Chungin “Roy” Lee stepped onto Columbia University’s campus this past fall and, by his own admission, proceeded to use generative artificial intelligence to cheat on nearly every assignment. As a computer-science major, he depended on AI for his introductory programming classes: “I’d just dump the prompt into ChatGPT and hand in whatever it spat out.” By his rough math, AI wrote 80 percent of every essay he turned in. “At the end, I’d put on the finishing touches. I’d just insert 20 percent of my humanity, my voice, into it,” Lee told me recently.

Lee was born in South Korea and grew up outside Atlanta, where his parents run a college-prep consulting business. He said he was admitted to Harvard early in his senior year of high school, but the university rescinded its offer after he was suspended for sneaking out during an overnight field trip before graduation. A year later, he applied to 26 schools; he didn’t get into any of them. So he spent the next year at a community college, before transferring to Columbia. (His personal essay, which turned his winding road to higher education into a parable for his ambition to build companies, was written with help from ChatGPT.) When he started at Columbia as a sophomore this past September, he didn’t worry much about academics or his GPA. “Most assignments in college are not relevant,” he told me. “They’re hackable by AI, and I just had no interest in doing them.” While other new students fretted over the university’s rigorous core curriculum, described by the school as “intellectually expansive” and “personally transformative,” Lee used AI to breeze through with minimal effort. When I asked him why he had gone through so much trouble to get to an Ivy League university only to off-load all of the learning to a robot, he said, “It’s the best place to meet your co-founder and your wife.”

By the end of his first semester, Lee checked off one of those boxes. He met a co-founder, Neel Shanmugam, a junior in the school of engineering, and together they developed a series of potential start-ups: a dating app just for Columbia students, a sales tool for liquor distributors, and a note-taking app. None of them took off. Then Lee had an idea. As a coder, he had spent some 600 miserable hours on LeetCode, a training platform that prepares coders to answer the algorithmic riddles tech companies ask job and internship candidates during interviews. Lee, like many young developers, found the riddles tedious and mostly irrelevant to the work coders might actually do on the job. What was the point? What if they built a program that hid AI from browsers during remote job interviews so that interviewees could cheat their way through instead?

In February, Lee and Shanmugam launched a tool that did just that. Interview Coder’s website featured a banner that read F*CK LEETCODE. Lee posted a video of himself on YouTube using it to cheat his way through an internship interview with Amazon. (He actually got the internship, but turned it down.) A month later, Lee was called into Columbia’s academic-integrity office. The school put him on disciplinary probation after a committee found him guilty of “advertising a link to a cheating tool” and “providing students with the knowledge to access this tool and use it how they see fit,” according to the committee’s report.

Lee thought it absurd that Columbia, which had a partnership with ChatGPT’s parent company, OpenAI, would punish him for innovating with AI. Although Columbia’s policy on AI is similar to that of many other universities’ — students are prohibited from using it unless their professor explicitly permits them to do so, either on a class-by-class or case-by-case basis — Lee said he doesn’t know a single student at the school who isn’t using AI to cheat. To be clear, Lee doesn’t think this is a bad thing. “I think we are years — or months, probably — away from a world where nobody thinks using AI for homework is considered cheating,” he said.

In January 2023, just two months after OpenAI launched ChatGPT, a survey of 1,000 college students found that nearly 90 percent of them had used the chatbot to help with homework assignments. In its first year of existence, ChatGPT’s total monthly visits steadily increased month-over-month until June, when schools let out for the summer. (That wasn’t an anomaly: Traffic dipped again over the summer in 2024.) Professors and teaching assistants increasingly found themselves staring at essays filled with clunky, robotic phrasing that, though grammatically flawless, didn’t sound quite like a college student — or even a human. Two and a half years later, students at large state schools, the Ivies, liberal-arts schools in New England, universities abroad, professional schools, and community colleges are relying on AI to ease their way through every facet of their education. Generative-AI chatbots — ChatGPT but also Google’s Gemini, Anthropic’s Claude, Microsoft’s Copilot, and others — take their notes during class, devise their study guides and practice tests, summarize novels and textbooks, and brainstorm, outline, and draft their essays. STEM students are using AI to automate their research and data analyses and to sail through dense coding and debugging assignments. “College is just how well I can use ChatGPT at this point,” a student in Utah recently captioned a video of herself copy-and-pasting a chapter from her Genocide and Mass Atrocity textbook into ChatGPT.

Sarah, a freshman at Wilfrid Laurier University in Ontario, said she first used ChatGPT to cheat during the spring semester of her final year of high school. (Sarah’s name, like those of other current students in this article, has been changed for privacy.) After getting acquainted with the chatbot, Sarah used it for all her classes: Indigenous studies, law, English, and a “hippie farming class” called Green Industries. “My grades were amazing,” she said. “It changed my life.” Sarah continued to use AI when she started college this past fall. Why wouldn’t she? Rarely did she sit in class and not see other students’ laptops open to ChatGPT. Toward the end of the semester, she began to think she might be dependent on the website. She already considered herself addicted to TikTok, Instagram, Snapchat, and Reddit, where she writes under the username maybeimnotsmart. “I spend so much time on TikTok,” she said. “Hours and hours, until my eyes start hurting, which makes it hard to plan and do my schoolwork. With ChatGPT, I can write an essay in two hours that normally takes 12.”

Teachers have tried AI-proofing assignments, returning to Blue Books or switching to oral exams. Brian Patrick Green, a tech-ethics scholar at Santa Clara University, immediately stopped assigning essays after he tried ChatGPT for the first time. Less than three months later, teaching a course called Ethics and Artificial Intelligence, he figured a low-stakes reading reflection would be safe — surely no one would dare use ChatGPT to write something personal. But one of his students turned in a reflection with robotic language and awkward phrasing that Green knew was AI-generated. A philosophy professor across the country at the University of Arkansas at Little Rock caught students in her Ethics and Technology class using AI to respond to the prompt “Briefly introduce yourself and say what you’re hoping to get out of this class.”

It isn’t as if cheating is new. But now, as one student put it, “the ceiling has been blown off.” Who could resist a tool that makes every assignment easier with seemingly no consequences? After spending the better part of the past two years grading AI-generated papers, Troy Jollimore, a poet, philosopher, and Cal State Chico ethics professor, has concerns. “Massive numbers of students are going to emerge from university with degrees, and into the workforce, who are essentially illiterate,” he said. “Both in the literal sense and in the sense of being historically illiterate and having no knowledge of their own culture, much less anyone else’s.” That future may arrive sooner than expected when you consider what a short window college really is. Already, roughly half of all undergrads have never experienced college without easy access to generative AI. “We’re talking about an entire generation of learning perhaps significantly undermined here,” said Green, the Santa Clara tech ethicist. “It’s short-circuiting the learning process, and it’s happening fast.”

Before OpenAI released ChatGPT in November 2022, cheating had already reached a sort of zenith. At the time, many college students had finished high school remotely, largely unsupervised, and with access to tools like Chegg and Course Hero. These companies advertised themselves as vast online libraries of textbooks and course materials but, in reality, were cheating multi-tools. For $15.95 a month, Chegg promised answers to homework questions in as little as 30 minutes, 24/7, from the 150,000 experts with advanced degrees it employed, mostly in India. When ChatGPT launched, students were primed for a tool that was faster, more capable.

But school administrators were stymied. There would be no way to enforce an all-out ChatGPT ban, so most adopted an ad hoc approach, leaving it up to professors to decide whether to allow students to use AI. Some universities welcomed it, partnering with developers, rolling out their own chatbots to help students register for classes, or launching new classes, certificate programs, and majors focused on generative AI. But regulation remained difficult. How much AI help was acceptable? Should students be able to have a dialogue with AI to get ideas but not ask it to write the actual sentences?

These days, professors will often state their policy on their syllabi — allowing AI, for example, as long as students cite it as if it were any other source, or permitting it for conceptual help only, or requiring students to provide receipts of their dialogue with a chatbot. Students often interpret those instructions as guidelines rather than hard rules. Sometimes they will cheat on their homework without even knowing — or knowing exactly how much — they are violating university policy when they ask a chatbot to clean up a draft or find a relevant study to cite. Wendy, a freshman finance major at one of the city’s top universities, told me that she is against using AI. Or, she clarified, “I’m against copy-and-pasting. I’m against cheating and plagiarism. All of that. It’s against the student handbook.” Then she described, step-by-step, how on a recent Friday at 8 a.m., she called up an AI platform to help her write a four-to-five-page essay due two hours later.

Whenever Wendy uses AI to write an essay (which is to say, whenever she writes an essay), she follows three steps. Step one: “I say, ‘I’m a first-year college student. I’m taking this English class.’” Otherwise, Wendy said, “it will give you a very advanced, very complicated writing style, and you don’t want that.” Step two: Wendy provides some background on the class she’s taking before copy-and-pasting her professor’s instructions into the chatbot. Step three: “Then I ask, ‘According to the prompt, can you please provide me an outline or an organization to give me a structure so that I can follow and write my essay?’ It then gives me an outline, introduction, topic sentences, paragraph one, paragraph two, paragraph three.” Sometimes, Wendy asks for a bullet list of ideas to support or refute a given argument: “I have difficulty with organization, and this makes it really easy for me to follow.”

Once the chatbot had outlined Wendy’s essay, providing her with a list of topic sentences and bullet points of ideas, all she had to do was fill it in. Wendy delivered a tidy five-page paper at an acceptably tardy 10:17 a.m. When I asked her how she did on the assignment, she said she got a good grade. “I really like writing,” she said, sounding strangely nostalgic for her high-school English class — the last time she wrote an essay unassisted. “Honestly,” she continued, “I think there is beauty in trying to plan your essay. You learn a lot. You have to think, Oh, what can I write in this paragraph? Or What should my thesis be? ” But she’d rather get good grades. “An essay with ChatGPT, it’s like it just gives you straight up what you have to follow. You just don’t really have to think that much.”

I asked Wendy if I could read the paper she turned in, and when I opened the document, I was surprised to see the topic: critical pedagogy, the philosophy of education pioneered by Paulo Freire. The philosophy examines the influence of social and political forces on learning and classroom dynamics. Her opening line: “To what extent is schooling hindering students’ cognitive ability to think critically?” Later, I asked Wendy if she recognized the irony in using AI to write not just a paper on critical pedagogy but one that argues learning is what “makes us truly human.” She wasn’t sure what to make of the question. “I use AI a lot. Like, every day,” she said. “And I do believe it could take away that critical-thinking part. But it’s just — now that we rely on it, we can’t really imagine living without it.”

Most of the writing professors I spoke to told me that it’s abundantly clear when their students use AI. Sometimes there’s a smoothness to the language, a flattened syntax; other times, it’s clumsy and mechanical. The arguments are too evenhanded — counterpoints tend to be presented just as rigorously as the paper’s central thesis. Words like multifaceted and context pop up more than they might normally. On occasion, the evidence is more obvious, as when last year a teacher reported reading a paper that opened with “As an AI, I have been programmed …” Usually, though, the evidence is more subtle, which makes nailing an AI plagiarist harder than identifying the deed. Some professors have resorted to deploying so-called Trojan horses, sticking strange phrases, in small white text, in between the paragraphs of an essay prompt. (The idea is that this would theoretically prompt ChatGPT to insert a non sequitur into the essay.) Students at Santa Clara recently found the word broccoli hidden in a professor’s assignment. Last fall, a professor at the University of Oklahoma sneaked the phrases “mention Finland” and “mention Dua Lipa” in his. A student discovered his trap and warned her classmates about it on TikTok. “It does work sometimes,” said Jollimore, the Cal State Chico professor. “I’ve used ‘How would Aristotle answer this?’ when we hadn’t read Aristotle. But I’ve also used absurd ones and they didn’t notice that there was this crazy thing in their paper, meaning these are people who not only didn’t write the paper but also didn’t read their own paper before submitting it.”

Still, while professors may think they are good at detecting AI-generated writing, studies have found they’re actually not. One, published in June 2024, used fake student profiles to slip 100 percent AI-generated work into professors’ grading piles at a U.K. university. The professors failed to flag 97 percent. It doesn’t help that since ChatGPT’s launch, AI’s capacity to write human-sounding essays has only gotten better. Which is why universities have enlisted AI detectors like Turnitin, which uses AI to recognize patterns in AI-generated text. After evaluating a block of text, detectors provide a percentage score that indicates the alleged likelihood it was AI-generated. Students talk about professors who are rumored to have certain thresholds (25 percent, say) above which an essay might be flagged as an honor-code violation. But I couldn’t find a single professor — at large state schools or small private schools, elite or otherwise — who admitted to enforcing such a policy. Most seemed resigned to the belief that AI detectors don’t work. It’s true that different AI detectors have vastly different success rates, and there is a lot of conflicting data. While some claim to have less than a one percent false-positive rate, studies have shown they trigger more false positives for essays written by neurodivergent students and students who speak English as a second language. Turnitin’s chief product officer, Annie Chechitelli, told me that the product is tuned to err on the side of caution, more inclined to trigger a false negative than a false positive so that teachers don’t wrongly accuse students of plagiarism. I fed Wendy’s essay through a free AI detector, ZeroGPT, and it came back as 11.74 AI-generated, which seemed low given that AI, at the very least, had generated her central arguments. I then fed a chunk of text from the Book of Genesis into ZeroGPT and it came back as 93.33 percent AI-generated.

There are, of course, plenty of simple ways to fool both professors and detectors. After using AI to produce an essay, students can always rewrite it in their own voice or add typos. Or they can ask AI to do that for them: One student on TikTok said her preferred prompt is “Write it as a college freshman who is a li’l dumb.” Students can also launder AI-generated paragraphs through other AIs, some of which advertise the “authenticity” of their outputs or allow students to upload their past essays to train the AI in their voice. “They’re really good at manipulating the systems. You put a prompt in ChatGPT, then put the output into another AI system, then put it into another AI system. At that point, if you put it into an AI-detection system, it decreases the percentage of AI used every time,” said Eric, a sophomore at Stanford.

Most professors have come to the conclusion that stopping rampant AI abuse would require more than simply policing individual cases and would likely mean overhauling the education system to consider students more holistically. “Cheating correlates with mental health, well-being, sleep exhaustion, anxiety, depression, belonging,” said Denise Pope, a senior lecturer at Stanford and one of the world’s leading student-engagement researchers.

Many teachers now seem to be in a state of despair. In the fall, Sam Williams was a teaching assistant for a writing-intensive class on music and social change at the University of Iowa that, officially, didn’t allow students to use AI at all. Williams enjoyed reading and grading the class’s first assignment: a personal essay that asked the students to write about their own music tastes. Then, on the second assignment, an essay on the New Orleans jazz era (1890 to 1920), many of his students’ writing styles changed drastically. Worse were the ridiculous factual errors. Multiple essays contained entire paragraphs on Elvis Presley (born in 1935). “I literally told my class, ‘Hey, don’t use AI. But if you’re going to cheat, you have to cheat in a way that’s intelligent. You can’t just copy exactly what it spits out,’” Williams said.

Williams knew most of the students in this general-education class were not destined to be writers, but he thought the work of getting from a blank page to a few semi-coherent pages was, above all else, a lesson in effort. In that sense, most of his students utterly failed. “They’re using AI because it’s a simple solution and it’s an easy way for them not to put in time writing essays. And I get it, because I hated writing essays when I was in school,” Williams said. “But now, whenever they encounter a little bit of difficulty, instead of fighting their way through that and growing from it, they retreat to something that makes it a lot easier for them.”

By November, Williams estimated that at least half of his students were using AI to write their papers. Attempts at accountability were pointless. Williams had no faith in AI detectors, and the professor teaching the class instructed him not to fail individual papers, even the clearly AI-smoothed ones. “Every time I brought it up with the professor, I got the sense he was underestimating the power of ChatGPT, and the departmental stance was, ‘Well, it’s a slippery slope, and we can’t really prove they’re using AI,’” Williams said. “I was told to grade based on what the essay would’ve gotten if it were a ‘true attempt at a paper.’ So I was grading people on their ability to use ChatGPT.”

The “true attempt at a paper” policy ruined Williams’s grading scale. If he gave a solid paper that was obviously written with AI a B, what should he give a paper written by someone who actually wrote their own paper but submitted, in his words, “a barely literate essay”? The confusion was enough to sour Williams on education as a whole. By the end of the semester, he was so disillusioned that he decided to drop out of graduate school altogether. “We’re in a new generation, a new time, and I just don’t think that’s what I want to do,” he said.

Jollimore, who has been teaching writing for more than two decades, is now convinced that the humanities, and writing in particular, are quickly becoming an anachronistic art elective like basket-weaving. “Every time I talk to a colleague about this, the same thing comes up: retirement. When can I retire? When can I get out of this? That’s what we’re all thinking now,” he said. “This is not what we signed up for.” Williams, and other educators I spoke to, described AI’s takeover as a full-blown existential crisis. “The students kind of recognize that the system is broken and that there’s not really a point in doing this. Maybe the original meaning of these assignments has been lost or is not being communicated to them well.”

He worries about the long-term consequences of passively allowing 18-year-olds to decide whether to actively engage with their assignments. Would it accelerate the widening soft-skills gap in the workplace? If students rely on AI for their education, what skills would they even bring to the workplace? Lakshya Jain, a computer-science lecturer at the University of California, Berkeley, has been using those questions in an attempt to reason with his students. “If you’re handing in AI work,” he tells them, “you’re not actually anything different than a human assistant to an artificial-intelligence engine, and that makes you very easily replaceable. Why would anyone keep you around?” That’s not theoretical: The COO of a tech research firm recently asked Jain why he needed programmers any longer.

The ideal of college as a place of intellectual growth, where students engage with deep, profound ideas, was gone long before ChatGPT. The combination of high costs and a winner-takes-all economy had already made it feel transactional, a means to an end. (In a recent survey, Deloitte found that just over half of college graduates believe their education was worth the tens of thousands of dollars it costs a year, compared with 76 percent of trade-school graduates.) In a way, the speed and ease with which AI proved itself able to do college-level work simply exposed the rot at the core. “How can we expect them to grasp what education means when we, as educators, haven’t begun to undo the years of cognitive and spiritual damage inflicted by a society that treats schooling as a means to a high-paying job, maybe some social status, but nothing more?” Jollimore wrote in a recent essay. “Or, worse, to see it as bearing no value at all, as if it were a kind of confidence trick, an elaborate sham?”

It’s not just the students: Multiple AI platforms now offer tools to leave AI-generated feedback on students’ essays. Which raises the possibility that AIs are now evaluating AI-generated papers, reducing the entire academic exercise to a conversation between two robots — or maybe even just one.

It’ll be years before we can fully account for what all of this is doing to students’ brains. Some early research shows that when students off-load cognitive duties onto chatbots, their capacity for memory, problem-solving, and creativity could suffer. Multiple studies published within the past year have linked AI usage with a deterioration in critical-thinking skills; one found the effect to be more pronounced in younger participants. In February, Microsoft and Carnegie Mellon University published a study that found a person’s confidence in generative AI correlates with reduced critical-thinking effort. The net effect seems, if not quite Wall-E, at least a dramatic reorganization of a person’s efforts and abilities, away from high-effort inquiry and fact-gathering and toward integration and verification. This is all especially unnerving if you add in the reality that AI is imperfect — it might rely on something that is factually inaccurate or just make something up entirely — with the ruinous effect social media has had on Gen Z’s ability to tell fact from fiction. The problem may be much larger than generative AI. The so-called Flynn effect refers to the consistent rise in IQ scores from generation to generation going back to at least the 1930s. That rise started to slow, and in some cases reverse, around 2006. “The greatest worry in these times of generative AI is not that it may compromise human creativity or intelligence,” Robert Sternberg, a psychology professor at Cornell University, told The Guardian, “but that it already has.”

Students are worrying about this, even if they’re not willing or able to give up the chatbots that are making their lives exponentially easier. Daniel, a computer-science major at the University of Florida, told me he remembers the first time he tried ChatGPT vividly. He marched down the hall to his high-school computer-science teacher’s classroom, he said, and whipped out his Chromebook to show him. “I was like, ‘Dude, you have to see this!’ My dad can look back on Steve Jobs’s iPhone keynote and think, Yeah, that was a big moment. That’s what it was like for me, looking at something that I would go on to use every day for the rest of my life.”

AI has made Daniel more curious; he likes that whenever he has a question, he can quickly access a thorough answer. But when he uses AI for homework, he often wonders, If I took the time to learn that, instead of just finding it out, would I have learned a lot more? At school, he asks ChatGPT to make sure his essays are polished and grammatically correct, to write the first few paragraphs of his essays when he’s short on time, to handle the grunt work in his coding classes, to cut basically all cuttable corners. Sometimes, he knows his use of AI is a clear violation of student conduct, but most of the time it feels like he’s in a gray area. “I don’t think anyone calls seeing a tutor cheating, right? But what happens when a tutor starts writing lines of your paper for you?” he said.

Recently, Mark, a freshman math major at the University of Chicago, admitted to a friend that he had used ChatGPT more than usual to help him code one of his assignments. His friend offered a somewhat comforting metaphor: “You can be a contractor building a house and use all these power tools, but at the end of the day, the house won’t be there without you.” Still, Mark said, “it’s just really hard to judge. Is this my work? ” I asked Daniel a hypothetical to try to understand where he thought his work began and AI’s ended: Would he be upset if he caught a romantic partner sending him an AI-generated poem? “I guess the question is what is the value proposition of the thing you’re given? Is it that they created it? Or is the value of the thing itself?” he said. “In the past, giving someone a letter usually did both things.” These days, he sends handwritten notes — after he has drafted them using ChatGPT.

“Language is the mother, not the handmaiden, of thought,” wrote Duke professor Orin Starn in a recent column titled “My Losing Battle Against AI Cheating,” citing a quote often attributed to W. H. Auden. But it’s not just writing that develops critical thinking. “Learning math is working on your ability to systematically go through a process to solve a problem. Even if you’re not going to use algebra or trigonometry or calculus in your career, you’re going to use those skills to keep track of what’s up and what’s down when things don’t make sense,” said Michael Johnson, an associate provost at Texas A&M University. Adolescents benefit from structured adversity, whether it’s algebra or chores. They build self-esteem and work ethic. It’s why the social psychologist Jonathan Haidt has argued for the importance of children learning to do hard things, something that technology is making infinitely easier to avoid. Sam Altman, OpenAI’s CEO, has tended to brush off concerns about AI use in academia as shortsighted, describing ChatGPT as merely “a calculator for words” and saying the definition of cheating needs to evolve. “Writing a paper the old-fashioned way is not going to be the thing,” Altman, a Stanford dropout, said last year. But speaking before the Senate’s oversight committee on technology in 2023, he confessed his own reservations: “I worry that as the models get better and better, the users can have sort of less and less of their own discriminating process.” OpenAI hasn’t been shy about marketing to college students. It recently made ChatGPT Plus, normally a $20-per-month subscription, free to them during finals. (OpenAI contends that students and teachers need to be taught how to use it responsibly, pointing to the ChatGPT Edu product it sells to academic institutions.)

In late March, Columbia suspended Lee after he posted details about his disciplinary hearing on X. He has no plans to go back to school and has no desire to work for a big-tech company, either. Lee explained to me that by showing the world AI could be used to cheat during a remote job interview, he had pushed the tech industry to evolve the same way AI was forcing higher education to evolve. “Every technological innovation has caused humanity to sit back and think about what work is actually useful,” he said. “There might have been people complaining about machinery replacing blacksmiths in, like, the 1600s or 1800s, but now it’s just accepted that it’s useless to learn how to blacksmith.”

Lee has already moved on from hacking interviews. In April, he and Shanmugam launched Cluely, which scans a user’s computer screen and listens to its audio in order to provide AI feedback and answers to questions in real time without prompting. “We built Cluely so you never have to think alone again,” the company’s manifesto reads. This time, Lee attempted a viral launch with a $140,000 scripted advertisement in which a young software engineer, played by Lee, uses Cluely installed on his glasses to lie his way through a first date with an older woman. When the date starts going south, Cluely suggests Lee “reference her art” and provides a script for him to follow. “I saw your profile and the painting with the tulips. You are the most gorgeous girl ever,” Lee reads off his glasses, which rescues his chances with her.

Before launching Cluely, Lee and Shanmugam raised $5.3 million from investors, which allowed them to hire two coders, friends Lee met in community college (no job interviews or LeetCode riddles were necessary), and move to San Francisco. When we spoke a few days after Cluely’s launch, Lee was at his Realtor’s office and about to get the keys to his new workspace. He was running Cluely on his computer as we spoke. While Cluely can’t yet deliver real-time answers through people’s glasses, the idea is that someday soon it’ll run on a wearable device, seeing, hearing, and reacting to everything in your environment. “Then, eventually, it’s just in your brain,” Lee said matter-of-factly. For now, Lee hopes people will use Cluely to continue AI’s siege on education. “We’re going to target the digital LSATs; digital GREs; all campus assignments, quizzes, and tests,” he said. “It will enable you to cheat on pretty much everything.”

Es un enigma: los equipos de clientes tienen más datos de los que pueden comenzar a usar, desde las notas de Salesforce, los boletos JIRA, los paneles de proyectos, los documentos de Google, pero tienen dificultades para reunirlo todo al elaborar mensajes de clientes que realmente resuenan.

Las herramientas existentes a menudo dependen de plantillas o diapositivas genéricas y no pueden proporcionar una imagen completa de viajes de clientes, hojas de ruta, objetivos del proyecto y objetivos comerciales.

Korl, una startup lanzada hoy, espera superar estos desafíos con una nueva plataforma que funciona en múltiples sistemas para ayudar a crear comunicaciones altamente personalizadas. La herramienta multimodal múltiple utiliza una mezcla de modelos de OpenAI, Géminis y antrópico para obtener datos y contextualizar los datos.

“Los ingenieros tienen herramientas de IA potentes, pero los equipos orientados al cliente están atrapados con soluciones poco profundas y desconectadas”, dijo Berit Hoffmann, CEO y cofundador de Korl, a VentureBeat en una entrevista exclusiva. “La innovación central de Korl se basa en nuestras tuberías avanzadas de múltiples agentes diseñados para construir el contexto del cliente y el producto que carecen las herramientas genéricas de presentación”.

Creación de materiales de cliente personalizados a través de una vista de múltiples fuentes

Los agentes de AI de Korl agregan información de diferentes sistemas, como la documentación de ingeniería de JIRA, contornos de Google Docs, diseños de Figma y datos de proyectos de Salesforce, para construir una vista de múltiples fuentes.

Por ejemplo, una vez que un cliente conecta a Korl con JIRA, su agente estudia las capacidades de productos existentes y planificadas para descubrir cómo mapear datos e importar nuevas capacidades de productos, explicó Hoffmann. La plataforma coincide con los datos del producto con la información del cliente, como el historial de uso, las prioridades comerciales y la etapa del ciclo de vida, que llena los vacíos con el uso de la IA.

“Los agentes de datos de Korl recopilan, enriquecen y estructuran diversos conjuntos de datos de fuentes internas y datos públicos externas”, dijo Hoffmann.

Luego, la plataforma genera automáticamente revisiones comerciales trimestrales (QBR) personalizadas, lanzamientos de renovación, presentaciones a medida y otros materiales para su uso en hitos importantes del cliente.

Hoffmann dijo que el diferenciador central de la compañía es su capacidad para ofrecer “materiales pulidos listos para el cliente”, como diapositivas, narraciones y correos electrónicos, “en lugar de simplemente análisis o ideas crudas”.

“Creemos que esto ofrece un nivel de valor operativo que los equipos orientados al cliente necesitan hoy dadas las presiones para hacer más con menos”, dijo.

Cambiar entre OpenAi, Géminis, Anthrope, basado en el rendimiento

Korl orquesta un “conjunto de modelos” en OpenAi, Gemini y Anthrope, seleccionando el mejor modelo para el trabajo en el momento basado en la velocidad, la precisión y el costo, explicó Hoffmann. Korl necesita realizar tareas complejas y diversas (narraciones matizadas, computación de datos, imágenes), por lo que cada caso de uso coincide con el modelo más desempeñado. La compañía ha implementado “mecanismos sofisticados de respaldo” para mitigar las fallas; Al principio, observaron altas tasas de falla al confiar en un solo proveedor, informó Hoffman.

La startup desarrolló un aplazamiento de automóviles patentado para manejar diversos esquemas de datos empresariales en JIRA, Salesforce y otros sistemas. La plataforma se asigna automáticamente a los campos relevantes en Korl.

“En lugar de solo una coincidencia semántica o de nombre de campo, nuestro enfoque evalúa factores adicionales como la escasez de datos para obtener y predecir coincidencias de campo”, dijo Hoffmann.

Para acelerar el proceso, Korl combina modelos de baja latencia y alto rendimiento (como GPT-4O para respuestas rápidas de construcción de contexto) con modelos analíticos más profundos (Claude 3.7 para comunicaciones más complejas y orientadas al cliente).

“Esto garantiza que optimizemos para la mejor experiencia del usuario final, haciendo compensaciones basadas en el contexto entre inmediatez y precisión”, explicó Hoffmann.

Debido a que “la seguridad es primordial”, Korl busca garantías de privacidad de grado empresarial de los proveedores para garantizar que los datos del cliente estén excluidos de los conjuntos de datos de capacitación. Hoffmann señaló que su orquestación múltiple y contextual, lo que impulsa adicional limita la exposición inadvertida y las fugas de datos.

Lidiar con datos que son ‘demasiado desordenados’ o ‘incompletos’

Hoffman señaló que, al principio, Korl escuchó de los clientes que les preocupaba que sus datos fueran “demasiado desordenados” o “incompletos” para ser aprovechados. En respuesta, la compañía construyó tuberías para comprender las relaciones de los objetos comerciales y llenar los vacíos, como cómo posicionar las características externamente o cómo alinear los valores en torno a los resultados deseados.

“Nuestro agente de presentación es lo que aprovecha esos datos para generar diapositivas de clientes y pista de conversación [guide conversations with potential customers or leads] dinámicamente cuando sea necesario ”, dijo Hoffmann.

También dijo que Korl presenta “verdadera multimodalidad”. La plataforma no es solo extraer datos de varias fuentes; Está interpretando diferentes tipos de información, como texto, datos estructurados, gráficos o diagramas.

“El paso crítico es ir más allá de los datos sin procesar para responder: ¿Qué historia cuenta este gráfico? ¿Cuáles son las implicaciones más profundas aquí, y realmente resonarán con este cliente específico?”, Dijo. “Hemos creado nuestro proceso para realizar esa diligencia debida crucial, asegurando que la producción no sea solo datos agregados, sino un contenido genuinamente rico entregado con un contexto significativo”.

Dos de los competidores cercanos de Korl incluyen Gainsight y Clari; Sin embargo, Hoffmann dijo que Korl se diferencia al incorporar un contexto profundo de productos y hoja de ruta. Las estrategias efectivas de renovación y expansión del cliente requieren una comprensión profunda de lo que hace un producto, y esto debe combinarse con el análisis de los datos y el comportamiento del cliente.

Además, Hoffmann dijo que Korl aborda dos “deficiencias fundamentales” de las plataformas existentes: contexto comercial profundo y precisión de la marca. Los agentes de Korl recopilan el contexto comercial de múltiples sistemas. “Sin esta inteligencia integral de datos, las cubiertas automatizadas carecen de valor comercial estratégico”, dijo.

Cuando se trata de la marca, la tecnología patentada de Korl extrae y replica las pautas de los materiales existentes.

Reducir el tiempo de preparación de la cubierta de ‘varias horas a minutos’

Las primeras indicaciones sugieren que Korl puede desbloquear al menos una mejora de 1 punto en la retención de ingresos netos (NRR) para las compañías de software del mercado medio, dijo Hoffmann. Esto se debe a que descubre el valor del producto previamente no realizado y facilita la comunicación de los clientes antes de que se conviertan o toman decisiones de renovación o expansión.

La plataforma también mejora la eficiencia, reduciendo el tiempo de preparación de la plataforma para cada llamada del cliente de “varias horas a minutos”, según Hoffman.

Los primeros clientes incluyen la plataforma de construcción de habilidades Datacamp y Gifting y Direct Mail Company Sendoso.

“Abordan un desafío crítico y pasado por alto: con demasiada frecuencia, las características del producto se lanzan mientras que los equipos de mercado (GTM) no están preparados para venderlas, apoyarlas o comunicarlas de manera efectiva”, dijo Amir Younes, director de clientes de Sendoso. “Con la IA de Korl, [go-to-market] La habilitación de GTM y la creación de activos podrían estar a solo un clic de distancia, sin agregar sobrecarga para los equipos de I + D “.

Korl ingresó hoy al mercado con $ 5 millones en fondos iniciales en una ronda co-liderada por Mac Venture Capital y subrayado VC, con la participación de Perceptive Ventures y Diane Greene (fundador de VMware y ex CEO de Google Cloud).

Insights diarias sobre casos de uso comercial con VB diariamente

Si quieres impresionar a tu jefe, VB Daily te tiene cubierto. Le damos la cuenta interior de lo que las empresas están haciendo con la IA generativa, desde cambios regulatorios hasta implementaciones prácticas, por lo que puede compartir ideas para el ROI máximo.

Lea nuestra Política de privacidad

Gracias por suscribirse. Mira más boletines de VB aquí.

Ocurrió un error.