To show off how easy it is for users to earn money by using his new chatbot platform, Pankaj Gupta offers to cash out $1 worth of Yupp credits, sending it to me over Venmo or PayPal. I’m talking with Gupta in the WIRED office during a prelaunch demo of Yupp, which comes out of stealth mode today. Journalistic ethics forbid accepting gifts from sources, so I politely decline. He proceeds to send it over PayPal to his Stanford alumni email.
Gupta is the CEO of Yupp, which is free to use and available globally. The website looks similar to other generative AI tools like ChatGPT. There’s a prompt box, a way to attach files, and a log of past conversations.
The main difference is that every time users ask Yupp a question, they’ll see two answers, generated by two different models and displayed side by side. Yupp routes prompts to a pair of LLMs, choosing from a pool of over 500 models that includes products from leading US generative AI companies like OpenAI, Google, and Anthropic, as well as international releases, like models from Alibaba, DeepSeek, and Mistral.
After looking over the two answers, users pick the response they like best, then provide feedback explaining why. For their effort, they earn a digital scratch card with Yupp credits.
“You’re not being employed, but you can make a little bit of money,” says Gupta. In my testing, the Yupp credits on the scratch cards typically ranged from zero to around 250, though they occasionally went higher. Every 1,000 credits can be exchanged for $1. Users can cash out a maximum of $10 a day and $50 a month.
Courtesy of Yupp
Not sure where to start while testing this web app, I turned to the range of pre-written topics flickering beneath Yupp’s prompt bar, which spanned from news topics, like David Hogg leaving the DNC, to ideas for image-creation prompts, like generating a crochet-looking surfer. (Yupp’s models can generate text or images.) I eventually chose to have the bots explain different perspectives on the current Los Angeles protests.
I was interested in how it would pull from news reports and other sources to generate the analysis about a political issue. Yupp notified me that generating this answer would cost 50 of my 8,639 Yupp credits; users have to spend credits to make credits on Yupp.
It generated two answers, one from Perplexity’s Sonar, on the left side, and one from an “AI agent” for news built by Yupp, on the right. AI agents are buzzy right now; they’re basically task-based AI programs that can perform a string of simple operations on your behalf when given a single prompt.
The output based on Perplexity’s model answered the question citing five online sources, including CBS News and a YouTube video uploaded by the White House titled “Third-World Insurrection Riots on American Soil.” The other answer, generated by the news agent, cited twice as many sources, including the socialist magazine Jacobin and MSNBC.
In addition to having more sources, the answer on the right side included more context about what Los Angeles mayor Karen Bass has been doing. I clicked the button saying I preferred that generation and gave my feedback, which Yupp anonymizes before aggregating.
A shiny card resembling a lottery scratcher ticket popped up afterwards, and I used my mouse to scratch it off. I got a measly 68 credits for that feedback, not exactly a windfall. But since I spent 50 credits to run the prompt, it put me up by 18 credits.
After about an hour of messaging with the chatbot about different topics and giving my feedback on the models, the total points accrued equaled about $4. The cash-out options include PayPal and Venmo, but also cryptocurrencies like Bitcoin and Ethereum. “Crypto and stablecoin allow us to instantly reach anywhere in the world,” Gupta says.
While I didn’t earn much money, the free outputs did include answers generated by newly released models that are often locked behind subscription paywalls. If someone wants to use a free chatbot and doesn’t mind the friction of providing feedback as the web interface flips between models, Yupp could be worth trying out.
During the demo, Gupta asked Yupp where the WIRED office was located. Both models spit out wrong answers initially, though subsequent tries got it right. Still, he sees the side by side outputs as potentially helpful for users who are concerned about AI-generated errors, which are still quite prevalent, and want another point of comparison.
Courtesy of Yupp
“‘Every AI for everyone’ is kind of our tagline,” says Gupta. “We have organized all the AI models we can find today.” Yupp’s website encourages developers to reach out if they want their language or image model added to the options. It doesn’t currently have any deals with AI model builders, and provides these responses by making API calls.
Every time someone uses Yupp they are participating in a head-to-head comparison of two chatbot models, and sometimes getting a reward for providing their feedback and picking a winning answer. Basically, it’s a user survey disguised as a fun game. (The website has lots of emoji.)
He sees the data trade off in this situation for users as more explicit than past consumer apps, like Twitter—which he’s quick to tell me that he was the 27th employee at and now has one of that company’s cofounders, Biz Stone, as one of his backers. “This is a little bit of a departure from previous consumer companies,” he says. “You provide feedback data, that data is going to be used in an anonymized way and sent to the model builders.”
Which brings us to where the real money is at: Selling human feedback to AI companies that desperately want more data to fine tune their models.
“Crowdsourced human evaluations is what we’re doing here,” Gupta says. He estimates the amount of cash users can make will add up to enough for a few cups of coffee a month. Though, this kind of data labeling, often called reinforcement learning with human feedback in the AI industry, is extremely valuable for companies as they release iterative models and fine tune the outputs. It’s worth far more than the bougiest cup of coffee in all of San Francisco.
The main competitor to Yupp is a website called LMArena, which is quite popular with AI insiders for getting feedback on new models and bragging rights if a new release rises to the top of the pack. Whenever a powerful model is added to LMArena, it often stokes rumors about which major company is trying to test out its new release in stealth.
“This is a two-sided product with network effects of consumers helping the model builders,” Gupta says. “And model builders, hopefully, are improving the models and submitting them back to the consumers.” He shows me a beta version of Yupp’s leaderboard, which goes live today and includes an overall ranking of the models alongside more granular data. The rankings can be filtered by how well a model performs with specific demographic information users share during the sign-up process, like their age, or on a particular prompt category, like health-care related questions.
Near the end of our conversation, Gupta brings up artificial general intelligence—the theory of superintelligent, human-like algorithms—as a technology that is imminent. “These models are being built for human users at the end of the day, at least for the near future,” he says. It’s a fairly common belief, and marketing point, among people working at AI companies, despite many researchers still questioning whether the underlying technology behind large language models will ever be able to produce AGI.
Gupta wants Yupp users, who may be anxious about the future of humanity, to envision themselves as actively shaping these algorithms and improving their quality. “It’s better than free, because you are doing this great thing for AI’s future,” he says. “Now, some people would want to know that, and others just want the best answers.”
And even more users might just want extra cash and be willing to spend a few hours giving feedback during their chatbot conversations. I mean, $50 is $50.
