vishing Incident Response Framework

Pre-written wire recall playbook, covering bank fraud-team contact procedures, session revocation, credential rotation, and forensic capture of call metadata
Mandatory callback verification through independently sourced contact information for any request involving funds transfer, credential reset, or access changes
Layered channel verification for high-risk requests — requiring confirmation through at least two independent channels (e.g., a callback plus an internal messaging system confirmation) rather than relying on any single channel, however convincing
Regular, realistic vishing simulation exercises modeled on actual scenarios (bank fraud alerts, executive impersonation, SaaS support calls) rather than generic phishing awareness content alone, given the roughly 40% higher click-through vulnerability documented on phone-based channels

Frequently Asked Questions

How much audio does it take to clone someone’s voice in 2026?

As little as 3 to 10 seconds of audio is sufficient to produce a convincing voice clone using current AI tools, with resulting deepfake audio achieving over 90% accuracy in mimicking the real voice.

What was the Arup deepfake case?

In 2024, an employee at UK engineering firm Arup authorized 15 wire transactions totaling $25.6 million after joining a video call featuring real-time deepfakes of the company’s CFO and several executives — a case widely cited as the reference incident for enterprise multi-channel deepfake fraud risk.

How are banks defending against AI voice phishing in 2026?

Banks are shifting toward out-of-band verification on channels the caller cannot control, behavioral and telephony metadata analysis instead of voice-identity checks alone, mandatory delay windows for high-value transfers, and pre-established fraud-team relationships to speed wire recalls.

Conclusion

The 2026 vishing threat landscape reflects a broader pattern seen across AI-enabled fraud: the technology did not create a new category of crime so much as it removed the practical constraints — cost, technical skill, adaptability — that previously kept an old category of crime in check. Financial institutions rewriting security protocols around out-of-band verification, behavioral metadata, and multi-channel confirmation are responding to a threat model where “it sounded right” and “it looked right” have both stopped being reliable signals of anything at all.

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Top 5 AI ETFs and Stocks to Buy Before Anthropic Goes Public

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Top 5 AI ETFs and Stocks to Buy Before Anthropic Goes Public

Published

1 day ago

September 5, 2026

Abdul Rahman

With Anthropic’s IPO reportedly targeted for September or October 2026 and a valuation debate centered around $2 trillion, many retail investors are looking for ways to gain AI exposure right now rather than waiting for a listing they may not get full access to at the offer price. The good news: you don’t need to wait. A handful of publicly traded ETFs and stocks already offer meaningful exposure to the same enterprise AI infrastructure boom fueling Anthropic’s growth.

Key Takeaways

Semiconductor and infrastructure ETFs have been the strongest-performing AI trade of 2026, with names like the Invesco Semiconductors ETF up over 130% year-to-date.
Diversified AI ETFs such as the Global X Artificial Intelligence & Technology ETF (AIQ) spread risk across chipmakers, cloud providers, and software companies rather than betting on a single winner.
Individual mega-cap stocks — Nvidia, Broadcom, Microsoft, Amazon, Meta — all have direct financial exposure to the same compute demand driving Anthropic’s growth.
Pre-IPO platforms exist for direct Anthropic exposure but carry liquidity, accreditation, and fee-structure risks not present in publicly listed ETFs and stocks.
No single ETF or stock is a perfect proxy for Anthropic specifically — this is about sector exposure, not a substitute for owning the company itself.

Why Consider AI-Adjacent Exposure Before the IPO?

Retail investors are structurally disadvantaged when it comes to accessing shares at the actual IPO offer price — that allocation typically goes to institutional clients and high-net-worth wealth management relationships tied to the underwriting banks (Morgan Stanley, Goldman Sachs, and JPMorgan, in Anthropic’s case). Building exposure to the broader enterprise AI ecosystem ahead of time is one practical way to participate in the theme without needing IPO-day access.

It’s also a risk-management move. Anthropic’s reported valuation target implies a multiple of roughly 30x its trailing $65 billion revenue run rate — a single-name bet at that pricing carries real valuation risk if growth decelerates even modestly. Diversified exposure spreads that risk across dozens of companies at various points in the AI value chain.

1. Semiconductor ETFs: The Infrastructure Backbone

AI models like Claude don’t run without chips. The VanEck Semiconductor ETF (SMH) and the Invesco Semiconductors ETF (PSI) both offer concentrated exposure to the companies building the physical infrastructure behind every large language model’s training and inference workloads — including Nvidia, Broadcom, and equipment makers whose revenue scales directly with AI compute demand.

VanEck Semiconductor ETF (SMH): Tracks a market-cap-weighted index of roughly 25 semiconductor companies; heavily concentrated in Nvidia and Taiwan Semiconductor Manufacturing (TSMC).
Invesco Semiconductors ETF (PSI): A narrower, 30-stock portfolio focused specifically on chip production; posted triple-digit percentage gains in 2026 amid the broader AI infrastructure buildout.

Trade-off: These funds are more exposed to Nvidia- and TSMC-specific risk than diversified software-focused funds, and don’t capture the enterprise software/SaaS side of the AI value chain where Anthropic itself operates.

2. Diversified AI & Technology ETFs

For investors who want exposure across the full AI stack — chips, cloud, software, and applications — rather than concentrated semiconductor risk, broader thematic ETFs offer a more balanced approach.

Global X Artificial Intelligence & Technology ETF (AIQ): Holds a mix of established tech leaders and faster-growing innovators across machine learning, cloud computing, and data analytics, with top holdings including Taiwan Semiconductor, Nvidia, and Apple. Roughly $7.6 billion in assets under management.
Invesco AI and Next Gen Software ETF (IGPT): Leans more heavily toward AI software developers and cloud infrastructure providers rather than pure semiconductor exposure, with holdings including Micron, Meta, and AMD.

Trade-off: Diversification reduces concentration risk but also dilutes the magnitude of any single winner’s outperformance relative to a concentrated bet.

3. Data Center & Digital Infrastructure Exposure

Every additional dollar of AI revenue — Anthropic’s included — requires physical data center capacity. The Global X Data Center & Digital Infrastructure ETF (DTCR) offers a distinctive angle: roughly split between technology stocks and real estate investment trusts (REITs) tied to data center construction and operation, capturing the physical buildout side of the AI boom rather than the model layer.

Trade-off: REIT exposure introduces interest-rate sensitivity that pure tech ETFs don’t carry, which can be a benefit or drawback depending on the broader rate environment.

4. Individual Mega-Cap Stocks With Direct AI Compute Exposure

For investors comfortable with single-stock risk, several established companies have direct financial ties to the same compute demand fueling Anthropic’s growth:

Stock	Ticker	AI Exposure
Nvidia	NVDA	Dominant AI accelerator/GPU supplier
Broadcom	AVGO	Custom AI chips and networking infrastructure for hyperscalers
Amazon	AMZN	AWS Bedrock offers enterprise access to multiple AI models, including Anthropic’s
Microsoft	MSFT	Azure cloud infrastructure and enterprise AI software integration
ASML	ASML	Monopoly-like position in EUV lithography equipment used to manufacture advanced AI chips

Amazon in particular has a direct commercial relationship with Anthropic through AWS, which has both invested in and hosts Anthropic’s models for enterprise customers — making AMZN one of the more directly linked mega-cap plays on Anthropic’s specific success, short of owning Anthropic stock itself.

5. Quantum & Next-Generation Compute (Higher Risk, Longer Horizon)

For investors willing to take on more speculative, longer-horizon exposure, the Defiance Quantum ETF (QTUM) invests in companies developing next-generation computing technology that could eventually reshape AI training economics, including Tower Semiconductor, Rigetti Computing, and Teradyne.

Trade-off: Quantum computing remains years away from mainstream commercial application in AI workloads — this is a long-duration, speculative complement to core AI exposure, not a near-term Anthropic proxy.

Comparing the Options

Fund/Stock	Focus	Risk Level	Best For
SMH / PSI	Semiconductors	High concentration	Direct infrastructure exposure
AIQ / IGPT	Diversified AI/software	Moderate	Broad sector participation
DTCR	Data centers + REITs	Moderate, rate-sensitive	Physical infrastructure angle
NVDA, AVGO, AMZN, MSFT	Individual mega-caps	Single-stock risk	Targeted, liquid exposure
QTUM	Quantum computing	High, speculative	Long-horizon diversification

What None of These Options Replace

It’s worth being direct: no ETF or adjacent stock replicates Anthropic’s specific growth trajectory, its ~$65 billion revenue run rate, or its potential re-rating catalyst around IPO day. These are sector proxies, not substitutes. Investors specifically seeking Anthropic exposure will eventually need to either buy shares in the open market after listing or explore pre-IPO platforms — each with materially different risk profiles than a liquid, exchange-traded fund.

FAQ

Is there an ETF that already holds Anthropic stock? Not currently, since Anthropic is not yet publicly traded. Once it lists, some broad-based AI and technology ETFs may add it to their holdings depending on index methodology and market-cap weighting rules.

What’s the safest way to get AI exposure before the Anthropic IPO?

Diversified ETFs like AIQ or IGPT generally carry lower single-name risk than concentrated semiconductor funds or individual stocks, making them a more conservative way to participate in the broader AI theme ahead of the listing.

Does Amazon benefit directly from Anthropic’s growth?

Yes — Amazon has an investment and infrastructure relationship with Anthropic through AWS, which hosts Anthropic’s models for enterprise customers via AWS Bedrock, giving AMZN a more direct (though indirect, non-equity) link to Anthropic’s commercial success.

Should I wait for the Anthropic IPO instead of buying AI ETFs now?

That depends on your risk tolerance and time horizon. Many financial advisors suggest building diversified sector exposure over time rather than trying to time a single event like an IPO, which can carry significant first-day volatility.

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What Morgan Stanley & Goldman Sachs’ Roles Mean for Anthropic Investors

Published

1 day ago

September 5, 2026

Abdul Rahman

When a company chooses its underwriters, it’s telling the market something before a single share trades. Anthropic’s reported selection of Morgan Stanley and Goldman Sachs — alongside JPMorgan — as lead banks on its expected IPO is being read by Wall Street as a signal of confidence in the company’s ability to command a valuation near $2 trillion. Here’s what these roles actually mean, mechanically and strategically, for anyone considering an investment.

Key Takeaways

Morgan Stanley reportedly holds the “pole position” for the coveted lead-left spot on Anthropic’s IPO, according to sources cited by the Financial Times.
Goldman Sachs is running “neck-and-neck” with Morgan Stanley for a top-tier underwriting role.
JPMorgan, Citigroup, and Barclays are expected to round out the broader syndicate.
These same three lead banks — Morgan Stanley, Goldman Sachs, and JPMorgan — anchored the SpaceX IPO in June 2026, the current record-holder for largest offering.
The banks previously provided Anthropic with debt financing, including work toward a reported $15 billion pre-IPO credit facility.
Underwriter selection influences pricing strategy, institutional allocation, and after-market stabilization — all of which affect retail investors indirectly.

What “Lead-Left” Actually Means

In IPO terminology, the lead-left bank is the underwriter listed first (traditionally on the left side) on the cover of the prospectus — a position that comes with outsized responsibility and outsized reward. The lead-left bank typically:

Runs the bookbuilding process, collecting and aggregating institutional investor orders
Sets the final offer price in coordination with the issuer’s board
Takes the largest underwriting fee allocation among the syndicate
Leads after-market stabilization activities, including exercising the “greenshoe” over-allotment option if the stock trades up
Serves as the primary point of contact between the company and public market investors during the roadshow

If Morgan Stanley secures this role for Anthropic, as reporting suggests is likely, it puts the bank in the driver’s seat for what could be the largest IPO ever completed — surpassing even its own recent work, alongside Goldman Sachs and JPMorgan, on the SpaceX offering.

Why Two (or Three) Top-Tier Banks Matters for Investors

A syndicate anchored by Morgan Stanley and Goldman Sachs — both perennially ranked among the top global equity underwriters — sends a specific signal: institutional demand is expected to be deep enough to require serious distribution muscle. For investors, this translates into a few practical implications:

Broader institutional reach. These banks’ wealth management and institutional sales networks span pension funds, sovereign wealth funds, and large asset managers globally, which typically supports stronger initial demand and a more orderly aftermarket.
More rigorous pricing discipline. Top-tier lead underwriters have reputational incentive to avoid a “busted IPO” — a listing that trades below its offer price shortly after debut — because it damages their standing for future mandates.
Deeper aftermarket support. Lead banks typically commit capital to stabilize the stock in early trading through the over-allotment mechanism, which can reduce (though not eliminate) early volatility.

The Debt-Equity Connection: Why the $15 Billion Credit Facility Matters Here

It’s not a coincidence that the banks reportedly structuring Anthropic’s equity offering previously provided the company with debt financing. Morgan Stanley, Goldman Sachs, and JPMorgan are also reportedly involved in finalizing a $15 billion pre-IPO credit facility for Anthropic — capital that gives the company balance sheet flexibility to fund continued compute infrastructure buildout independent of the equity raise itself.

This dual relationship — debt financier and equity underwriter — is common for large-cap tech IPOs and gives the lead banks unusually deep visibility into Anthropic’s financials heading into the roadshow. For investors, that can be read two ways:

Bullish read: The banks have extensive due diligence exposure and are still willing to lead a ~$2 trillion offering.
Cautious read: The banks have a strong financial incentive (underwriting fees plus debt relationship preservation) to see the deal price successfully, which doesn’t guarantee the valuation is fundamentally sound.

Historical Precedent: The SpaceX Playbook

Morgan Stanley, Goldman Sachs, and JPMorgan ran the book on SpaceX’s IPO in June 2026, which priced at $135 per share and raised approximately $75 billion at a valuation near $1.8 trillion — the current record for largest IPO in history. That stock has since traded in a range from a first-day peak near $2.1 trillion market cap down to roughly $1.5 trillion by late July, before stabilizing.

The reuse of essentially the same underwriting trio for Anthropic suggests the banks are applying lessons learned from the SpaceX process — particularly around managing a low free-float listing, which both companies share as a structural feature.

Deal Element	SpaceX (June 2026)	Anthropic (Expected)
Lead underwriters	Morgan Stanley, Goldman Sachs, JPMorgan	Morgan Stanley, Goldman Sachs, JPMorgan (reported)
IPO valuation	~$1.8 trillion	~$2 trillion (target, unconfirmed)
Capital raised	~$75 billion	Not yet disclosed
Post-IPO price action	Peaked ~$2.1T, settled ~$1.5T	Unknown
Free float	Low	Reportedly low (~4% range in some estimates)

Risks the Underwriter Roster Doesn’t Solve

Even the strongest underwriting syndicate can’t eliminate fundamental risk. Investors should keep in mind:

A low float amplifies volatility regardless of which bank is managing the book — SpaceX’s post-IPO price swing from $2.1T to $1.5T illustrates this even with top-tier underwriters involved.
Underwriter confidence is not a valuation guarantee. Banks earn substantial fees regardless of long-term stock performance; their willingness to lead the deal reflects market appetite and relationship value, not a certification of fair value.
Multiple additional banks joining the syndicate (Citigroup, Barclays) spreads risk but also dilutes any single bank’s accountability for pricing outcomes.

FAQ

What does it mean that Morgan Stanley is the “lead-left” bank on Anthropic’s IPO? It means Morgan Stanley would run the bookbuilding process, help set the final offer price, and lead after-market stabilization — the most influential and highest-fee role in the underwriting syndicate.

Does Goldman Sachs having a top role change the IPO outlook?

Having two top-tier global banks (Morgan Stanley and Goldman Sachs) sharing lead roles typically signals strong expected institutional demand and broader distribution capacity, though it doesn’t guarantee post-IPO stock performance.

Are Morgan Stanley and Goldman Sachs also lending Anthropic money?

Yes — reporting indicates these banks previously provided debt financing to Anthropic and are involved in structuring a reported $15 billion pre-IPO credit facility alongside their equity underwriting roles.

Did the same banks handle the SpaceX IPO?

Yes. Morgan Stanley, Goldman Sachs, and JPMorgan anchored the SpaceX IPO in June 2026, which currently holds the record for the largest offering in history.

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Analysis

OpenAI vs. Anthropic IPO: Which AI Giant Will Dominate Wall Street?

Published

1 day ago

September 5, 2026

Abdul Rahman

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

Metric	Anthropic	OpenAI
Confidential S-1 filed	June 1, 2026	Reported, date less clear
Expected IPO window	September–October 2026	Reportedly 2027
Reported revenue run rate	~$65 billion (July 2026)	~$40 billion
Last private valuation	$965 billion (May 2026)	Not covered in current reporting
Reported IPO valuation target	~$2 trillion	Not yet reported
Lead underwriters	Morgan Stanley, Goldman Sachs, JPMorgan	Not yet confirmed
Growth trajectory	~7x run rate growth in ~7 months	~2x run rate growth year-over-year

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:

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.
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.
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.
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.