Want to get featured here? Explore premium visibility opportunities.

Contact us

AI NewsHow VCs and founders use inflated ‘ARR’ to crown AI startups

How VCs and founders use inflated ‘ARR’ to crown AI startups

3:00 AM IST ¡ May 23, 2026

How VCs and founders use inflated ‘ARR’ to crown AI startups

Last month, Scott Stevenson, co-founder and CEO of the legal AI startup Spellbook, took to X in an effort to expose what he called a “huge scam” among AI startups: inflation of the revenue figures that they announce publicly. “The reason many AI startups are crushing revenue records is because they are using a dishonest metric. The biggest funds in the world are supporting this and misleading journalists for PR coverage,” he wrote in his tweet. Stevenson isn’t the first to claim that annual recurring revenue (ARR) — a metric historically used to sum up annual revenue of active customers under contract — is being manipulated by some AI companies beyond recognition. Certain aspects of ARR shenanigans have been the subject of multipleother newsreportsandsocialmedia posts. However, Stevenson’s tweet seemed to have struck a particular nerve within the AI startup community, drawing over 200 reshares and comments fromhigh-profile investors, manyfounders, anda fewheadlines. “Scott at Spellbook did a great job of highlighting some of what you might describe as bad behavior on the part of some companies,” Jack Newton, co-founder and CEO of legal startup Clio, told TechCrunch, adding that the post brought much-needed awareness to the topic, referring to anexplanatory postfrom YC’s Garry Tan about proper revenue metrics. TechCrunch spoke with over a dozen founders, investors, and startup finance professionals to assess whether the ARR inflation is as pervasive as Stevenson suggests. Indeed, our sources, many of whom spoke on the condition of anonymity, confirmed that fudged ARR in public declarations is a common occurrence among startups, and how, in many cases, investors are aware of the exaggerations. The main obfuscation tactic is substituting “contracted ARR,” sometimes referred to as “committed ARR” (CARR), and simply calling it ARR. “For sure they are reporting CARR” as ARR, one investor said. “When one startup does it in a category, it is hard not to do it yourself just to keep up.” ARR is a metric established and trusted since the cloud era to indicate total sales of products where usage, and therefore payments, is metered out over time. Accountants don’t formally audit or sign off on ARR primarily because generally accepted accounting principles (GAAP) focus on historical, already-collected revenue, rather than future revenue. ARR was intended to show the total value of signed-and-sealed sales, typically multiyear contracts. (Today, this concept tends to go by another name: remaining performance obligations.) Meanwhile, the term “revenue” is typically reserved for money already collected. CARR is supposed to be another way to track growth. But it’s a much squishier metric than ARR because it counts revenue from signed customers that aren’t onboarded yet. One VC told TechCrunch that he has seen companies where CARR is 70% higher than ARR, even though a significant chunk of that contracted revenue will never actually materialize. CARR “builds on the ARR concept by adding committed but not yet live contract values to total ARR,” Bessemer Venture Partners (BVP)wrote in a blog postback in 2021. Critically, though, BVP says, the startup is supposed to adjust CARR to take into account expected customer churn (how many customers leave) and “downsell” (those who decide to buy less). The main problem with CARR is counting revenue before a startup’s product is implemented. If implementation is lengthy or goes awry, clients might cancel during the trial before all — or any — of the contracted revenue has been collected. Several investors told TechCrunch that they directly know of at least one high-profile enterprise startup that reported it surpassed $100 million in ARR, when only a fraction of that revenue came from currently paying customers. The rest was from contracts that hadn’t been deployed yet and in some cases may take a long time to implement the technology. One former employee at a startup that routinely reported CARR as ARR told TechCrunch that the company counted at least one substantial, yearlong free pilot as ARR. The company’s board, including a VC from a large fund, was aware that the revenue from the eventual paying part of the contract had been counted in ARR during the lengthy pilot program, the person said. The board was also aware that the customer could cancel before paying the full contract amount. The obvious problem with using CARR and calling it ARR is that it is far more susceptible to being “gamed” than traditional ARR. If a startup doesn’t account realistically for churn and downsell, CARR could be inflated. For instance, a startup could offer big discounts for the first two years of a three-year contract and count the whole three years as CARR (or ARR), even though customers may not stick around to pay the higher prices in year three. “I think Scott [Stevenson] is right. I’ve heard all sorts of anecdotes as well,” Ross McNairn, co-founder and CEO of legal AI startup Wordsmith told TechCrunch about ARR misrepresentations. “I speak to VCs all the time. They’re like, ‘There are some choppy, choppy standards out.’” Most cases are slightly less extreme. For instance, an employee at another startup described a discrepancy where marketing materials claimed $50 million in ARR, while the actual figure was $42 million. However, this person claimed that investors had access to the company’s books, which accurately reflected the lower amount. The source said some startups and their investors are comfortable playing fast and loose with their public metrics in part because AI startups are growing so quickly that an $8 million gap is viewed as a rounding error they’ll grow into quickly. There’s another issue surrounding all those public ARR declarations. Sometimes founders use another measurement with the same “ARR” acronym and a similar name: annualized run-rate revenue. This ARR is also controversial because it extrapolates current revenue over the next 12 months based on a given period’s haul (e.g., a quarter, month, week, or even a day). Since many AI companies charge based on usage or outcomes, that method of calculating annualized run-rate ARR can be misleading because revenue is no longer locked into predictable contracts. Most people interviewed for this story said that ARR overstatements of all kinds are hardly a novel phenomenon, but startups have become far more aggressive amid the AI hype. “The valuations have gotten higher, and so the incentives are stronger to do it,” Michael Marks, a founding managing partner at Celesta Capital, told TechCrunch. In the age of AI, startups are expected to grow much faster than ever before. “Going from 1 to 3 to 9 to 27 is not interesting,” Hemant Taneja, CEO and managing director of General Catalyst, said on the20VC podcastlast September, referring to the millions in ARR a startup is traditionally projected to hit each year. “You got to go like 1 to 20 to 100.” The pressure to show rapid growth is prompting some VCs to support, or at least overlook, startups presenting inflated ARR figures to the public. “There are definitely VCs in on this because they’re incentivized to create a narrative that they have runaway winners. They’re incentivized to get press coverage for their companies,” Stevenson told TechCrunch. Newton, whose legal AI startup Clio was valued at$5 billionlast fall, also alleges that VCs are often aware but silent about ARR misrepresentations. “We see some investors looking the other way when their own companies are inflating numbers because it makes them look good from the outside in,” he told TechCrunch. Other investors who spoke with TechCrunch say there is no reason for VCs to expose the overstatements. By turning a blind eye to public pronouncements of inflated ARR, VCs are effectivelyhelping to kingmaketheir own portfolio companies. When a startup publicly reports high revenue, it is more likely to attract the best talent and customers who believe the company is the undisputed winner in its category. “Investors can’t call it out,” a VC told TechCrunch. “Everyone has a company monetizing CARR as ARR.” Still, anyone intimately familiar with the industry’s intricacies has a hard time believing that some of these startups actually reached $100 million in ARR within a few years of launch. “To everyone who’s inside, it just feels fake,” said Alex Cohen, co-founder and CEO of health AI startup Hello Patient. “You read the headlines and you’re like, ‘I don’t believe it.’” However, not all startups feel comfortable representing growth by reporting CARR instead of ARR. They prefer to be clean and clear about their numbers in part because they understand that public markets measure software companies on ARR rather than CARR. These founders prioritize transparency. Wordsmith’s McNairn, who remembers the struggle startups faced justifying high valuations after the 2022 market correction, said he doesn’t want to create an even higher hurdle by exaggerating his startup’s revenue. “I think it is short-sighted, and I think that when you do things like that for a short-term gain, you’re overinflating already crazy high multiples,” he said. “I think it’s super bad hygiene, and it’s going to come back and bite you.”

read more

Latest AI News

View All News →
Nobel-Winning AlphaFold Scientist John Jumper Leaves Google DeepMind for Anthropic

Nobel-Winning AlphaFold Scientist John Jumper Leaves Google DeepMind for Anthropic

For his work on AlphaFold, Jumper shared the 2024 Nobel Prize in Chemistry with Demis Hassabis and scientist David Baker.

5 hours ago

View

How Hexaware's GIFT City Move Gives Indian IT a New Financial Frontier

How Hexaware's GIFT City Move Gives Indian IT a New Financial Frontier

Hexaware plans to create nearly 1,000 high-skilled jobs over the next three years.

9 hours ago

View

Encryption, spyware, and now Mythos: History shows why cyber export control doesn’t work

Encryption, spyware, and now Mythos: History shows why cyber export control doesn’t work

Last Friday, citing unspecified national security concerns, the White Houseordered Anthropicto restrict the export of its powerful AI models Fable and Mythos to anyone outside of the United States, as well as foreign nationals inside the country. Shortly after, the AI giant hastily pulled the plug on both models, which have now been unavailable to anyone for a week. The episode is the first real test of whether the U.S. government can use export controls to contain frontier AI the way it has tried, with very uneven results, to contain encryption and spyware before it. And dramatic as it may sound, how this standoff gets resolved could shape not just Anthropic’s access to foreign markets but the rulebook that other AI labs will have to build around. Some context first.Ever since Anthropic launched Mythos in April, the company has marketed it assome kind of Doomsday cyber machinethat could wreak havoc on the internet if released too widely — which is why, before the ban,only around 150 vetted companies and government organizationshad access to it at all. The goal was helping defenders secure their software and services before the bad guys could reach Mythos-like capabilities. So what triggered the ban? Two subsequent events, reportedly. The first: Anthropic gave a South Korean telecom access to Mythos through its limited partner program, and U.S. officials grew alarmed after identifying the company as one they suspected had ties to China. (The company,widely reportedto be SK Telecom, hasdeniedany China connection.) Amazon CEO Andy Jassy also reportedlyalerted the administrationafter Amazon’s own researchers, he said, found a way around Fable 5’s safeguards. Anthropic disputes the “jailbreak” label, calling it a narrow, already-patched issue rather than a wholesale defeat of the model’s safety measures. The result was the same: the Commerce Department issued an export control directive, and Anthropic had to scramble to immediately limit access to its products — within roughly 90 minutes of being notified, by some accounts. None of this is new, though. Governments have tried to use export controls to limit the proliferation of what they see as dangerous cyber technology for decades, but their track record has been middling at best. The U.S. government was behind what is perhaps history’s most spectacular failure of this approach in the early to mid-1990s. At the time, computer scientists were developing encryption technologies to secure data as it traveled over the internet. One of those encryption products was called Pretty Good Privacy, or PGP, a popular software that could encrypt data and make it virtually impossible to unscramble even if intercepted as it traveled to its intended recipient over the internet. The U.S. government initially saw PGP as a dangerous weapon, fearing it would prevent its intelligence agencies from snooping on emails as they crossed their wires. To stop the distribution of PGP, the U.S. Customs Serviceopened a criminal investigationagainst PGP’s creator Phil Zimmermann for allegedly violating arms export controls. He fought back by publishing PGP’s source codeas a printed book, igniting what is known today as the “Crypto Wars.” Zimmermann later won a key battle when the investigation was closed, paving the way for crucial end-to-end encryption algorithms such as the one used by billions of Signal and WhatsApp users. Later during the early 2010s, researchers began discovering Western-made spyware used against dissidents in the Middle East. In response, several governments agreed to expandthe Wassenaar Arrangement, an international treaty that limits the export of dual-use software and technologies that are used in both civilian and military applications. The idea was to classify surveillance and hacking software as dual-use, thus forcing spyware makers to get export licenses to sell their products abroad. Contact UsDo you have more information about the Mythos ban? From a non-work device and network, you can contact Lorenzo Franceschi-Bicchierai securely on Signal at +1 917 257 1382, or via Telegram and Keybase @lorenzofb, oremail. But Wassenaar has always had two inherent weaknesses. There are several countries that don’t adhere to the agreement, including Israel, which houses some of the world’s most active spyware makers. The agreement also depends on countries applying it to companies within their borders at their own discretion. For a time, the Italian government allowed one of the country’s then-top spyware makers, Hacking Team, a license to export its tools around the world, despite the company’s track record of selling spyware tooppressivegovernmentsthatused itto hack journalists and human rights activists. Since then,othercountriesin Europe have been lax with spyware makers like Italy. Despite numerous scandals, Europe, home tomany spyware and hacking tools makers, hascontinually failed to curb the export of spywareto authoritarian regimes. Critics say that a recently renewed effort across the bloc of 27 member states to tackle its growing problem of spyware exports to authoritarian states “does not go far enough.” Several spyware makers, such as Intellexa, a sanctioned consortium of spyware companies,  have simply moved their operations to countries with lax export controls. Other spyware makers sought to move their operations to Saudi Arabia for similar reasons. There have been some wins. Germany-based spyware maker FinFishershut down in 2022after a multi-year investigation by German prosecutors into the company forallegedly selling spywareto Turkey without an export license. Investigators previously found the FinFisher spyware had beendeployed on the phonesof critics of Turkey’s government. As of the time of writing, the impasse between Anthropic and the Trump administration remains. There is a reasonable chance the administration will buckle and lift the restriction in the interest of keeping American AI companies competitive worldwide — a move that would amount to tacit acknowledgment that AI labs elsewhere, including in China, will likely reach similar capabilities regardless of what the U.S. restricts. Or, American AI companies could end up needing government approval before serving foreign customers at all, a compliance burden that would invariably dent their bottom line. Given the past experiences that world governments have had with trying to control the reach of software, government-mandated export controls are unlikely to be the right approach to stop malicious actors from abusing powerful dual-use cyber technologies.

13 hours ago

View

Billionaire Ambani wants AI in every call, app, and home

Billionaire Ambani wants AI in every call, app, and home

As India searches for a homegrown contender in the global artificial intelligence race, billionaire Mukesh Ambani is positioning Reliance Industries as a national champion, rolling out AI services for phone calls, mobile apps, and connected homes. At itsannual shareholder meetingon Friday, the Mumbai-based conglomerate announced Jio Call Agent, an AI assistant that can join phone calls to transcribe conversations, generate summaries, and perform tasks such as booking cabs, ordering food, and making reservations. The service, which can be activated by saying “Hey Jio,” is expected to launch later this year for Jio’s more than 500 million users. By embedding the service directly into its telecom network rather than offering it as a stand-alone app, Jio is betting AI assistance can become a native feature of phone calls. The approach could reduce consumers’ reliance on third-party call-assistant apps and give Reliance a powerful distribution advantage in an increasingly crowded AI market. Reliance also unveiled an AI-powered version of its MyJio app that can perform tasks on behalf of users, from activating eSIMs to selecting roaming plans, through natural-language requests. The company further introduced TeleFrame, a home display that uses AI agents to proactively surface information and recommendations, such as weather alerts, schedules, and household reminders. The product appears to echo a broader industry push toward ambient AI assistants for the home, an area being explored by companies such asAmazonandGoogle. The announcements mark the next phase of Reliance’s AI ambitions as India seeks to build domestic capabilities in a field largely dominated by U.S. and Chinese technology companies. The push follows thelaunch of Reliance Intelligencelast year, through which the conglomerate aims to develop AI infrastructure and services for consumers, businesses, and governments, including applications that support 22 Indian languages. “India should not be a mere consumer of AI created elsewhere. It must become a creator, adopter, and a global leader in AI,” Ambani, age 69, said. Reliance has been ramping up its AI ambitions through partnerships withGoogle,Meta, andNvidia. Earlier this year, the company announced plans toinvest $110 billion in AI infrastructureas it seeks to establish itself as a major player in India’s emerging AI ecosystem. At the shareholder meeting, Reliance also unveiled a suite of AI services for healthcare, education, agriculture, and small businesses. The products, branded JioHealthIQ, JioLearnIQ, JioKrishiIQ, and AI Vyapar, are designed to operate across multiple Indian languages and cater to local needs, the company said. The shareholder meeting also brought a major development for investorsawaiting Jio’s stock market debut. Ambani said Jio Platforms’ board had approved a draft prospectus for an initial public offering that would include a fresh issue of up to 270 million shares, according to a stock exchange filing. The announcements also raise questions about how Reliance will handle user data as it expands AI services across phone calls, mobile apps, and connected homes. While the company said the services would operate with user consent, it did not answer questions about whether data generated through the products could be used to train AI models or shared with technology partners. Reliance’s AI ambitions come as Indian companies remain heavily reliant on foreign AI models and cloud providers.Recent restrictions on accessto some of Anthropic’s latest models have underscored that dependency, showing how decisions made overseas can affectstartups and businessesbuilding AI products in India — the kind of supply-chain risk that’s pushing Indian conglomerates toward building their own stack rather than renting someone else’s. Last week, Reliance announced acollaboration with Meta to establish an AI data centerin the western state of Gujarat, building on Meta’s earlier investment in Jio Platforms and a joint venture launched last year to develop AI solutions for enterprise customers in India and overseas markets. Reliance is not alone in pursuing AI opportunities.Tata Consultancy Services,Infosys, and rivalAdani Grouphave also expanded their AI initiatives and partnerships with global players, including Anthropic, Google, and OpenAI, as India’s largest corporations race to secure a leading role in the country’s AI future. Nonetheless, for Reliance, the stakes are particularly high; it’s preparing Jio for a long-awaited stock market debut and needs new growth drivers, with the conglomerate’s shares down about 17% this year.

21 hours ago

View

How VCs and founders use inflated ‘ARR’ to crown AI startups