How People Are Actually Using Jev

Nathaniel Whittemore (0:00)

Jev is one of the busiest models we've had in a long time, and that's because it's not just another LLM, like a GPT-6 or an OPUS or Fable model, it is something fundamentally different. But because it's different, it's not necessarily clear at the beginning exactly what it's going to be best used for. With the benefit of a week and a half under our belts now, though, people are discovering and sharing a slew of different use cases that take advantage of what makes Jev unique, and today we're going to get into the best of them and where they might be relevant for you.

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Welcome back to the AI Daily Brief. Over the last couple of weeks, one of the busiest new things to come up in the AI world has been a new model called Jev. Now, what makes Jev interesting is that it is not just another LLM that you would use for the same thing as GPT-6 or Opus 55, but actually works in a slightly differently and as we will see, complimentary way. In the 10 days or so since launch, not only has there been a ton of buzz, I'm talking hundreds of different posts on X, which each themselves have hundreds or even thousands of likes and shares, but that attention has also translated into significant financial opportunity, with the information reporting that the company is in talks to raise as much as $1 billion at a $10 billion or higher valuation. That is a decent jump from its $40 million seed that was completed at a $200 million valuation. But now that we've had a chance for people to actually get their hands on Jev itself, I wanted to go back through and talk about how people are actually using this thing outside of the buzzy visual and game demos that have been all over social media. Basically, is Jev something that the average person who's not a game designer or not a developer should be paying attention to and even thinking about as part of their larger AI stack?

Now, to recap what Jev is, previously I called it a judgment model. What Jev can't do is write in the traditional way that you think about Chatbot's writing.

Instead, the team at TypeSafe who built Jev calls it a System 1 model after a concept from Daniel Kahneman's Thinking Fast and Slow.

System 1 thinking is fast instinctive pattern matching, i.e. snap judgments, and that's what Jev is built for. A good way to think about a test for whether something is a good job for Jev is where you repeatedly read something, make a small judgment, then take a predictable next step. So take, for example, a file landing in your downloads folder. An ordinary rule that existing software might have would be to classify it as a PDF. But what Jev can do is go a step further, identifying is it an invoice, which projects is it for, and does someone need to see it. Once the judgment is made, it can be predictably moved onto the next step in a system. There are three core types of questions that you can ask Jev. The first is pick one or what Jev calls choice. It answers which of these fits. You list up to 255 options and Jev can find the best fit. So an example of this might be which team should handle this ticket? Is it billing, tech support, sales or other? The Jev model is going to give you back the pick plus a probability for every other option. The second type of question that you can ask Jev is what they call a score, which is basically rating on a scale. You describe each level in words from two to 10 levels and Jev can answer where something falls. So for example, on the scale from calm to very angry, how frustrated is this customer? You're going to get back from Jev a position on that scale as well as an indication of how sure the model is. The last type of question is a yes or no question, what Jev calls a null, which is short for Bernoulli, and it simply answers, is this true? So to stay on this customer service example, is this person explicitly asking for a refund? What you're going to get back is a probability from zero to one that the statement is true.

Hey, Stefaninex did a really cool, very simple visualization where he put a pile of emojis at the bottom of a screen, and then into a text box, he wrote, you can type in classifiers like things you can wear, or things you can wear in winter. The emojis that match raise to the top or fall off if the classification has changed. So what's happening in the background is that every emoji is getting the same yes or no question. Does this match what you typed? Importantly, nobody labeled or tagged the emoji as would happen in traditional machine learning. There's no rule, in other words, for winter. Every emoji gets the same question at the same moment. Does this match? And part of what you might be recognizing if you're watching this demo is that because what Jev does is limited and specific, it can do it incredibly fast. When TypeSafe launched the model, they said it can work 20 to 200 times faster and 40 to 400 times cheaper than comparable processes with LLMs. Now, instead of this pile of emojis, imagine that the pile is your leads, your tickets, your documents or some other big undifferentiated mass of information.