When it was founded in 2015, artificial intelligence research lab OpenAI was a nonprofit organization. The idealistic mission: to make sure the high-stakes work they were doing on artificial intelligence served the whole world. This was necessary because — according to the founders’ fervent belief, at least — it would transform the whole world.
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OpenAI is transitioning to a for-profit business. The stakes are enormous.
In some ways since then, OpenAI has succeeded beyond its wildest dreams. “General artificial intelligence” sounded like a pipe dream in 2015, but today we have talking, interactive, creative AI that can pass most tests of human competence we’ve put it to. Many serious people believe that full general intelligence is just around the corner. OpenAI, which in the years since its founding morphed from a nonprofit lab into one of the most highly valued startups in history, has been at the center of that transformation. (Disclosure: Vox Media is one of several publishers that has signed partnership agreements with OpenAI. Our reporting remains editorially independent.)
In other ways, of course, things have been a bit of a mess. Even as it basically became a business, OpenAI used nonprofit governance to keep the company focused on its mission. OpenAI CEO Sam Altman reassured Congress he had no equity in the company, and the nonprofit board still held all authority to change course if they thought the company had gone astray from its mission.
But that ultimately put the board at odds with Altman last November in a messy conflict that the CEO ultimately won. Nearly the entire original leadership team departed. In the year since, the board has largely been replaced and high-profile employees have left the company in waves, some of them warning they no longer believe OpenAI will build superintelligence responsibly. Microsoft, OpenAI’s largest investor, increasingly seems eager for the company to stop building superintelligence and start building a profitable product.
Now, OpenAI is attempting a transition to a more conventional corporate structure, reportedly one where it will be a for-profit public benefit corporation like its rival Anthropic. But nonprofit to for-profit conversions are rare, and misinformation has swirled about what, exactly, “OpenAI becoming a for-profit company” even means.
Elon Musk, who co-founded OpenAI but left after a leadership dispute, paints the for-profit transition as a naked power grab, arguing in a recent lawsuit that Altman and his associates “systematically drained the non-profit of its valuable technology and personnel” in a scheme to get rich off a company that had been founded as a charity. (OpenAI has moved to dismiss Musk’s lawsuit, arguing that it is an “increasingly blusterous campaign to harass OpenAI for his own competitive advantage”).
While Musk — who has his own reasons to be competitive with OpenAI — is among the more vocal critics, many people seem to be under the impression that the company could just slap on a new “for-profit” label and call it a day.
Can you really do that? Start a charity, with all the advantages of nonprofit status, and then declare one day it’s a for-profit company? No, you can’t, and it’s important to understand that OpenAI isn’t doing that.
Rather, nonprofit lawyers told me that what’s almost certainly going on is a complicated and fraught negotiation: the sale of all of the OpenAI nonprofit’s valuable assets to the new for-profit entity, in exchange for the nonprofit continuing to exist and becoming a major investor in the new for-profit entity.
The key question is how much are those assets worth, and can the battered and bruised nonprofit board get a fair deal out of OpenAI (and Microsoft)?
So far, this high-stakes wrangling has taken place almost entirely behind the scenes, and many of the crucial questions have gotten barely any public coverage at all. “I’ve been really kind of baffled at the lack of curiosity about where the value goes that this nonprofit has,” nonprofit law expert Timothy Ogden told me.
Nonprofit law might seem abstruse, which is why most coverage of OpenAI’s transition hasn’t dug into any of the messy details. But those messy details involve tens of billions of dollars, all of which appear to be up for negotiation. The results will dramatically affect how much sway Microsoft has with OpenAI going forward and how much of the company’s value is still tied to its founding mission.
This might seem like something that only matters for OpenAI shareholders, but the company is one of the few that may just have a chance of creating world-changing artificial intelligence. If the public wants a transparent and open process from OpenAI, they have to understand what the law actually allows and who is responsible for following it so we can be sure that OpenAI pursues this transition in a transparent and accountable way.
How OpenAI went from nonprofit to megacorp
In 2015, OpenAI was a nonprofit research organization. It told the IRS in a filing for nonprofit status that its mission was to “advance digital intelligence in the way that is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.”
Understanding OpenAI’s expansive reach
OpenAI, the maker of ChatGPT, is one of the most important companies in artificial intelligence and one of the most controversial. I’ve been covering the ins and outs of OpenAI for years; here are some highlights:
Have questions or comments? Email me at kelsey.piper@vox.com.
By 2019, that idealistic nonprofit model was running into some trouble. OpenAI had attracted an incredible staff and published some very impressive research. But it was becoming clear that the lofty goal the company had set itself — building general artificial intelligence, machines that can do everything humans can do — was going to be very expensive. It was naturally hard to raise billions of dollars for an effort that was meant to be nonprofit. “We realized that we’d reached the limits of our fundraising ability as a pure nonprofit,” co-founder Ilya Sutskever (who has since departed the company) told me at the time.
The company would attempt to split the difference with a hybrid structure: a nonprofit board controlling a for-profit company. An additional twist: Investors in the for-profit company’s returns were capped at 100x their original investments so that, if world-altering superintelligence was achieved as the OpenAI leadership believed it might, the benefits would accrue to all humanity and not just investors. After all, investors needed to be enticed to invest, but if the company truly ended material scarcity and built a God on Earth, as they essentially said they wanted to, the hope was that more than just the investors would come out ahead.
The nonprofit, therefore, was still supposed to be preeminent. “It would be wise to view any investment in OpenAI Global, LLC in the spirit of a donation,” an enormous black-and-pink disclaimer box on OpenAI’s website alerts would-be investors, “with the understanding that it may be difficult to know what role money will play in a post-AGI world. The Company exists to advance OpenAl, Inc.‘s mission of ensuring that safe artificial general intelligence is developed and benefits all of humanity. The Company’s duty to this mission and the principles advanced in the OpenAl, Inc. Charter take precedence over any obligation to generate a profit.”
One might expect that a prominent disclaimer like that would give commercial investors pause. You would be mistaken. OpenAI had Altman, a fantastic fundraiser, at the helm; its flagship product, ChatGPT, was the fastest app to 100 million users. The company was a gamble, but it was the kind of gamble investors can’t wait to get in on.
But that was then, and this is now. In 2023, in an unexpected and disastrously under-explained move, the nonprofit board fired OpenAI CEO Sam Altman. The board had that authority, of course — it was preeminent — but the execution was shockingly clumsy. The timing of the firing looked likely to disrupt an opportunity for employees to sell millions of dollars of stock in the company. The board gave a few examples of underhanded, bizarre, and dishonest behavior by Altman, including being “not consistently candid” with the board. (One board member later expanded the allegations, saying that Altman had lied to board members about private conversations with other board members, but provided nothing as clear as confused and frustrated employees hoped.)
Employees threatened to resign en masse. Microsoft offered to hire them all and reconstitute the company. Sutskever, who was among the board members who’d voted for Altman’s removal, suddenly changed his mind and voted for Altman to stay. That meant the members who had fired Altman were suddenly in the minority. Two of the board members who had opposed Altman resigned, and the once and future CEO returned to the helm.
Many people concluded that it had been a serious mistake to try to run a company worth 11 figures as a nonprofit instead of as the decidedly for-profit company it was clearly operating as, whatever its bylaws might say. So it’s not surprising that ever since the aborted Altman coup, rumors swirled that OpenAI meant to transition to a fully for-profit entity.
In the last few weeks, those rumors have gotten much more concrete. OpenAI’s latest funding round has been reported to include commitments that the nonprofit-to-for-profit transition will get done in the next two years on pain of the more than $6 billion raised being paid back to those investors. Microsoft and OpenAI — both of whom have enormous amounts to gain in the wrangling over who owns the resulting for-profit company — have hired dueling investment banks to negotiate the details.
We are moving into a new era for OpenAI, and it remains to be seen what that will mean for the humble nonprofit that has ended up owning tens of billions of dollars of the company’s assets.
How do you turn a charity into a for-profit?
If OpenAI were really just taking the nonprofit organization’s assets and declaring them “converted” into a for-profit — as if they were playing a game of tag and suddenly decided a tree was “base” — that would absolutely be illegal. The takeaway, though, shouldn’t be that a crime is happening in plain sight, but that something much more complicated is being negotiated. Nonprofit law experts I talked to said that the situation was being widely and comprehensively misunderstood.
Here are the rules. First off, assets accumulated by a nonprofit cannot be used for private benefit. “It’s the job of the board first, and then the regulators and the court, to ensure that the promise that was made to the public to pursue the charitable interest is kept,” UCLA law professor Jill Horwitz told Reuters.
If it looks as though a nonprofit isn’t pursuing its charitable interest, and especially if it appears to be handing some of its board members bargain-bin deals on billion-dollar assets during a transition to for-profit status? That will have the IRS investigating, along with the state’s Attorney General.
But a nonprofit can sell anything it owns. If a nonprofit owns a piece of land, for example, and it wants to sell that land so that it has more money to spend on its mission, it’s all good. If the nonprofit sold the land for well below market value to the director’s nephew, it would be a clear crime, and the IRS or the state’s Attorney General might well investigate. The nonprofit has to sell the land at a fair market price, take the money, and keep using the money for its nonprofit work.
At a much larger scale, that is exactly what is at stake in the OpenAI transition. The nonprofit owns some assets: control over the for-profit company, a lot of AI IP from OpenAI’s proprietary research, and all future returns from the for-profit company once they exceed the 100x cap set up by the capped profit company — which, should the company achieve its goals, could well be limitless. If the new OpenAI wants to extract all of its assets from the nonprofit, it has to pay the full market price. And the nonprofit has to continue to exist and to use the money it has earned in that transfer for its mission of ensuring that AI benefits all of humanity.
There have been a few other cases in corporate legal history of a nonprofit making the transition to a for-profit company, most prominently the credit card company Mastercard, which was founded as a nonprofit collaboration among banks. When that situation happens, the nonprofit’s assets still belong to the nonprofit.
Mastercard, in the course of transitioning to a public company, ended up founding the now-$47 billion Mastercard Foundation, one of the world’s wealthiest private foundations. Far from the for-profit walking away with all the nonprofit’s assets, the for-profit emerges as an independent company and the nonprofit emerges not only still extant but very rich.
OpenAI’s board has indicated that this is exactly what they are doing. “Any potential restructuring would ensure the nonprofit continues to exist and thrive, and receives full value for its current stake in the OpenAI for-profit with an enhanced ability to pursue its mission.” OpenAI board chairman Bret Taylor, a technologist and CEO, told me in a statement. (What counts as “full value”? We’ll come back to that.)
Outside actors, too, expect to be applying oversight to make sure that the nonprofit gets a fair deal. A spokesperson for the California Attorney General’s office told the Information that their office is “committed to protecting charitable assets for their intended purpose.” OpenAI is registered in Delaware, but the company operates primarily in California, and California’s AG is much less deferential to business than Delaware’s.
So, the OpenAI entity will definitely owe the nonprofit mind-boggling amounts of money. Depending who you ask, it could be between $37 billion and $80 billion. The OpenAI for-profit entity does not have that kind of money on hand — don’t forget that OpenAI is projected to lose tens of billions of dollars in the years ahead — so the plans in the works are reportedly for the for-profit to make the nonprofit a major shareholder in the for-profit.
The Information reported last week that “the nonprofit is expected to own at least a 25% stake in the for-profit — which on paper would be worth at least $37 billion.” In other words, rather than buying the assets from the non-profit with cash, OpenAI will trade equity.
That’s a lot of money. But many experts I spoke to thought it was actually much too low.
What’s a fair price for control of a mega company?
Everyone agrees that the OpenAI board is required to negotiate and receive a fair price for everything the OpenAI nonprofit owns that the for-profit is purchasing. But what counts as a fair price? That’s an open question, one that people stand to earn or lose tens of billions of dollars by getting answered in their favor.
But first: What does the OpenAI nonprofit own?
It owns a lot of OpenAI’s IP. How much exactly is highly confidential, but some experts speculate that the $37 billion number is probably a reflection of the easily measured, straightforward assets of the nonprofit, like its IP and business agreements.
Secondly, and most crucially, it owns full control over the OpenAI for-profit. As part of this deal, it is definitely going to give that up, either becoming a minority shareholder or ending up with nonvoting shares entirely. That is, substantially, the whole point of the nonprofit-to-for-profit conversion: After Altman’s ouster, the Wall Street Journal reported, “[I]nvestors began pushing OpenAI to turn into a more typical company.” Investors throwing around billions of dollars don’t want a nonprofit board to be able to fire the CEO because they’re worried he’s too dishonest to make good decisions around powerful new technology. Investors want a normal board that will fire the CEO for normal reasons, like that he’s not maximizing shareholder value.
Control is generally worth a lot more, in for-profit companies, than shares that come without control — often something like 40 percent more. So if the nonprofit is getting a fair deal, it should get some substantive compensation in exchange for giving up control of the company.
Thirdly, investors in OpenAI under its old business model agreed to a “capped profit” model. For most investors, that cap was set at 100x their original investment, so if they invested $1 million, they would get a maximum of $100 million in return. Above that cap, all returns would go to the nonprofit. The logic for this setup was that, under most circumstances, it’s the same as investing in a normal company. Investments don’t usually produce 100x returns, after all, with the exception of early investments in massively successful tech companies like Google or Amazon.
The capped profit setup would be most significant in the unlikely world where OpenAI attained its ambitious goals and built an AI that fundamentally transformed the world economy. (How likely is that? Experts disagree, rather heatedly, but we shouldn’t discount it altogether.) If that does happen, its value will be nearly unfathomably huge. “OpenAI’s value is mostly in the extreme upside,” AI analyst Zvi Mowshowitz wrote in an analysis of the valuation question.
The company might fail entirely; it might muddle along as a midsized company. But it also might be worth trillions of dollars, or more than that, and most investors are investing on the premise it might be worth trillions of dollars. That means the share of profits owned by the nonprofit would also be worth trillions of dollars. “Most future profits still likely flow to the nonprofit,” Mowshowitz concludes. “OpenAI is shooting for the stars. As every VC in this spot knows, it is the extreme upside that matters. That is what the nonprofit is selling. They shouldn’t sell it cheap.”
So what would be an appropriate valuation? $60 billion? $100 billion? Mowshowitz’s analysis is that a fair price would involve the nonprofit still owning a majority of shares in the for-profit, which is to say at least $80 billion. (Presumably these would be nonvoting shares.)
The only people with full information are the ones with access to the company’s confidential balance sheets, and they aren’t talking. OpenAI and Microsoft will be negotiating the answer to the question, but it’s not clear that either of them particularly wants the nonprofit to get a valuation that reflects, for example, the expected value of the profits in excess of the cap because there’s more money for everyone else who wants a piece of the pie if the nonprofit gets less.
There are two forces working toward the nonprofit getting fair compensation: the nonprofit board — whose members are capable people, but also people handpicked by Altman not to get ideas and get in the way of his control of the company — and the law. Experts I spoke with were a bit cynical about the board’s willingness to hold out for a good deal in what is an extremely awkward circumstance for it. “We have kind of already seen what’s going on with the OpenAI board,” Ogden told me.
“I think the common understanding is they’re friendly to Sam Altman, and the ones who were trying to slow things down or protect the nonprofit purpose have left,” Rose Chan Loui, the director of UCLA Law’s nonprofit program, observed to the Transformer.
If the board is inclined to go with the flow, the Delaware Attorney General or the IRS could object. These are fundamentally complicated questions about the valuation of a private company, and the law isn’t always good at consistent and principled enforcement in cases like this one. “When you’re talking about numbers like $150 billion,” UCLA law professor Jill Horwitz warned, “the law has a way of getting weak.”
Does that mean that Elon Musk’s allegation — that we’re witnessing a bait-and-switch before our eyes, a massive theft of resources that were originally dedicated to the common good — is right after all? I’m not inclined to grant him that much.
Firstly, having spoken to OpenAI leadership and OpenAI employees over the six years I’ve been reporting on the company, I genuinely come away with the impression that the bait-and-switch, to the extent it happened, was completely unintentional.
In 2015, the involved parties really were — including in private emails leaked in Musk’s lawsuits — convinced that a research organization serving the public was the way to achieve their mission. And then over the next few years, as the power of big machine learning models became apparent, they became sincerely convinced they needed to find clever ways to raise money for their research. In 2019, when I spoke with Brockman and Sutskever, they were enthusiastic about their capped profit structure and saw it as a model for how a company could raise money but ensure most of its benefits if it succeeded went to humanity as a whole.
Altman has a habit of being all things to all people, even when that may require being less than truthful. His detractors say he’s “deceptive, manipulative, and worse”, and even his supporters will say he’s “extremely good at becoming powerful,” which VCs might consider more of a compliment than the general public does.
But I don’t think Altman was aiming for this predicament. OpenAI did not inflict its current legal headache on itself out of cunning chicanery, but out of a desire to satisfy a number of different early stakeholders, many of them true believers. It was due chiefly to understandable failures of foresight about how much power corporate governance law would really have once employees had millions riding on the company’s continued fundraising and once investors had billions riding on its ability to make a profit.
Secondly, I think it’s far too soon to call this a bait-and-switch. The nonprofit’s control of OpenAI was meant to give it the power to stop the company from putting profits before the mission. But it turns out that being on a nonprofit board does not come with enough access to the company, or enough real power, to productively turn OpenAI away from the brink, as we discovered last November.
It seems entirely possible that a massive and highly capitalized nonprofit foundation with the aim of ensuring AI benefits humanity is a better approach than a corporate governance agreement with power on paper and none in practice. If the nonprofit gets massively undervalued in the conversion and shooed away with a quarter of the company when more careful estimates suggest it currently controls a majority of the company’s value, then we can call it a bait-and-switch.
But that hasn’t happened. The correct attitude is to wait and see, to demand transparency, to hold the board to account for getting the valuation it is legally obligated to pursue, and to pursue OpenAI to the full extent of the law if it ends up convincing the board to give up its extraordinary bequest at bargain-basement prices.
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Después de anunciar la gran reestructuración de OpenAI con fines de lucro que todos esperaban, OpenAi acaba de presentar una nueva iniciativa de IA que podría hacer de ChatGPT la opción de IA predeterminada para los países democráticos y darle al chatbot una ventaja importante sobre los competidores. Operai lo llama “OpenAi para países”, esencialmente la versión internacional de Stargate.
Project Stargate es el plan de OpenAI para invertir $ 500 mil millones en infraestructura de IA en los próximos años, con ayuda y apoyo del gobierno de los Estados Unidos. Operai está trabajando con Microsoft, SoftBank y otros en el proyecto. Stargate le dará a OpenAI el poder de cómputo masivo que necesita para el futuro de ChatGPT.
Operai quiere replicar ese tipo de asociación con otros gobiernos de todo el mundo que están interesados en sus propios proyectos similares a Stargate. La iniciativa Operai for Country se lanzará con 10 países no identificados, donde OpenAI construirá infraestructura ChatGPT adaptada a las necesidades de cada mercado, con la bendición del gobierno de los Estados Unidos.
Operai anunció el Proyecto Operai for Country en una publicación de blog, presentándolo como una forma de hacer que la IA segura y democrática sea el valor predeterminado, en lugar de la IA construida por regímenes autoritarios:
Queremos ayudar a estos países, y en el proceso, difundir la IA democrática, lo que significa el desarrollo, el uso y el despliegue de IA que protege e incorpora principios democráticos de larga data. Ejemplos de esto incluyen la libertad para que las personas elijan cómo trabajan y dirigen la IA, la prevención del uso gubernamental de la IA para acumular el control y un mercado libre que garantiza la libre competencia. Todas estas cosas contribuyen a una amplia distribución de los beneficios de la IA, desalientan la concentración de poder y ayudan a avanzar en nuestra misión. Del mismo modo, creemos que asociarse estrechamente con el gobierno de los Estados Unidos es la mejor manera de avanzar en la IA democrática.
Como usuario de ChatGPT desde hace mucho tiempo, estoy de acuerdo con todo eso. No usaría software de IA como Deepseek, sin importar cuán avanzado, porque incluye barandillas de censura alineadas con los valores del gobierno chino. También envía todos sus datos a China.
De hecho, si Deepseek no se hubiera convertido en una alternativa tan grande a ChatGPT, no estoy seguro de que Operai hubiera avanzado con esta iniciativa tan fácilmente. Deepseek ofrece un fuerte rival de chatgpt que es de uso gratuito. Puede instalar la versión de código abierto en su computadora para evitar la censura y el intercambio de datos con China. Pero aún se origina en un país no democrático, y eso es parte de lo que impulsa la campaña demócrata de IA de OpenAI.
Dejando a un lado la política, OpenAi para países será una asociación comercial entre Operai e internacionales. Estos acuerdos implicarán la construcción de infraestructura de IA localmente, por lo que los datos de ChatGPT de los usuarios en esas regiones pueden almacenarse dentro de sus fronteras.
Operai también creará versiones personalizadas de CHATGPT para los ciudadanos de cada país. No está claro si estas versiones coincidirán con el CHATGPT disponible internacionalmente, pero esperaría que lo hagan:
Esto ayudará a ofrecer una mejor atención médica y educación, servicios públicos más eficientes y más. Esto será AI de, por y para las necesidades de cada país en particular, localizado en su idioma y para su cultura y respetar futuros estándares globales.
Operai también dice que continuará construyendo modelos de IA seguros que respeten los valores democráticos y los derechos humanos. OpenAi para países incluirá recaudar fondos nacionales de inicio para apoyar “ecosistemas nacionales de IA saludables”.
Todo esto sucedería a través de “colaboraciones de infraestructura formalizadas y en coordinación con el gobierno de los Estados Unidos”.
Todo eso suena muy bien en teoría. En la práctica, podría darle a OpenAi un nivel de poder e influencia que hace que sea casi imposible competir.
Una de las principales prioridades de OpenAI en este momento es asegurar la energía de cálculo y la energía necesarias para desarrollar AGI. Eso requiere capital, por lo que está creando un brazo con fines de lucro. Operai también quiere monetizar el chatgpt. No hay nada de malo en eso.
OpenAi para países ayuda en todos los frentes. La financiación del gobierno para la infraestructura permitirá que OpenAi siga construyendo mejores modelos de chatgpt en su camino hacia una IA más avanzada.
Pero las asociaciones también brindarán ventajas significativas a OpenAI y posiciones dominantes en el mercado, potencialmente sofocando la competencia en el espacio de IA.
Si un gobierno democrático invierte en infraestructura similar a Stargate, ChatGPT podría convertirse en la IA predeterminada en industrias como la atención médica y la educación. Ese gobierno querrá ver un retorno de su inversión en AI entregando beneficios inmediatos a sus ciudadanos.
A su vez, esos ciudadanos se acostumbrarán a confiar en ChatGPT para tareas personales de IA, especialmente si también es lo que usan en el trabajo.
Si bien Operai será un socio, también se beneficiará de las instituciones gubernamentales que utilizan modelos CHATGPT personalizados y de personas que pueden suscribirse a versiones pagas de la herramienta.
La IA democrática es una base sólida para la iniciativa Operai para los países, pero también podría llevar a OpenAi a convertirse en un monopolio de IA. Hemos visto un dominio similar antes con compañías como Microsoft y Google, y eso sucedió sin apoyo del gobierno. Un monopolio de IA sería mucho más grave que uno que involucre a Internet Explorer o Google Search.
Digo eso como suscriptor de chatgpt más que no tiene planes de irse. Pero tampoco quiero que ninguna compañía de IA domine el espacio. Los países que exploran la infraestructura de estilo Stargate pueden querer apoyar el desarrollo local de IA antes de recurrir a OpenAI para países.
El ejemplo más cercano que tenemos en este momento es una asociación multimillonaria entre Operai y SoftBank en Japón. Los dos crearon una empresa conjunta local llamada SB OpenAi Japón para administrar productos de IA con alimentación de chatgpt que SoftBank se extenderá entre sus negocios. Ese es un acuerdo privado, pero puede ofrecer una idea de cómo puede operar OpenAi para los países.
Illustration: New York Magazine
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.”
Noticias
Korl lanza una plataforma que orquestan agentes de IA de OpenAi, Gemini y Anthrope para hipercustomizar la mensajería de los clientes
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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).
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