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AI startup valuations are doubling and tripling within months as back-to-back funding rounds fuel a stunning growth spurt

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Everyone keeps asking: “Are we in an AI bubble?” But just as often, I hear a different question, followed by recognition: “Wait—they raised another round?”

This year, a handful of top AI startups—some now so large that calling them “startups” feels vaguely ironic—have raised not just one giant round of funding, but two or more. And with each round, the startups’ valuations are doubling, sometimes even tripling, to reach astonishing new heights.

Take Anthropic. In March it raised a $3.5 billion Series E at a $61.5 billion valuation. Just six months later, in September, it pulled in a $13 billion Series F round. New valuation: $183 billion.

OpenAI, the startup that ignited the AI boom with ChatGPT, remains the pace setter, fetching an unprecedented $500 billion valuation in a tender offer last month. That’s up from the $300 billion valuation it garnered during a March funding round, and the $157 billion valuation it started off this year with as a result of an October 2024 funding.

In other words, in the 12 months between October 2024 and October 2025, OpenAI’s valuation increased by roughly $29 billion every month—almost $1 billion per day.

It’s not just the LLM giants. Further down (but still high on) the AI food chain, recruiting startup Mercor in February raised its $100 million Series B at a $2 billion valuation—and then by October raised another $350 million as the company’s valuation leapt to $10 billion. 

Well over a dozen startups have raised two or more funding rounds this year with escalating valuations, including Cursor, Reflection AI, OpenEvidence, Lila Sciences, Harmonic, Fal, Abridge, and Doppel. Some, like Harvey and Databricks, are currently reported to be in their third rounds. 

These valuation growth spurts, especially at a scale of billions and tens of billions of dollars, are extraordinary and raise a number of dizzying questions, beginning with: Why is this even happening? Is the phenomenon a reflection of the strength of these startups, or the unique business opportunity presented by the AI revolution, or a bit of both? And how healthy is this kind of thing—what risks are the startups, and the broader market, taking on by raising so much capital so fast and pumping valuations up so quickly? 

The specter of 2021

To hear some industry insiders explain it, there’s more to the current phenomenon than frothy market conditions. While the ZIRP, or zero interest rate policy, era that peaked in 2021 saw its share of startups raising multiple back-to-back rounds (Cybersecurity startup Wiz was valued at $1.7 billion in its May 2021 round, and when it raised $250 million in October its valuation sprung to $6 billion), the underlying dynamics were completely different back then (not least because ChatGPT hadn’t launched yet).

Tom Biegala, founding partner at Bison Ventures, said that he doesn’t believe this is anything like 2021, when “companies would raise a round… not because they’ve made any sort of real progress or any technical or commercial milestones.” Investor enthusiasm was so high and capital flowed so effortlessly back then that the perception of momentum was often enough to draw more than one round of capital in a year, Biegala said.

And for every successful Wiz, there were numerous startups in the ZIRP-era that also raised two or more rounds within 12 months that have since struggled (like grocery delivery app Jokr, NFT marketplace OpenSea, and telehealth startup Cerebral).

Terrence Rohan, managing director at Otherwise Fund, says today’s multi-round startups are demonstrating real business traction: “The revenue growth we’re seeing in select companies is without precedent. In certain cases, one could argue that we are dealing with a new phenotype of startup,” Rohan said via email.

Many of today’s high-flying AI startups are putting up impressive numbers, even if we should be suspicious of ARR at this moment. You have young companies like vibe coding startup Lovable, which went from zero to $17 million in ARR in three months, and conversational AI startup Decagon hit “seven figures” in ARR over its first half-year. Cursor is perhaps the most famous of all: The developer-focused AI coding tool went from zero to $100 million in ARR in one year. 

Felicis Ventures founder and managing partner Aydin Senkut describes the back-to-back fundings as a sign of a high velocity market where the costs of being wrong are higher than ever. “The prize now goes to those who identify and support these outliers earliest,” Senkut says, “because being in the wrong sector or too late may not just reduce returns, it may zero them out.”

“The prize is so big”

While broad excitement over generative AI is fueling the series of funding rounds, startups pushing the boundaries in certain verticals are among the biggest beneficiaries of the trend.

Cursor, the buzzy AI coding startup, finished 2024 with a healthy $2.6 billion valuation. Its valuation jumped to $10 billion in June 2025, when Cursor raised $900 million in funding. This month, Cursor announced that it’s now worth $29.3 billion, as it scooped up $2.3 billion in additional capital from investors including Accel, Thrive, and Andreessen Horowitz.

Harvey, an AI startup aimed at the legal industry, raised a total of $600 million in two separate funding rounds within the first six months of 2025, lifting its valuation first to $3 billion and then to $5 billion. In October, several outlets, including Bloomberg and Forbes, reported that Harvey just raised another round of funding that gives the startup an $8 billion valuation. 

Each is representative of their respective sectors: Both coding and legal AI are booming right now. Legal AI company Norm AI in November raised $50 million from Blackstone—shortly after raising a $48 million Series B raised in March. Likewise, in coding, Lovable raised its $15 million seed round in February, followed up with a $200 million Series A at a $1.8 billion valuation by July. 

Healthcare and AI is also hot, with companies like OpenEvidence raising its July Series B of $210 million at $2.5 billion valuation, only to follow up in October with another $200 million at a $6 billion valuation. Abridge (last valued at $5.3 billion) and Hippocratic AI (last valued at $3.5 billion) fall into this category, as well.

Max Altman, Saga Ventures cofounder and managing partner, says the trend isn’t simply the result of exuberant startup investors throwing money around. For some startups, rapid-fire fundraising is becoming part of the strategic playbook—an effective means of taking on competition. 

“What these companies are doing is, very smartly, salting the Earth for their competitors,” Altman told Fortune. “The prize is so big now, with so many people going after it. So, a really amazing strategy is to suck up all the capital, have the best funds invest in your company so they’re not investing in your competitors. Stripe did this really early on, it was smart—you become this force of nature that’s too big to fail.”

That said, that doesn’t mean everyone attracting massive capital is a winner waiting in the wings. 

When the foundation isn’t set

If raising multiple rounds quickly can be a strategic advantage, it can also become a dangerous liability. Or, as Andreessen Horowitz general partner Jennifer Li puts it, these back-to-back fundraisings can go right—and they can go wrong.

“They go right when the capital directly fuels product market fit and execution,” Li said via email. “For example, when the company uses new resources to expand infrastructure, improve models, or meet outsized demand.”

So when do they go wrong?

“When the focus shifts from building to fundraising before the foundation is set,” said Li.

Like a skyscraper built on unstable ground, startups that can’t support overly lofty valuations risk a painful comedown. The valuations of some of hyped AI startups may look untenable (perhaps even unhinged) in the public markets, should the startup make it that far. The resulting recalibration manifests itself in the plummeting value of employees’ equity, creating talent retention and recruiting risks. Many of 2025’s biggest IPOs, such as Chime and Klarna, were decisive valuation cuts from their 2021 highs.

Within the private markets, rapid rounds of fund raising means cap tables can get quickly complex as founder stakes dilute. And then perhaps, the biggest risk of all: That some of these excessively funded startups end up with wild burn rates that they can’t roll back if times get tough and capital dries up. That can lead to layoffs, or worse.

Ben Braverman, Altman’s Saga cofounder and managing partner, said this is ultimately a story about both the concentration of capital in AI and about how VCs have evolved their strategies in the aftermath of 2021. Venture capital has always been about the Power Law—that big winners keep winning big—but that’s become especially true as VCs chase consensus favorites more than ever.

“The story of 2021 to now, on all sides of the market, is a flight to quality,” said Braverman. “Seemingly VCs made the same decision over the last cycle: ‘We’re going to put the majority of our dollars into a few brand names we really trust. And obviously, that has its own consequences.”

One of those consequences is that more capital than ever is flowing into a limited set of AI darlings. And while term sheets are being signed at a feverish pace today, even bullish investors acknowledge that, like any cycle, there will be winners and losers.

“In this type of environment, investors sometimes fall into a trap where they think every new AI model company is going to look like OpenAI or Anthropic,” Bison Ventures’s Biegala told Fortune.

“They’re assigning big valuations to those businesses, and it’s an option value on those companies becoming the next OpenAI or Anthropic,” Biegala said. But, he notes, “a lot of them are not necessarily going to grow into those valuations…and you’re going to see some losses for sure.”



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Mark Zuckerberg renamed Facebook for the metaverse. 4 years and $70B in losses later, he’s moving on

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In 2021, Mark Zuckerberg recast Facebook as Meta and declared the metaverse — a digital realm where people would work, socialize, and spend much of their lives — the company’s next great frontier. He framed it as the “successor to the mobile internet” and said Meta would be “metaverse-first.”

The hype wasn’t all him. Grayscale, the investment firm specializing in crypto, called the Metaverse a “trillion-dollar revenue opportunity.” Barbados even opened up an embassy in Decentraland, one of the worlds in the metaverse. 

Five years later, that bet has become one of the most expensive misadventures in tech. Meta’s Reality Labs division has racked up more than $70 billion in losses since 2021, according to Bloomberg, burning through cash on blocky virtual environments, glitchy avatars, expensive headsets, and a user base of approximately 38 people as of 2022.

For many people, the problem is that the value proposition is unclear; the metaverse simply doesn’t yet deliver a must-have reason to ditch their phone or laptop. Despite years of investment, VR remains burdened by serious structural limitations, and for most users there’s simply not enough compelling content beyond niche gaming.

A 30% budget cut 

Zuckerberg is now preparing to slash Reality Labs’ budget by as much as 30%, Bloomberg said. The cuts—which could translate to $4 billion to $6 billion in reduced spend—would hit everything from the Horizon Worlds virtual platform to the Quest hardware unit. Layoffs could come as early as January, though final decisions haven’t been made, according to Bloomberg. 

The move follows a strategy meeting last month at Zuckerberg’s Hawaii compound, where he reviewed Meta’s 2026 budget and asked executives to find 10% cuts across the board, the report said. Reality Labs was told to go deeper. Competition in the broader VR market simply never took off the way Meta expected, one person said. The result: a division long viewed as a money sink is finally being reined in.

Wall Street cheered. Meta’s stock jumped more than 4% Thursday on the news, adding roughly $69 billion in market value.

“Smart move, just late,” Craig Huber of Huber Research told Reuters. Investors have been complaining for years that the metaverse effort was an expensive distraction, one that drained resources without producing meaningful revenue.

Metaverse out, AI in

Meta didn’t immediately respond to Fortune’s request for comment, but it insists it isn’t killing the metaverse outright. A spokesperson told the South China Morning Post that the company is “shifting some investment from Metaverse toward AI glasses and wearables,” point­ing to momentum behind its Ray-Ban smart glasses, which Zuckerberg says have tripled in sales over the past year.

But there’s no avoiding the reality: AI is the new obsession, and the new money pit.

Meta expects to spend around $72 billion on AI this year, nearly matching everything it has lost on the metaverse since 2021. That includes massive outlays for data centers, model development, and new hardware. Investors are much more excited about AI burn than metaverse burn, but even they want clarity on how much Meta will ultimately be spending — and for how long.

Across tech, companies are evaluating anything that isn’t directly tied to AI. Apple is revamping its leadership structure, partially around AI concerns. Microsoft is rethinking the “economics of AI.” Amazon, Google, and Microsoft are pouring billions into cloud infrastructure to keep up with demand. Signs point to money-losing initiatives without a clear AI angle being on the chopping block, with Meta as a dramatic example.

On the company’s most recent earnings call, executives didn’t use the word “metaverse” once.



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Robert F. Kennedy Jr. turns to AI to make America healthy again

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HHS billed the plan as a “first step” focused largely on making its work more efficient and coordinating AI adoption across divisions. But the 20-page document also teased some grander plans to promote AI innovation, including in the analysis of patient health data and in drug development.

“For too long, our Department has been bogged down by bureaucracy and busy-work,” Deputy HHS Secretary Jim O’Neill wrote in an introduction to the strategy. “It is time to tear down these barriers to progress and unite in our use of technology to Make America Healthy Again.”

The new strategy signals how leaders across the Trump administration have embraced AI innovation, encouraging employees across the federal workforce to use chatbots and AI assistants for their daily tasks. As generative AI technology made significant leaps under President Joe Biden’s administration, he issued an executive order to establish guardrails for their use. But when President Donald Trump came into office, he repealed that order and his administration has sought to remove barriers to the use of AI across the federal government.

Experts said the administration’s willingness to modernize government operations presents both opportunities and risks. Some said that AI innovation within HHS demanded rigorous standards because it was dealing with sensitive data and questioned whether those would be met under the leadership of Health Secretary Robert F. Kennedy Jr. Some in Kennedy’s own “Make America Health Again” movement have also voiced concerns about tech companies having access to people’s personal information.

Strategy encourages AI use across the department

HHS’s new plan calls for embracing a “try-first” culture to help staff become more productive and capable through the use of AI. Earlier this year, HHS made the popular AI model ChatGPT available to every employee in the department.

The document identifies five key pillars for its AI strategy moving forward, including creating a governance structure that manages risk, designing a suite of AI resources for use across the department, empowering employees to use AI tools, funding programs to set standards for the use of AI in research and development and incorporating AI in public health and patient care.

It says HHS divisions are already working on promoting the use of AI “to deliver personalized, context-aware health guidance to patients by securely accessing and interpreting their medical records in real time.” Some in Kennedy’s Make America Healthy Again movement have expressed concerns about the use of AI tools to analyze health data and say they aren’t comfortable with the U.S. health department working with big tech companies to access people’s personal information.

HHS previously faced criticism for pushing legal boundaries in its sharing of sensitive data when it handed over Medicaid recipients’ personal health data to Immigration and Customs Enforcement officials.

Experts question how the department will ensure sensitive medical data is protected

Oren Etzioni, an artificial intelligence expert who founded a nonprofit to fight political deepfakes, said HHS’s enthusiasm for using AI in health care was worth celebrating but warned that speed shouldn’t come at the expense of safety.

“The HHS strategy lays out ambitious goals — centralized data infrastructure, rapid deployment of AI tools, and an AI-enabled workforce — but ambition brings risk when dealing with the most sensitive data Americans have: their health information,” he said.

Etzioni said the strategy’s call for “gold standard science,” risk assessments and transparency in AI development appear to be positive signs. But he said he doubted whether HHS could meet those standards under the leadership of Kennedy, who he said has often flouted rigor and scientific principles.

Darrell West, senior fellow in the Brooking Institution’s Center for Technology Innovation, noted the document promises to strengthen risk management but doesn’t include detailed information about how that will be done.

“There are a lot of unanswered questions about how sensitive medical information will be handled and the way data will be shared,” he said. “There are clear safeguards in place for individual records, but not as many protections for aggregated information being analyzed by AI tools. I would like to understand how officials plan to balance the use of medical information to improve operations with privacy protections that safeguard people’s personal information.”

Still, West, said, if done carefully, “this could become a transformative example of a modernized agency that performs at a much higher level than before.”

The strategy says HHS had 271 active or planned AI implementations in the 2024 financial year, a number it projects will increase by 70% in 2025.



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Construction workers are earning up to 30% more in the data center boom

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Big Tech’s AI arms race is fueling a massive investment surge in data centers with construction worker labor valued at a premium. 

Despite some concerns of an AI bubble, data center hyperscalers like Google, Amazon, and Meta continue to invest heavily into AI infrastructure. In effect, construction workers’ salaries are being inflated to satisfy a seemingly insatiable AI demand, experts tell Fortune.

In 2026 alone, upwards of $100 billion could be invested by tech companies into the data center buildout in the U.S., Raul Martynek, the CEO of DataBank, a company that contracts with tech giants to construct data centers, told Fortune.

In November, Bank of Americaestimated global hyperscale spending is rising 67% in 2025 and another 31% in 2026, totaling a massive $611 billion investment for the AI buildout in just two years.

Given the high demand, construction workers are experiencing a pay bump for data center projects.

Construction projects generally operate on tight margins, with clients being very cost-conscious, Fraser Patterson, CEO of Skillit, an AI-powered hiring platform for construction workers, told Fortune.

But some of the top 50 contractors by size in the country have seen their revenue double in a 12-month period based on data center construction, which is allowing them to pay their workers more, according to Patterson.

“Because of the huge demand and the nature of this construction work, which is fueling the arms race of AI… the budgets are not as tight,” he said. “I would say they’re a little more frothy.”

On Skillit, the average salary for construction projects that aren’t building data centers is $62,000, or $29.80 an hour, Patterson said. The workers that use the platform comprise 40 different trades and have a wide range of experience from heavy equipment operators to electricians, with eight years as the average years of experience.

But when it comes to data centers, the same workers make an average salary of $81,800 or $39.33 per hour, Patterson said, increasing salaries by just under 32% on average.

Some construction workers are even hitting the six-figure mark after their salaries rose for data center projects, according to The Wall Street Journal. And the data center boom doesn’t show any signs it’s slowing down anytime soon.

Tech companies like Google, Amazon, and Microsoft operate 522 data centers and are developing 411 more, according to The Wall Street Journal, citing data from Synergy Research Group. 

Patterson said construction workers are being paid more to work on building data centers in part due to condensed project timelines, which require complex coordination or machinery and skilled labor.

Projects that would usually take a couple of years to finish are being completed—in some instances—as quickly as six months, he said.

It is unclear how long the data center boom might last, but Patterson said it has in part convinced a growing number of Gen Z workers and recent college grads to choose construction trades as their career path.

“AI is creating a lot of job anxiety around knowledge workers,” Patterson said. “Construction work is, by definition, very hard to automate.”

“I think you’re starting to see a change in the labor market,” he added.



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