• Ballot Initiative Filed: California Charitable Assets Protection Act filed with AG to review and reverse nonprofit conversions in scientific/tech research since January 2024.
• Coalition Backing: Coalition for AI Nonprofit Integrity (CANI) supports measure after failing to block OpenAI conversion earlier.
• OpenAI Conversion: Completed conversion of for-profit subsidiary to public-benefit corporation in late October after AG negotiations.
• Strategic Importance: Conversion enables OpenAI fundraising amid growth and investments in AI, consumer devices, computing hardware.
• OpenAI Founding: Co-founded in 2015 as nonprofit by Sam Altman and Elon Musk to advance beneficial digital intelligence.
• Musk's Involvement: Musk left OpenAI in 2018; CANI appeals to him for funding ballot measure appearing in November 2026 if signatures gathered.
• OpenAI Statement: Spokeswoman describes effort as baseless attempt to relitigate approved decision.
• Filing Details: Paperwork submitted by Poornima Ramarao, mother of OpenAI whistleblower Suchir Balaji who died by suicide.

By

Keach Hagey

Dec. 2, 2025 12:36 am ET

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Sam Altman is chief executive of OpenAI, the company behind ChatGPT. Al Drago/Bloomberg News

• A coalition is proposing a California ballot initiative aimed at limiting OpenAI’s power.

• The initiative aims to review and potentially reverse conversions to nonprofit organizations engaged in scientific and technological research that occurred since January of 2024.

• The Coalition for AI Nonprofit Integrity, which previously opposed OpenAI’s conversion, is backing the measure.

An artificial-intelligence tool created this summary, which was based on the text of the article and checked by an editor. Read more about how we use artificial intelligence in our journalism.

• A coalition is proposing a California ballot initiative aimed at limiting OpenAI’s power.

  View more

A coalition that tried and failed to block OpenAI’s conversion earlier this year is back with a new tactic: a California ballot initiative aimed at reining in the startup’s power.

The planned initiative, dubbed the California Charitable Assets Protection Act, was filed Monday with California’s attorney general. It doesn’t mention OpenAI by name, but calls for the creation of an oversight board empowered to review and potentially reverse conversions to nonprofit organizations engaged in scientific and technological research that have happened in the state since January of 2024.

In late October, after a year of negotiating with the attorneys general of California and Delaware, OpenAI announced that it was converting its for-profit subsidiary to a public-benefit corporation.

The conversion was key to OpenAI’s fundraising efforts. The startup has seen explosive growth in recent years and has placed a host of expensive and high-profile bets on the future of AI, consumer devices and computing hardware.

OpenAI’s ascendance has drawn scrutiny from some AI safety groups, critics of Silicon Valley’s quest for superhuman intelligence and the startup’s business foes. The ballot initiative is backed by the Coalition for AI Nonprofit Integrity, or CANI, which earlier this year tried to block OpenAI’s conversion, saying it was a betrayal of the startup’s mission and would “unduly concentrate power in the hands of a few.”

“This is a baseless attempt by CANI to relitigate a decision that has already been made,” said an OpenAI spokeswoman.

OpenAI was co-founded by Sam Altman and Elon Musk in 2015 as a nonprofit organization with the goal “to advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.”

Elon Musk left OpenAI in 2018. Tom Brenner/Reuters

CANI is appealing directly to Musk for help funding the measure, which would appear on the ballot in November of next year if its proponents can gather the hundreds of thousands of signatures required. Musk, who left OpenAI in 2018, has tried to block OpenAI’s conversion in court.

The coalition said it has the initial funding to get the ballot measure in front of voters, “but to see it through to November 2026, we’re appealing to Elon Musk and others who understand what’s at stake.” Musk didn’t immediately respond to a request for comment.

To gather support, the coalition is launching a website called <a href="http://OpenTheft.com" rel="nofollow">OpenTheft.com</a> to raise funds, organize and spread its message. “We can’t do this alone. This is going to take resources to fight one of the most well-funded companies in the world,” the coalition said.

The paperwork for the ballot initiative was filed by Poornima Ramarao, the mother of an OpenAI whistleblower who died shortly after publicly accusing his former employer of breaking copyright law to train its models. The San Francisco medical examiner determined he had died by suicide.

OpenAI has said the company was “devastated” by Suchir Balaji’s death and called him a “valued member of our team.”

CANI tried earlier this year to push forward a bill in the California legislature that would have blocked OpenAI’s conversion. It sought to “prevent any individual, business or group from creating a startup venture capital nonprofit to exploit the charitable contributions of contributors and stakeholders, including knowledge, resources, and donations.”

News Corp, owner of The Wall Street Journal, has a content-licensing partnership with OpenAI.

Write to Keach Hagey at Keach.Hagey@wsj.com

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• Nvidia's Profitability: Generated $110 billion in operating income over four quarters, equating to 59% operating margin, exceeding PHLX Semiconductor Index average of 25%.
• Gross Margins: Reached 70% annually, up from 62% pre-AI demand surge, attributed to software and complexity in data-center products.
• Stock Performance: Shares down 12% from $5 trillion peak, trading at 26 times projected earnings, near five-year low multiple.
• Competition Concerns: Investors worry about sustainability amid AI spending, with potential price pressure from rivals.
• AMD Challenge: Plans MI450 chips for 2025 with OpenAI customer, targets 35% operating margins from current 22%, enabling underpricing Nvidia.
• Google's Entry: Developing TPU for sale, backed by Alphabet's $151.4 billion operating cash flow, highest on S&P 500; in talks with Meta.
• Analyst Views: Morgan Stanley sees no near-term Nvidia impact from Meta-Google TPU use; Google spends $20 billion yearly on Nvidia chips.
• Future Pressures: Rising competition from AMD, Google, Amazon's Trainium 3 challenges Nvidia's pricing power and 70% gross margins.

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Nvidia Chief Executive Jensen Huang and Google CEO Sundar Pichai.

The AI revolution has created a unique and high-quality problem for Nvidia : The chip maker makes too much money.

Nvidia has fallen under scrutiny over the past month as investors have come to worry about the sustainability of blowout artificial-intelligence spending. But another fear surfaced last week with reports that Google is looking to enter the AI chip market by selling the chip it designed in-house, known as a tensor processing unit or TPU, for its own computing needs.

Nvidia’s stock price has picked up some ground this week, but the shares are still down 12% since the company peaked at a $5 trillion market cap in late October. The Nasdaq composite has lost about 2% in that same time.

The recent decline has made the world’s most valuable company look cheap. Nvidia’s stock now trades around 26 times projected earnings for the next four quarters—close to its lowest multiple over the past five years, according to FactSet data. But that ratio is based on earnings expected over the next year that could come under pressure if stronger competition forces Nvidia to lower its prices. 

A haircut to earnings would still leave Nvidia more profitable than most other chip companies, but the stock wouldn’t be quite the bargain it seems to be, despite the company’s still-commanding lead in the lucrative AI-chip market.

How lucrative? Nvidia has generated a little over $110 billion in operating income over the last four quarters. That equates to about 59 cents of operating profit for every dollar of revenue, far above what any other chip company on the PHLX Semiconductor Index commanded over the same period, according to data from S&P Global Market Intelligence. TSMC—the Taiwan manufacturing powerhouse that actually makes most of Nvidia’s chips—produced an operating margin of just under 50% for the same period, while the average for companies on the index is around 25%. 

Nvidia’s gross margins, which reflect the direct costs of producing a company’s products, now stand at 70% on an annual basis. That compares with an average of around 62% for the five-year period before sales exploded in mid-2023 from AI demand. Nvidia then credited the gross margin jump to “a significant amount of software and complexity” in its data-center products. Fabless chip companies Qualcomm, Advanced Micro Devices and Marvell, which also outsource their manufacturing, currently command annual gross margins in the low 50% range, according to data from S&P Global Market Intelligence.

AMD’s margins are particularly notable given that the company is gearing up for a major challenge to Nvidia’s dominance in the AI-computing market. The MI450 chips that the company plans to start shipping next year already have landed a major customer in OpenAI, and AMD Chief Executive Lisa Su told analysts last month that more major customers are coming.

Neither Nvidia nor AMD has publicly disclosed prices for their AI chips, which are generally customized for each major customer, making direct comparisons difficult. But their sharply different profitability levels gives AMD the advantage of being able to underprice Nvidia and still boost its margins. The company, in fact, has publicly committed to surpassing 35% in annual pro forma operating margins over the next three to five years, compared with 22% now. AMD’s pro forma margins exclude the cost of stock-based compensation and other charges.

Now, competition from Google adds a new wrinkle, as the search giant already has a highly profitable advertising business that can support an ambitious new venture like directly entering the AI-chip market.

Parent company Alphabet’s operating cash flow of $151.4 billion over the past four quarters is actually the highest on the S&P 500. And landing a customer like Facebook-parent Meta Platforms would be a big win for Google’s TPU efforts. Meta is a major Nvidia customer but has a strong need to secure as many AI chips as it can given Mark Zuckerberg’s ambitious goal of developing “superintelligence” before rivals like OpenAI get there.

News Corp, owner of The Wall Street Journal, has a commercial agreement to supply content on Google platform.

Analysts aren’t panicking about Nvidia yet. “In the near term, Meta using TPUs doesn’t materially affect our viewpoint on Nvidia despite the narrative headwind it creates,” Morgan Stanley’sJoe Moore wrote in a note to clients last week. Google itself is still a major Nvidia customer and frequently touts its access to the company’s most advanced products. Moore estimates Google now spends about $20 billion a year on Nvidia’s chips.

But Wall Street is still keenly focused on Nvidia maintaining gross margins in the 70% range—even with sharply rising costs for key components like memory. And Google’s latest chip advances, plus new offerings from AMD and Amazon’s latest Trainium 3 chip announced on Tuesday, show competition keeps picking up for a key market that Nvidia has long dominated. Nvidia’s ability to maintain pricing power—and its high margins—will only get harder from here.

Write to Dan Gallagher at dan.gallagher@wsj.com

What’s Next for Nvidia

• Google Factors Into AI Trade More Strongly
• The Chip CEO Staring Down Nvidia
• Meta in Talks to Use Google Chips
• Nvidia’s Results Show AI Fears Premature

• AI Sleeper Agents: Models harbor undetectable triggers activated under specific conditions.
• Chinese Threats: Intelligence operations like Salt Typhoon and Volt Typhoon target infrastructure; Google engineer indicted for stealing AI secrets for Beijing.
• AI Alignment: Ensures stable objectives, predictable reasoning faithful to missions amid new situations and adversarial pressures.
• US Investments: Genesis Mission accelerates AI scientific discovery, but alignment requires comparable funding to prevent compromise.
• China's Approach: $8.2 billion AI fund and policies emphasize safe, reliable, controllable systems under command.
• Alignment Benefits: Structured frameworks improve capabilities, similar to F-16 design constraints unlocking success.
• Necessary Properties: Stable long-term objectives, interpretable reasoning, verified shutdowns, resistance to manipulation for military use.
• Proposed Steps: Fund neglected research, mandate alignment in DoD contracts, create federal zones with built-in requirements.

Recent research has demonstrated that AI models can harbor sleeper agents that can be triggered under specific conditions without detection. The same adversaries that compromised U.S. telecommunications through Salt Typhoon and targeted critical infrastructure through Volt Typhoon can exploit vulnerabilities in military AI systems. We know that Chinese intelligence has penetrated frontier AI labs: In 2024, a former Google engineer was indicted on charges that he stole AI trade secrets while secretly working for Beijing. (He has pleaded not guilty and his trial is scheduled to begin next month.)

The way to address this is with AI alignment research—ensuring that systems’ objectives and reasoning stay stable, predictable and faithful to their intended mission across new situations, long time horizons and adversarial pressures. The administration’s newly announced Genesis Mission at the Department of Energy launches a coordinated national effort to accelerate AI-enabled scientific discovery. These are appropriate investments in cutting-edge capability, but a failure to invest comparably in alignment can allow adversaries to compromise American AI capabilities.

While American defense procurement treats alignment as an afterthought, China is moving systematically. In January, Beijing launched an $8.2 billion National AI Industry Investment Fund. Since the 2017 New Generation AI Development Plan, Chinese policy has emphasized building AI that is “safe, reliable and controllable.” Chinese military strategists stress the importance of systems that remain under operational command. Beijing grasps what many American policymakers miss: Alignment research accelerates AI capabilities. Whoever solves these technical problems builds more capable systems and wins the race.

Many AI policy discussions instinctively cast alignment as compliance overhead that slows development. But historically, such constraints have often unlocked capabilities. The F-16 became the most successful fighter in history not despite its strict design constraints, but because of them. The same principle applies to AI. Giving models structured frameworks for how they think produces dramatic capability improvements, and techniques designed to make AI more aligned have been adopted by most major labs.

Yet most promising alignment directions remain unexplored. The systems that defense planners need for extended autonomous operations require alignment properties we’ve barely begun to develop: stable long-term objectives, interpretable reasoning across complex decision chains, verified shutdown protocols that can’t be circumvented, and principled resistance to adversarial manipulation. Military-grade reliability demands military-grade commitment to alignment research.

Unlike cybersecurity, where we’re locked in a cycle of patching vulnerabilities after adversaries exploit them, AI alignment offers a chance to build security from the ground up. We can establish verified shutdown protocols, interpretable reasoning systems, and resistance to adversarial manipulation before these systems are deployed.

But this chance won’t last forever. Frontier lab leaders expect systems that can match human experts across all cognitive domains within 12 to 18 months. Once models can autonomously design successor models, and once powerful AI systems are embedded in critical infrastructure and military operations, we’ll face the same reactive posture that has plagued cybersecurity for decades.

Three steps would position America to win:

First, launch broad-scale programs that target neglected alignment research. Private labs optimize for commercial performance, which means critical security challenges go unsolved. These problems lack commercial importance, but they determine whether AI systems can be trusted with national-security operations. The government must fund this research directly, just as it has historically funded cryptography, semiconductor security and other dual-use technologies for which market incentives misalign with defense needs.

Second, require military-grade alignment research and development in major Defense Department AI contracts. The director of the Defense Advanced Research Projects Agency has already set a goal to achieve military-grade AI, and the Pentagon is already spending hundreds of millions on frontier capabilities. Those contracts should mandate that computing resources go toward interpretability, verified shutdown protocols, the elimination of sleeper agents and long-term objective stability. This approach would motivate private-sector research while ensuring that the systems we deploy meet defense-grade security standards.

Third, build special zones for AI research on federal land with alignment requirements built in from the start.

The Pentagon already demands the best equipment, training and strategic doctrine. It should demand the same from AI systems: reliably steerable toward American strategic objectives across time horizons that matter.

America built the atomic bomb when the physics seemed impossible. We reached the moon when the engineering seemed fantastical. We created the internet when networked computing seemed impractical.

Americans have always risen to civilizational challenges when we’ve seen them clearly and moved with conviction. The challenge now is to build AI systems we can trust with the future. That future is closer than most realize, and the window for shaping it is open. But it won’t stay open forever.

Mr. Rosenblatt is CEO and Mr. Berg a research director of AE Studio.

America is struggling to build ships, missiles and drones, even as war rages in eastern Europe and tensions rise in Asia. Kate Odell speaks with analyst Seth Jones about his new book “The American Edge,” and how the U.S. can still meet the world's threats.

• Recurring anti-Israel claims: Gaza war depicted as uniquely lethal and catastrophic using selective 21st-century statistics on fatalities, casualty rates, women/children killed, and destruction.
• Most deadly war claim false: Tigray War killed 800,000 in two years; Syrian Civil War 620,000; Yemeni Civil War 377,000; South Sudan 190,000 direct plus famine deaths.
• Second Congo War scale: 5.4 million deaths from 1998-2003, averaging 75,000 per month; post-war excess deaths at 45,000 monthly.
• Sudanese Civil War comparison: 400,000 fatalities since 2023, 12 million displaced, 25 million in hunger, far exceeding Gaza metrics.
• Highest population percentage killed false: Mariupol 6-17% in 2022; Korean War South Korea 6.5%, North Korea 12-15%; Biafra 4-15%; Cambodia 20%.
• Highest women/children percentage false: Tigray 58%; Mosul 54%; Raqqa 51%, all in urban anti-terror battles like Gaza.
• Unprecedented destruction false: Raqqa 80% uninhabitable; Mosul 80%+; Marawi 95%; Mariupol near-total, with Gaza at 70% damaged.
• Low civilian-combatant ratio: Gaza 1.8:1 using Hamas figures, lower than Mosul 3:1 and other Western urban operations; critics manipulate data.

A recurring claim pushed by anti-Israel activists and critics is that the Gaza war is a

uniquely lethal

and

catastrophic

conflict. They cite statistics such as

total fatalities

,

casualty rates

, the number or

proportion

of

women and children

killed, and the

scale of destruction

, often limiting the comparison to “

the 21st century

.” The aim is to depict Israel’s conduct as inherently criminal and to reinforce the false

allegation of genocide

by insisting that nothing in modern warfare compares. War is horrific and Gaza’s losses are tragic, but tragedy does not transform a conventional urban conflict into an unprecedented event. When these claims are examined against actual historical data, every one of them collapses. Gaza is not unprecedented, not the “worst,” and not an outlier in modern warfare, even this century. These claims depend on selective statistics, misrepresentation and a refusal to acknowledge far deadlier conflicts. This article dismantles the main arguments, though no one should expect those invested in this narrative to suddenly start telling the truth.

False Claim: Gaza is the "Most Deadly" War

Claims that Gaza is the most deadly war of this century are demonstrably false. The

Tigray War

in Ethiopia, fought from November 2020 to November 2022, killed an estimated

800,000 people

in two years. Every metric of displacement, starvation and civilian suffering in Tigray far exceeded anything in Gaza, even using Hamas's

unreliable statistics

.

The Syrian Civil War from 2011 to 2019 saw roughly

620,000 deaths

. It could be even higher after

mass graves

were found this year near Damascus. The Syrian Network for Human Rights documented

30,600 children killed

out of a subset of 234,000 fatalities, far surpassing Gaza’s child fatality numbers. Other deadlier conflicts this century include the Yemeni Civil War with

377,000 deaths

and the South Sudan Civil War with

190,000 direct fatalities

plus 193,000 more from famine.

Nothing compares to the

Second Congo War

from 1998 to 2003 in terms of total fatalities or rate of death. The war caused an estimated

5.4 million deaths

, averaging 75,000 per month. A 2008 report in

The New York Times

noted that even after the war ended, 45,000 people were still dying each month from the effects of the war.

There is also a current conflict far deadlier than Gaza: the Sudanese Civil War that began in April 2023, with an estimated

400,000 fatalities

, 12 million displaced people and 25 million facing extreme hunger. Despite producing human suffering on a

vastly larger scale

, it receives almost

no attention

from the same “experts,” NGOs and humanitarians who claim Gaza represents a

uniquely catastrophic

event.

False Claim: Gaza has the Highest Percentage of a Population Killed

Haaretz journalist Nir Hasson

claimed

that no war has seen 3% of its population killed within two years based on Hamas’s assertion that

70,000

Gazans have died out of a 2.1 million pre-war population. Setting aside that roughly

25,000

of these are combatants, Hasson is simply wrong, both for this century and the last.

The brutal three-month battle for Mariupol in 2022 offers a stark and recent benchmark that immediately disproves claims of Gaza’s unprecedented casualty rates. Ukrainian authorities initially estimated

25,000 civilian deaths

, but an

AP report

suggested the real figure may be 75,000. With a pre-war population of 430,000, the lower estimate equals 6% of the city killed, while the higher estimate exceeds 17%, both far greater than Gaza. Critics may argue that Mariupol is just one city, not all of Ukraine, but Gaza is also just one territory where Palestinians live, with 3 million in the West Bank. But even setting Mariupol aside, many conflicts have exceeded the 3% threshold.

The Korean War was extraordinarily deadly. South Korea lost an estimated

1.3 million

people out of a pre-war population of 20 million, a loss rate of 6.5%.

North Korea’s losses

were even more severe, with estimates ranging from 12%-15% of the total population killed. Other modern conflicts produced similarly devastating percentages. The

Biafran War

in Nigeria (1967–1970) saw about 4% of the population killed in direct fighting, and 15% when including deaths from the actual famine that occurred. Separately, under the Khmer Rouge and during the

war with Vietnam

(1975–1979), Cambodia lost

2 million

people, more than 20% of its population. There are many additional examples, but the point is clear: Gaza is not even close to breaking statistical ground.

False Claim: Gaza has Highest Percentage of Women & Children Killed

Claims that Gaza has seen the

highest percentage

of women and children killed, about 48% according to

Hamas' latest figures

, are also false. Recent conflicts show higher rates. The challenge is that in the deadliest wars reliable demographic data is generally unavailable, but where data does exist it contradicts the Gaza narrative.

In the Tigray War, a

study

of airstrikes across 24 districts found that approximately 28% of casualties were children and 30% were women, a combined 58%. A

study of mortality

from the Battle of Mosul, fought by US-led coalition and Iraqi forces against ISIS from 2016 to 2017, recorded that more than 54% of those killed were women and children. One does not need to reach back to obscure cases or actual genocides to find higher rates; a recent, well-documented battle involving Western militaries surpasses Gaza’s claims. Similar results appeared in the

Battle of Raqqa

. Among roughly 1,000 identified deaths, 51% were women and children, including 28% children.

The

Battles of Mosul

and

Raqqa

are particularly

relevant comparisons to Gaza

because each involved dense urban warfare against a terrorist force embedded within a civilian population. These battles were not labeled genocide or unprecedented crimes, but recognized as the harsh reality of

urban combat

against an enemy that used

human shields

. ISIS also did not have seventeen years to construct a

1,000-kilometer tunnel system

, making the IDF’s results in Gaza even more consistent with the norms of Western warfare in unusually difficult circumstances.

False Claim: Gaza has Unprecedented Physical Destruction

The destruction in Gaza is

often cited

as prima facie

proof of genocide

, with commentators insisting that such damage cannot result from lawful warfare. This claim ignores the extensive record of modern urban battles that produced equal or greater devastation. Gaza is severe but typical of high-intensity urban combat, not evidence of genocidal intent.

UNCTAD’s

November 2025 report estimates that 70% of Gaza was damaged or destroyed. Certain areas remain mostly

intact

. Importantly, if 3% of Gazans were killed, including combatants, then the massive gap between the percentage of physical destruction and fatalities reflects highly successful

evacuation

s by Israel, not an effort to exterminate a population.

Raqqa and Mosul demonstrate this clearly. Raqqa was assessed as nearly

completely destroyed

, with one estimate finding

80% of buildings

uninhabitable. A RAND study described the battle in ways that

closely mirror

Gaza, and

UNOSAT imagery

shows virtually the entire city destroyed or heavily damaged. ISIS had tunneled between buildings, rigged the city with explosives, concealed streets from aerial surveillance and used

civilians as shields

—the same conditions faced by the IDF.

Mosul suffered comparable devastation, particularly in the western and Old City districts, with

destruction

estimated at more than 80%. A

World Bank assessment

of Iraqi cities after the war with ISIS found destruction levels of 94% in Bayji and 96% in Al-Ba’aj, with destruction and damage across sixteen governorates, an area far larger than Gaza, averaging 59%.

Outside the Middle East, the five-month

Battle of Marawi

in the Philippines in 2017 between government forces and 1,000 Islamic State militants caused massive destruction in a city of 200,000 people. Civilian casualties remained low due to effective evacuations, yet in a large area more than

95% of structures

were

destroyed

or damaged. As a

Stimson Center report

noted, “Marawi thus illustrates that even when casualties are low and a population is evacuated, damage to infrastructure can still severely impact both the city’s people and those of surrounding areas.”

Mariupo

l further reinforces the point. The city, spanning 166 square kilometers (nearly half the size of Gaza), saw destruction or damage across almost its entire urban area.

True Statistic: Gaza has a Comparatively Low Civilian-Combatant Ratio

Based on available data, the civilian to combatant ratio in Gaza is roughly 1.8 to 1 (and probably

even lower

), using Hamas’ claim of 70,000 total fatalities and an estimated

25,000 combatants

killed. This ratio is far lower than in recent Western-led urban battles. In Mosul, an estimated

10,000 civilians

were killed compared to about 2,000 to 3,000

ISIS fighter

s, a ratio of 3 to 1 at the low end. Broader operations in Iraq and Afghanistan have produced ratios in the range of

3 to 1 up to 5 to 1

. The Gaza ratio therefore contradicts accusations of genocide or indiscriminate targeting.

Critics who cannot accept this reality have attempted to manipulate both sides of the ratio to fabricate a higher figure. On the denominator, they

undercount combatants

by relying only on the number of fighters the IDF can literally identify by first and last name and match to a pre-war roster. By this absurd standard, any combatant the IDF could not fully identify in the midst of battle, combatants remaining in tunnels or beneath rubble, or any individual recruited by Hamas after the war began, is automatically labeled a civilian. This is how the

false claim

of “

83% civilians killed

” is manufactured.

On the numerator, these same critics assert, without evidence, that total fatalities are

undercounted

by some 40%. They

never explain

how this is possible when Gazans could and did

report

thousands of deaths without needing to present bodies, and given the compensation incentives to do so. Two years into the conflict, the notion that thirty thousand or more deaths remain unreported by their families has no evidentiary basis.

Taken together, the credible data leaves Gaza’s civilian combatant ratio well under 2 to 1, low for high-intensity urban warfare. And tellingly, when this metric contradicts their genocide narrative, the same critics who inflated every other statistic suddenly work to discredit it, proving that accurate numbers were never the point; the manipulation exists solely to promote an anti-Israel agenda.

Conclusion

When the facts invalidate the claims, the predictable response is to move the goalposts. After portraying Gaza as an unprecedented, genocidal conflict, critics suddenly dismiss all comparative evidence, insisting that previous catastrophic wars are

too terrible to cite

as data points. The impulse to portray Israel as uniquely criminal, rather than any commitment to truth, drives this constant reframing. It exposes the ideological goal driving the narrative: to cast Israel as uniquely criminal, even when the

evidence shows otherwise

. In the end, tragedy does not prove genocide, and facts still matter, even to those determined to ignore them.

• Polish dubbing issues: Single lektor delivers all dialogue in monotone, no cast variation, hated by young audiences.
• ElevenLabs founding: Mati Staniszewski and Piotr Dabkowski quit jobs in 2022 to build AI text-to-speech generator.
• AI voice capabilities: Generates voices with emotions like happiness, excitement, laughter; clones any voice including users' own.
• Early adoption and growth: Used by authors for audiobooks, YouTubers for translations in 29 languages; deals with HarperCollins, Bertelsmann.
• Funding and valuation: Raised over $300 million, reached $6.6 billion valuation; cofounders worth $1 billion each.
• Revenue and profitability: $193 million trailing revenue, $116 million net profit; half from corporates like Cisco, Epic Games.
• Competitive edge: Largest library of 10,000 voices, fewer errors than OpenAI; charges up to three times more than rivals.
• Challenges and expansions: Faced deepfake misuse, settled audiobook lawsuit; launching music generator, AI avatars, voice agents platform.

Dubbed films in Poland are horrible. A lone lektor delivers all the dialogue in an enervated Slavic monotone. There is no cast. No variation between speakers. Young audiences hate it. “Ask any Polish person and they will tell you it’s terrible,” says Mateusz (Mati) Staniszewski, the cofounder of AI speech outfit ElevenLabs. “I guess it was a communist thing that stuck as a cheap way to produce content.”

While working at Palantir, Staniszewski teamed up with high school friend and Google engineer Piotr Dabkowski to experiment with artificial intelligence. The pair realized that one project, a particularly promising AI public speaking coach, could solve the uniquely Polish horror of Leonardo DiCaprio or Scarlett Johansson being drowned out by a lektor “star” like Maciej Gudowski.

Cody Pickens for Forbes

The pair pooled their savings and by May 2022 had quit their jobs to work full-time on ElevenLabs. Out of the gate, their new AI text-to-speech generator was leagues better than the robotic voices of Apple’s Siri and Amazon’s Alexa. ElevenLabs’ AI voices were capable of happiness, excitement, even laughter.

In January 2023 ElevenLabs launched its first model. It could take any piece of text and use AI to read it aloud in any voice—including a clone of your own (or, worryingly, someone else’s). There was immediate demand. Authors could instantly spawn audiobooks with the software (pro rates now start from $99 a month for higher quality and more time). YouTube creators used ElevenLabs to translate their videos into other languages (its models can now speak in 29). The Warsaw- and London-based startup landed deals with lang­uage learning and meditation apps; then media companies like HarperCollins and Germany’s Bertelsmann jumped in. “It was obvious that this was the best model and everyone was picking it off the shelf,” says investor Jennifer Li of Andreessen Horowitz, which co-led a $19 million round in May 2023. A year later, the cofounders were honored as part of Forbes 30 Under 30 Europe.

Others, though, found more unnerving uses: AI soundalikes of public figures such as President Trump crassly narrating video game duels, actress Emma Watson reading Mein Kampf and podcaster Joe Rogan touting scams quickly went viral. Worse, fraudsters began using AI cloning tools to impersonate loved ones’ voices and steal millions in sophisticated deepfake swindles.

None of it stopped venture capitalists from pouring in money. ElevenLabs has raised more than $300 million in all, soaring to a $6.6 billion valuation in October to become one of Europe’s most valuable startups. Staniszewski, 30, who acts as CEO (the firm has no traditional titles), and research head Dabkowski, 30, are now both billionaires, worth just over $1 billion each, per Forbes estimates.

Around half of ElevenLabs’ $193 million in trailing 12-month revenue comes from corporates like Cisco, Twilio and Swiss recruitment agency Adecco, which use its tech to field customer service calls or interview job seekers. Epic Games uses it to voice characters in Fortnite, including a chat with Darth Vader (with the consent of James Earl Jones’ estate). The other half of its revenue comes from the YouTubers, podcasters and authors who were early adopters**.** “When you talk to them, it’s mind-blowing how good they are,” says Gartner analyst Tom Coshow. Unlike most AI firms, too, ElevenLabs is profitable. Forbes estimates it netted a $116 million in the last 12 months (a 60% margin).

It’s now competing against giants like Google, Microsoft, Amazon and OpenAI to become the de facto voice of AI. It’s not a new space: Tech companies started spinning up products to listen, transcribe and generate speech around a decade ago. While it’s somewhat of a sideline for Microsoft, Satya Nadella was willing to shell out $20 billion to buy Nasdaq-listed voice transcription service Nuance in March 2022. OpenAI launched its own voice tool, which can feed human conversations into ChatGPT, in October 2024.

It Goes to 11 | ElevenLabs’ numero­phile cofounders, Mati Staniszewski (left) and Piotr Dabkowski (right), love the number 11, especially the “rule of 11” divisibility trick. Their next goal? An $11 billion valuation, naturally.

Cody Pickens for Forbes

But ElevenLabs’ 300-person team isn’t playing catch-up. Its models are so good that it’s able to get away with charging up to three times as much as these American rivals. Its library of 10,000 uncannily human-sounding voices is the largest by far and now includes A-listers Michael Caine and Matthew McConaughey. It’s also more reliable. Data training startup Labelbox tested six of the top voice models with a reading quiz and found that ElevenLabs made half as many errors as its closest competitor, OpenAI. “We are one of the very few companies that are ahead of OpenAI—not only on speech, but speech-to-text and music. That’s hard,” Staniszewski says. ElevenLabs’ recipe is simple. A tight cadre of machine learning researchers, with obsessive focus on one narrow problem, and a tight budget (the cofounders fronted the first $100,000 training run) drove model breakthroughs. “Having a ton of compute can be a curse because you don’t think how to solve it in a smart way,” Dabkowski says.

But a lawsuit from a pair of audiobook narrators hints at another ingredient. Karissa Vacker and Mark Boyett allege that ElevenLabs used thousands of copyright-protected audiobooks to train its models. They claim so many of their books were scraped that clones of their voices ended up as default options on ElevenLabs. The case, in which ElevenLabs denied wrongdoing, was settled out of court in November. (Vacker and Boyett did not respond to a comment request; ElevenLabs declined further comment.)

Maturity is setting in. The company finally drew up a list of “no go” voices (mostly politicians and celebrities) after an ElevenLabs-made clone of Joe Biden’s voice was used to discourage voting in a robocall campaign around the 2024 Democratic primary. ElevenLabs now has seven full-time human moderators (plus AI, natch) scouring its clips for misuse. Newly cloned voices need to pass a consent check, and the company offers a free deepfake detector.

Staniszewski and Dabkowski have big plans beyond voice. Both cash-strapped creators and budget-conscious media companies wanted royalty-free background music, so they delivered an AI music generator in August. Don’t have time to shoot a video? ElevenLabs will have AI avatars to front Sora-style videos next year. Their boldest bet is that they can translate their expertise to provide a single hub for clients to manage all their AI tools. “We are building a platform that allows you to create voice agents and deploy them smoothly,” Staniszewski says.

Of course, that puts ElevenLabs on a collision course with a gaggle of other startups hoping to do the same thing. It helps that it’s been profitable since its earliest days, but its startup competitors are richly funded, and the tech giants have virtually unlimited resources. Still, it must innovate. Voice models will soon be commoditized. When other models catch up, fickle customers that already balk at ElevenLabs’ pricing will likely switch.

As it broadens beyond voices to more computationally intensive music and video, ElevenLabs needs to expand its own GPU farms to stay in the race. It has already spent $50 million on a data center project in Oregon. “If we are to build the generational company in AI, you need to build scale, and we are building,” Staniszewski says.

Back in Poland, the aging corps of lektors are still in business, for now. Dabkowski hasn’t forgotten ElevenLabs’ original pitch, boasting that his next model will translate and voice an entire movie in one shot. “We never give up on our missions,” he says.

More from Forbes

ForbesVibe Coding Turned This Swedish AI Unicorn Into The Fastest Growing Software Startup EverBy Iain MartinForbesHow An AI Notetaker Became One Of The Few Profitable AI StartupsBy Iain MartinForbesThis AI Founder’s Audacious Plan To Buy Out His Own VCsBy Iain MartinForbesMagic Money: The Mysterious Case Of The $15 Billion Metaverse Startup And Its Anonymous Multi-Billion Dollar InvestorBy Phoebe Liu

• High-profile endorsements: Backed by figures like Sam Altman and Jensen Huang, and called one of the fastest-growing products by insiders.
• Valuation vs. profitability: Valuation jumped from $2.5 billion to about $29.3 billion while the company remains unprofitable.
• Reliance on external models: Cursor depends heavily on AI models from OpenAI, Anthropic and others for core functionality.
• Rapid revenue growth: Annualized sales reportedly rose from $100 million to $1 billion in 2025.
• Platform risk: Customers and executives worry major model providers could cut access or build competing tools.
• Composer launch and funding: Cursor released its own model, Composer, and shortly after raised over $2 billion to reduce dependency.
• High operating costs: Significant expenses come from compute power and paying for model access and training data.
• Founding story and culture: Founded by four MIT alumni in 2023 from a living room pivot, with a North Beach office, informal workplace customs and growth driven largely by word-of-mouth and pop-up events.

By

Angel Au-Yeung

Dec. 1, 2025 12:00 pm ET

Michael Truell, co-founder and CEO of Cursor. Andria Lo

Silicon Valley insiders have started calling Cursor, the AI coding tool, the fastest-growing product of all time.

Two of its biggest fans: OpenAI’s Sam Altman and Nvidia’s Jensen Huang, who has said he wants all of his 40,000 coders to use it. 

Even though the company’s valuation soared from $2.5 billion in January to $29.3 billion today, it loses money, according to people familiar with its finances. And the tool relies heavily on the underlying artificial-intelligence models of companies such as OpenAI and Anthropic, which are actively competing for the same software engineering users who have made Cursor a runaway hit.

That has put the startup’s staying power in question and at the heart of a raging debate in Silicon Valley. Millions of users love it, but they aren’t sure it will last.

“The risk that we see is, what happens if Google comes along, turns off model access for a company like Cursor and then makes their own version of Cursor?” said Kyle Cesmat, an engineering manager at Coinbase Global, who is responsible for the crypto exchange’s AI strategy.

So far, an army of coders who love it have silenced many skeptics.

“Coding has been the first breakout category of tools revolutionized by AI,” said Barry McCardel, co-founder and chief executive of data analytics startup Hex, based in San Francisco. Earlier this year, enough of his developers had individual accounts that he decided to get an enterprise account for the company. He pays roughly $40,000 a year for 70 of his engineers, designers and product managers to use Cursor. 

The company says the pricing can vary based on how often it is being used and what features they are choosing. Users say it and other tools that make use of the expert coding capabilities of AI can make some engineers up to 10 times as productive, enough to lead some to fear its impact and how it will affect their jobs.

Cursor Chief Executive Michael Truell, 25 years old, dismissed such concerns, saying it is a problem for another day.

“We are so far away from anything that looks like coding going away,” he said in an interview. “We will always exist to be a tool that helps humans take an idea in their head, a vision of what they want to see on the computer, and have it show up on the screen. That is why we exist.” 

The startup’s cultural oddities have added to its allure. Cursor works out of North Beach, the historic Italian neighborhood with a bohemian flare that is decidedly separate from the AI epicenter in downtown San Francisco.

Cursor’s office in San Francisco. Andria Lo

Its four founders—Truell, Sualeh Asif, Arvid Lunnemark and Aman Sanger—started building Cursor out of a living room in 2023. There is a no-shoes policy in the office, the floor is lined with ornate rugs for socked feet, and a chef named Fausto serves lunch six days a week for its 250 employees.

For a company that started as an encrypted messaging startup just two years ago, Cursor has come a long way.

Truell grew up in New York City, the son of two journalists who have worked for The Wall Street Journal and the New York Times. He started programming in middle school and met his three MIT co-founders in 2018.

After graduating, they moved to San Francisco in 2022 and after a month-long brainstorming session, embarked on a plan to build an encrypted messaging system, working on it for a few months before deciding the market was too small.

It took three months for them to build the first iteration of what is now known as Cursor. For the rest of 2023, they “lived like monks,” resisting outside hires and focused maniacally on building a user-friendly tool. By the end of 2023, their startup had less than 10 employees.

In 2024, Cursor mostly spread through word-of-mouth among developers. The company spent almost no money on external marketing. Tech executives who heard about it from their employees found it difficult to get in touch with the company.

“I sought out Michael [Truell] on LinkedIn,” said Coinbase’s Cesmat. “My original contact was with a temp executive assistant because I couldn’t find anyone else to talk to.”

Eventually, Truell and the team grew more aggressive about hiring, flying around the world to persuade people to join them, even if they had previously said no.

They pursued Oskar Schulz, a former classmate at MIT, for more than a year. “Now, we try to close people a bit faster,” he said.

The company started organizing Cursor pop-up cafe events this fall, taking over local coffee shops and turning them into a co-working space for engineers to connect. They have gone around the world, from San Francisco and New York to Mumbai and Karachi, Pakistan (one of the co-founders is from Karachi).

A few weeks ago when the company was preparing for a big launch, they decided to buy a full-page ad in a newspaper. An ad in a national newspaper felt too flashy, so they opted for the Sunday edition of a local paper in San Francisco. The ad included a link to a Cursor webpage along with a heartfelt letter that started with: “Dear Developers.” 

“We monitored the traffic to the link and it was like, 112 people, of which probably were 50 people of our own team figuring out where this link went,” said Schulz.

Even with a somewhat haphazard marketing strategy, Cursor’s growth has ballooned in 2025.

The company’s annualized sales—an extrapolation of the next 12 months’ revenue based on recent sales—grew from $100 million to $1 billion this year.

By this metric, some Silicon Valley insiders have started calling Cursor the fastest-growing product of all time. “I’ve never seen this in any other company since I’ve been investing,” said Miles Clements, a partner at venture firm Accel since 2009 who led Cursor’s most recent funding round.

While growth has been robust, Cursor has faced high costs for computing firepower and AI model access operated by OpenAI, Anthropic, Google and others. 

SHARE YOUR THOUGHTS

Do you think Cursor has staying power? Join the conversation below.

In October, it launched its own AI model called Composer, partially to reduce its dependency on the biggest AI labs. Two weeks after the model launch, the company announced it had raised over $2 billion in venture funding—an indication of significant interest and just how much it faces in expenses.

Composer is fueled by years worth of product use data that Cursor has been collecting from developers since its launch in 2023. This data is valuable: a one-hour session of developer data using an AI coding tool could cost as much at $500, according to startups that hire contractors to help train AI models. Some users praised its capabilities for quick tasks but said they still rely on bigger AI models like those from Anthropic for more complex work.

The ultimate fate of the company rests on the question of what will become a commodity first, said McCardel, the startup founder whose employees persuaded him to use Cursor.

Will model builders such as Anthropic or OpenAI become a commodity as they compete with cheaper models such as DeepSeek, which emerged from China? Or will it be the likes of Cursor, which hosts the software built by others that does most of the programming magic?

“It’s just an interesting dynamic to see who will be able to commoditize who faster,” he said.

Write to Angel Au-Yeung at angel.au-yeung@wsj.com

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