OpenAI, not yet public, raises $3B from retail investors in monster $122B fund raise - TechCrunch
Hey there, little explorer! Guess what?
Remember our friend ChatGPT, the super-smart computer brain that can tell stories and answer questions? Well, the people who made ChatGPT are called OpenAI.
Imagine OpenAI is building a giant, super-duper toy factory for smart robots! To build all those cool robots, they need lots of special blocks and tools.
So, some grown-ups, like you might share your toys with a friend, gave OpenAI lots and lots of pretend money – like a super big pile of shiny coins! They gave so much money, it's like having a hundred thousand ice cream cones! 🍦🍦🍦
This helps OpenAI keep making their smart computer friends even smarter and build even more amazing things for us to play with! Isn't that neat?
<a href="https://news.google.com/rss/articles/CBMitgFBVV95cUxNNl9JaTZkb0V5YTBPYnAzcXdoZW9PT042SWJqZDFaelVYS0JpalRXT0otTFhnTUZ1OUxXOFkwOVItQXp6Z0FqMGJ4dHpEUXlPTjFhUE5vMGJNRVpjcVVEUVhjWkQ1andxVzVyUTNSbnloQXVSaGJ3OEJvMEpSR3ZFdWxYT1g4Q010MnJKWXBjNlhRUnc3WHFRckIwOUFQcFQxOUxmWTR0RzNIQlJtZ0JqNzZQQXF4dw?oc=5" target="_blank">OpenAI, not yet public, raises $3B from retail investors in monster $122B fund raise</a> <font color="#6f6f6f">TechCrunch</font>
Could not retrieve the full article text.
Read on Google News: OpenAI →Sign in to highlight and annotate this article
Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Analyst News
Help Converts Newcomers, Not Veterans: Generalized Reciprocity and Platform Engagement on Stack Overflow
arXiv:2604.03209v1 Announce Type: cross Abstract: Generalized reciprocity -- the tendency to help others after receiving help oneself -- is widely theorized as a mechanism sustaining cooperation on online knowledge-sharing platforms. Yet robust empirical evidence from field settings remains surprisingly scarce. Prior studies relying on survey self-reports struggle to distinguish reciprocity from other prosocial motives, while observational designs confound reciprocity with baseline user activity, producing upward-biased estimates. We address these empirical challenges by developing a matched difference-in-differences survival analysis that leverages the temporal structure of help-seeking and help-giving on Stack Overflow. Using Cox proportional hazards models on over 21 million questions,
Generative AI Use in Professional Graduate Thesis Writing: Adoption, Perceived Outcomes, and the Role of a Research-Specialized Agent
arXiv:2604.02792v1 Announce Type: cross Abstract: This paper reports a survey of generative AI use among 83 MBA thesis students in Japan (target population 230; 36.1% response rate), conducted after thesis examiner evaluation. AI use was nearly universal: 95.2% reported at least some use and 77.1% heavy use. Students engaged AI across the full research-writing workflow - literature review, drafting, and consultation when stuck - reporting benefits centered on clearer argument and structure (82.3%), better revision quality (73.4%), and faster writing (70.9%), with a mean perceived quality improvement of 6.27 out of 7. Concerns about output accuracy (75.9%) and citation handling persisted alongside these gains. Among respondents who rated GAMER PAT, a research-specialized agent, against othe
From Gaussian Fading to Gilbert-Elliott: Bridging Physical and Link-Layer Channel Models in Closed Form
arXiv:2604.03160v1 Announce Type: cross Abstract: Dynamic fading channels are modeled at two fundamentally different levels of abstraction. At the physical layer, the standard representation is a correlated Gaussian process, such as the dB-domain signal power in log-normal shadow fading. At the link layer, the dominant abstraction is the Gilbert-Elliott (GE) two-state Markov chain, which compresses the channel into a binary ``decodable or not'' sequence with temporal memory. Both models are ubiquitous, yet practitioners who need GE parameters from an underlying Gaussian fading model must typically simulate the mapping or invoke continuous-time level-crossing approximations that do not yield discrete-slot transition probabilities in closed form. This paper provides an exact, closed-form bri
Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!