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OpenAI is removing ChatGPT conversations from Google

OpenAI is removing ChatGPT conversations from Google

Engadget3 days ago
OpenAI has removed a feature that made shared ChatGPT conversations appear in search results. The "short-lived experiment" was based on the chatbot's link creation option. After complaints, OpenAI's chief information security officer, Dane Stuckey, said the company is working to remove the chats from search engines.
The public outrage stems from a Fast Company article from earlier this week (via Ars Technica ). Fast Company said it found thousands of ChatGPT conversations in Google search results. The indexed chats didn't explicitly include identifying information. But in some cases, their contents reportedly contained specific details that could point to the source.
To be clear, this wasn't a hack or leak. It was tied to a box users could tick when creating a shareable URL directing to a chat. In the pop-up for creating a public link, the option to "Make this chat discoverable" appeared. The more direct explanation ("allows it to be shown in web searches") appeared in a smaller, grayer font below. Users had to tick that box to make the chat indexed.
You may wonder why people creating a public link to a chat would have a problem with its contents being public. But Fast Company noted that people could have made the URLs to share in messaging apps or as an easy way to revisit the chats later. Regardless, the public discoverability option is gone now.
In Fast Company 's report, Stuckey defended the feature's labeling as "sufficiently clear." But after the outcry grew, OpenAI relented. "Ultimately, we think this feature introduced too many opportunities for folks to accidentally share things they didn't intend to, so we're removing the option," Stuckey announced on Thursday.
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USA Today

timean hour ago

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Lex Wire Journal Launches to Help Attorneys Gain Visibility in AI-Powered Legal Search

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Top LLM Providers for Enterprises
Top LLM Providers for Enterprises

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00:00 In the enterprise domain in particular, you guys are saying that anthropic leads in terms of market share, then open air is just behind. Just for transparency. While our audience of course, Menlow Low is on the cap table of anthropic and you have a sort of partnership with them for compute access but break down the data and how in Prop X pulled ahead in that space. Yeah, I mean, there's really three things that stood out in the report. First is, you know, Anthropic really pulled ahead in terms of enterprise spend. It's something on the order of 32% of the spend in the enterprise right now. I think the other thing that really stood out and maybe didn't mention yet is that closed source models are really dominating how people use these things these days. The open source models are really starting to fall behind in terms of usage. And we see it when we talk to developers. I mean, the way that we invest in companies is we basically disarticulated architectures and understand how they're building their products. And you can see it anecdotally when we talk to developers in those companies. An executive from Openai I was posting this morning on X that chat GPT is up to 700 million weekly active users. But therein lies the distinction, right? There's the consumer use. You have a subscription basis revenue that comes in and then enterprise that seems where the volume of dollars are going right now. What do you make of that chat GPT stat? Yeah, it's really not that it's not that surprising and we hear it when we talk to folks again in the same way that we talk to developers, we talk to users of these products. It's pretty well known in Silicon Valley, at least in the circles that Openai sort of really wins in the consumer space, at least for now. Anthropic is really winning in the enterprise spend with developers who use it programmatically. Tim I'm really interested to go more on this close source versus open source, considering that the action plan really is leaning into this open moment. At the same time, people reading between the lines from matter's earnings, for example, and Mark Zuckerberg's put pitch that maybe because of safety they'll have to build more close rather than fully open as they had previously seem committed to. What does an enterprise need right now? Yeah. What really an enterprise needs is what's in these syntactical circles called the so-called ellipses of a product, the manageability of the product, the security, the observability, everything that sort of comes with being able to run that product. It's really hard to just take the weight of a model and just go run it. I couldn't, you know, put weight of the model, put it on a hard drive and just hand it to you and say, Hey, go run it. There's a lot of services and sort of software that has to stand up around it to be able to make it usable. And so that's really what you're seeing is is reaching for enterprise, closed for solutions that have programmatic access to these capabilities. And going back to anthropic numbers, leading enterprise data provider, 32% of the market. How early are we, though, in this rush on this commitment to money by companies into large language models? How much more is there to take? How quickly could we see the landscape shift? I think you're still seeing early days, the amount of spend that's going to you're going to see from model spend over over the next few years is going to continue to increase. In the report you saw just in the last six months, spend went from 3.5 billion to 8.4 billion, which is more than double again just in the last six months. So it's early days and I think the spend is going to continue to increase dramatically. So I've I've read through the report and I always look for data that kind of gives us the other side of the story. So you have all this enterprise revenue coming in anthropic, but what is that cost? OpEx cost compute cost on a per token basis, Like is that data that you track that tells us if these companies will ever be profitable? Yeah, that's not data that we put into the report, but Dario has been pretty open that he feels like the gross margins in the company are going to be respectable and increase over time. And I think what you're going to see is efficiency gains from the models and how they're used. You're going to see things like token caching really start to dominate how people use the models, and so they'll become more efficient over time and the cost of running them will sort of go down over time, I fully expect.

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