Buy now, pay later firms like Klarna and Block’s Afterpay could be about to face tougher rules in the U.K.
Nikolas Kokovlis | Nurphoto | Getty Images
LONDON — More startups are being spun out of Swedish digital payments firm Klarna than any other financial technology unicorn in Europe, according to a new report from venture capital firm Accel.
Accel’s “Fintech Founder Factory” report shows that alumni from Klarna have gone on to create a total of 62 new startups, including the likes of Swedish lending technology firm Anyfin, regulatory compliance platform Bits Technology and AI-powered coding platform Pretzel AI.
That is more than any other venture-backed fintech startup worth $1 billion or more in the region.
This includes the digital banking app Revolut, whose former employees have founded 49 startups. It also includes money transfer app Wise and online-only bank N26, where ex-staff at both firms have started 33 companies each, according to Accel’s data.
‘Founder factories’
Accel labels these companies “founder factories,” on the basis that they have become breeding grounds for talent that often go on to establish their own firms.
“We now have a very long list of large, durable, successful companies in Europe across the different ecosystems — including London, Berlin and Stockholm — that have been generating interesting outcomes,” Luca Bocchio, partner at Accel, told CNBC.
Out of 98 venture-backed fintech unicorns in Europe and Israel, 82 have produced 635 new tech-enabled startups, according to Accel’s report, which was published Tuesday ahead of a fintech event the firm is hosting in London Wednesday.
The data also factors in fintech unicorns based in Israel. However, most of the biggest fintech founder factories come from Europe.
Klarna’s workforce reduction
Klarna has attracted headlines in recent months due to commentary from the buy now, pay later giant’s founder and CEO, Sebastian Siemiatkowski, about using artificial intelligence to help reduce headcount.
Klarna, which currently has a company-wide hiring freeze in place, cut its overall employee headcount by roughly 24% to 3,800 in August this year. Siemiatkowski has said that Klarna was able to reduce the number of people it hires thanks to its implementation of generative AI.
He is looking to further reduce Klarna’s headcount to 2,000 employees — but has yet to specify a time for this target.
Asked about why Klarna topped the ranking of fintech founder factories in Europe, Bocchio said: “Klarna is an organization that is coming of age now.”
That means it is currently “well positioned to produce interesting founders,” Bocchio added — both because it’s large and has been around for a long time, and because of the “interesting” ways its staff work internally.
Staying close to home
Another notable finding from Accel’s report is that most companies founded by former fintech unicorn employees tend to do so in the same cities and hubs their employer was founded in.
Nearly two-thirds (61%) of companies founded by former employees of fintech unicorns were founded in the same city as the unicorn, according to Accel.
More broadly, the numbers show that Europe is seeing a “flywheel effect,” according to Bocchio, as tech firms are scaling to such a large size that staff can take learnings from them and leave to set up their own ventures.
“I think the flywheel is spinning because that talent is remaining inside the flywheel. That talent is not going anywhere.” This, he said, “speaks to the maturity and appetite” of individuals within Europe’s fintech founder factories. “We expect this trend to continue. I don’t see any reason why it should stop.”
With the U.S. restricting China from buying advanced semiconductors used in artificial intelligence development, Beijing is placing hopes on domestic alternatives such as Huawei.
The task has been made more challenging by the fact that U.S. curbs not only inhibit China’s access to the world’s most advanced chips, but also restrict availing technology vital for creating an AI chip ecosystem.
Those constraints span the entire semiconductor value chain, ranging from design and manufacturing equipment used to produce AI chips to supporting elements such as memory chips.
Beijing has mobilized tens of billions of dollars to try to fill those gaps, but while it has been able to “brute force” its way into some breakthroughs, it still has a long way to go, according to experts.
“U.S. export controls on advanced Nvidia AI chips have incentivized China’s industry to develop alternatives, while also making it more difficult for domestic firms to do so,” said Paul Triolo, partner and senior vice president for China at advisory firm DGA-Albright Stonebridge Group.
Here’s how China stacks up against the rest of the world in four key segments needed to build AI chips.
AI chip design
Nvidia is regarded as the world’s leading AI chip company, but it’s important to understand that it doesn’t actually manufacture the physical chips that are used for AI training and computing.
Rather, the company designs AI chips, or more precisely, graphics processing units. Orders of the company’s patented GPU designs are then sent to chip foundries — manufacturers that specialize in the mass production of other companies’ semiconductor products.
While American competitors such as AMD and Broadcom offer varying alternatives, GPU designs from Nvidia are widely recognized as the industry standard. The demand for Nvidia chips is so strong that Chinese customers have continued to buy any of the company’s chips they can get their hands on.
But Nvidia is grappling with Washington’s tightening restrictions. The company revealed in April that additional curbs had prevented it from selling its H20 processor to Chinese clients.
Nvidia’s H20 was a less sophisticated version of its H100 processor, designed specifically to skirt previous export controls. Nevertheless, experts say, it was still more advanced than anything available domestically. But China hopes to change that.
In response to restrictions, more Chinese semiconductor players have been entering the AI processor arena. They’ve included a wide array of upstarts, such as Enflame Technology and Biren Technology, seeking to soak up billions of dollars in GPU demand left by Nvidia.
But no Chinese firm appears closer to providing a true alternative to Nvidia than Huawei’s chip design arm, HiSilicon.
Huawei’s most advanced GPU in mass production is its Ascend 910B. The next-generation Ascend 910C was reportedly expected to begin mass shipments as early as May, though no updates have emerged.
Dylan Patel, founder, CEO and chief analyst at SemiAnalysis, told CNBC that while the Ascend chips remain behind Nvidia, they show that Huawei has been making significant progress.
“Compared to Nvidia’s export-restricted chips, the performance gap between Huawei and the H20 is less than a full generation. Huawei is not far behind the products Nvidia is permitted to sell into China,” Patel said.
He added that the 910B was two years behind Nvidia as of last year, while the Ascend 910C is only a year behind.
But while that suggests China’s GPU design capabilities have made great strides, design is just one aspect that stands in the way of creating a competitive AI chip ecosystem.
AI chip fabrication
To manufacture its GPUs, Nvidia relies on TSMC, the world’s largest contract chip foundry, which produces most of the world’s advanced chips.
TSMC complies with U.S. chip controls and is also barred from taking any chip orders from companies on the U.S. trade blacklist. Huawei was placed on the list in 2019.
That has led to Chinese chip designers like Huawei to enlist local chip foundries, the largest of which is SMIC.
SMIC is far behind TSMC — it’s officially known to be able to produce 7-nanometer chips, requiring less advance tech than TSMC’s 3-nanometer production. Smaller nanometer sizes lead to greater chip processing power and efficiency.
There are signs that SMIC has made progress. The company is suspected to have been behind a 5-nanometer 5G chip for Huawei’s Mate 60 Pro, which had rocked confidence in U.S. chip controls in 2023. The company, however, has a long way to go before it can mass-produce advanced GPUs in a cost-efficient manner.
According to independent chip and technology analyst Ray Wang, SMIC’s known operation capacity is dwarfed by TSMC’s.
“Huawei is a very good chip design company, but they are still without good domestic chipmakers,” Wang said, noting that Huawei is reportedly working on its own fabrication capabilities.
But the lack of key manufacturing equipment stands in the way of both companies.
Advanced Chip equipment
SMIC’s ability to fulfill Huawei’s GPU requirements is limited by the familiar problem of export controls, but in this case, from the Netherlands.
While Netherlands may not have any prominent semiconductor designers or manufacturers, it’s home to ASML, the world’s leading supplier of advanced chipmaking equipment — machines that use light or electron beams to transfer complex patterns onto silicon wafers, forming the basis of microchips.
In accordance with U.S. export controls, the country has agreed to block the sale of ASML’s most advanced ultraviolet (EUV) lithography machines. The tools are critical to making advanced GPUs at scale and cost-effectively.
EUV is the most significant barrier for Chinese advanced chip production, according to Jeff Koch, an analyst at SemiAnalysis. “They have most of the other tooling available, but lithography is limiting their ability to scale towards 3nm and below process nodes,” he told CNBC.
SMIC has found methods to work around lithography restrictions using ASML’s less advanced deep ultraviolet lithography systems, which have seen comparatively fewer restrictions.
Through this “brute forcing,” producing chips at 7 nm is doable, but the yields are not good, and the strategy is likely reaching its limit, Koch said, adding that “at current yields it appears SMIC cannot produce enough domestic accelerators to meet demand.”
SiCarrier Technologies, a Chinese company working on lithography technology, has reportedly been linked to Huawei.
But imitating existing lithography tools could take years, if not decades, to achieve, Koch said. Instead, China is likely to pursue other technologies and different lithography techniques to push innovation rather than imitation, he added.
AI memory components
While GPUs are often identified as the most critical components in AI computing, they’re far from the only ones. In order to operate AI training and computing, GPUs must work alongside memory chips, which are able to store data within a broader “chipset.”
In AI applications, a specific type of memory known as HBM has become the industry standard. South Korea’s SK Hynix has taken the industry lead in HBM. Other companies in the field include Samsung and U.S.-based Micron.
“High bandwidth memory at this stage of AI progression has become essential for training and running AI models,” said analyst Wang.
As with the Netherlands, South Korea is cooperating with U.S.-led chip restrictions and began complying with fresh curbs on the sale of certain HBM memory chips to China in December.
In response, Chinese memory chip maker ChangXin Memory Technologies, or CXMT, in partnership with chip-packaging and testing company Tongfu Microelectronics, is in the early stages of producing HBM, according to a report by Reuters.
According to Wang, CXMT is expected to be three to four years behind global leaders in HBM development, though it faces major roadblocks, including export controls on chipmaking equipment.
SemiAnalysis estimated in April that CXMT remained a year away from ramping any reasonable volume.
Chinese foundry Wuhan Xinxin Semiconductor Manufacturing is reportedly building a factory to produce HBM wafers. A report from SCMP said that Huawei Technologies had partnered with the firm in producing HBM chips, although the companies did not confirm the partnership.
Huawei has leaned on HBM stockpiles from suppliers like Samsung for use in their Ascend 910C AI processor, SemiAnalysis said in an April report, noting that while the chip was designed domestically, it still relies on foreign products obtained prior to or despite restrictions.
“Whether it be HBM from Samsung, wafers from TSMC, or equipment from America, Netherlands, and Japan, there is a big reliance on foreign industry,” SemiAnalysis said.
Ali Ghodsi, co-founder and CEO of Databricks, speaks at the company’s Data and AI Summit in San Francisco on June 11, 2025.
Jordan Novet | CNBC
Databricks, a data analytics software vendor, said on Wednesday that it expects to generate $3.7 billion in annualized revenue by July, with year-over-year growth of 50%.
CFO Dave Conte delivered the numbers at a briefing for investors and analysts tied to the company’s Data and AI Summit in San Francisco on Wednesday. Growth in the October quarter was 60%, Databricks said in late 2024.
Databricks is one of the most highly valued tech startups, announcing in December that it raised $10 billion at a $62 billion valuation. Snowflake, its closest public market competitor, has a market cap of about $70 billion on annualized revenue of just over $4 billion, based on its latest quarter.
Conte didn’t give any indication of when Databricks might file for an IPO. On Wednesday, fintech company Chime priced its IPO, and stablecoin issuer Circlestarted trading on the New York Stock Exchange last week.
Databricks had $2.6 billion in revenue in its fiscal year that ended in January, with a net retention rate exceeding 140%, unchanged from last year. In the first quarter of the new fiscal year, nearly 50 of Databricks’ 15,000-plus customers were spending over $10 million annually, Conte said.
“We want to combine good revenue growth and good product velocity with profitability,” Conte said.
The company has roughly 8,000 employees. Earlier on Wednesday, Databricks CEO Ali Ghodsi said the company is hiring 3,000 people in 2025. Databricks was close to being free cash flow positive for the first time in the most recent fiscal year, Conte said.
In addition to Snowflake, competition also comes from cloud providers that sell their own data warehousing software.
Also on Wednesday, Databricks announced a preview of Lakebase database software drawing on technology from its recent $1 billion acquisition of startup Neon. Lakebase stands to expand the size of Databricks’ market opporunity, Conte said.
Databricks ranked third on CNBC’s newly release 2025 Disruptor 50 list, behind only Anduril and OpenAI.
Elon Musk, during a news conference with President Donald Trump on May 30, 2025 inside the Oval Office at the White House in Washington.
Tom Brenner | The Washington Post | Getty Images
Elon Musk’s official role in the Trump administration recently came to an end. Many Republicans won’t be sad to see less of him, according to the results of Quinnipiac University’s latest public opinion survey.
While a majority of Republicans still hold a favorable view of Musk, the number fell to 62% in the poll out Wednesday, down from 78% in March, Quinnipiac said.
Overall, the Quinnipiac poll found that 30% of self-identified voters surveyed in the U.S. hold a favorable opinion of Musk, according to polling from June 5 to June 9. Republican and Democratic voters remain deeply divided in their views of the world’s richest man, who contributed nearly $300 million to propel President Donald Trump back to the White House.
Only 3% of Democrats surveyed said they held a favorable of view of the Tesla CEO, who was once seen as an environmental leader appealing to liberal values.
Musk didn’t respond to a request for comment.
Musk and Trump had a very public falling out last week that started with Musk’s disapproval of the president’s spending bill and escalated into an all-out war of words that played out on social media. Musk said on Wednesday that he regretted some of the posts he made about Trump last week, adding that “they went too far.”
Even with a slide in his favorability, Musk is still popular among Republicans after his time running the Department of Government Efficiency (DOGE), an effort to dramatically slash the size of the federal government.
Among the Republican respondents to the early June poll, 80% rated Musk and DOGE’s work as either excellent or good, while 13% said it was either not so good or poor. In the March poll, 82% of Republicans surveyed said they thought Musk and DOGE were helping the country.