As the person in charge of Airbnb’s worldwide ban on parties, she’s spent more than three years figuring out how to battle party “collusion” by users, flag “repeat party houses” and, most of all, design an anti-party AI system with enough training data to halt high-risk reservations before the offender even gets to the checkout page.
It’s been a bit like a game of whack-a-mole: Whenever Banerjee’s algorithms flag some concerns, new ones pop up.
Airbnb defines a party as a gathering that occurs at an Airbnb listing and “causes significant disruption to neighbors and the surrounding community,” according to a company rep. To determine violations, the company considers whether the gathering is an open-invite one, and whether it involves excessive noise, trash, visitors, parking issues for neighbors, and other factors.
Bannerjee joined the company’s trust and safety team in May 2020 and now runs that group. In her short time at the company, she’s overseen a ban on high-risk reservations by users aged 25 and under, an pilot program for anti-party AI in Australia, heightened defenses on holiday weekends, a host insurance policy worth millions of dollars, and this summer, a global rollout of Airbnb’s reservation screening system.
Some measures have worked better than others, but the company says party reports dropped 55% between August 2020 and August 2022 — and since the worldwide launch of Banerjee’s system in May, more than 320,000 guests have been blocked or redirected from booking attempts on Airbnb.
Overall, the company’s business is getting stronger as the post-pandemic travel boom starts to fade. Last month, the company reported earnings that beat analysts’ expectations on earnings per share and revenue, with the latter growing 18% year-over-year, despite fewer-than-expected number of nights and experiences booked via the platform.
Turning parental party radar into an algorithm
Courtesy: Airbnb
Airbnb says the pandemic and hosts’ fears of property damage are the main drivers behind its anti-party push, but there have been darker incidents as well.
A Halloween party at an Airbnb in 2019 left five people dead. This year between Memorial Day and Labor Day weekends, at least five people were killed at parties hosted at Airbnbs. In June, the company was sued by a family who lost their 18-year-old son in a shooting at a 2021 Airbnb party.
When Banerjee first joined Airbnb’s trust team in summer 2020, she recalls people around her asking, “How do you solve this problem?” The stream of questions, from people above and below her on the corporate ladder, contributed to her anxiety. Airbnb’s party problem was complex, and in some ways, she didn’t know where to start.
As a mother of five, Banerjee knows how to sniff out a secretive shindig.
Last summer, Banerjee’s 17-year-old daughter had a friend who wanted to throw an 18th birthday party – and she was thinking about booking an Airbnb to do it. Banerjee recalls her daughter telling her about the plan, asking her whether she should tell her friend not to book an Airbnb because of the AI safeguards. The friend ended up throwing the party at her own home.
“Being a mother of teenagers and seeing teenage friends of my kids, your antenna is especially sharp and you have a radar for, ‘Oh my God, okay, this is a party about to happen,” Banerjee said. “Between our data scientists and our machine learning engineers and us, we started looking at these signals.”
For Banerjee, it was about translating that antenna into a usable algorithm.
In an April 2020 meeting with Nate Blecharczyk, the company’s co-founder and chief strategy officer, Banerjee recalls strategizing about ways to fix Airbnb’s party problem on three different time scales: “right now,” within a year, and in the general future.
For the “right now” scale, they talked about looking at platform data, studying the patterns and signals for current party reports, and seeing how those puzzle pieces align.
The first step, in July 2020, was rolling out a ban on high-risk reservations by users under the age of 25, especially those who either didn’t have much history on the platform or who didn’t have good reviews from hosts. Although Airbnb says that blocked or redirected “thousands” of guests globally, Banerjee still saw users trying to get around the ban by having an older friend or relative book the reservation for them. Two months later, Airbnb announced a “global party pan,” but that was mostly lip service – at least, until they had the technology to back it up.
Around the same time, Banerjee sent out a series of invitations. Rather than to a party, they were invites to attend party risk reduction workshops, sent to Airbnb designers, data scientists, machine learning engineers and members of the operations and communications teams. In Zoom meetings, they looked at results from the booking ban for guests under age 25 and started putting further plans in motion: Banerjee’s team created a 24/7 safety line for hosts, rolled out a neighborhood support line, and decided to staff up the customer support call center.
One of the biggest takeaways, though, was to remove the option for hosts to list their home as available for gatherings of more than 16 people.
Courtesy: Airbnb
Now that they had a significant amount of data on how potential partiers might act, Banerjee’s had a new goal: Build the AI equivalent of a neighbor checking on the house when the high-schooler’s parents leave them home alone for the weekend.
Around January 2021, Banerjee recalled hearing from Airbnb’s Australia offices that disruptive parties at Airbnbs were up and coming, just like they were in North America, as travel had come to a relative standstill and Covid was in full swing. Banerjee considered rolling out the under-25 ban in Australia, but after chatting with Blecharczyk, she decided to experiment with a party-banning machine learning model instead.
But Banerjee was nervous. Soon after, she phoned her father in Kolkata, India – it was between 10pm and 11pm for her, which was mid-morning for him. As the first female engineer in her family, Banerjee’s father is one of her biggest supporters, she said, and typically the person she calls during the most difficult moments of her life.
Banerjee said, “I remember talking to him saying, ‘I’m just very scared – I feel like I’m on the verge of doing one of the most important things of my career, but I still don’t know if we are going to succeed, like we have the pandemic going on, the business is hurting… We have something that we think is going to be great, but we don’t know yet. I’m just on this verge of uncertainty, and it just makes me really nervous.'”
Banerjee recalled her father telling her that this has happened to her before and that she’d succeed again. He’d be more worried, he told her, if she was overconfident.
In October 2021, Banerjee’s team rolled out the pilot program for their reservation screening AI in Australia. The company saw a 35% drop in parties between regions of the country that had the program versus those that did not. The team spent months analyzing the results and upgraded the system with more data, as well as safety and property damage incidents and records of user collusion.
How the AI system works to stop parties
Listings on Airbnb
Source: Airbnb
Imagine you’re a 21-year-old planning a Halloween party in your hometown. Your plan: Book an Airbnb house for one night, send out the “BYOB” texts and try to avoid posting cliched Instagram captions.
There’s just one problem: Airbnb’s AI system is working against you from the second you sign on.
The party-banning algorithm looks at hundreds of factors: the reservation’s closeness to the user’s birthday, the user’s age, length of stay, the listing’s proximity to where the user is based, how far in advance the reservation is being made, weekend vs. weekday, the type of listing and whether the listing is located in a heavily crowded location rather than a rural one.
Deep learning is a subset of machine learning that uses neural networks – that is, the systems process information in a way inspired by the human brain. The systems are certainly not functionally comparable to the human brain, but they do follow the pattern of learning by example. In the case of Airbnb, one model focuses specifically on the risk of parties, while another focuses on property damage, for instance.
“When we started looking at the data, we found that in most cases, we were noticing that these were bookings that were made extremely last-minute, potentially by a guest account that was created at the last minute, and then a booking was made for a potential party weekend such as New Year’s Eve or Halloween, and they would book an entire home for maybe one night,” Banerjee told CNBC. “And if you looked at where the guest actually lived, that was really in close proximity to where the listing was getting booked.”
After the models do their analysis, the system assigns every reservation a party risk. Depending on the risk tolerance that Airbnb has assigned for that country or area, the reservation will either be banned or greenlit. The team also introduced “heightened party defenses” for holiday weekends such as the Fourth of July, Halloween and New Year’s Eve.
Source: Airbnb
In some cases, like when the right decision isn’t quite clear, reservation requests are flagged for human review, and those human agents can look at the message thread to gauge party risk. But the company is also “starting to invest in a huge way” in large language models for content understanding, to help understand party incidents and fraud, Banerjee said.
“The LLM trend is something that if you are not on that train, it’s like missing out on the internet,” Banerjee told CNBC.
Banerjee said her team has seen a higher risk of parties in the U.S. and Canada, and the next-riskiest would probably be Australia and certain European countries. In Asia, reservations seem to be considerably less risky.
The algorithms are trained partly on tickets labeled as parties or property damage, as well as hypothetical incidents and past ones that occurred before the system went live to see if it would have flagged them. They’re also trained on what “good” guest behavior looks like, such as someone who checks in and out on time, leaves a review on time, and has no incidents on the platform.
But like many forms of AI training data, the idea of “good” guests is ripe for bias. Airbnb has introduced anti-discrimination experiments in the past, such as hiding guests’ photos, preventing hosts from viewing a guest’s full name before the booking is confirmed, and introducing a Smart Pricing tool to help address earnings disparities, although the latter unwittingly ended up widening the gap.
Airbnb said its reservation-screening AI has been evaluated by the company’s anti-discrimination team and that the company often tests the system in areas like precision and recall.
Going global
Courtesy: Airbnb
Almost exactly one year ago, Banerjee was at a plant nursery with her husband and mother-in-law when she received a call from Airbnb CEO Brian Chesky.
She thought he’d be calling about the results of the Australia pilot program, but instead he asked her about trust in the platform. Given all the talk she did about machine learning models and features, she recalled him asking her, would she feel safe sending one of her college-bound kids to stay at an Airbnb – and if not, what would make her feel safe?
That phone call ultimately resulted in the decision to expand Banerjee’s team’s reservation screening AI worldwide the following spring.
Things kicked into high gear, with TV spots for Banerjee, some of which she spotted in between pull-ups on the gym television. She asked her daughter for advice on what to wear. The next thing she knew, the team was getting ready for a live demo of the reservation screening AI with Chesky. Banerjee was nervous.
Last fall, the team sat down with Chesky after working with front-end engineers to create a fake party risk, showing someone booking an entire mansion during a holiday weekend at the last minute and seeing if the model would flag it in real-time. It worked.
Chesky’s only feedback, Banerjee recalled, was to change the existing message – “Your reservation cannot be completed at this point in time because we detect a party risk” – to be more customer-friendly, potentially offering an option to appeal or book a different weekend. They followed his advice. Now, the message reads, “The details of this reservation indicate it could lead to an unauthorized party in the home. You still have the option to book a hotel or private room, or you can contact us with any questions.”
Over the next few months, Banerjee remembers a frenzy of activity but also feeling calm and confident. She went to visit her family in India in April 2023 for the first time in about a year. She told her father about the rollout excitement, which happened in batches the following month.
This past Labor Day, Banerjee was visiting her son in Texas as the algorithm blocked or redirected 5,000 potential party bookings.
But no matter how quickly the AI models learn, Banerjee and her team will need to continue to monitor and change the systems as party-inclined users figure out ways around the barriers.
“The interesting part about the world of trust and safety is that it never stays static,” Banerjee said. “As soon as you build a defense, some of these bad actors out there who are potentially trying to buck the system and throw a party, they will get smarter and they’ll try to do something different.”
Co-founder and chief executive officer of Nvidia Corp., Jensen Huang attends the 9th edition of the VivaTech trade show in Paris on June 11, 2025.
Chesnot | Getty Images Entertainment | Getty Images
Nvidia has just shelled out over $900 million to hire Enfabrica CEO Rochan Sankar and other employees at the artificial intelligence hardware startup, and to license the company’s technology, CNBC has learned.
In a deal reminiscent of recent AI talent acquisitions made by Meta and Google, Nvidia is paying cash and stock in the transaction, according to two people familiar with the arrangement. The deal closed last week, and Enfabrica CEO Rochan Sankar has joined Nvidia, said the people, who asked not to be named because the matter is private.
Nvidia has served as the backbone of the AI boom that began with the launch of OpenAI’s ChatGPT in late 2022. The company’s graphics processing units (GPUs), which are generally purchased in large clusters, power the training of large language models and allow for big cloud providers to offer AI services to clients.
Enfabrica, founded in 2019, says its technology can connect more than 100,000 GPUs together. It’s a solution that could help Nvidia offer integrated systems around its chips so clusters can effectively serve as a single computer.
A spokesperson for Nvidia declined to comment, and Enfabrica didn’t provide a comment for this story.
While Nvidia’s earlier AI chips like the A100 were single processors slotted into servers, its most recent products come in tall racks with 72 GPUs installed working together. That’s the kind of system inside the $4 billion data center in Wisconsin that Microsoft announced on Thursday.
Nvidia previously invested in Enfabrica as part of a $125 million Series B round in 2023 that was led by Atreides Management. The company didn’t disclose its valuation at the time, but said that it was a fivefold increase from its Series A funding.
Late last year, Enfabrica raised another $115 million from investors including Spark Capital, Arm, Samsung and Cisco. According to PitchBook, the post-money valuation was about $600 million.
Tech giants Meta, Google, Microsoft and Amazon have all poured money into hiring top AI talent through deals that resemble acquihires. The transactions allow the companies to bring in top engineers and researchers without worrying about the regulatory hassles that come with acquisitions.
The biggest such deal came in June, when Meta spent $14.3 billion on Scale AI founder Alexandr Wang and others and took a 49% stake in the AI startup. A month later, Google announced an agreement to bring in Varun Mohan, co-founder and CEO of artificial intelligence coding startup Windsurf, and other research and development employees in a $2.4 billion deal that also included licensing fees.
Last year, Google made a similar deal to bring in the founders of Character.AI. Microsoft did the same thing for Inflection, as did Amazon for Adept.
While Nvidia has been a big investor in AI technologies and infrastructure, it hasn’t been a significant acquirer. The company’s only billion-dollar-plus deal was for Israeli chip designer Mellanox, a $6.9 billion purchase announced in 2019. Much of Nvidia’s current Blackwell product lineup is enabled by networking technology that it acquired through that acquisition.
Nvidia tried to buy chip design company Arm, but that deal collapsed in 2022 due to regulatory pressure. In the past year, Nvidia closed a $700 million purchase of Run:ai, an Israeli company whose technology helps software makers optimize their infrastructure for AI.
On Thursday, Nvidia announced one of its most sizable investments to date. The chipmaker said it’s taken a $5 billion stake in Intel, and announced that the two companies will collaborate on AI processors. Nvidia also said this week that it invested close to $700 million in U.K. data center startup Nscale.
— Correction: A prior version of this story mistakenly included the name of a company as an investor in Enfabrica.
CrowdStrike logo is seen in this illustration taken July 29, 2024.
Dado Ruvic | Reuters
CrowdStrike shares popped about 13%, a day after the cybersecurity firm issued better-than-expected long-term guidance at its investor day.
The company on Wednesday said it expects net new annual recurring revenues to grow at least 20% in 2027, ahead of analysts’ expectations. CrowdStrike plans for ARR to hit $10 billion by 2031, and then double to $20 billion by 2036.
“CrowdStrike is by far the most advanced security platform in the industry, and the plethora of AI-based solutions announced today will further separate CrowdStrike from the competition,” wrote Wells Fargo analyst Andrew Nowinski in a note following the event.
Some Wall Street firms also boosted their price targets.
Read more CNBC tech news
Cybersecurity has taken center stage this year as businesses beef up security in the age of artificial intelligence. Many companies have harnessed AI tools to strengthen their offering as threats rise in sophistication.
Nvidia CEO Jensen Huang attends the “Winning the AI Race” Summit in Washington D.C., U.S., July 23, 2025.
Kent Nishimura | Reuters
Nvidia CEO Jensen Huang said that the company’s $5 billion investment and technology collaboration with Intel comes after the two companies held discussions for nearly a year.
Huang said that he communicated personally with Intel CEO Lip-Bu Tan about the partnership. He called Tan a “longtime friend” on a Thursday call with reporters after the companies announced that Nvidia would co-develop data center and PC chips with Intel as part of the investment deal. On the call, Tan said he and Huang have known each other for 30 years.
“We thought it was going to be such an incredible investment,” Huang said.
Nvidia said it will collaborate with the chipmaker to create artificial intelligence systems for data centers that combine Intel’s x86-based central processors with Nvidia’s graphics processors and networking.
Intel will also sell CPUs for PCs and notebooks that integrate Nvidia graphics processors, or GPUs.
The transaction itself took a few months to come together, Intel’s revenue chief Greg Ernst wrote in a LinkedIn post, adding that the agreement was reached on Saturday.
The investment highlights how the fortunes of the two companies have switched atop Silicon Valley’s pecking order as a result of the AI explosion ushered in by OpenAI’s launch of ChatGPT in late 2022.
Intel shares are down 31.78% in the last five years, while Nvidia shares are up 1,348% as of opening prices on Thursday. Nvidia is worth over $4.25 trillion, while Intel is only worth $143 billion.
How Intel and Nvidia will collaborate
For decades, the most important part in a PC or server was the central processor, and Intel dominated the market for those chips. But AI infrastructure, like the machines in the $4 billion data center Microsoft announced on Thursday, often needs two or more Nvidia GPUs for every one CPU.
Nvidia AI systems, like the NVL72 used by Microsoft, come with Arm-based CPUs, instead of Intel x86-based CPUs. On the call, Huang said Nvidia will soon support Intel’s CPUs in its NVLink racks for AI.
“We’ll buy those CPUs from from Intel, and then we’ll connect it into super chips that then becomes our compute node, that then gets integrated into a rack scale AI supercomputer,” Huang said.
Nvidia will also contribute GPU technology to Intel chips that ship in laptops and PCs, which is an underserved market, Huang said. In total, the addressable markets for the two product collaborations are worth $50 billion, Huang said.
“We’re going to become a very large customer of Intel CPUs, and we’re going to be a large supplier of GPU chiplets into Intel” chips, he said.
Huang said the deal with Intel will have “no” impact on Nvidia’s business relationship with Arm.
Thursday’s investment deal is focused on the relationship between Nvidia and Intel’s product division, not its foundry. The two companies, however, did not rule out future foundry partnerships.
“We’ve always evaluated Intel’s foundry technology, and we’re going to continue to do it, but today, this announcement, is squarely focused on these custom CPUs,” Huang said. Nvidia currently uses Taiwan Semiconductor Manufacturing Company to manufacture its chips.
The collaboration will use Intel’s packaging, which is a part chip manufacturing that occurs toward the end of the process and combines several chip components into a single part that can be installed in machines.
Intel CEO Lip-Bu Tan makes a speech on stage in Taipei, Taiwan May 19, 2025.
Ann Wang | Reuters
Tan said he was grateful for Nvidia’s vote of confidence.
“‘I’d like to thank Jensen for the confidence in me, and our team and Intel will work really hard to make sure it’s a good return for you,” Tan said.
Last year, Intel’s board removed previous CEO Pat Gelsinger because of rising costs in its manufacturing business and the company’s failure to gain a foothold in AI chips. In March, Intel named Tan, a well-connected investor who had turned around chip software firm Cadence Design Systems, its new chief executive.
Tan has focused on cutting costs and raising money in his short tenure leading Intel even as the future of the company’s manufacturing business, called Intel Foundry, remains unclear.
In addition to the $5 billion from Nvidia and $8.9 billion from the U.S. government, Intel has taken a $2 billion investment from SoftBank, sold a majority stake in its ASIC subsidiary Altera to Silver Lake for $3.3 billion and sold $1 billion in stock from Mobileye, its self-driving car subsidiary.
Intel has also cut significant staff, saying in July that it would eliminate 15% of its workforce by the end of the year.
The company develops its own chips as well as manufacturing them. It wants to manufacture chips for companies like Nvidia or Apple, but has yet to secure them as customers. Analysts say Intel needs a big foundry client to signal that its technology is stable and ready for volume production.
But cutting-edge chip manufacturing is expensive, and Intel has signaled that if it can’t get enough customers, it may not continue investing in its foundry. That could spark a reaction from Washington, whose politicians and lobbyists consider Intel to be strategically important for the nation because it is the only American company capable of manufacturing the most advanced chips.
The Trump administration took a 10% stake in Intel in August. Intel was previously in line to receive $8.9 billion in grants and loans from the CHIPS Act, but the Trump administration asked and received an equity stake in the chipmaker in exchange for the money.
Huang was with Trump this week in England to attend a State Dinner at Windsor Palace and announce new projects and investments in the U.K. But the Trump administration wasn’t involved in this deal, according to a White House official and Huang.
“Intel’s new partnership with Nvidia is a major milestone for American high-tech manufacturing,” White House spokesman Kush Desai said in a statement.