Jun 3, 2022

PROTOCOL Coverage | Newsletter | Intel | Events How policy is shaping tech — and how the tech industry is responding. Email Address Policy Thank you for signing up. Please check your inbox to verify your email. Email me an authentication link A login link has been emailed to you - please check your inbox. Hirsh Chitkara ( @HirshChitkara ) is a reporter at Protocol focused on the intersection of politics, technology and society. Before joining Protocol, he helped write a daily newsletter at Insider that covered all things Big Tech. He's based in New York and can be reached at hchitkara@protocol.com. Cast AI uses Kubernetes automation technology to optimize spending and performance for cloud-native apps by matching the right amount of computing power and memory to those apps. Cast AI was born out of its co-founders’ frustrations with their cloud bills while they operated a prior startup. Photo: Cast AI Donna Goodison ( @dgoodison ) is Protocol's senior reporter focusing on enterprise infrastructure technology, from the 'Big 3' cloud computing providers to data centers. She previously covered the public cloud at CRN after 15 years as a business reporter for the Boston Herald. Based in Massachusetts, she also has worked as a Boston Globe freelancer, business reporter at the Boston Business Journal and real estate reporter at Banker & Tradesman after toiling at weekly newspapers. June 3, 2022 Cloud customers pay an average three times more on cloud compute costs for AWS, Microsoft Azure and Google Cloud than they should, according to Cast AI. Helping them manage those costs is turning into a business itself. The startup specializes in Kubernetes automation and cost optimization and reporting for cloud-native applications. Its platform uses artificial intelligence to identify which compute resources are needed for specific Kubernetes workloads and automatically selects the best combinations, configuring CPUs and memory to prevent over-provisioning. It continuously adds or removes resources as needed, ensuring customers aren’t overspending without compromising workload availability or performance, according to the company. “It's impossible to do this exercise as a human,” co-founder and Chief Product Officer Laurent Gil said. “We decomplexify capabilities. We make Kubernetes or containers serverless by saying we're going to take care of the servers, and we will make the servers cost-efficient.” Cast AI was born out of its co-founders’ frustrations with their cloud bills while they operated a prior startup: Zenedge, a cloud-based, AI-driven cybersecurity startup acquired by Oracle in 2018. “In the beginning of that company, I would spend about $1,000 to $2,000 a month on AWS,” Gil said. “Three years later … that became $2 million dollars — by far the highest cost of the company, and we were very, very frustrated. We had a nice ride with customers, but every time we would add a client, our AWS bill would go through the roof.” AWS’ answer was for Zenedge to prepay for three years to cut their cloud bill by 40%, but Zenedge didn’t want to be locked in, according to Gil. With Cast AI, they built the spending-management product they wished they had at the time. The overspending tax Companies using Cast AI’s services can reduce their cloud compute spending by 65% on average, according to Gil. It works with Amazon Elastic Kubernetes Service (EKS), Google Kubernetes Engine (GKE), Azure Kubernetes Service (AKS) and Kubernetes Operations (kOps) on AWS. “The engine is instantly going to understand what applications you have … and how much compute and memory they currently consume, and how much they cost to run based on the machine that these applications are installed on,” Gil told Protocol. “Then we are going to give you another number, which is, ‘Hey, considering what this application does and uses, this should really be the cost.’” While there are no big differences between the Big Three cloud providers’ prices, Gil said, within each cloud itself, there are cost differences when it comes to processors. “Most … are cheaper with AMD than they are with Intel,” Gil said. “That makes our engine use more AMD sometimes for compute-intensive [workloads]. But the machine has been trained to know this, so we will always select the lowest-cost option.” Image: Cast AI Cast AI is currently optimizing about 1,000 applications for hundreds of customers, according to Gil. “One thing that was very surprising to us … is that the average cost-savings we provide to anybody using us … is 65%,” he said. “Sixty-five percent means you are spending three times more than you should on Amazon. So if you think of this the other way, you say, 'Well, out of $100 of your cloud bill, $66 of this is for Mr. Bezos, because it does nothing for you … and $33 is what you really use.” Cast AI says that, on average, its customers weren’t using 37% of the CPUs that they were paying for. They could save an additional 7% by changing one type of virtual machine (VM) for another and another 22% by switching VMs to discounted spot instances. “We're not changing anything [with] our customer environment,” Gil said. “It's like … how you defragment disk drives. We defragment your application by moving the boxes around so that you can fill the machine more [by] using all the empty space.” This is the way It’s a task that’s impossible for developers to tackle on their own, and the cloud providers don’t make it easy , according to Gil. One of Cast AI’s customers — an adtech company with a large consumer app in India — saw 84% in compute savings after turning on its engine, according to Gil. Another publicly traded company, a SaaS business, saw its cloud compute costs reduced by 72%. Branch, a late-stage startup specializing in deep linking, mobile analytics and attribution, is a Cast AI customer that sees about 25 billion events per day and is running all of its compute inside Kubernetes clusters. “Our cloud hosting needs to be very efficient to be able to process all that data in real time to be able to make real-time decisions … as well as to be able to aggregate and show all of the statistics inside of the analytics,” said Mark Weiler, Branch’s head of Engineering. Branch, which uses AWS as its preferred cloud provider, started a proof of concept with Cast AI in May of 2021 and deployed it across all of its clusters within two months. “They have saved us on the order of a couple million dollars per year on our AWS cloud bill, which is one of the highest ROI cost-savings projects that we've done in the past five or six years,” Weiler said. “The promise was they would allow us to dynamically determine what sorts of optimal spot instances to use based on our workloads without incurring any negative effects on our uptime SLAs [service-level agreements] when Amazon revokes those instances. They came through.” “Manually configuring all that, keeping that up to date, having all the fallback scenarios set up and up to date, is extremely complicated to do on your own. It's begging for an automated solution that can monitor the actual spot market and your instances and determine what the optimal reallocation would be,” Weiler said. Cast AI is currently adding new features for observability and cost-reporting, but Gil sees an opportunity to even further reduce other areas of customers’ cloud bills. “We’re just scratching the surface,” he said. Keep ReadingShow less Internet for Growth, an initiative of the Interactive Advertising Bureau , supports the transformative role the advertising-supported internet plays in empowering America's small businesses, helping entrepreneurs bring their ideas to life. Supported by a diverse community of over 700 IAB members including marketers, agencies, publishers, platforms and ad tech providers, as well as hundreds of small businesses and creators, Internet for Growth highlights the benefits the internet delivers to local economies, expanding opportunities for innovators to reach markets far beyond their neighborhoods. Their work ensures people understand the limitless opportunity the internet provides for creativity and commerce, fair competition, and connecting with consumers on mutually shared values and interests, no matter the background or geography. May 31, 2022 Logan Niles, Founder, Pot Pie Factory Smaller companies like ours are buckling under the weight of unprecedented price increases, supply chain shortages and rising labor prices. To increase our marketing reach on a slim budget, the internet is our best option. Internet marketing is critical to the survival of our business. It's one of the most affordable, effective forms of marketing at our disposal. Limiting our options will only hurt us at a time when we need every opportunity possible to stay in business. Small companies like ours are competing with much larger competitors to reach the same customers in a busy, crowded space. How many Valpaks, grocery store flyers and random postcards from local businesses have you discarded in the last month? We’re all overloaded with physical junk mail. Even if an offer catches our eye, there’s no instant online access or interactivity. Generational shifts have also impacted marketing. For younger generations, digital media is a part of everyday life. How they shop, date and travel: It’s all digital. For most of our customers, shopping online is the norm, and their payment choices are digital too, including at pop-up and live events. The digital economy is a way of life and here to stay. Congress needs to be careful tampering with digital advertising tools that Pot Pie Factory needs to stay in business. From "Lean In" to Jan. 6 and everything in between. Meta has defined Sheryl Sandberg’s career thus far, but she plans to “write the next chapter of her life” after leaving the company. Photo: Antoine Antoniol/Getty Images for Cannes Lions June 2, 2022 Issie Lapowsky ( @issielapowsky ) is Protocol's chief correspondent, covering the intersection of technology, politics, and national affairs. She also oversees Protocol's fellowship program. Previously, she was a senior writer at Wired, where she covered the 2016 election and the Facebook beat in its aftermath. Prior to that, Issie worked as a staff writer for Inc. magazine, writing about small business and entrepreneurship. She has also worked as an on-air contributor for CBS News and taught a graduate-level course at New York University's Center for Publishing on how tech giants have affected publishing. June 2, 2022 There was a time early on at Facebook when pretty much every other company in Silicon Valley was on the hunt for its own “Sheryl.” It was shorthand for a female executive who could transform a company from a scrappy, bro-ey startup to a fast-growing business, as Sheryl Sandberg had famously done with Facebook. But all these years later, as Sandberg prepares to leave the company after 14 years, the mythology surrounding her — and what it means to be a “Sheryl” — has become decidedly more mixed. She is both responsible for making Meta one of the most valuable companies in the world through its ad business and also responsible for normalizing the vast privacy intrusions that enable that business model. She is the most recognizable female executive in America and an inspiration to innumerable women around the world, as well as the person who has wielded her power behind the scenes to protect Facebook — and her own reputation — at all costs. The evolution of Sandberg’s legacy can be traced back to key moments in Facebook’s history. 2008: Dividing duties with Zuck One of the early decisions that would wind up having a domino effect on Facebook’s future was the way Mark Zuckerberg and Sandberg decided to divvy up responsibility. Building products was — and remains — Zuckerberg’s primary passion. Sandberg, by contrast, had always worked on Google’s ad business and had ties to Washington, having worked at the Treasury Department under former Secretary Larry Summers. In an interview with Steven Levy for the book “Facebook: The Inside Story,” Sandberg described the division of labor as being “very easy — he took product and I took the rest.” “The rest” wound up including not just the business operations, but Facebook’s communications and relationship with D.C. As Facebook’s public relations and political reputation began to take a beating nearly a decade later, a lot of the blows would wind up landing on Sandberg. 2010: ‘Lean In’ In 2010, Sandberg delivered the TED talk of all TED talks. The kind that makes one of the world’s most intractable issues — bias against women — seem utterly fixable in 15 minutes or less. The talk, titled “Why we have too few women leaders,” urged women to take a seat at the table, get their partners to pitch in and, above all, to lean in to promotions and opportunities and whatever else women sacrifice in anticipation of starting a family. The talk became the basis of a book released in 2013, which spawned a global movement, with “Lean In” circles — groups of women supporting women — popping up in 188 countries around the world. “Lean In” made Sandberg a household name and, for a time at least, shielded her from some of the scrutiny that would begin to come Facebook’s way. 2012: Facebook’s IPO When Facebook went public in 2012, it made Sandberg, who is now a billionaire, very, very rich. But more than that, it cemented Sandberg’s reputation as a business genius. Facebook went from losing money in 2008, the year Sandberg joined, to making money hand-over-fist, setting the company up for a public-market debut that, despite early stumbles, quickly saw Facebook’s stock price soar. Sandberg got a lot of the credit for making that happen. (The New York Times quoted one Stanford engineering professor at the time, who called Sandberg the “Justin Bieber of tech.”) Facebook had the product, the fast-growing audience and the buzz, but not the discipline to make money off of it all until she got there. 2015: Tragedy strikes Sandberg’s life was forever changed in 2015 with the sudden death of her husband, SurveyMonkey CEO Dave Goldberg. That tragedy not only temporarily affected Sandberg’s day-to-day work at Facebook, but it created a new outlet for her advocacy: this time, focused on dealing with grief. She wrote at length about how she coped in the days and months after his death, including what helped her and what didn’t, and channeled it all into another self-help book called “Option B,” which deals with lessons on resilience and facing adversity. 2017: Breaking ranks on FOSTA/SESTA Facebook’s fortunes in Washington were already in trouble by the fall of 2017. The 2016 election had started a backlash on the right over alleged censorship of conservatives on the platform. On the left, folks were already starting to blame fake news and targeted ads by the Trump campaign for Hillary Clinton’s loss. Into this environment came FOSTA/SESTA: a bipartisan package of bills that would whittle away at Section 230 protections for the first time in the name of stopping sex trafficking. The tech industry hated the bill, but Sandberg broke ranks with her fellow Silicon Valley executives, coming out in favor of a modified version of the bill in November 2017. Facebook’s backing is widely viewed as having pushed FOSTA/SESTA over the line. For Sandberg, the moment was also defining, revealing the ways she worked behind the scenes to burnish the company’s reputation in Washington. Early 2018: Cambridge Analytica scandal Outside of Facebook, Zuckerberg bore most of the blame for allowing so much Facebook user data to be scooped up and sold to Cambridge Analytica for political purposes. It was Zuckerberg who appeared first in front of Congress to apologize for Facebook’s missteps. But inside the company, Cambridge Analytica was reportedly a turning point in Zuckerberg and Sandberg’s relationship, with Zuckerberg personally blaming his No. 2 for being too slow to address that and other issues at the company, according to The Wall Street Journal. Fall 2018: Delay, deny, deflect A November 2018 exposé in The New York Times, published in the aftermath of both the Cambridge Analytica debacle and the Russian troll scandal, focused attention on Sandberg’s alleged misdeeds like never before. According to the Times, Sandberg had tried to limit the amount of detail Facebook shared about Russian intrusion and even reprimanded the company’s then-head of Security for sharing information about it with Facebook’s board. That story also exposed Facebook’s efforts to push negative coverage of competitors through an opposition research group and investigate ties between George Soros and Facebook’s critics. Sandberg, who initially attempted to distance herself from that work, eventually accepted blame for it, writing , “I want to be clear that I oversee our Comms team and take full responsibility for their work and the PR firms who work with us.” The story started a new round of rumblings about whether Sandberg’s days at Facebook were numbered. 2021: The Capitol riot Through the Trump years, Sandberg, a known Democrat and Clinton supporter, took a less central role in representing Facebook in Washington. But as the company’s second in command, she was still called on to answer for Facebook’s failure to prevent the Stop the Steal movement from spreading before the Jan. 6 riot. In an interview with Reuters, she pushed blame onto other platforms, saying , “These events were largely organized on platforms that don’t have our abilities to stop hate, don’t have our standards and don’t have our transparency.” Sandberg’s reluctance to accept any responsibility for the riot struck people both inside and outside of Facebook as a sign of how little the company and its most senior executives had learned about their impact on the world. 2022: Defending Bobby Kotick Sandberg’s most recent scandal came to light just months ago, when the Journal reported that she twice intervened in reporting by The Daily Mail on a since-retracted temporary restraining order that had been taken out against her ex-boyfriend Bobby Kotick. The Journal reported that Meta employees had been involved in trying to squash the story and that the ordeal had sparked an internal investigation of Sandberg. It’s unclear what that investigation found, though a Meta spokesperson told the Journal that Sandberg never “threatened the MailOnline’s business relationship with Facebook in order to influence an editorial decision.” Meta has defined Sandberg’s career thus far, but she plans to “write the next chapter of her life” after leaving the company this fall. Whether she likes it or not, Facebook will continue to be a main character. Owen Thomas contributed reporting. June 2, 2022 The Consumer Financial Protection Bureau is getting tired of letting fintech play in the sandbox. In unveiling a recent restructuring of the agency's innovation office, CFPB Director Rohit Chopra said regulators were shifting away from policies that place "special regulatory treatment on individual companies." That spells twilight for a Trump-era policy that offered some new financial products safe regulatory harbor through a sandbox and a related effort to publish no-action letters. The sandbox initiative, led by the former Office of Innovation, tried to make it easier to bring new products to market, but was unpopular with consumer groups. The CFPB's newly named Office of Competition and Innovation will focus on creating "market conditions where consumers have choices, the best products win and large incumbents cannot stifle competition by exploiting their network effects or market power,” the agency said in May. As part of the restructuring, the CFPB said it reviewed the sandbox and no-action letter programs and found they "proved to be ineffective" and that some firms used the programs to indicate the bureau "conferred benefits upon them that the bureau expressly did not." Despite declaring the program ineffective, a CFPB spokesperson said the agency "is still processing no-action letter and sandbox applications at this time." The agency's release encouraged companies to instead file formal rule-making petitions to ask for greater clarity on particular regulations. The no-action letter policies and sandbox have not been widely used. But along with a series of other actions taken by the CFPB, their apparent sunset indicates a shifting regulatory stance on financial technology. "The CFPB is sending clear signals that the growth of the fintech industry has resulted in increased risks to consumers," said Michael Gordon, an attorney with Ballard Spahr and former top CFPB official under the Obama administration. "While many fintech innovations are indeed consumer-friendly, I expect the CFPB to ramp up its scrutiny and take action against firms that fall short of their obligations." The sandbox as a concept has had hype for some time in policy circles. The U.K.’s Financial Conduct Authority launched an early effort in 2015, allowing companies to test new products on a small subset of customers without fear of regulatory reprisal. There are 11 states with sandboxes that offer testing grounds in restricted markets, according to a list kept by Libertas Institute, a Utah-based think tank that promotes the sandbox concept nationally. The CFPB's effort was the most prominent on the federal level. Utilizing either a template approval through the agency's compliance sandbox or a no-action letter from the CFPB, companies could apply to get a provisional OK to test out a new product within certain guidelines. The agency granted six applications for no-action letters and three approvals under the sandbox program, mostly under the Office of Innovation launched in 2018. Online lending service Upstart got the first no-action letter, in 2017, and a three-year extension in 2020. Bank of America received a no-action letter in November 2020 for its Balance Assist small-dollar credit product. Synchrony Bank that same year got approval under the sandbox program for a credit card designed to help people with poor credit improve their score. Sandboxes in general have attracted plenty of scrutiny from consumer groups and even other regulators, who fear that the regulatory relief would be used to offer harmful products to consumers. When a Treasury Department report suggested expanding the concept to more federal regulators in 2018, Maria Vullo, the superintendent of New York State’s Department of Financial Services, fired off a statement that declared: "Toddlers play in sandboxes. Adults play by the rules." Vullo, who now runs an advisory firm, said in an email that the CFPB is creating a more level playing field by declaring that "companies cannot experiment with consumers but instead must abide by all consumer protection laws and regulations." The CFPB under Chopra had yet to approve any new no-action letters, so the reorganization of the office did not come as a total surprise. It also comes as the CFPB has pushed to expand its oversight authority over non-bank institutions and demanded banks and lenders explain their algorithms , showing the agency now aims to take a more aggressive stance toward the industry. The announcement did open the door for companies working to improve access to banking options. "We will be looking at ways to clear obstacles and pave the path to help people have more options and more easily make choices that are best for their needs,” Chopra said in a statement. President Biden ordered the agency last summer to kickstart open banking by creating rules that make it easier for consumers to access their data. "I do not think the current CFPB leadership is anti-fintech," said Dan Quan, a venture capital investor and former director of Project Catalyst at the CFPB, a precursor to the innovation office. "Innovation plays an important role in competition, which is central to their current agenda. The rebranding of this office simply means they will take a different approach from the previous administration when it comes to promoting innovation." While the announcement did not say that the sandbox program will be shut down entirely, the CFPB's emphasis is now on rule-making petitions for companies that seek regulatory clarity on a product. That would allow for greater protections, as any rules would apply to the industry as a whole and be harder to revoke. But, as one legal analysis noted, there has been just one granted rule-making petition in the CFPB's nearly 11-year history. So far, fintechs are taking a wait-and-see approach to the reorganized innovation office. “Competition from financial technology companies drives lower costs, better services and more consumer choice," said Penny Lee, chief executive officer of the Financial Technology Association. "We look forward to working closely with the bureau to continue encouraging consumer-friendly competition and innovation." Keep ReadingShow less Kate Kaye is an award-winning multimedia reporter digging deep and telling print, digital and audio stories. She covers AI and data for Protocol. Her reporting on AI and tech ethics issues has been published in OneZero, Fast Company, MIT Technology Review, CityLab, Ad Age and Digiday and heard on NPR. Kate is the creator of RedTailMedia.org and is the author of "Campaign '08: A Turning Point for Digital Media," a book about how the 2008 presidential campaigns used digital media and data. June 2, 2022 When credit card giant American Express began offering bank accounts for the first time last year, it had a foundation of fraud detection to bring to an entirely new product arena. That meant in some cases, the company could port over AI and machine-learning models used to spot phony identities or dodgy transactions for its credit card products to its consumer and business checking accounts. But it’s been a process, and now, AmEx plans to invest in bringing additional AI techniques used to protect against credit card fraud to its banking products. “We have models which run to detect whether it's you or whether somebody else is logging into your account. Very straightforwardly, we transferred it to the banking product,” said Abhinav Jain, vice president for Global Fraud Decision Science at AmEx, who is responsible for the company’s fraud detection models. “We had at least the technical side of the model ready to prevent this kind of fraud happening for a customer.” The fraud models are designed to recognize odd behavior or suspicious patterns of activity that are not typical of a particular customer. After kicking off a business checking account product in October followed by a consumer-aimed checking account in February, AmEx’s models already are picking up on an emerging trend. “Fraudsters show up very quickly once you launch a new financial product. And one of the patterns that we have seen starting to emerge very quickly is fraudsters attempting to take over a customer's profile and apply to open a checking account in their name – so, identity theft,” said Ana Palaghita, vice president and head of Banking Fraud and Deposit Risk at AmEx. “This has been a typical pattern that I've seen just in the month and a half to two months that we've been in-market,” said Palaghita, who leads the company’s fraud strategy team and works closely with Jain’s data science and ML modeling group. Business checking accounts are attracting their own distinct fraud patterns, Palaghita said. “On the business side, we've seen different patterns. We've seen more of the synthetic identity where fraudsters are attempting to open accounts with businesses that are either not real or they're just a front in an attempt to deposit fake checks and extract money that way.” Some companies are using machine- and deep-learning models to detect fraudulent behavior by entities sanctioned by the U.S. Treasury Department in relation to the war in Ukraine. AmEx sources said they would not address the war in Ukraine or sanctioned entities in more general terms. False positives and speedy data The AmEx fraud detection models react and optimize automatically by adjusting how they weigh certain data points in the decision-making process, for instance. “In the places we’re seeing more fraud, they will be more aggressive,” said Jain, explaining that the models self-calibrate by attributing higher probability of fraud to certain data elements that are reflective of other recent fraud. They might weigh more heavily particular geographic regions, currencies or types of products associated with attempted transactions, Jain said. Fraudsters show up very quickly once you launch a new financial product. Of course, without proper tuning, automated fraud detection systems can be overly sensitive, halting legitimate transactions and annoying customers in the process. However, if a transaction sets off a fraud alert, AmEx doesn’t necessarily stop the transaction right away. Sometimes the company puts transactions on hold, then sends a text or email alert to the customer asking whether they’ve made the purchase or taken the action in question. That information gets fed back into the fraud model to optimize it. “As soon as we identify this was a false positive, that information also feeds back in real time to the model,” Jain said. Indeed, financial services companies increasingly are reliant on real-time data and data processing to run fraud detection models. Not only do they need sophisticated machine learning to keep up with evolving fraud approaches, but they need speedy data processing on the back end to ingest fresh data inputs into fraud models and ensure those models recognize and react to quickly-morphing fraud patterns. “Within milliseconds, we should be able to link that IP address, that email address, to the fraud database. If a second attempt comes from similar entities, we should be able to stop it,” Jain said. A new crop of database startups is emerging to help financial institutions and other enterprises grab real-time data and make use of it immediately to update machine-learning models for things like ecommerce recommendations and dynamic pricing. Because AmEx has fewer “silos and fences” separating the data and technology systems behind its product lines than other financial services companies, it has been able to more readily transfer machine-learning models for use across products than other companies have, said Palaghita, who worked in various roles at Capital One since 2007 before joining AmEx in 2021. That capability “is not necessarily something that every financial institution can do so quickly and seamlessly,” she said. AmEx is still in the process of porting fraud models used on its credit card side over to its banking side, Jain said. Up next: incorporating time series data for neural networks used to detect identity fraud or online account takeovers. “Getting the time series view helps the model further drill down into exactly what is fraud and what is not,” Jain said. “There are neural network algorithms that help us do that.” In data used for more standard modeling, each transactional data point used to train a model is assessed individually as good or bad. Models that incorporate time series data consider history and context by viewing not only the current transaction, but a customer’s previous 10 or 20 transactions. For example, if a customer’s last few recent transactions happened at a mall, but soon after a transaction is attempted in a faraway location, a model looking at time series data would be able to detect it as fraudulent. “We are investing heavily into those types of algorithms, and we've had really good success in the initial launches that we’ve had, and we want to expand that more and more to different types of fraud — and even start leveraging it much more,” Jain said Keep ReadingShow less