Sumesh Sasidharan of the Faculty of Medicine at Aix-Marseille University explores how transformations in medtech may not impact all patients equally.

AI-powered digital twin technology could transform how doctors understand and treat heart disease. But if the medical data used to build these virtual models overlook biological differences between women and men, the promise of truly personalised medicine may remain incomplete.

Artificial intelligence is beginning to reshape how doctors study and treat heart disease. One of the most ambitious ideas is the ‘digital twin’, a computer model built from a patient’s medical data that allows researchers to simulate how a disease might develop and how treatments might work.

In cardiology, these models combine medical imaging, clinical records and biological data to create a virtual version of the heart. In the future, doctors could potentially test treatment strategies on this digital model before applying them to the patient.

But an important scientific question is emerging: What if the medical data used to build these models are missing important biological differences between women and men?

As digital health technologies move closer to clinical practice, ensuring these tools reflect the full diversity of human biology is becoming increasingly important.

In our research at Aix Marseille University on patient-specific computational models of inflammatory heart disease (MYOCAR3 funded by Civis Alliance), we are beginning to see how differences in immune responses between women and men could influence how these diseases develop and how they might appear in future digital models.

The promise of digital twins in heart medicine

Digital twins are attracting growing attention across Europe as a way to advance precision medicine.

Instead of treating patients based on average responses observed in large populations, researchers hope to build personalised models that capture the unique biological characteristics of each individual. Several European initiatives are exploring this approach.

The European Virtual Human Twin Initiative, supported by the European Commission, aims to accelerate the development of digital twin technologies for healthcare. Other projects, such as SimCardioTest, focus on building patient-specific cardiovascular models to improve diagnosis and treatment planning.

These efforts bring together engineers, clinicians and data scientists to better understand complex heart diseases. But the success of these models depends heavily on one crucial factor: the quality and representativeness of the data used to build them.

When medical data fails to represent everyone

Over the past decade, researchers have increasingly recognised that biomedical research has sometimes treated male biology as the default.

A widely cited analysis published in Nature reported that male animals historically outnumbered females by roughly five to one in many preclinical studies.

In cardiovascular medicine, this imbalance matters.

Heart disease remains the leading cause of death worldwide, responsible for nearly 18 million deaths each year, according to the World Health Organization.

Yet heart disease does not affect women and men in exactly the same way. Symptoms, disease mechanisms and responses to treatment can differ.

Inflammatory heart disease provides a striking example. Myocarditis, an inflammation of the heart muscle, can occur after viral infections and, in rare cases, after vaccination.

Global estimates suggest that myocarditis affects around 1.8 million people each year and occurs two to four times more frequently in men than in women, particularly among young adults.

Research published in journals such as Circulation suggests that these differences may be linked to variations in immune responses, hormonal influences and cardiac tissue biology.

For scientists developing digital heart models, this raises an important question: if datasets do not fully capture these biological differences, can digital twins accurately reproduce how the disease behaves in different patients?

From sex differences to gender-sensitive medicine

These concerns are part of a broader shift in biomedical research towards what is known as sex and gender-sensitive medicine.

This emerging field recognises that both biological sex and sociocultural gender factors influence health, disease progression and responses to treatment.

Researchers are increasingly working to integrate these dimensions into medical research, clinical practice and healthcare education.

For example, the University Hospital Zurich Heart Center has developed consultations dedicated to gender-sensitive cardiology. Researchers analyse international datasets, identify patterns across large patient cohorts and generate new clinical data to better understand how sex and gender influence cardiovascular disease.

At the same time, European scientific collaborations are working to strengthen how sex differences are considered in research.

The European Initiative COST Action EU-SABV is the first Europe-wide effort that focuses on improving how “sex as a biological variable” is integrated into biomedical research, helping ensure studies produce findings that are both rigorous and relevant for diverse patient populations.

Together, these efforts aim to generate better data sets, the essential foundation for reliable digital health technologies.

Building better digital medicine

Digital twins represent one of the most exciting frontiers in cardiovascular medicine. In the future, these models could allow doctors to simulate disease progression, test therapies virtually and tailor treatments to individual patients.

But the promise of digital medicine ultimately depends on the data that shape these models.

If those data fail to reflect biological differences between women and men, even the most advanced algorithms may miss part of the picture.

Ensuring that digital twins reflect the full diversity of human biology will, therefore, be essential. Only then can these technologies fulfil their promise of truly personalised medicine, not for an ‘average’ patient, but for every patient.

Sumesh Sasidharan

Sumesh Sasidharan is a biomedical engineer and senior researcher at Aix-Marseille University‘s Faculty of Medicine and a CIVIS3i Laureate, funded by the European Union. He was ranked among the top selected candidates globally and is the first Indian researcher to receive this prestigious fellowship. His research focuses on developing patient-specific computational models and digital twin frameworks for inflammatory heart diseases, with particular emphasis on acute myocarditis and immune-mediated cardiotoxicity. 

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K-12 education is navigating an AI landscape that is shifting faster than most school policies can keep up, and teachers are getting caught in the middle. Join us on This Week with EdSurge where we look at new data on where ed tech decision makers actually stand on AI adoption, and hear from a leading expert on what it would take to bring educators meaningfully into the conversation.

Districts Are All In on AI, But Their Budgets Are Not.

A new report suggests that districts are moving on AI faster than many expected, but the infrastructure needed to do it responsibly is struggling to keep up. EdSurge reporter Lauren Coffey digs into the latest CoSN State of Ed Tech report, which finds that three-quarters of districts now have AI guidelines in place, a notable jump from just a year ago. The same report, however, surfaces a harder question: as schools move faster, who is making sure the tools and systems they are building on are actually secure, accessible, and ready?

82 Percent of Teachers Have Received No Formal AI Guidance. Here’s Why.

A new survey from Gallup and the Walton Family Foundation finds that the vast majority of teachers have not received formal guidance on how to use AI in their work, and about a third have gotten none. Joseph South, Chief Innovation Officer at ISTE+ASCD, joins the show to unpack what is driving the disconnect and what schools can do about it. South points to a combination of factors: AI was not part of most teacher preparation programs, administrators are hesitant to lead with something they do not fully understand yet, and schools are already stretched thin. Two districts are starting to show what is possible, and South thinks the path forward is more accessible than most administrators assume.

The districts getting this right have a head start. Find out what they are doing and whether yours can do the same. Listen to the episode.

This Week with EdSurge is produced by the EdSurge newsroom. Subscribe to the EdSurge newsletters for education news and analysis delivered to your inbox every week.

Stay informed, stay curious.

Code sleuths are diving into iOS 27 and finding hints about the unreleased iPhone Fold, and finding subroutines that report just how open or shut the foldable might be.

Apple does its best to hide any code that might give away some future product, but it isn’t always perfect. Code references in iOS 27 show some of what Apple is preparing for the upcoming iPhone Fold.

Even though there hasn’t been a single component leak or information about mass production starting, some believe iPhone Fold is still coming in September. These bits of code certainly suggest Apple is working to release iPhone Fold, but say little else.

iOS 27 framework references “foldState” and “angleDegrees” but I’m sure that’s nothing pic.twitter.com/PcYNVvymms

— sam henri gold (@samhenrigold) June 8, 2026

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The code shared by the social media user shows items for “foldState,” “angleDegrees,” and a reference to the number of present hardware displays. These are all clear references to the existence of an iPhone Fold.

Of course, there have been rumors, patents, and various dummy units confirming an iPhone Fold has been in the works since at least 2019. This is the first instance of iOS directly referencing the future foldable, which means it could be imminent.

However, Apple has feature flags meant for operating systems that are years away, so the discovered code could also provide nothing in terms of the release timeline. Time will tell if the product will actually be ready for its expected September debut.

Apple has delivered the deeper Siri overhaul that many expected years earlier. The new version, officially called Siri AI, moves beyond simple voice commands into a system that reads the screen, pulls relevant personal details from across apps, and taps current information from the web to give answers that actually fit the moment.

Users are no longer required to repeat the same basic information each time they seek assistance; this is simply inconvenient. Siri may now catch up on restaurant details in Messages or hotel confirmations in Mail and return to them later without prompting. Similarly, for photographs from prior vacations or calendar entries, the algorithm evaluates them discreetly and extracts the necessary information.

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On-screen awareness has changed how people interact with the information in front of them. Ask Siri a question about a place you see in an open Instagram post or a document on your screen, and she’ll respond without you having to explain or copy anything. This works in compatible apps, such as screenshots on iPad and quick picks on Mac.

Real-world actions feel more natural now that Siri can generate an email or message that sounds exactly like what you’d normally send to someone. It even promises to edit and proofread your material across a range of apps. The Camera app can even break down a meal bill using a photo of a receipt, which is excellent. With expanded Visual Intelligence, you can simply aim your camera or snap a screenshot, then ask follow-up questions or initiate associated steps.

The interface has also been updated. Using the Dynamic Island results in a cleaner, more subdued show instead of the original vibrant radiance. A new Siri app allows you to sync your conversation history across all of your devices, including iPhone, iPad, and Mac, using private iCloud syncing, so you can always retrieve earlier conversations. Voice capabilities have also been expanded. Siri’s pace and expressiveness can now be modified to fit the context, making long conversations feel more natural, and dictation has improved dramatically, so you don’t have to worry about spelling, punctuation, or capitalization.

The majority of the heavy lifting is still done on the device, allowing for speedy results while remaining private. When greater power is required, requests are sent through Apple’s Private Cloud Compute, which secures your personal information. This foundation underpins the rest of Apple Intelligence’s operations, which are now integrated with system-wide search, writing tools, and app interactions.

Betas of iOS 27, iPadOS 27, macOS 27, and visionOS 27 are now being tested by developers. Compatible devices include the iPhone 15 Pro and later, modern iPads, Macs with Apple hardware, and a variety of Apple Watch as well as Vision Pro models. Improved on-device voice and dictation capabilities require cutting-edge technology and enough of RAM.
[Source]

OpenAI has filed confidential paperwork for an initial public offering, the company announced on Monday, kicking off what could be a monthslong process toward debuting on a US stock exchange. The move makes it the third company to file for what could be a trillion-dollar IPO this year.

Tech companies pursuing the most powerful AI models, including publicly traded giants Alphabet, Amazon, Meta, and Microsoft, are hungry for tens of billions dollars each to build out more data centers and recruit scientists to grow their services.

An IPO would be yet another fundraising opportunity for OpenAI after the company privately raised $122 billion in March. Going public, which brings many workers closer to a life-altering payday and increases transparency about a business’ financial health, could also boost employee morale and customer confidence as OpenAI tries to regain its position as the clear front-runner in frontier AI.

OpenAI did not specify timing for the IPO nor how much it plans to raise. “We recently submitted a confidential S-1,” the company said in an unsigned, one-paragraph blog post. “We expect it to leak so we’re just announcing it. We have not decided on timing yet; it may be a while because there are things we want to do that are likely easier as a private company. But it’s a complicated set of tradeoffs and this gives us the option to go public sooner if that ends up being best.”

OpenAI declined to comment further. But by having paperwork ready, the company could draft off a potentially successful debut by Anthropic. If the rival runs into any challenges, OpenAI could hold off and recalibrate.

A Three-Way Race

Anthropic, which was founded in 2021 by former OpenAI employees, filed its confidential IPO paperwork on June 1. Just days before the filing, Anthropic’s latest fundraising brought its valuation to $965 billion, topping OpenAI’s $852 billion mark—both record-breaking figures in the world of tech venture capital. Elon Musk’s SpaceX, which makes rockets, sells satellite internet, and also develops some of the world’s most capable AI models, publicly filed its IPO papers last month.

The IPOs could value each of these companies at over $1 trillion despite all of them being unprofitable and having roughly 80 percent to 90 percent lower sales than nearly every existing trillion-dollar public company. The only IPO to have broken the $1 trillion mark was oil company Saudi Aramco in 2019.

OpenAI’s revenue from subscriptions, ads, and service fees grew to somewhere between $10 billion and $20 billion last year, according to previous company disclosures. But it spent far more money on cloud computing and thousands of staff, leading to billions of dollars in losses. In recent months, the company has carried out several restructurings due to executive illnesses and an attempt to focus on fewer projects.

OpenAI executives have debated for months whether the company is prepared to go public, according to two people familiar with the matter but not authorized to discuss confidential information. At one point last year, OpenAI was targeting an IPO in late 2027 or early 2028, according to another person familiar with the discussions.

Last week, President Donald Trump said his administration would look into the possibility of the US government taking a stake in AI companies as they go public. OpenAI has discussed the idea for months as a way to broaden public benefits of AI development, according to one of the people familiar with company discussions. An OpenAI blog post coauthored by CEO Sam Altman on Monday said “a good AI future” requires that “many people, companies, communities, and countries can build, benefit, and hold power.”

Legal Challenges

In 2019, OpenAI created a for-profit subsidiary to allow it to fundraise vastly more sums than it believed people would be willing to donate. Today, the nonprofit owns roughly 25 percent, or more than $200 billion, of the company. It also has the power to block major business decisions and fire the company’s executives. Undoing the nonprofit is legally challenging.

Recently, OpenAI cleared a major hurdle on the road to its IPO by defeating a lawsuit brought by Musk, which accused the ChatGPT-maker of straying from its nonprofit mission. Musk’s claims were dismissed last month after a federal judge and jury ruled that he filed his lawsuit too late.

Can’t a multi-trillion-dollar company have secrets any more?

Dozens of cryptographically verified open source packages from Microsoft were compromised late last week to add advanced credential-stealing code that was triggered when developers opened them in AI coding agents.

In all, multiple researchers said, 73 packages were flagged as malicious when automated systems on GitHub blocked them on the platform. Rather than noting they are malicious—and that developers who used AI agents to work with them should assume their systems are compromised—the Microsoft-owned GitHub said it disabled the packages “due to a violation of GitHub’s terms of service.” The text went on to encourage the package owner to contact GitHub.

Devs: Assume compromise and proceed accordingly

It wasn’t until Monday that Microsoft even raised the possibility the packages were infected. In an email, the company stated: “We have temporarily removed some repositories as we investigate potential malicious content.”

The incident is the second supply-chain attack in as many months to breach an official Microsoft repository account. In mid May, the firm StepSecurity documented the compromise of Microsoft’s durabletask Python SDK on PyPI. The package is a framework for building fault-tolerant workflows and orchestrations to automate distributed transactions and other workflows. It receives 400,000 downloads per month.

The compromise packages executed a 28 KB payload that steals credentials from AWS, Azure, GCP, Kubernetes, password managers, and over 90 developer tool configurations. It then spreads laterally through cloud infrastructures to infect other developer machines. The attack, which has been linked to a threat actor tracked as TeamPCP, poisoned the durabletask package after compromising Microsoft credentials for publishing the package. The technique allows attackers to bypass the repository’s build pipeline entirely.

The malware used in the attack is tracked as Miasma. It’s essentially a clone of TeamPCP’s Mini Shai-Hulud toolkit, which the threat actor open-sourced recently. Security firm Cloudsmith said the malware harvests OIDC (OpenID-Connect) token credentials that are used in SLSA (Supply-chain Levels for Software Artifacts) provenance attestation, a method for providing cryptographically signed guarantees of a software’s integrity.

As was the case in the May compromise of Microsoft’s durabletask, the one last week made use of the functionality to steal a legitimate Microsoft OIDC token. It was also used in a separate supply-chain attack poisoning dozens of Red Hat packages.

New York City was the backdrop of this year’s IEEE Honors Ceremony, held on 24 April.

The event celebrates engineering pioneers who have developed technologies that have changed how people connect and learn about the world. This year’s celebrants included the engineers behind innovations such as text-to-donate technology, AI-powered diagnostic tools, and the graphics processing unit, among many others.

Prior to the Honors Ceremony, IEEE hosted a forum on 23 April for a select group of early-career achievers to exchange ideas and experiences with laureates and awardees, speakers, and IEEE leaders. Attendees from around the world, working in a variety of technical areas, shared their journeys and explored the intersections of technologies, disciplines, and missions.

The event culminated in Friday evening’s black tie Honors Ceremony, where IEEE celebrated medal laureates, including Jensen Huang, who received IEEE’s highest recognition, the IEEE Medal of Honor. Huang is a cofounder of Nvidia and its chief executive.

“IEEE has always been a home to those who see the future before others see it,” Mary Ellen Randall, IEEE president and CEO, said in her welcome speech.

Video highlights and photos from the event are available on the IEEE Awards website.

Exploring mission-driven tech and AI in art

Friday morning began with a conversation between Randall and Marian Croak, the recipient of this year’s IEEE Founders Medal. Croak was honored for “leadership in communication networks, including acceleration of digital equity, responsible artificial intelligence, and the promotion of diversity and inclusion.”

Croak, who serves as vice president of engineering at Google, headquartered in Mountain View, Calif., pioneered Voice over Internet Protocol (VoIP) technologies. When a person speaks into a telephone, VoIP converts their voice into digital signals that are transmitted over the Internet rather than traditional phone lines. Her work enabled audio and video conferencing. She also developed text-to-donate technology to raise money for those affected by Hurricane Katrina, which devastated New Orleans in 2005. The technology enables customers to donate money to a charity via their mobile service provider, which then bills them.

“Empathy has always been a driving force in the engineering that I’ve done,” she said.

She shared advice on how to stay creative: “Get out of the office. Go to an art museum, exercise, or play with children.” Croak said her grandchildren inspire her.

The daytime program also spotlighted AI’s use in the visual arts. Kathleen Kramer, the 2025 IEEE president, interviewed artist Refik Anadol, who is scheduled to open an AI art museum on 20 June in Los Angeles. Dataland’s exhibits are powered by an open-access model developed by Anadol’s studio.

For the museum’s first exhibition, “Machine Dreams: Rainforest,” the model collected visual data about the natural world from the Smithsonian National Museum of Natural History, London’s Natural History Museum, and the Cornell Lab of Ornithology, with their permission. The information, including up to a half billion images, will form the basis for a variety of AI-produced art, Anadol said.

Anadol said he was inspired to mix AI with art by the movie Blade Runner. He said he believes “machines can become collaborators,” as “data is a form of pigment.”

Data also plays an important role in the work of artist and author Giorgia Lupi. The artist is a partner at design firm Pentagram.

Lupi said she uses data to tell stories, including chronicling her struggles with a chronic illness.

“Data is an abstraction of our reality,” she said.

One of her recent projects, “A Data Love Letter to the Subway,” was shown last year in the Dey Street Passageway in New York City. The video was made using data from the Metropolitan Transportation Authority about each train line, including timetables, ridership, and people’s travel habits. Based on the information Lupi gathered, she documented how commuters traveling on different subway lines encountered one another without realizing it.

By exploring data on this year’s IEEE award recipients, she collaborated with IEEE to create an animated video illustrating the shared pathways and collaborations among the honorees. It debuted at the Honors Ceremony.

Honoring engineering giants

The Honors Ceremony, held at Cipriani 42nd Street, recognized more than 20 laureates and innovators.

More than 92 million selfies are taken worldwide every day, PhotoAiD estimates. A selfie wouldn’t be possible without Eric Fossum’s invention of the CMOS image sensor. Developed at NASA’s Jet Propulsion Laboratory, in Pasadena, Calif., the “camera on a chip” was intended for use in space, but it is now found in smartphones, medical devices, and vehicles. Fossum, an IEEE Life Fellow, received the IEEE Jun-ichi Nishizawa Medal, which recognizes outstanding contributions to materials and device science and technology.

“Engineering is a pursuit of what must be possible. [IEEE is] the spirit, the conscience, of our profession.” —Jensen Huang, founder and CEO of Nvidia

The medal, he said, “is at the top of the IEEE staircase of being recognized by your peers.”

The IEEE Holonyak Medal for Semiconductor Optoelectronic Technologies went to Steven P. DenBaars, a professor of materials and electrical and computer engineering at the University of California, Santa Barbara. DenBaars was honored for his work in semiconductors, which laid the foundation for high-resolution LED and laser displays, modern solid-state lighting, and more.

“This work has always been a team effort…I’m excited and curious about the role gallium nitride micro LEDs will play in optical communications,” he said in his acceptance speech.

The ceremony ended with the Medal of Honor presentation to Huang, who received a standing ovation. He was recognized for his “leadership in the development of graphics processing units and their application to scientific computing and artificial intelligence.”

The IEEE honorary member donated his cash prize to IEEE TryEngineering, which provides teachers with a library of lesson plans and offers educational summer camps. The Jen-Hsun and Lori Huang Foundation matched his gift, and the additional donation is destined to fund scholarships for new graduates.

“Engineering is a pursuit of what must be possible. [IEEE is] the spirit, the conscience, of our profession,” Huang said.

In recent days, founders and founders-turned-investors took to X to share horror stories about being mistreated by VCs. Their complaints ranged from VCs falling asleep during pitch meetings to investors suggesting a founder fire a co-founder.

Brendan Foody, co-founder of the AI talent platform Mercor, which was last valued at $10 billion, went so far as to call out Sequoia, arguably one of the most elite VC firms in the world.

“The “sequoia scam” is worse than a single horror story,” Foody wrote on X. “in the last 6 [months] ive seen a half dozen rounds where sequoia invests in 2 tranches. everyone pretends they only did the higher valuation. founders misrepresent this to their employees & then shop it to angels too.”

TechCrunch has previously reported on VCs investing in the same round at different valuations. Under this mechanism, the lead VC firm invests a significant chunk of its capital at a lower, preferential valuation, while putting a much smaller portion of capital in at a drastically higher price. The massive “headline” valuation that gets announced manufactures the perception of a dominant market winner, masking the fact that the lead investor’s actual average entry price was significantly lower.

The disparity can be stark. For example, when the AI-driven IT helpdesk startup Serval announced a $75 million Series B at a $1 billion valuation, the announcement didn’t tell the whole story. According to The Wall Street Journal, Sequoia’s actual lowest entry point valued the company at just $400 million — less than half the headline figure. The gap between those two numbers is the gap between perception and reality that Foody is pointing at.

Serval isn’t alone. At Aaru, a startup that uses AI to simulate user behavior for market research, lead investor Redpoint backed the company at a $450 million valuation despite an announced $1 billion headline price.

Sequoia’s Shaun Maguire pushed back on Foody’s characterization directly. “TBH I have seen some of this behavior but I think it’s unfair to call it the ‘Sequoia scam,’” Maguire wrote in response to Foody on X. “This has happened approximately five times during my seven years at Sequoia. What happens is other investors are willing to pay a high price for a hot company — usually AI — at multiples above what we’re willing to pay. So we try to decouple the company-building relationship with our partner from the capital, and this leads to two tranches at different valuations in close succession.

“I’m not aware of anything shady here,” Maguire continued, “but if you’ve seen it I’d love to know. VC is a repeated game, so it just doesn’t make sense for us to try to mislead people. And if anyone has, I’d love to know. And in general, congrats on the success of Mercor — it was a miss for us.”

Maguire’s response frames the practice as a market reality rather than a deliberate maneuver — Sequoia, he suggests, is simply unwilling to pay what competitors will pay for the hottest deals, so it structures its participation differently. Whether that explanation fully holds up depends on a question Maguire doesn’t address: what founders are telling the people who don’t already know about the lower tranche.

Although Sequoia appears to use this pricing mechanism most frequently, Foody acknowledged it isn’t the only firm using this tactic. And while the dual-pricing structures certainly inflate a startup’s perceived worth and help attract top talent, calling the practice a “scam” may be going too far.

That’s because the stock options granted to employees should theoretically be valued based on the blended price of all tranches, said Jason Woo, partner in valuation and financial modeling at Armanino. Woo’s group provides the independent 409A valuations that startups use to price employee stock options. A 409A is an independent appraisal that’s supposed to set the fair market value of a private company’s shares — it’s the number that determines what employees actually pay for their options, regardless of what valuation gets announced in a press release.

There’s a catch, however: 409A valuations are widely understood to skew low. Because the 409A sets the strike price of employee options — and a lower strike price means a smaller tax bill for the company — there is a structural incentive to keep that number down. Which means the independent appraisal that’s supposed to protect employees from an inflated headline valuation is also, by design, not trying particularly hard to reach the top of the range. Employees may not be paying the headline price for their options, but they may not be getting the full picture either.

The angel question is more complicated. Unlike employees, angels are writing checks, not receiving options. There is no independent appraiser standing between an angel investor and whatever number a founder chooses to share.

The dual-pricing structure is just one of way VCs and founders game the perception of success in a hyper-competitive market. Another, more pervasive tactic involves manipulating or outright overstating annual recurring revenue (ARR).

The VC Niko Bonatsos, a longtime veteran of General Catalyst who more recently founded Verdict Capital, addressed this issue during one of TechCrunch’s events in Athens last month. “We [at Verdict] mostly invest before metrics, before product, before the company [has fully taken shape] but I do have a past portfolio, and sometimes the conversations are telling. I’ll get a call or an email with a very high ARR number. I’ll think: I didn’t remember that company doing so well. So I reach out to the founder: ‘What happened? Why are the numbers so strong?’ And the answer is: ‘Oh yeah, it’s 365 times the revenue we made yesterday because one of our campaigns hit.’ So yeah, some of these terms have lost meaning.”

Foody declined to comment further. Sequoia didn’t immediately respond to a request for comment.

— With additional reporting from Connie Loizos