Business
Startups are staying private longer thanks to alternative capital
A version of this article appeared in CNBC’s Inside Alts newsletter, a guide to the fast-growing world of alternative investments, from private equity and private credit to hedge funds and venture capital. Sign up to receive future editions, straight to your inbox.
Even as the IPO market is starting to rebound, startups are staying private for longer thanks in large part to alternative capital, according to new data.
The median age of companies that have gone private so far this year is 13 years since founding, up from a median of 10 years in 2018, according to new data from Renaissance Capital.
A separate, recent study by Jay Ritter at the University of Florida found that between 1980 and 2024, the average age of companies going public has more than doubled.
Companies going public also have much larger revenue, since they’re maturing longer in private hands. In 1980, the median revenue for IPO companies was $16 million, or $64 million in inflation-adjusted 2024 dollars. By 2024, their median revenue had soared to $218 million, according to Ritter’s study.
The number of so-called “unicorns,” or private companies with valuations of more than $1 billion, has swelled to over 1,200 as of July, according to CB Insights. OpenAI’s valuation of $500 billion, notched with last week’s sale of employee shares topped SpaceX’s $400 billion valuation to become the world’s most highly valued private company.
Analysts and economists largely blame the regulatory burden and short-term pressures associated with being a publicly traded company for the urge to stay private. Yet the surge in alternative investments and private capital – from sovereign wealth funds and family offices to venture capital, private equity and private credit – are providing more than enough capital for today’s tech startups.
Global private-equity assets under management have risen over 15% a year over the past decade to over $12 trillion, according to Preqin. Over the next decade, they’re expected to double to around $25 trillion.
Venture capital assets under management in North America are expected to increase from $1.36 trillion at the start of 2025 to $1.8 trillion in 2029, according to PitchBook.
“One of the main reasons for going public is to raise capital,” Ritter said. “Now there are a lot of good alternatives to raising capital without going public.”
Ritter said that the growth of new digital marketplaces for selling shares of private companies – like Forge Global and EquityZen – give employees liquidity for their equity instead of having to wait for an IPO.
Klarna, the Swedish fintech startup, was founded 20 years ago and experienced wild swings in valuation before going public last month. It was valued at $45.6 billion in 2021 thanks to a funding round led by SoftBank, but saw its valuation plunge to $6.7 billion in 2022. Its funding along the way came from Sequoia Capital, IVP, Atomico, GIC and Heartland, the family office of Danish billionaire Anders Holch Povlsen.
Klarna’s current market cap is $15 billion.
While private equity and venture capital firms argue that the fastest growth stage for startups is in the early years, with the best returns gone by the time they go public, Ritter said the evidence is more complicated. While returns for private equity and venture capital have outperformed public markets in the past, he said the rush of capital flowing into alternatives and the huge prices paid by private investors for assets in recent years could mark a turning point.
“Money flows into an asset class as long as there are abnormal returns,” he said. “But so much money has poured in, I don’t expect there to be abnormal returns in the future.”
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Prabhkiran Singh, Co-Founder and CEO of Bewakoof, steps down after 14 years, leaving a Mumbai slum startup turned national youth brand.
Prabhkiran Singh announces his decision to step down as CEO of Bewakoof after 14 years.
Prabhkiran Singh, Co-Founder and CEO of Bewakoof, has announced that he will be stepping away from the company he built over the past 14 years, marking the end of a long entrepreneurial chapter that began in 2011.
Bewakoof is a D2C fashion brand, which is popular among GenZ.
From Mumbai Slum Startup To National Youth Brand
In a LinkedIn post shared on Tuesday, Singh reflected on starting Bewakoof at the age of 21 with a fellow engineering graduate. The company was launched from a small room in a Mumbai slum at a time when direct-to-consumer (D2C) fashion brands were still a nascent concept in India and equity funding was limited.
He recalled personally handling early operations, including making T-shirt deliveries via local trains and responding to customer queries. Over time, the brand scaled significantly, growing from campus T-shirt sales to shipping over 20,000 products daily.
Bewakoof went on to become one of India’s prominent youth-focused fashion brands and, according to Singh, was the first D2C fashion startup in the country to cross ₹100 crore in revenue. The company also built a social media community of more than 6 million followers.
Backed By TMRW And Aditya Birla Group
Singh stated that the company is now “structurally ready” for its next phase, supported by a strong leadership team and the backing of TMRW and the Aditya Birla Group.
He added that after 14 years of building the business, he intends to prioritize his health, family, and personal goals. Singh will continue to lead Bewakoof until the end of March.
Calling the startup his “firstborn,” Singh said he will continue to support the brand from the sidelines as it works toward building a long-term legacy.
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February 24, 2026, 15:54 IST
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New data series: Real GDP growth data calculation methodology overhauled to improve accuracy – here’s what changes – The Times of India
India is set to release its first set of GDP or Gross Domestic Product data on the basis of a new series that may also address recent criticism from economists. The government is revamping the methodology used to estimate real GDP growth under a new national accounts series scheduled to be released this week. The revised framework will incorporate more detailed price deflation techniques to respond to concerns raised by economists.Real GDP in India is calculated by adjusting nominal growth figures for inflation through the use of price indices. Critics have argued that the existing approach is outdated because it depends largely on the wholesale price index rather than the more widely followed consumer price index.In November, the International Monetary Fund highlighted shortcomings in India’s national accounts system. It pointed to the continued use of the 2011–12 base year, heavy dependence on wholesale price data and extensive reliance on single-deflation techniques. The IMF assigned the methodology a “C” rating.
New GDP data series: What changes
“We will now use about 500–600 items from the new CPI and the old WPI series, compared with about 180 earlier, to deflate the output and improve accuracy of the data,” Saurabh Garg, secretary in the Ministry of Statistics and Programme Implementation, said in an interview according to a Reuters report.He noted that this approach will remain in place until a revised WPI series is introduced, which is expected in the near term.Under the earlier system, periods marked by subdued nominal GDP expansion and low wholesale inflation often resulted in inconsistencies, as they tended to produce comparatively higher real growth estimates.As per the current data series, India’s economy, which is one of the fastest-expanding among major global economies, is projected to grow by 7.4% in 2025–26. This is compared with an estimated 6.5% growth in 2024–25.Nominal GDP, which measures economic output at prevailing market prices, is expected to increase by 8.0% during the current financial year.A revised GDP series with 2022–23 as the base year will be released on February 27, along with updated historical data covering the previous four years.These modifications form part of a wider overhaul of India’s statistical framework, following the introduction of a new retail inflation series earlier this month. Updates to the wholesale price index and industrial production data are also in progress.A key element of the revised framework is the adoption of double deflation, which adjusts both output prices and input costs separately to derive real value added.Garg said the changes are expected to enhance data precision, especially in the manufacturing sector, where differences between input and output price movements had previously raised concerns about distortions under the single-deflation approach.
Two stories from the past few weeks capture something essential about where we are with AI.The first concerns Salesforce, the enterprise software giant that aggressively embraced AI for customer service. CEO Marc Benioff proudly announced that AI deployment had allowed the company to cut support staff from 9,000 to roughly 5,000. Then reality intervened. Reports from late 2025 indicate that the company is now withdrawing from AI due to widespread failure. The AI agents confidently gave wrong answers, dropped instructions when given more than eight steps, and lost focus when users asked unexpected questions. Customers complained that AI support took longer than the simple old search function. Salesforce is now retreating to rigid, rule-based scripting–essentially admitting they were, in their own words, “more confident” than the technology warranted.The second story is a zeitgeist shift. Over the past couple of months, the conversation around AI and coding has transformed completely. People who were skeptical six months ago–senior developers who actually write code for a living–are now saying the age of human beings writing code is ending. Not in some distant future, but imminently. Entire features are being shipped by AI with minimal human intervention. The productivity gains are no longer incremental; they’re structural.How can both be true? How can AI fail comprehensively in customer service–seemingly straightforward–while revolutionising software development, which appears far more complex?The answer is that we’ve been thinking about AI wrong. We treat it as a single phenomenon that will sweep through the economy at roughly the same pace. However, AI in business is not a single story. It’s many parallel stories, moving at wildly different speeds. And the distinction has almost nothing to do with how intelligent the AI is.I’ve written about this tension before. A year ago, I argued that “the fact that a revolution is real doesn’t mean that every business claiming to be part of it will succeed.” More recently, I observed that “the gap between what AI demos well in controlled environments and what it actually delivers when confronting the messy real world remains enormous.” I now think there’s a more precise way to understand this gap. It’s not random. It’s structural.Consider what makes coding fertile ground for AI. Code is formally structured and machine-verifiable–it runs and passes tests, or it doesn’t. The feedback loop is immediate. When AI makes a mistake, a developer (or another AI agent) notices, fixes it, and moves on. Errors are private and reversible. Now consider customer service. Customers don’t speak in data schemas. Emotion, sarcasm, and cultural context matter enormously. One wrong answer can escalate to social media outrage or regulatory complaints. The failures are public and often irreversible.The difference isn’t intelligence. It’s what I’d call error economics. AI thrives where mistakes are cheap, private, and correctable. It struggles where mistakes are expensive, public, and permanent.We received a clear illustration of executive disconnect just a few days ago. During Bajaj Finance’s Q3 call, CEO Rajeev Jain announced that AI had listened to 2 crore calls and generated 100,000 new customer offers. “We’ll be able to listen to 100 million calls next year,” he said proudly. The response on social media was predictable hilarity. As the entire country, except apparently Mr Jain knows, Bajaj Finance’s incessant spam calls are the butt of countless jokes. Here was a CEO using sophisticated technology to optimize something customers actively despise. Machine learning works perfectly; the learning about customers is absent.For investors, the implications are significant. When you hear “AI” attached to a business function, ask: what happens when it’s wrong? If the answer involves customers, regulators, or reputations, progress will be slower than vendor PPTs claim. If the answer is “someone notices and fixes it,” that’s a different world entirely.The story of AI in business is not one of universal acceleration. It’s one of the selective escape velocities. Coding has left the atmosphere and gone into orbit. Customer service is still fighting gravity. Most other functions lie somewhere in between–mistakenly assumed to be closer to the rocket than they really are. The many worlds of AI are not converging. They’re diverging. And that divergence will determine which investments succeed and which disappoint.(Dhirendra Kumar is Founder and CEO of Value Research)
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