56 Min Read

[The AI Show Episode 142]: ChatGPT’s New Image Generator, Studio Ghibli Craze and Backlash, Gemini 2.5, OpenAI Academy, 4o Updates, Vibe Marketing & xAI Acquires X

By Claire Prudhomme on April 1, 2025

Podcasts

This week, Paul and Mike are together again with a 60+-minute podcast episode focused on another wild week in AI.

From ChatGPT’s jaw-dropping new image generator and the viral Studio Ghibli craze (and controversy) to Google’s Gemini 2.5 update and the launch of OpenAI Academy—there’s no shortage of major moves. Plus: updates to GPT-4o, the rise of “vibe marketing,” xAI’s acquisition of X, and what it all means for the future of work, creativity, and coding.

Listen or watch below—and see below for show notes, timestamps, articles discussed, and the transcript.

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Timestamps

00:03:01 — ChatGPT’s New Image Generator

00:13:59 — Backlash Against ChatGPT, Meta Copyright Violations

00:23:49 — Google Gemini 2.5

00:29:52 — OpenAI Academy

00:34:07 — More OpenAI Updates (GPT-4o, New Features, and OpenAI Revenue, Funding)

00:38:35 — Vibe Marketing

00:44:37 — xAI Acquires X

00:48:43 — New Anthropic Paper Traces the Thoughts of LLMs

00:53:04 — Replit CEO: “I No Longer Think You Should Learn to Code”

Post from Amjad Masad

00:56:37 — McKinsey State of AI Research

The state of AI: How organizations are rewiring to capture value

01:00:01 — Inside the Drama and Deception at OpenAI

01:04:01 — Runway Gen-4

01:07:06 — Microsoft Researcher and Analyst

Introducing Researcher and Analyst in Microsoft 365 Copilot

01:09:53 — Listener Questions

Summary

ChatGPT’s New Image Generator

OpenAI has introduced 4o Image Generation, a powerful new capability built directly into the GPT-4o model—meaning users can now create stunning visuals right within ChatGPT itself. This marks a major step forward in multimodal AI, blending text and image generation into a single, seamless experience.

In its launch announcement, OpenAI describes the feature by saying:

“GPT‑4o image generation excels at accurately rendering text, precisely following prompts, and leveraging 4o’s inherent knowledge base and chat context—including transforming uploaded images or using them as visual inspiration. These capabilities make it easier to create exactly the image you envision, helping you communicate more effectively through visuals and advancing image generation into a practical tool with precision and power.”

This isn’t just an upgrade to image generation—it’s a fundamental shift. Because it’s fully integrated into the 4o model, the tool now benefits from the model’s full intelligence and contextual awareness. That means your prompts are interpreted more accurately, resulting in better, more aligned images than what was previously possible.

One major improvement: it’s now far better at rendering text within images—historically a weak point for image generators. You can also upload existing images and edit or manipulate them, changing specific elements or applying new styles directly within the conversation.

Among the most impressive capabilities is the model’s “in-context” visual refinement. This allows users to evolve and fine-tune an image across multiple iterations, simply by conversing with GPT-4o. The result is consistent, nuanced imagery—ideal for use cases like character development, branding work, or complex visual storytelling.

At the moment, 4o image generation is available to ChatGPT Plus, Pro, and Team users. And due to overwhelming demand, OpenAI CEO Sam Altman shared that their GPUs are “melting,” which has delayed rollout to the Free tier.

Studio Ghibli Craze and Backlash

OpenAI’s new 4o image generation capabilities have gone wildly viral—thanks in large part to one specific use case. People are using 4o to transform their personal photos into illustrations that mimic the beloved style of Studio Ghibli.

Studio Ghibli, the legendary Japanese animation studio, is known for its beautifully crafted, often hand-drawn films that captivate both children and adults. Often referred to as the “Pixar of Japan,” Ghibli’s style is more dreamlike, poetic, and serene—a calm, peaceful, and deeply thoughtful aesthetic that has resonated with global audiences for decades.

Naturally, this distinct animation style was one that users quickly embraced while experimenting with 4o. Now, platforms like X are flooded with people—many of them outside the core AI community—using the tool to “Ghiblify” their photos. The trend has been widely viewed as fun, wholesome, and creative.

However, it hasn’t been without controversy. The viral success of these Ghibli-style images has sparked backlash, with many questioning whether OpenAI may have used copyrighted Studio Ghibli content to train the model, raising fresh concerns about intellectual property and ethical AI development.

Gemini 2.5

The buzz surrounding ChatGPT’s new image generation feature completely dominated the conversation, but there was still time for Google to unveil Gemini 2.5, which it’s calling its “most intelligent AI model” to date.

The first release in this new line is an experimental version called Gemini 2.5 Pro Experimental, and it’s already making a significant impact in the AI community by outperforming industry benchmarks with impressive margins.

What sets Gemini 2.5 apart is its classification as a “thinking model,” a term Google uses for AI systems designed to reason through their responses before delivering them. According to Google, this approach goes beyond basic classification and prediction—it allows the AI to analyze information, draw logical conclusions, incorporate context and nuance, and make more informed decisions.

Currently, Gemini 2.5 Pro sits at the top of the LMArena leaderboard, which measures human preferences for AI-generated responses. It excels in reasoning and code generation tasks, particularly in common coding, math, and science benchmarks—without relying on costly computational tricks.

In practical terms, Gemini 2.5 Pro is delivering exceptional results on a range of challenging tests, including state-of-the-art performance on complex math and science benchmarks like GPQA and AIME 2025. Notably, it also scored 18.8% on Humanity’s Last Exam, a rigorous dataset crafted by hundreds of experts to reflect the cutting edge of human knowledge and reasoning.

Gemini 2.5 Pro is now available in Google AI Studio and in the Gemini app for Gemini Advanced subscribers, with integration into Vertex AI coming soon.

This week's episode is brought to you by our fifth-annual State of Marketing AI Report. Last year’s report shared never-before-seen data from almost 1,800 marketing and business leaders on how they actually use and adopt AI.

This year, we’re aiming to get even more respondents. And you can help by taking a few minutes to fill out this year’s survey at www.stateofmarketingai.com. There, you’ll see a link to take the survey—and you can download 2024’s report.

Once we publish the 2025 report, we’ll also send you a copy of that as a thank you for taking the survey.

Read the Transcription

Disclaimer: This transcription was written by AI, thanks to Descript, and has not been edited for content.

[00:00:00] Paul Roetzer: It's like this future of work, like what does it even look like? And this definitely makes my brain start to hurt a little bit, trying to visualize that. But the idea of a single interface for all of your communications and strategy sure. Seems like a logical target for them.

[00:00:13] Paul Roetzer: Welcome to the Artificial Intelligence Show, the podcast that helps your business grow smarter by making AI approachable and actionable. My name is Paul Roetzer. I'm the founder and CEO of Marketing AI Institute, and I'm your host. Each week I'm joined by my co-host and Marketing AI Institute. To Chief Content Officer Mike Kaput, as we break down all the AI news that matters and give you insights and perspectives that you can use to advance your company and your career.

[00:00:43] Paul Roetzer: Join us as we accelerate AI literacy for all.

[00:00:51] Paul Roetzer: Welcome to episode 142 of the Artificial Intelligence Show. I'm your host, Paul Roetzer. Back again with my co-host Mike put after my solo, [00:01:00] talking into the screen for an hour and 40 minute session of 1 41, the Road to a GI. So if you didn't catch that one, 1 41 was the first in our road to a GI series and I kind of walked through, a theoretical AI timeline of sort of what's happening now, what's coming next, and what it means.

[00:01:18] Paul Roetzer: So you can go check that out. That is available now and we are back to our regular weekly format today. This episode is brought to us by the State of Marketing AI survey, soon to be report. This is your last chance, so if you haven't, if you are a marketer or business leader, want to be a part of our state of marketing AI report for 2025.

[00:01:37] Paul Roetzer: This is our. Fifth year, Mike. Fourth year. Fifth year. Yeah. Fifth year. This is the fifth year we've done it. So we've got a ton of fascinating historical benchmarks and data. That will build into the report. More than 1800 people have already been a part of the survey, so we'd love to have you join in.

[00:01:51] Paul Roetzer: You can go to state of marketing ai.com and just click on the link to take part in the survey. You can download last year's while you're there. Check that out. [00:02:00] But the new one will be coming out. Um. When, Mike, when are we thinking?

[00:02:04] Mike Kaput: We are shooting for the end of April. Okay. For it to come out, or mid-April rather for it to come. It's going to be a fast turnaround.

[00:02:11] Paul Roetzer: All right. So we're going to turn this thing around fast and, so you will have some fresh data. We'll definitely talk about that data on the podcast once that comes out. But again, go to state of marketing ai.com. It takes what, three minutes, Mike, go through and take the survey.

[00:02:25] Paul Roetzer: Yeah. Give us your feedback. We would love to hear, it just talks about piloting scaling AI within organizations and your own pers perspectives on AI and your career and a little bit into your life. So we'd love to have you be a part of that survey. All right. With that, I mean, we had a couple of ads this morning, so we're recording this Monday, March 31st, 1140 am and just this morning there was like two major things that got added.

[00:02:50] Paul Roetzer: So yeah, it's things that are moving fast, but we, we had big week last week with some new models and new capabilities and other models. So let's, let's dive right into that, Mike. [00:03:00]

[00:03:01] ChatGPT’s New Image Generator

[00:03:01] Mike Kaput: Yeah, it was a huge week. Paul. Our first main topic, OpenAI has introduced four oh image generation. So this is a new image generation capability directly within the GBT four oh model, meaning you can now generate stunning imagery right within ChatGPT, much, much more advanced than the previous dolly image generation capabilities according to OpenAI in the launch announcement, quote, GPT-4 oh image Generation excels at accurately rendering text, precisely following prompts, and leveraging four ohs and inherent knowledge base in chat context, including transforming uploaded images or using them as visual inspiration.

[00:03:44] Mike Kaput: These capabilities make it easier to create exactly the image you envision, helping you communicate more effectively through visuals and advancing image generation into a practical tool with precision and power. Now, you might get from that quote, [00:04:00] this is not just a better image generation, image generation tool.

[00:04:04] Mike Kaput: It is a fundamentally different one. So this is actually baked right into the four oh model. So that model is truly multimodal, and as a result, it gives it new capabilities so it can produce much better images because the full intelligence of 4.0 is brought to bear on the prompt, which didn't use to happen with image generation In ChatGPT, it's better at generating text and images way, way better, which was a weakness of past image generation models.

[00:04:34] Mike Kaput: And you can also upload and edit or manipulate images using the tool so you can change any element or apply any style to an existing photo or picture. Now what's really cool here is it has quite the capacity for quote in context visual refinement. So you can kind of progressively prompt and shape the image through conversation with GPT-4 oh and still maintain visual [00:05:00] consistency across multiple iterations.

[00:05:01] Mike Kaput: So if you start a new iteration, it's not going to look. Totally different than the image you started with. So right now this is available in chat, GPT plus Pro and team, Sam Altman posted it because of insanely high demand, their GPUs are quote melting, and the rollout to the free tier is going to be delayed for some time.

[00:05:25] Mike Kaput: So Paul, there is actually a whole piece of this we're going to address as its own topic in the next topic, which is the fact that this image generation has gone viral with people generating images in a popular animation style. But before we get to all that, I want to first get your initial impressions of this.

[00:05:43] Mike Kaput: Like, I know you've used it, like have you found it as impressive as everyone's claiming it is? Yeah,

[00:05:48] Paul Roetzer: so I did have a chance to finally experiment with it on, Wednesday. I think it was, I think it came out on Tuesday, so I guess it wasn't too long after. I thought it was interesting timing. [00:06:00] Always OpenAI likes to drop things right after Google drops things.

[00:06:03] Paul Roetzer: So we'll talk about 2.5 from Google, which came out at like 11:00 AM or 10:00 AM that morning. And then at 1:00 PM OpenAI drops their image generation thing and sort of like steals the thunder, but we will come back on the Gemini model. It's quite impressive on its own. So I wanted a use case to test the text part of it, because that has been a massive flaw of these models previously.

[00:06:26] Paul Roetzer: And so, I actually make my kids' birthday signs. My mom used to do this for me when I was a kid. It was like one of my favorite things. My birthday, you wake up in the morning and there's like signs made around the house. And so I carry on that tradition. And so every year for my kids and my, my wife, I make them birthday signs and hang 'em up around the house.

[00:06:43] Paul Roetzer: And so this year I thought, well, let me see if I can make these. With, ChatGPT image generation. And so I just went in and gave my son's name and said he is, you know, turned 12. And, I want to do like some fun and clever sayings on signs. I'm going to give you some themes and like, let's develop [00:07:00] some stuff.

[00:07:00] Paul Roetzer: And so it was really fascinating because the first thing I I did was, Pokemon. And so it would create it and I asked for specific characters and it would create it, and then it would just disappear. And I was like, what the hell was that? Like it was there and then it would say, oh, due to copyright or whatever, we can't generate this image.

[00:07:18] Paul Roetzer: I'm like, yeah, you can, you just did like do it again. And it's like, well, I can't do that one, but I can make one that looks like this. And I was like, fine, do that. And it would come out looking almost exactly like the actual character, but it wasn't the character. And so I immediately realized like, okay, there's some filter like classifier here that's not.

[00:07:38] Paul Roetzer: Rooting out your initial request for copyrighted images, but it's, it's like extracting it. So I don't, anyway, we'll come back, we'll come back to the copyright thing in a minute. But it was able to start creating these things. So I asked for like a eight bit baseball one 'cause he loves video games. I asked for like Minecraft related things and it was able to roughly do those and that it nailed the text.

[00:07:57] Paul Roetzer: It had like one typo in, in like 12 [00:08:00] signs that I made. It was like, one, it smelled his name wrong and I was like, Hey, you did that wrong. And it fixed it. So it definitely is quite impressive. You know, I think that I immediately started, you know, when you're online on Twitter, you just see all of these, not only the studio Ghibli things that we'll talk about, but you were seeing like ads being made, people taking like coke signs and dropping them into backgrounds and you start realizing like, if people weren't.

[00:08:28] Paul Roetzer: Aware of the impact these tools are going to have on creative workers, creative firms, movie production, stock photography. It is quite apparent when you spend some time looking at the samples of what people are building or when you just start building things yourself. These capabilities are significant and you can definitely start to imagine a world where, you're using AI more and more in creative work.

[00:08:57] Paul Roetzer: And then the other thing that I thought about when I tested [00:09:00] this is sous next, like the video generation stuff, is this is the prelude to that. And so imagine this level of control and consistency, but applied to 10, 15, 20 second videos. I gotta imagine when the GPU shortage sort of goes away and they have more capacity, that capability's probably already sitting in there is my guess.

[00:09:23] Paul Roetzer: They just don't have enough GPUs to roll it out. So image and video you had just like whole nother world. And that's, you can go look at VO two from Google, their video gen model and imagine three from Google, like I. They're similar, certainly, you know, probably on par with some of this stuff. So yeah, we're taking some leaps I think this year in image and and video generation for sure.

[00:09:47] Mike Kaput: I want to just talk for a quick second about maybe some of the bigger implications here that you alluded to for say creatives or like business use cases. Because like I've always found it pretty fun and creative, no doubt to [00:10:00] like generate images. It's impressive. Yeah, like it's really cool the stuff we had before, but honestly now with the text being accurate, I was generating literally mockups of ads in old styles of like really good ads with their logos, the exact font and stuff.

[00:10:14] Mike Kaput: It was crazy how accurate it was. Like you could see yourself now actually using this to create high quality ads, business visuals. I could see infographics, charts, things like that at some point.

[00:10:26] Paul Roetzer: Yeah, and I think I. I, so if anybody's watching this on YouTube in my background, there's a, a extra size copy of the cover of our book, the artificial intelligence book.

[00:10:37] Paul Roetzer: There's a logo of Macon. Like there's, there's different things back there. And I start thinking, like, as a non designer, I wouldn't say non-creative, but like, I have no design capabilities at all. And when we want to do projects like that, you are relying on the designer to like get the vision out of your head.

[00:10:55] Paul Roetzer: But all I have is words like I can't sketch it. I'm not just not good at that. And now to [00:11:00] think about Mike, like to your point, whether it's a logo, a webpage design, interior design of your home . The design of a, of a book or a digital asset, all of it, you can now just use words or a driving image like, Hey, I love these three book covers.

[00:11:19] Paul Roetzer: D develop some in this, this theme. But here's the words for the cover. And then say, oh, no, no, no. Like, oh, that's awesome. Let's do that in blue. The, and all of a sudden non-designers have these abilities and I don't, I don't know what that means, honestly. And I don't, I don't think OpenAI knows what it means.

[00:11:36] Paul Roetzer: I don't think Google knows what it means, but I think it's really important that we have these conversations because I just feel like these tools are starting to truly. Creep in to democratize the ability to build things. And I don't know what that means to the people who do that work daily, for a living.

[00:11:56] Paul Roetzer: I think some, you know, some of them obviously are just going to take these tools and have [00:12:00] superpowers and continue on and, you know, a lot of the world's going to just be ignorant to the fact that these are even possible like that, you know, you can have business leaders who don't know you could go do some of this stuff yourself, or at least mock things up yourself.

[00:12:13] Paul Roetzer: But I think that's more and more what's going to happen in all the knowledge work is whether you're working with an attorney, an accountant, a graphic designer. You're going to have the ability to do the first drafts yourself now for anything, basically. And you still may rely on the experts to do the final products and bring it home, but some of that early work might just be done by the ai.

[00:12:33] Paul Roetzer: You know, here's a draft of my talk, like, and then give it to the speech writer and let the speech writer fine tune it. Here's a financial analysis of the business, and then let the financial analysts do their final work. Like, I don't know, like, again, I don't understand what the implications are, but.

[00:12:48] Paul Roetzer: It's a reality. Like these tools are there, they're beyond first draft capability in most of these things.

[00:12:54] Mike Kaput: Yeah. And just quickly thinking out loud, I wonder even if you accept that your designer or [00:13:00] creative professional has superpowers with these tools, I wonder how that'll change expectations. Like I'd be like, why can't you give me a hundred variations today, next two hours or whatever.

[00:13:10] Mike Kaput: Yeah. Maybe I'm, why is this taking two weeks? Maybe I'm a jerk for saying that, but I feel like the expectations of what's possible if you assume someone is enabled, they're empowered with these tools, has just changed

[00:13:21] Paul Roetzer: a hundred percent. Yeah. And you, I mean, we worked in an agency, I could, I a hundred percent could see that like the expectations just become way faster, way cheaper, way better.

[00:13:32] Paul Roetzer: And I think that's going to be a reality for service firms and internal, you know. Creatives and writers and things is like once everyone catches on to what these things can do, the expectations for what you do is going to change. And I think the faster you get there and be proactive about this, the better prepared you'll be.

[00:13:51] Paul Roetzer: You don't want to like sit and wait around until all your clients figured out that you could do things way faster.

[00:13:59] Backlash Against ChatGPT, Meta Copyright Violations

[00:13:59] Mike Kaput: Alright, our [00:14:00] second main topic is very closely linked to the first one. So. The new four oh image generation capabilities have gone truly like insanely viral, primarily due to a single use case.

[00:14:13] Mike Kaput: And that is people are using four oh to turn their personal photos into animated illustrations in the style of Studio Ghibli and Studio Ghibli, if you don't know, is a legendary Japanese animation studio. It is. Famous for producing beautiful, often hand-drawn animated films that appeal to both children and adults.

[00:14:35] Mike Kaput: Like one good way of thinking about it, it's like kind of the Pixar of Japan, but with a much more dreamlike and like poetic vibe. I would say It's very calm, peaceful, thoughtful, distinct animation style that is really, really well known, and it also happens to be the animation style that users latched onto when experimenting with 4.0 image generation.

[00:14:58] Mike Kaput: Now it seems [00:15:00] like X in particular, though I've seen elsewhere now, like LinkedIn is just flooded with everyone, not just ai, you know, early adopters using tools to apply like a Studio Ghibli filter to all their photos. Now a lot of people have found this really fun, wholesome, and creative. The style is really kind of joyful to look at.

[00:15:20] Mike Kaput: I think. But it's also generated a ton of backlash because people are wondering just how much of Studio Ghibli's copyrighted work may have been used to train this model. So Paul, I want to kind of frame this through one post which we saw that kind of really illustrated a, I don't know if it's surprising, but like a deep well of anger about this issue.

[00:15:42] Mike Kaput: So a former Googler and a leading voice in ai, Cassie Ov, who posted about this trend on LinkedIn. She showed off some Ghibli photos that she had done, of herself, and she basically kind of commented like, Hey, this might be some great marketing for [00:16:00] Studio Ghibli. Everyone's talking about it now.

[00:16:03] Mike Kaput: Comments, though, kind of disagree. They were almost 300 of them. They are very majority negative. People are just kind of raging and extremely upset about how Ghibli's work is just being essentially ripped off for this use case. Again, it's a really distinctive style that has been around for decades. So I guess I wanted to start this off by asking like, are we about to see a wider backlash here, at least among creatives towards ai?

[00:16:34] Paul Roetzer: Yeah. I think it's coming. In addition to like the books being stolen from . For training, which we'll touch on as well. So I tweeted, um. Sometime last week when it became pretty apparent that OpenAI was steering into this Studio Ghibli thing and Sam himself changed his icon on Twitter.

[00:16:55] Paul Roetzer: And, and I said that the AI model companies had entered the [00:17:00] quote, don't give a f phase of ip, meaning like, we're just going to do it. And then interestingly, 'cause I was referring earlier to how these things work, where they, it's, it's very, very obvious. They were trained on copyright material. Like ask it to do something for the Simpsons or Marvel or Disney.

[00:17:15] Paul Roetzer: It'll do it at least as of like Friday. It would do it, it'll, it takes about 20 seconds to create the image and it's sort of appears from top down. It's using this auto aggressive model to like build these things. And so you'll like say, turn me into Homer Simpson and it'll do it, and then it just goes away.

[00:17:32] Paul Roetzer: And so obviously it knows who Homer Simpson is. It was trained on Homer Simpson. It has the ability to output Homer Simpson or any of these other copyrighted characters and materials. Um. By disappearing it. They're basically like not hopefully going to get sued. But in the case of Studio Ghibli based in Japan, which doesn't actually have, you're not allowed to sue for copyright infringement in Japan.

[00:17:57] Paul Roetzer: They're allowed to train the model. So the [00:18:00] belief, and I don't think OpenAI has confirmed this yet, is that the reason that Sam and others allowed this, like steered into this and even like joked about this character or this studio being used, this style is because they can't get sued by this company.

[00:18:15] Paul Roetzer: Now again, I don't know that a hundred percent to be fact, but I do know that that is the rule in Japan. So. I think what they're doing is basically like they trained on everything and the ones where they won't not get sued, they're going to kind of just let it go. Now interestingly, there was a couple people who started saying like, Hey, this thing's getting nerfed already by the weekend.

[00:18:34] Paul Roetzer: Meaning they're making it safer and not like allowing it to do all these other things. 'cause it can do a lot of things, not just copyrighted things. And he said, quote, we are going to do the opposite of nerfing it meaning. They have every intention of like pushing the limits here, they're going to let this thing go.

[00:18:52] Paul Roetzer: So I don't know if they've just become convinced they're going to win these lawsuits or they just have enough billions set aside for the lawsuits that they just [00:19:00] don't care. . But it's obvious that they're just full steam ahead. XAI is go going to be full steam ahead. I gotta mention Meta's going to do the same thing thing.

[00:19:08] Paul Roetzer: I don't, I wouldn't have thought Google, but they've definitely been more, lenient I would suppose with the things that their models are creating. So I feel like we're just kind of pushing the limit here and then society's just going to kind of get used to that limit having been pushed and then all of a sudden it's like, ah, you can just make anything you want.

[00:19:27] Paul Roetzer: So. You know, I don't know. I think that there is this definite, frustration with people related to the impact it has on creative work. And then the other side of this was last week, Mike, there was a lot about like the lib gen books thing. You want to give us a rundown of what that was? Yeah. Because it's in the same vein.

[00:19:50] Mike Kaput: Yeah, absolutely. So on March 20th, the Atlantic, the publication published a database of all the books that meta may have used [00:20:00] to train its models books, which it doesn't have the rights to, because. Meta was proven to have, trained on books from a database called Library Genesis or Lib Gen, and it's a pirated book database.

[00:20:12] Mike Kaput: You can go on it and get books that you have to typically pay for for free. Now, another post about this has gone even much more viral than the one from Cassie, and this is from marketer and writer Anne Hanley, who we know. She posted on LinkedIn about how all three of her books she found from this database were used without permission to train me LAMA models.

[00:20:35] Mike Kaput: And this post has almost 850 comments, 438 reposts on LinkedIn, which is insane. That might be like one more crazier posts I've ever seen. And the comments are just as far as I could tell, just all very, very negative towards meta, but towards AI companies. So I guess maybe unpack this a bit more for us, because it just really feels like these two things, maybe it's just bad timing for them, but it really feels like creatives don't [00:21:00] have much of a leg to stand on here in terms of doing something about

[00:21:03] Paul Roetzer: this.

[00:21:03] Paul Roetzer: Yeah. So I think, and I'm just kind of thinking out loud here, I think two things are happening. One, people are becoming aware of how this has been working for years. This is not a secret that this is how this has been done. Your books have been being stolen for years and used to train models for years, as has your creative outputs, your designs, your photography, your paintings.

[00:21:22] Paul Roetzer: All of it's been stolen for years. That is not new. People's awareness of it is new.

[00:21:28] Paul Roetzer: .

[00:21:28] Paul Roetzer: And then the second component that I think is only going to. Throw some fuel on the fire here is, go back to the last section where we talked about the impact on creatives, where I think this is the year where people actually start to feel it where, you know, maybe I'm not making what I used to make to do logo designs, or I'm not getting paid what I used to get paid to do writing.

[00:21:51] Paul Roetzer: Or the client's kind of doing their own writing now, or the CEO is able to write his own scripts because they're just using ChatGPT and they build a co [00:22:00] CEO that writes their speeches for them. Like I think this is the year where the rest of the world starts realizing what these things can do and starts doing things themselves that they used to use other people to do.

[00:22:14] Paul Roetzer: . And so I think combined with an awareness and understanding of how these models work with an actual impact on people's livelihood or perceptions of value and fulfillment, I. That is a recipe for a lot of backlash. And I would not be surprised at all to see these sorts of posts continue. And it just takes a few well-placed influencers who decide to make this a talking point for all of their followers to now realize what's happening too.

[00:22:45] Paul Roetzer: And you know, Anne is wonderful. Anne is a great friend. She has been on, she was our, keynote for last year's AI for Writers Summit. I believe. She and I had a wonderful talk about the impact of AI in writing. So, you know, and Ann is one of the most [00:23:00] trustworthy and honorable people I've ever met. And if Anne has a problem, Anne's followers are going to have a problem with what Ann has a problem with.

[00:23:08] Paul Roetzer: So yeah, I think it's, it's fascinating to see. And I don't know, I like, part of me is involved in this because I am a writer. You're a writer, Mike. Yeah, my wife's an artist. You know, I think I live this personally and then I observe it. Comment on it for, for our podcast. So I live in this weird world where I actually, I feel both sides of this.

[00:23:31] Paul Roetzer: Like I, I'm inspired by what you can build now, the democratization of the abilities to do these things. And I like using the tools. And then the other part of me is like, but I know how they're trained and I know the impact they're going to have on people. And sometimes I'm not really sure how to feel about it all.

[00:23:49] Google Gemini 2.5

[00:23:49] Mike Kaput: So our third main topic this week, I almost hate to say this, but kind of flew under the radar, which is crazy shocking when you hear it's a huge topic, but, you know, Chachi PTs [00:24:00] image generation like sucked the oxygen out of the room and kind of overshadowed the fact that Google unveiled Gemini 2.5, which they are calling their quote, most intelligent AI model to date.

[00:24:12] Mike Kaput: So this first release in this new line is an experimental version called Gemini 2.5 Pro Experimental. It is making waves in the AI community because it is topping industry benchmarks with significant margins. So, Gemini 2.5 pro experimental is in a category of models Google calls thinking models. These are AI systems designed to reason through their thoughts before they respond, which results in better performance.

[00:24:40] Mike Kaput: Better accuracy. This means that they do much more than just classification and prediction. They have the ability to analyze information, draw logical conclusions, incorporate context and nuance, and make informed decisions. So right now, the new Gemini 2.5 Pro sits at number one on the [00:25:00] LM Arena leaderboard, which measures human preferences for AI responses.

[00:25:04] Mike Kaput: It shows particularly strong capabilities in reasoning and code generation. It leads in common coding, math and science benchmarks. And in practical terms, it is demonstrating really impressive reasoning skills across a range of challenging tests. So it's achieved state-of-the-art results on math and science benchmarks.

[00:25:24] Mike Kaput: It scored 18.8% on human's last exam, which we've talked about before, which is a data set designed by hundreds via of experts to capture the frontier of human knowledge and reasoning. 18.8% is a very high score so far on that for all the models out there, and it is now available if you want to try it in Google AI Studio and in the Gemini app, if you're a Gemini advanced subscriber.

[00:25:51] Mike Kaput: So Paul, what do we need to pay attention to here? This is clearly a really, really powerful model, like 4.0. It has [00:26:00] multimodal image generation. It has a long context window, so that's a million tokens right now, which is about 750,000 words that can hold in its memory and pay attention to at any given time.

[00:26:12] Mike Kaput: Just seems like these models Google is putting out are just getting really, really robust.

[00:26:18] Paul Roetzer: Yeah, and this is sort of a preview of the next generation of models. So the, that they're, if we go back a year or so, it was text in, text out. So you could put text into your prompt, it could generate text back to you.

[00:26:31] Paul Roetzer: Now you could do image generation and you could do, some reasoning last year and things like that. But they were through separate models. Usually. It wasn't all baked into the same pie, I guess, for lack of a better analogy. And so what's going to happen now is like Claude four, GPT five, Gemini three, Lama four, all these next generation models, which I assume we will see all of them this year.

[00:26:55] Paul Roetzer: They will all be multimodal from the ground up. Right now. That means text and [00:27:00] image. And, and I guess voice and it'll, it'll eventually also include video and audio in there. So you can imagine like SOA from OpenAI being baked right into ChatGPT or, you know, VO two I mentioned earlier from Google being baked right into Gemini.

[00:27:18] Paul Roetzer: So you're going to have these multimodal models that it's able to input and output modalities, multiple modalities, and then you're going to have reasoning on top of it. And then you'll have some sort of classifier that actually knows which function to use for you. So if you go in and you're having a conversation, it knows whether you use reasoning and think more.

[00:27:35] Paul Roetzer: It knows whether to create an image or a video. And we don't have to pick from our 17 models in the dropdown like we've talked about many times. And then the context window, which is where Google has a, a pretty clear advantage at the moment. I. The reason for a non-developer like you and I, Mike, that that's important.

[00:27:53] Paul Roetzer: The average business user is, imagine it having access to your CRM system or to your [00:28:00] Google Drive where all of your knowledges, all of your documents are, the context window is basically what goes into the prompt, like the back end of the prompt. And what happens is, within that window, it dramatically improves accuracy.

[00:28:15] Paul Roetzer: Reliability reduces hallucinations. So if it can remember that information, those tokens, then it becomes way, way better and more practical for use in businesses. Otherwise it just kind of forgets things and it can make errors. And so the bigger the context window, the more accurate it becomes.

[00:28:33] Paul Roetzer: It's why Notebook LM works so well. Like if you build a notebook, lm, and you put, you put like five PDFs in there and a video script, it basically talks to you. Based on that context that those documents you've put into it. And so the bigger that context, and we know Google, last year they talked about 10 million tokens being, you know, tested and working.

[00:28:53] Paul Roetzer: Yeah. They've, I think Sundar's been on record is talking about basically infinite tokens. So the goal is to be able to stuff as much [00:29:00] information and you want into this system and it's like insanely accurate with what it outputs and recommends to you and the decisions it makes. So context window matters a lot to the average user.

[00:29:12] Paul Roetzer: It's just sort of an abstract concept.

[00:29:14] Mike Kaput: Yeah. And I wonder too with, I mean, 750,000 words already is an insane amount and with higher limits, you have to think. I mean that could comprise every document your company has created if you're relatively a small company. I would say totally. I mean our,

[00:29:29] Paul Roetzer: yeah, like a average business book's like 50,000 words.

[00:29:32] Paul Roetzer: Right. So do the math. I mean, it's, it's a lot of books. It's a lot of content. Just at the million tokens, so yeah, it's. and again, they can do multimodal. So you can put video and you can put different kinds of documents. So. Yeah, it's hard to comprehend.

[00:29:49] Mike Kaput: All right, let's dive into some rapid fire this week.

[00:29:52] OpenAI Academy

[00:29:52] Mike Kaput: So this first one, literally hot off the presses here. So we just found out before recording OpenAI has launched [00:30:00] OpenAI Academy, which is a new initiative aimed at democratizing AI literacy for people from all backgrounds. So this is right now a free community powered learning hub that features bite-sized video tutorials that cover everything from Basic Chat JPT usage to more advanced applications like creating videos in soa.

[00:30:22] Mike Kaput: Right now. The current offerings include content specifically tailored for educators, students, job seekers, nonprofit leaders, small business owners, and a couple other groups. Now, what makes this kind of interesting is the community focused model. So rather than simply building. Just a repository of content.

[00:30:41] Mike Kaput: They're creating an interactive ecosystem with both virtual and in-person events. The platform hosts regular workshops, discussions, collaborative sessions, led by both OpenAI experts and external innovators. And according to social media posts from OpenAI's, VP of [00:31:00] Education who apparently worked at Coursera.

[00:31:02] Mike Kaput: This launch represents just the first phase. The Academy is designed to be globally accessible, though it's currently only available in English, they're going to expand additional languages soon. They also indicate they're looking for motivated hosts across the world to help scale their in-person events globally.

[00:31:20] Mike Kaput: Now, right now you don't get any type of certificate or accreditation through the academy, though they say they've got at least one other big announcement about this coming soon. So we'll see kind of how that works out. Paul, this definitely validates the need we've seen. For widespread AI literacy, what do you make of their approach to achieving that goal?

[00:31:44] Paul Roetzer: Yeah, I was, I was actually really excited to see this. I think, um. it definitely validates what we've been saying, this need for AI literacy. It's nice to see them, you know, pushing that. You see similar things like HubSpot has AI classes, Salesforce has AI [00:32:00] classes, Google Cloud, Microsoft, like a lot of these AI model companies, AI software companies have, and are moving into the I literacy space.

[00:32:09] Paul Roetzer: And I think it's really important. There's a lot of value that can be created. From these, model and software companies. Now, the challenge sometimes they face is that they can't be brand agnostic. So like OpenAI's not going to have courses on here about Claude and Right. You know, co-pilots and, Gemini and things like that.

[00:32:28] Paul Roetzer: But if you look at how they've got it structured, 'cause I joined as soon as I saw it, they have collections. So they have like ChatGPT on campus. Awesome. Like, I think that's huge. That's, I assume that's mostly for like students and maybe teachers. They have ChatGPT at work, which gets into like some specifics about how to use different productivity components.

[00:32:45] Paul Roetzer: They have SOA tutorials for the video. They have an a. AI for K to 12 educators, which is fantastic. And there's four items in there now, but like, I think that's great. And then they've got a lot on the developer side, I would expect they would go pretty hard on the developer side. [00:33:00] But I did notice they've got like, just some, like AI for older adults.

[00:33:04] Paul Roetzer: Like that's awesome. And so some of these are functioned as livestream and so it's, it's real similar to how I'm actually envision envisioning building out our AI academy is a mix of on-demand courses, livestream in-person events. So you're truly kind of creating all this. So, just in my initial couple minutes of looking through this, there's actually a lot of it I could see where we, we may actually be recommending components of this as part of our, mastery program.

[00:33:30] Paul Roetzer: We say, Hey, some for additional learning, here's some great stuff on OpenAI. Here's some stuff on Coursera, here's some stuff on LinkedIn learning. So. I think that, it's fantastic to see. I expect we're going to see a lot more of this from these companies because at the end of the day, they need AI literate buyers.

[00:33:48] Paul Roetzer: And so for people to use ChatGPT to the level they want to grow, they need to educate them on how to use it. So it makes total sense that they would make a play like this. And it's interesting, they, they announced this last fall, but [00:34:00] this is not what they announced. No. Like they, they pivoted what AI Academy was going to be, I think, which is great.

[00:34:07] More OpenAI Updates

[00:34:07] Mike Kaput: Our next rapid fire topic consists of a few more important OpenAI updates. So in addition to the image generation update, the company has also released some other significant updates to four. Oh, so according to OpenAI, the updated model reportedly feels more intuitive and collaborative. There's particularly.

[00:34:29] Mike Kaput: Particular improvements in STEM and coding tasks. GPT-4 oh now generates cleaner frontend code. It more accurately analyzes existing code to identify necessary changes and consistently produces outputs that compile and run successfully. Now, I would say if you are not a developer, I'd personally encourage you to check it out.

[00:34:49] Mike Kaput: I've found it actually to be significantly better, and maybe that's just kind of the vibe I'm getting. But for a lot of non-developer tasks, it also seems to have [00:35:00] improved significantly. And for business customers. OpenAI is rolling out one of their most requested features, the ability to connect chat, GPT.

[00:35:08] Mike Kaput: To internal knowledge sources. So this is in beta for ChatGPT customers, and it allows the AI to access and pull information from an organization's Google Drive workspace in real time, which is allowing it to provide more personalized and contextually relevant responses. OpenAI says this is just the beginning.

[00:35:30] Mike Kaput: They have plans to support additional connectors for collaboration tools, project management systems, and CRMs. On the business front, OpenAI is projecting pretty extraordinary revenue growth according to sources familiar with the company's internal communications. OpenAI expects to more than triple its revenue in the coming year to 12.7 billion, up from 3.7 billion last year.

[00:35:54] Mike Kaput: And that growth trajectory is expected to continue to with projections [00:36:00] of 29.4 billion for the following year. And this all comes as Bloomberg reports that they are getting closer to finalizing a $40 billion funding round led by SoftBank. So Paul, do any of these updates seem particularly notable to you?

[00:36:17] Mike Kaput: I mean, I'm personally interested to see what becomes possible when you can connect this to internal knowledge sources.

[00:36:23] Paul Roetzer: I'm interested to see what happens to the knowledge sources themselves when I can just use ChatGPT. Right. So I think of like Asana we use for project management and Asana's got some baked in AI stuff now, but like, if I could just connect ChatGPT to it, what I use Asana's AI tools.

[00:36:41] Paul Roetzer: HubSpot has some AI capabilities, like even some new things that I've started seeing that I really like with, with doing like auto summaries of companies and things like that.

[00:36:49] Mike Kaput: Yeah.

[00:36:50] Paul Roetzer: With their breeze intelligence, which I'm pretty sure is actually built on, OpenAI APIs. So I like, I wonder, Google, same thing.

[00:36:59] Paul Roetzer: I've got [00:37:00] Gemini right in Google Drive, like what? I use chat BTS integrator instead of Geminis. So as a user slash. CEO buyer, I don't know what actually any of this means. Like I start to think ahead. It's like, wait, so am I just going to, will we centralize all of this into ChatGPT and just connect it to all of our tech stack?

[00:37:18] Paul Roetzer: Or am I going to use the AI native within each core piece of our tech stack? And I don't know the answer to that, but I do think this idea of being able to connect in is, makes a ton of sense. I can see that being valuable.

[00:37:32] Mike Kaput: So you're saying you could, see a future where ChatGPT is just so known and intuitive, you end up just using that as the interface with these tools?

[00:37:40] Paul Roetzer: Yeah. Like you just log into chat GT in the morning and it's connected to your project management system, your CRM, your Google Drive, and you just live in ChatGPT, like you just. Talking to it all day long. And it has access to everything you need. And it's like, oh, what are my top three tasks for the day?

[00:37:57] Paul Roetzer: And it goes to Asana and it grabs 'em and, alright, what was the [00:38:00] conversation Mike and I had last week about, AI Academy? And it goes into Google Drive and it grabs it and it's like, okay, great. Like draw me an email to follow up with Mike on that. And I never leave that thread. And maybe I just have a thread each day and it's like, I don't, I don't know.

[00:38:13] Paul Roetzer: Okay. Again, it's like this future of work, like what does it even look like? And this definitely makes my brain start to hurt a little bit, trying to visualize that. But the idea of a single interface for all of your communications and strategy sure. Seems like a logical target for them. I would imagine they'd be trying to build that.

[00:38:35] Vibe Marketing

[00:38:35] Mike Kaput: Next step in rapid fire in February, AI leader Andres Carpathy, posted on X about a concept he called quote, vibe coding. This is a new kind of coding. He kind of invented a term for where you quote fully given to the vibes when you're coding. Basically by just talking to AI over and over and having it do all the coding to complete your [00:39:00] projects.

[00:39:00] Mike Kaput: He notes, for instance, quote, I'm building a project or web app, but it's not really coding. I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works along those lines. There's now the introduction of this term called quote Vibe Marketing. So this is now gaming steam in some marketing circles based on a number of posts online.

[00:39:22] Mike Kaput: That say the era of vibe marketing may be here. So here's their argument. The current marketing landscape typically involves AI tools being used in isolated ways for specific outputs on individual channels. However, in the coming year, we're expected to see interconnected AI systems working together with shared context, these systems.

[00:39:45] Mike Kaput: So the argument goes, will feature multiple AI agents and workflows managed by quote manager agents that are trained by human experts. As a result, this transformation will fundamentally change the role of [00:40:00] marketers that will basically evolve from individual executors to orchestrators of complex AI systems.

[00:40:06] Mike Kaput: So basically, marketers will start operating on vibes, focused on strategy, storytelling, creative direction, while AI handles all the messy execution. The proponents of this argument say it could create incredible efficiency gains. A savvy solo vibe marketer backed by an orchestra of agents could outperform a typical entire agency.

[00:40:31] Mike Kaput: So Paul, this is definitely an interesting idea. I don't disagree with marketers becoming orchestrators of AI tools and agents. Um. But good luck trusting so much marketing at major brands to agents and vibes. I mean, at least today, do you think?

[00:40:46] Paul Roetzer: Yeah, I don't know. So, so my friend Allie Kay Miller was sending me some of these links.

[00:40:50] Paul Roetzer: She and I were catching up last week and she was sharing some of these links with me. And so I was, I was diving in a little bit to it and the way I think about this, 'cause honestly, like [00:41:00] I went back and re-read Andre's original tweet like five times a week or two ago, and I was like, I don't know if I get it.

[00:41:06] Paul Roetzer: Like, this is kind of a confusing topic. And so let me, I'm going to think out loud here, Mike, and tell me if this makes sense. What I'm envisioning for Vibe Marketing is, all right, we're doing a product launch in 30 days. I want to go in and I want to build a campaign, I want to build all the components of it.

[00:41:22] Paul Roetzer: Like, let's go. And I'm just talking to Gemini or Chad, GPT. Yeah. And it's like, great, like let's start with a plan, you know, the really excited helper assistant. And it's like, okay, yeah. Build, build out the plan and it builds and it's like, okay, that looks great. Let's go ahead and write that first email.

[00:41:36] Paul Roetzer: And it writes it and it's like, okay, that's actually really good. Build me a nurturing sequence now for like, when people open, don't open and it just builds it. It's like, okay, let's get to the landing page. Can you design a concept of a landing page? And it has image generation capability now with tech, so it like builds the landing page.

[00:41:51] Paul Roetzer: How's this? Like, that was great. Like, write me the code for that one going to drop. And I, I'm just envisioning your, basically just sitting there. And just doing the campaign. Yeah. And I think that's [00:42:00] the spirit of the concept here, is that you're just kind of feeling it as you go and you're like, you have an idea and you visualize it and it's like, that's cool.

[00:42:08] Paul Roetzer: And like maybe three months from now we can do video with it. Like, hey, knock me out like a, a 32nd trailer for this idea that I can use to put on X or LinkedIn and it creates a trailer. And I don't know that that isn't a thing. Like I do think that I could see people who are at the frontiers here and really understand the capabilities of these tools.

[00:42:32] Paul Roetzer: You could see doing this where you used to need five people. Like, you just, you just do it like that. That was, listen, I was listening to a podcast with Sam Altman, a, a yc, podcast he did with, oh shoot, what's the guy's name? Gary Tan. the president of, yeah. And he, it was in November and he is like.

[00:42:53] Paul Roetzer: With ai, you can, you can just do things. And I think that's the spirit here is like, when you know [00:43:00] what these things can do and you want to do a campaign, you can just do things like, you can just go in and design it and develop videos and write copy and create landing pages and build paid ad copy and like social media shares.

[00:43:13] Paul Roetzer: Like you can do all of it. And again, this goes back to what does that mean? I don't know. I just know you can. Yeah. And like if I had to do something tomorrow to launch something and our team was like stretched and they couldn't do it, I could sit down for an hour tonight and do everything I just outlined for people so someone on my team could do it.

[00:43:34] Paul Roetzer: But like as the CEO, if I just need to do something, I just go and do it. Right? I did it last night. I did this crazy research project like that. No joke would've taken me. I don't know, like three days. I did it while I was like getting my teeth brushed. I started the research project in the CHE GPT app. I said, here's what I need to do.

[00:43:54] Paul Roetzer: I need to prep for this meeting tomorrow. Here's everything I need to know and let go. And I brushed my [00:44:00] teeth, went and put my daughter to bed, came back, it wasn't done yet. Went and checked on my son, came back, laid down. Boom. I have this research report. So cool. Crazy. So I think that's the spirit here.

[00:44:11] Paul Roetzer: I don't know that I'm like in love with the vibe marketing. Like name, but I get what they're saying and I guess it gives a name to this thing.

[00:44:19] Mike Kaput: Yeah, it feels like almost like the ultimate triumph of the stereotypical idea guide, right? Yeah. It's just like, Hey, let's come up with a bunch of ideas, and then suddenly they can actually all get done

[00:44:30] Paul Roetzer: on.

[00:44:30] Paul Roetzer: So yeah, the idea people are now the creators and the developers and the, yeah. Yeah.

[00:44:37] xAI Acquires X

[00:44:37] Mike Kaput: Next up, Elon Musk's X AI has acquired X the social media site in an all stock transaction that values. X AI at $80 billion and the social media platform at 33 billion. This merger officially combines two companies that were already pretty deeply interconnected behind the scenes.

[00:44:59] Mike Kaput: So the [00:45:00] rationale behind this focuses on the overlap between the two companies. So X provides a massive stream of conversation data that can be used to train AI models. It also is a built-in distribution network for X AI's rock chatbot, and the combination creates one of the few foundation model companies with a widely used consumer facing product.

[00:45:22] Mike Kaput: Though analysts note that rock still lags behind competitors, OpenAI, anthropic, and Google in certain areas of state-of-the-art performance. So this move effectively transforms investors in Musk's original Twitter acquisition into shareholders of Xai. And it also formalizes what was already happening informally, the sharing of data, talent, resources between the two companies and Musk's Bush to remain a leader in ai.

[00:45:50] Mike Kaput: Some people point out this may also not be the final consolidation that Musk is looking at. Tesla with its fleet of approximately 5 million vehicles collecting multimodal [00:46:00] data represents an even more valuable data source that could eventually, in some way be integrated with X AIS operations. So Paul, like you've followed all these companies closely credit where credit is due.

[00:46:12] Mike Kaput: I feel like you predicted this like two years ago, that this was something like, this was the overall play. Like what do we need to know here?

[00:46:20] Paul Roetzer: Yeah, so I think this is the most predictable outcome in business ever. So when he bought Twitter recall, he didn't, he tried to back out. So like when, when Musk bought Twitter, he kind of half jokingly, you know, made the offer and then he tried to back out claiming it was like because of bots and all this stuff.

[00:46:38] Paul Roetzer: And so he had to buy them. He was forced to buy Twitter and then proceeded to tank it like it was, I don't know what the valuation was before this event, but it was under 10 billion. So you go from 44 billion to 10. Now he borrowed a lot of that money, so he owed people those billions or tens of billions of dollars.

[00:46:55] Paul Roetzer: Well, how do you get out of it? You create an AI lab because what is the most [00:47:00] valuable asset in the world right now, maybe besides Nvidia chips and data centers? It's to own an AI lab, and so they're worth. 20 billion more than Anthropic Now based on this data with no revenue, like, so this xai, it's, there's no revenue.

[00:47:18] Paul Roetzer: They have gr but like they don't have what OpenAI has in terms of the growth. And so the only way out of this was to do this exact thing. It's, we always knew that X became the training source for X ai. But to do that, I would imagine legally. You need them to be the same company, otherwise you're just, I don't, I don't know how it was working before.

[00:47:39] Paul Roetzer: Maybe they were licensing the data to 'em. So yeah, it's, it makes total sense. I, I, it's probably a smart move. I think you're just basically making up a number to like, make the investors and people you owe money to whole and you just roll on. And so it's just, it's such a weird world where like tens of billions can just get [00:48:00] like thrown around and put on paper.

[00:48:01] Paul Roetzer: It's like, ah, it's worth, it's worth 44 billion. It's like, Hmm, okay. Like XI guess it is 'cause the data source, but, pretty wild. So yeah, this, and this was like, what was this, like Saturday night or Sunday night? This was like a late night thing and he just announced it on Twitter. Like, Hey, by the way, bought X, from XAI bought X.

[00:48:21] Paul Roetzer: And yeah. Crazy. So, oh, we'll see. I for you, the average person at the moment, it just means that if, if your, all your data from X wasn't being fed to X ai, it is now,

[00:48:32] Mike Kaput: that's

[00:48:33] Paul Roetzer: pretty much anything you've ever said publicly, or I assume in dms, like it's all training data now for X ai.

[00:48:43] New Anthropic Paper Traces the Thoughts of LLMs

[00:48:43] Mike Kaput: Anthropic has just released some research that gives us a peek under the hood of large language models like Claude offering some insights in how these AI systems actually quote unquote think. So the company published two new papers that are focused on what they call [00:49:00] interpretability, which essentially creates what they call like an AI microscope to examine the billions of computations happening inside these models.

[00:49:08] Mike Kaput: This research actually addresses fundamental questions about AI cognition that have remained mysterious until now. So some examples of how it is working kind of under the hood. When Quad communicates in multiple languages, is it using separate language systems or thinking in some universal mental space?

[00:49:28] Mike Kaput: We didn't know the answer to that question. When it writes poetry that rhymes, is it planning ahead or just making up word by word when it explains its reasoning? Is it showing its actual thought process or sometimes constructing a plausible sounding explanation after the fact? The findings are surprising even to the researchers themselves.

[00:49:47] Mike Kaput: I. It turns out Claude often thinks in a shared conceptual space across languages suggesting it has developed a kind of universal quote, language of thought. When writing poetry, for instance, it actively [00:50:00] plans ahead thinking of potential rhyming words before crafting lines that lead to those endings that contradicted the researchers' initial hypothesis that it would simply proceed word by word, and perhaps most intriguingly.

[00:50:12] Mike Kaput: The research confirms that AI models can sometimes engage in what you might call BSing, providing plausible sounding explanations that don't represent their actual internal processes. In one example, when given an incorrect hint for a math problem, Claude was caught in the act of fabricating reasoning.

[00:50:30] Mike Kaput: To match the expected answer rather than working through the problem logically. So Paul, we've been saying for years, but it bears repeating often. This is your regular reminder that even the people building these models do not fully understand how they operate. I. So honestly, it does seem like this research should be a bit of a big deal if we're able to better start understanding what goes on inside of them.

[00:50:56] Paul Roetzer: Yeah. This research totally went under the radar. If you [00:51:00] think 2.5 from Google under the radar, like this is wild stuff now. They've been working on this. We've covered this stuff before. I think the term was mechan. Mechanistic. Interpretability, yeah. I think is like the technical term they use for this stuff.

[00:51:12] Paul Roetzer: So Anthropic has been pushing on this. I know Google does research like this. I'm sure OpenAI does this. Like everyone's trying to figure out how these things think, why they do what they do. Interestingly, the Sam Altman podcast I mentioned with Gary Tam was, he told the story of like why they built GT one, and it was actually because this internal researcher.

[00:51:35] Paul Roetzer: Was looking at like, I think it was like Amazon product reviews and a neuron in, in, in the system was flipping like on and off related to sentiment and they couldn't figure out why it was doing it. Like it was, it was understanding sentiment even though it hadn't been trained on it. I think it was like the concept and that led to them actually pursuing the path of building GPT one.

[00:51:55] Paul Roetzer: That wasn't what they set out to do originally. And so this whole idea that like [00:52:00] these models just do things that we don't understand and sometimes it leads to an entire research path and so. I think that that's what this research is demonstrating for people who haven't been following is this reaffirming the fact that we don't know how they do what they do.

[00:52:16] Paul Roetzer: And while there's people like Jan Koon who think these things are just probabilistic machines, just making token predictions and that's all they do. You look at research like this and you're like, are we sure that's all they're doing? Because it sure seems like there's something else going on in these models.

[00:52:33] Paul Roetzer: And so I think it's fascinating to like, follow along with this research. And I mean, I love these kinds of papers because it does give us a window into how it works. And the other one, it goes back to that, golden Gate Bridge thing I think we talked about in the fall. Yeah. Where they found a way to like get the thing to just tie everything back to the Golden Gate Bridge.

[00:52:50] Paul Roetzer: Like they found the neuron that was firing, basically that was causing it to do this thing. And so, yeah, it's, it's such a, like a open-ended research [00:53:00] area where there's just so few answers right now.

[00:53:04] Replit CEO: "I No Longer Think You Should Learn to Code"

[00:53:04] Mike Kaput: Next up in Rapid Fire, the CEO of Rept Amjad Mossad has made some waves in AI circles by saying in a recent interview quote, I no longer think you should learn to code.

[00:53:15] Mike Kaput: So Rept software uses AI to automate and augment coding work, and Mossad has long been a proponent of using AI to massively increase the leverage that great programmers have. And he advocated for a while learning at least how to do some coding in order to build even more with AI's help. Now in this interview, he says he's really starting to believe in agents and a path where they optimize to get better and better.

[00:53:43] Mike Kaput: And that in turn has altered his opinion from even a year ago when he was recommending that people. Learn to code even a bit now. Not anymore. He says, instead you should quote, learn how to think, learn how to break down problems, learn how to communicate clearly. He then said in a [00:54:00] follow-up post to the interview quote, I understand all the cope.

[00:54:03] Mike Kaput: It was hard to arrive at this conclusion. There are obvious domain exceptions, but the trend is hard to miss. In my work, I've popularized learning to code more than anyone else. A good chunk of my life's work bittersweet. Okay. Paul, you and I are not programmers, but we're talking about this because Amjad is a CEO of a major AI company.

[00:54:24] Mike Kaput: He said he spent a long time, like his whole career arguing this opposing view, but now it seems like he's convinced of a future where something like learning to code doesn't make as much sense as maybe prioritizing other skills. It sounds like.

[00:54:41] Paul Roetzer: This is one of the great unknowns. I mean, just 'cause this is his opinion, doesn't mean he is right.

[00:54:45] Paul Roetzer: He is someone who's very thoughtful about this and has a company where, you know, his goal is to build like a billion developers. So he wants everyone to be a developer. I mean, I've, I've met him. I, I've talked with him, I think he's, as [00:55:00] unbiased as one can be when this is your business. So, I don't think he's doing this for hype.

[00:55:04] Paul Roetzer: I don't think he's doing it to sell more subscriptions to Rep I, I. I would imagine he truly believes this. And there are a lot of people who don't. Like, there's a lot of people on the other side who, you know, still see the value in coding. And I think it's just representative of where we find ourselves.

[00:55:19] Paul Roetzer: You're, you're going to find experts on any side. So like I do the A GI podcast, you're going to have some people who are like, you're crazy. A GI is ridiculous. It's not a thing. It's not going to happen for 10 years, if ever. And then you're going to have other people who say, two years and they actually think two months.

[00:55:32] Paul Roetzer: Like it's, it's all over the board. And like Jan Koon is so strong in his beliefs that language models are not the path to intelligence. But Jan also is very strong in his beliefs. Jan Koon, the chief scientists of Meta, chief AI scientist at Meta. Um. He was also extremely strong in his beliefs that, back in 2016, that, AI couldn't win at the game of go.

[00:55:56] Paul Roetzer: And he was wrong, right? Like, people are wrong sometimes [00:56:00] Jeff Hinton is like so convinced that AI is going to destroy the world that he left Google and is like making his life's work to, to dismiss his previous life's work and say it, we went the wrong way. We shouldn't have done what we did. Like people have opinions, and I think that's the whole goal of our show is to share those opinions with you, share those perspectives so you can figure out your own perspective on this.

[00:56:21] Paul Roetzer: I have no idea if he's right or not. Like if my son was a senior in high school right now and he wanted to go into coding,

[00:56:26] Mike Kaput: right?

[00:56:27] Paul Roetzer: I don't know enough to say don't do it. I would just have this perspective in the back of my mind and make sure we're thinking, thinking about that as we're making these decisions.

[00:56:37] McKinsey State of AI Research

[00:56:37] Mike Kaput: McKinsey has released its latest state of AI report examining how organizations are restructuring to capture value from ai. So this research is based on a global survey of nearly 1500 participants across 101. Countries and it reveals that companies are beginning to make organizational changes designed to [00:57:00] generate future value from generative ai.

[00:57:03] Mike Kaput: So they find that the adoption of AI continues to accelerate dramatically. With 78% of respondents now reporting their organizations use AI and at least one business function that's up from 72%, in the previous survey and just 55% the year before that. Generative AI usage has similarly jumped to 71% with companies most frequently deploying it in marketing and sales, product development, service operations, and software engineering.

[00:57:31] Mike Kaput: Despite this rapid adoption, the survey finds we're still in the early stages of organizational transformation. Only 21% of companies say they have fundamentally redesigned workflows as they deploy ai and less than one in five say they are tracking KPIs for gen AI solutions. Interestingly, larger organizations appear based on their data to be moving more quickly than smaller ones with companies succeeding 500 million in annual [00:58:00] revenue, more than twice as likely to have dedicated roadmaps to drive adoption of gen AI solutions and dedicated teams to help drive that adoption.

[00:58:09] Mike Kaput: So Paul, I know we wanted to talk about. First the recency of this data, but also maybe touch on that for us and the overall takeaways you found in this research.

[00:58:19] Paul Roetzer: Yeah, I think there's a lot of, information here that supports a lot of the things we talk about. You know, just in terms of the early stage of adoption, the lack of education and understanding within companies.

[00:58:30] Paul Roetzer: So I think it's a worthwhile report for people to read. Give it a download, check it out. They do a nice job of summarizing the findings. We'll put the link in the show notes. it is interesting, like I, I've always said on the show, in time we talk about research, it's always like, go to see how it was done.

[00:58:43] Paul Roetzer: Yeah. Who,

[00:58:44] Mike Kaput: who,

[00:58:44] Paul Roetzer: who did they interview? When did they interview 'em? That kind of stuff. And I did find it interesting when I went to this, I thought like, oh great, this is like brand new study. Like it's going to be super relevant. And the survey was from, two week period in July of 2024. Right. And I thought, that's odd.

[00:58:59] Paul Roetzer: Like, why [00:59:00] would you wait eight months to release a state of AI report? Um. Which honestly like made me think about the role AI will play in research reports in the future. No kidding. Because like why, why wouldn't you just take all the data and either train a model to, to do this analysis so you don't wait eight months to release it.

[00:59:19] Paul Roetzer: Or at least like accelerate the review of the data. Like that's how we're doing it. Like the way we're going to turn around a survey in two weeks instead of eight months is by infusing AI into the process and, helping Mike and I do this way faster so we get a more relevant data out. So, yeah, I don't know.

[00:59:37] Paul Roetzer: I, again, I guess great report. Read it. Yeah. Secondary note. Maybe an example of how AI is going to speed some things up in, organizations.

[00:59:47] Mike Kaput: Yeah. And we, and we'll share more once we publish our report, but we're doing even more this year with that too, which would be really cool. I mean, just looking, even at the last year when we published the report, we used AI quite a bit to accelerate it, but it's night and day [01:00:00] now what we can do.

[01:00:01] Inside the Drama and Deception at OpenAI

[01:00:01] Mike Kaput: Yeah. Next up is a fascinating inside account, has just been published by the Wall Street Journal revealing some more details behind the dramatic November, 2023 firing and rapid reinstatement of OpenAI, CEO Sam Alman. So this article is adapted from an upcoming book by Wall Street Journal reporter Ke Hagi, and it sheds new light on what happened during those chaotic five days that briefly upended the AI industry's most influential company.

[01:00:33] Mike Kaput: So apparently just days before his sudden, ouster Altman was warned by Val people venture capitalist Peter Thiel over dinner in LA that the quote, AI safety people at OpenAI would destroy the company echoing concerns about effective altruism, advocates who worry about AI risks. But ironically, according to the article, it wasn't ideological differences that led to altman's firing.

[01:00:58] Mike Kaput: It was [01:01:00] something much more mundane, governance issues and management style. The real trouble began when opening AI's nonprofit board started discovering what they perceived as a pattern of deception by Altman. Some of the most damaging testimony came from within Altman's executive team, CTO. Mirati had privately shared concerns about Altman's toxic management style, documenting instances where he allegedly misrepresented safety approvals and pitted senior employees against each other.

[01:01:29] Mike Kaput: These complaints combined with board members catching Altman in what they said were direct lies, ultimately led them to vote to remove him. Now what they didn't anticipate, as we've covered before, was the massive employee backlash within days unless the entire company had threatened to quit unless he returned.

[01:01:47] Mike Kaput: And even more, both Ti and Skr, Ilia Sr. Formerly at OpenAI, who had both provided evidence against Altman, ended up signing the letter supporting his reinstatement. [01:02:00] So this book should be an interesting read, Paul. It's not the only book coming out about the inside story at OpenAI either. We also learned that journalist Karen Howe, who we know well, has announced pre-sales of her book and Empire of AI Dreams and Nightmares in Sam Altman's OpenAI, which relies on seven years of her reporting to tell that story.

[01:02:22] Mike Kaput: We always knew there was all this like deception and drama going on. We covered, gosh, it has a ton of it. Does what we're learning now about it surprise you at all.

[01:02:32] Paul Roetzer: No, and you know, I think obviously there's just a lot more coming. I think Sam is every podcast that I have listened to Sam on, which is probably over a dozen since this all happened, he gets asked this question about his firing, right?

[01:02:46] Paul Roetzer: It's always the same response. Like, okay, I will answer these questions again. Every once in a while, he like, lets the guard down and provides a little bit more perspective on it. Um. I mean, my general take is that there, there is how [01:03:00] Sam has viewed these things and then there's how others viewed these things.

[01:03:03] Paul Roetzer: And, he's been pretty consistent that, you know, there's probably things he could have done different or better, which may be the things that are being highlighted here. I. You know, if he looks back, does he think they were worthy of him being fired and humiliated and going through that craziness?

[01:03:18] Paul Roetzer: Probably not. But he also generally just takes the high road and it's like, man, we've learned a ton and I gotta keep moving thing. So I, I'll read the books. Like, I mean, it's fascinating to see and to hear more about what happened, but I don't, I don't think it like changes anything moving forward.

[01:03:36] Paul Roetzer: I think they're pretty focused on the future and, yeah, I don't know. It's always, it's always intriguing though to get a few insights, like the Peter Thiel dinner thing I had not heard. Right. There was definitely pieces of this I was not aware of. Oh.

[01:03:49] Mike Kaput: It could be one of those things too, almost like the Ghibli thing where it just becomes wider knowledge of kind of everything that's been happening within these companies.

[01:03:56] Mike Kaput: It's almost like the social network movie or something about meta as well, you [01:04:00] know? Yep. All right.

[01:04:01] Runway Gen-4

[01:04:01] Mike Kaput: Next up, runway has introduced Gen four, its latest AI video generation model that Bloomberg says, challenges OpenAI, SOA with more cohesive videos. So Gen four is a next generation AI video creation system.

[01:04:17] Mike Kaput: It represents a significant leap forward addressing one of the most persistent challenges in AI generated video, which is consistency across scenes. This new model introduces what runway calls, quote, world consistency, allowing creators to maintain coherent characters, locations, and objects through an entire project.

[01:04:38] Mike Kaput: So typically past video generation systems have struggled with maintaining that kind of visual continuity from one clip to the next. It's also able to work with minimal reference materials according to runway. The system can generate consistent characters across multiple scenes using just a single.

[01:04:56] Mike Kaput: Reference image. They also offer some impressive [01:05:00] capabilities for object consistency, allowing creators to place any subject in various environments while maintaining its core visual characteristics. So you can start thinking of this as applying to things like film and storyboarding to kind of capture multiple angles of the same scene by having all these references be consistent across each frame.

[01:05:22] Mike Kaput: Paul definitely seems like we've alluded to things are moving really fast in visual ai.

[01:05:27] Paul Roetzer: Yeah. Video is one of the things I talked about on the a GI podcast last week is just you're going to see these rapid improvements in this space and consistency length, things like that. And, you know, I think for runway, we've talked a lot about them.

[01:05:42] Paul Roetzer: At least last year we covered 'em quite a bit.

[01:05:45] Mike Kaput: Yeah.

[01:05:45] Paul Roetzer: Their, their CEOs on record is saying like, their goal is to do a feature length film from a single prompt. Like they're, they're not stopping at like 10, 15, 20 second clips here. And, you know, I think that they play an interesting role in the creative space and [01:06:00] the impact on creatives.

[01:06:01] Paul Roetzer: They've tried a lot to integrate creatives into what they're developing and let them use those tools. I do think. I mean, if I had to put some money on who's going to get acquired this year, I would put runway pretty high on that list. Because I think what's going to happen is video's going to become so integrated into the AI models platforms themselves that sustaining a standalone video gen tool.

[01:06:25] Paul Roetzer: Like I don't know that they can build the kind of customer base they're going to want to build once I just have Sora baked right into my thing. Right. Or I have VO two Bake right into the Gemini. So I don't know. I, good company, we've been following them for six years. I could see 'em being an acquisition target for sure, for somebody.

[01:06:42] Mike Kaput: I have no idea as to who that would be, but it does occur to me. There are two AI companies that also have TV studios and film studios, which are Apple and Amazon. So who knows? Those

[01:06:52] Paul Roetzer: are interesting ones. and two that don't have video yet, which is xai and philanthropic. Right. [01:07:00] Um. Yeah. Oh, that's interesting.

[01:07:02] Paul Roetzer: We could probably do a whole episode thinking about that one.

[01:07:06] Microsoft Researcher and Analyst

[01:07:06] Mike Kaput: All right. Next up in Rapid Fire, Microsoft has unveiled two powerful new AI reasoning agents for their Microsoft 365 co-pilot platform. These are called researcher and analyst. Researcher acts as an on-demand research assistant. It tackles complex multi-step projects with improved accuracy and insight.

[01:07:27] Mike Kaput: It's built on opening AI's deep research and enhanced with Microsoft's orchestration and search capabilities. So it could do things like develop market strategies by synthesizing internal company data with competitive information from across. The web. Researcher can also integrate data from third party sources, and the second agent analyst functions like a skilled data scientist.

[01:07:50] Mike Kaput: It transforms raw data into actionable insights within minutes. It is powered by OpenAI's oh three mini reasoning model and it uses chain of thought [01:08:00] reasoning to work through problems incrementally similar to how humans do analytical thinking. It can run Python code to handle complex data queries in real time, allowing users to verify its work as it processes.

[01:08:13] Mike Kaput: Now both of these agents will be rolling out to co-pilot license holders in April through a new Frontier program designed to give customers early access to developing innovations. So Paul, this like seems like a really cool update to co-pilot. I guess I immediately think of like the many, many knowledge workers I talk to or work with who use copilot.

[01:08:36] Mike Kaput: I just hope these kind of come with adequate education. 'cause I don't know if outta the gate, if you show me this announcement and I'm a co-pilot user, I'm going to immediately kind of get, how do I use these tools?

[01:08:49] Paul Roetzer: Yeah, they probably won't. The history of all these companies as anything, it's like, here, here's some really powerful tools.

[01:08:55] Paul Roetzer: Figure it out. My mind immediately went to like, when are they going to launch [01:09:00] the accountant and the wealth manager and the marketer and the writer. Yeah, it's a slippery slope. It's a hard position for these model companies to be in where, you know, you have the ability to build these tools that do jobs, you know, collection of tasks, a large collection of tasks that make up a job, and how they'll be received.

[01:09:23] Paul Roetzer: They can be nice, complimentary tools that help you do your job. They can also be viewed as replacements. Um. I don't know. I think we're going to see a lot more of these this year and even go back to that vibe marketing. If you just like lumped everything I said in that example into like a marketer copilot, like couldn't, couldn't you just bundle it and know it has those capabilities?

[01:09:46] Paul Roetzer: Yep. I don't know. More questions than answers I have.

[01:09:53] Listener Questions

[01:09:53] Mike Kaput: All right, so our last topic today is our recurring segment we're doing on listener questions where we are [01:10:00] answering all the questions that come up from listeners or from audience members in different contexts on different webinars. So we're just kind of cherry picking some that jump out as really helpful possibly for the audience to get an answer to.

[01:10:14] Mike Kaput: So this week's question, someone said, I want to master prompt engineering, but now that models are able to create prompts for you, is this even going to be important in 12 months?

[01:10:28] Paul Roetzer: Yeah. So in 2023, pretty early on, I was trying to look out and say like, is prompting like a thing? Like, isn't the model just going to like, write the prompts or improve your prompts?

[01:10:36] Paul Roetzer: And I, all I can say is we're like a couple years into this and prompting matters still. Like, you know, Mike does demos on this all the time, runs classes on it where you're showing like new prompting techniques for reasoning models, for example, right. Or prompting techniques for image generation models or video generation models.

[01:10:53] Paul Roetzer: Like, yes, the model companies are probably taking and improving your prompt and not showing it to you. They're like rewriting it and making it [01:11:00] better behind the scenes. But your ability to, to know what the system's capable of and convey what you want to convey, like what is the goal, what is the output I'm looking for?

[01:11:08] Paul Roetzer: That stuff still matters. Like, it definitely, I think of it as a skill and like when we're interviewing, people, you know, for, for roles in our company, I. I want to know their prompting skills. Like I want to know these things. So I would, I would encourage universities, high schools. I would be teaching prompting as a skill.

[01:11:26] Paul Roetzer: I don't think that that's going to go away. I think the systems will get better and better at helping you, but I do think that knowing how to talk to these systems is going to be a required part of everybody's job moving forward.

[01:11:38] Mike Kaput: Yeah, absolutely. All right, Paul, that's a wrap on a busy week. Just a quick reminder for everyone.

[01:11:44] Mike Kaput: Again, go to state of marketing ai.com to take the survey that takes just a few minutes to help us with this year's state of marketing AI report. You can find the link right on that page along with a copy to download of last year's report. And check [01:12:00] out the marketing ai newsletter, marketing ai institute.com/newsletter where we wrap up all of this news from today's episode, as well as all the stuff that didn't make the list, which is always lots of really interesting news.

[01:12:13] Mike Kaput: We just didn't have time for. Thanks again.

[01:12:17] Paul Roetzer: Thanks Mike. It was good to be back together, my solo session, a solo session, and we'll be back next week with another regular weekly episode. So thanks everyone for joining us.

[01:12:29] Paul Roetzer: Thanks for listening to the AI show. Visit marketing ai institute.com to continue your AI learning journey and join more than 60,000 professionals and business leaders who have subscribed to the weekly newsletter, downloaded the AI blueprints, attended virtual and in-person events, taken our online AI courses and engaged in the Slack community.

[01:12:52] Paul Roetzer: Until next time, stay curious and explore [01:13:00] ai.

Claire Prudhomme

Claire Prudhomme is the Marketing Manager of Media and Content at the Marketing AI Institute. With a background in content marketing, video production and a deep interest in AI public policy, Claire brings a broad skill set to her role. Claire combines her skills, passion for storytelling, and dedication to lifelong learning to drive the Marketing AI Institute's mission forward.

[The AI Show Episode 142]: ChatGPT’s New Image Generator, Studio Ghibli Craze and Backlash, Gemini 2.5, OpenAI Academy, 4o Updates, Vibe Marketing & xAI Acquires X

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Timestamps

Summary

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[00:03:01] ChatGPT’s New Image Generator

[00:13:59] Backlash Against ChatGPT, Meta Copyright Violations

[00:23:49] Google Gemini 2.5

[00:29:52] OpenAI Academy

[00:34:07] More OpenAI Updates

[00:38:35] Vibe Marketing

[00:44:37] xAI Acquires X

[00:48:43] New Anthropic Paper Traces the Thoughts of LLMs

[00:53:04] Replit CEO: "I No Longer Think You Should Learn to Code"

[00:56:37] McKinsey State of AI Research

[01:00:01] Inside the Drama and Deception at OpenAI

[01:04:01] Runway Gen-4

[01:07:06] Microsoft Researcher and Analyst

[01:09:53] Listener Questions

Claire Prudhomme

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[The AI Show Episode 142]: ChatGPT’s New Image Generator, Studio Ghibli Craze and Backlash, Gemini 2.5, OpenAI Academy, 4o Updates, Vibe Marketing & xAI Acquires X

Listen Now

Watch the Video

Timestamps

Summary

Read the Transcription

[00:03:01] ChatGPT’s New Image Generator

[00:13:59] Backlash Against ChatGPT, Meta Copyright Violations

[00:23:49] Google Gemini 2.5

[00:29:52] OpenAI Academy

[00:34:07] More OpenAI Updates

[00:38:35] Vibe Marketing

[00:44:37] xAI Acquires X

[00:48:43] New Anthropic Paper Traces the Thoughts of LLMs

[00:53:04] Replit CEO: "I No Longer Think You Should Learn to Code"

[00:56:37] McKinsey State of AI Research

[01:00:01] Inside the Drama and Deception at OpenAI

[01:04:01] Runway Gen-4

[01:07:06] Microsoft Researcher and Analyst

[01:09:53] Listener Questions

Claire Prudhomme

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[The AI Show Episode 142]: ChatGPT’s New Image Generator, Studio Ghibli Craze and Backlash, Gemini 2.5, OpenAI Academy, 4o Updates, Vibe Marketing & xAI Acquires X

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