Chegg Embraced AI. ChatGPT Ate Its Lunch Anyway - WIRED

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Chegg Embraced AI. ChatGPT Ate Its Lunch Anyway - WIRED

In subjects such as engineering, chemistry, and statistics, which drive significant traffic to Chegg but often involve diagrams, there was a sense that relying too heavily on AI to parse visual information was unreasonable, the former employees say. So the ethics of unleashing an imperfect product gave Chegg pause. “We knew generative was coming down the pike,” says one former executive. “Text analysis was easy to embrace in the short run.” In 2020, OpenAI’s GPT-3 model was released and made text generation much better. Some machine-learning leaders at Chegg wanted to get their hands on it, but one source says executives weren’t aggressive about securing access to the technology, which OpenAI did not open-source. Early this year, GPT-3’s successor was added to ChatGPT, and the centrality of generative AI to Chegg’s future became inarguable, carved as it was into the company's dented user growth. Fight Back Chegg is now focused on proving with its in-house bot CheggMate that it’s possible to outcompete ChatGPT when it charges onto your turf. “We happened to be one of the industries that's facing it first, and that gives us a wonderful opportunity to understand it deeper and sooner and come on to the other side of it with unique and value-creating products for our consumers,” says Schultz, the COO. The company has marshaled all extra hands onto CheggMate and AI development, including by reassigning teams that worked on collecting more data from users to personalize services through more traditional means. Brown, the CFO, told investors last month that the company’s summer interns will be fully focused on CheggMate. But Chegg doesn’t have the best record of developing products from scratch and has previously leaned on acquisitions, leaving some former executives closely following CheggMate unsure of its prospects. The new service also doesn’t exactly ease ethical implications. Chegg has long faced allegations from colleges and universities that it enables cheating, as students secretly turn to its tools to complete homework and exams. Officially, Chegg bars dishonest use and carries out and supports integrity investigations, says Nina Huntemann, the company’s chief academic officer. But former Chegg data scientist Eric Wang worries that CheggMate and similar applications could spread the cheating habit. Students feel overwhelmed and pressed for time, and feel they are competing for scarce opportunities, he says. “All of these forces drive students who know better to make decisions that are in hindsight not great,” Wang says, suggesting that there could be better ways to support students and educators. Select users, along with Chegg’s subject matter experts and academic advisers, began testing CheggMate over the past couple of weeks, but it isn’t expected to publicly launch until next year. That means it won’t be ready for the US fall semester, when Chegg typically generates its greatest sales. Schultz says he’s proud of the company’s response to ChatGPT’s arrival. “We weren't going to react overnight and just throw something up on the site,” he says. “We have a responsibility to be thoughtful.” When a user types a query to CheggMate, it first attempts to categorize whether the request is for help understanding a concept, solving a particular problem, or concerning a particular subject, Schultz says. The system then tries to direct the question to the best resource, with the options including prompting GPT-4, having a human expert answer, or re-airing an old answer from Chegg’s database. CheggMate is designed to keep users engaged through positive reinforcement and pushing related content. “We could say, ‘Why don't you try this similar problem? Why don't you guess a step?’” says Huntemann, the chief academic officer. “Conversation allows us to extend the experience.” Chegg executives hope tuning their chatbot to education that way will make ChatGPT look less attractive as a homework helper. Pricing for CheggMate has not been determined; operating generative models is expensive, and those costs rise with usage. But two former employees say that having a human expert answer a question costs about $2. Generating a comparable response through GPT-4 possibly runs half a US cent, and having an expert edit it might cost $1 overall, they say, suggesting the economi...