Category: Artificial Intelligence (AI)

While Santa’s loading his sleigh, Silicon Valley’s dropping AI breakthroughs by the hour.

OpenAI’s “12 Days of Shipmas” keeps the gifts coming with ChatGPT Canvas, an Apple Intelligence integration, and game-changing voice capabilities. Not to be outdone, Google jumps in with Gemini 2.0 and its impressive Deep Research tool. Join Paul Roetzer and Mike Kaput as they unwrap these developments, plus rapid-fire updates on Andreessen’s AI censorship bombshell, an OpenAI employee’s AGI claims, and the latest product launches and funding shaking up the industry.

Listen or watch below—and see below for show notes and the transcript.

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Timestamps

00:05:39 — OpenAI 12 Days of Shipmas: Days 4 – 8

00:18:54 — Gemini 2 Release + Deep Research

00:33:03 — Hands-On with o1

00:46:18 — Perplexity Growth 

00:50:46 —  Andreessen AI Tech Censorship Comments

00:56:22 — OpenAI AGI

01:00:38 — Amazon Agent Lab

01:03:38 — Pricing for AI Agents

01:07:45 — OpenAI Faces Opposition to For-Profit Status

01:11:13 —Ilya Sutskever at NeurIPS

01:14:20 — Mollick Essay on When to Use AI

01:16:15 — Product and Funding Updates

• Databricks Fundraise
• Grok
• Zuckerberg on Llama 4 and Open Source
• Google Veo 2 and Imagen 3 New Versions
• Pika 2.0 + Sora First Impressions
• Microsoft Recall
• NotebookLM Updates

Summary

OpenAI’s 12 Days of Shipmas: Days 4-8

OpenAI has continued its 12 Days of OpenAI event this past week, where it’s releasing new products and updates each weekday, 12 weekdays in a row. Since our last episode dropped, the company has released announcements for Days 4 through 7.

On Day 4, OpenAI announced the general release of Canvas in ChatGPT. Canvas is a side panel that has responses from ChatGPT on a shared, editable, and shareable page, so you can more effectively collaborate with ChatGPT on writing and coding tasks.

On Day 5, OpenAI unveiled its long-awaited integration with Apple Intelligence. The demonstration highlighted Siri’s improved abilities, the voice assistant now handling complex queries and offering more natural, context-aware responses powered by ChatGPT. 

Users can now switch seamlessly between Siri and the ChatGPT app, with Siri accessing tools like Canvas and DALL-E. It also integrates with Apple’s Visual Intelligence for advanced image analysis.

On Day 6, OpenAI finally delivered on its promise of video capabilities for ChatGPT’s Advanced Voice Mode, a feature first previewed during the GPT-4o launch back in May.  The update allows users to interact with ChatGPT through their phone’s camera, with the AI able to see and respond to what’s happening in real-time. The feature also includes screen-sharing capabilities, allowing ChatGPT to understand and comment on content displayed on a user’s device.

In a more festive twist, OpenAI has also introduced a Santa Mode for Voice Mode, complete with a deep, jolly voice and characteristic “ho-ho-hos.” 

On Day 7, OpenAI introduced Projects in ChatGPT, a feature to help users manage AI conversations more effectively. Projects function like folders, letting users group related chats and resources in a more intuitive way. 

Located in the ChatGPT sidebar, users can create projects, customize them with different colors, and set instructions to guide ChatGPT’s responses. The feature also allows adding files and existing conversations, making it easier to track ongoing work.

On Day 8, just before recording this week’s episode, OpenAI announced updates to ChatGPT Search, now faster and optimized for mobile. Search is also now going to be enabled in Advanced Voice Mode. Search is also rolling out to all logged in free users.

While this is our last formal episode of the year, obviously there are 4 more days to cover in this event. While they won’t be covered in the coming week on the podcast, they will be added to our regularly updated post on the 12 days of OpenAI, which we’ll link to in the show notes.

Gemini 2 Release + Deep Research

Not to be outdone by OpenAI, Google also made some huge announcements this week.

First, Google just unveiled Gemini 2.0, marking what the company calls its entry into “the agentic era” of artificial intelligence.  This update to Google’s flagship AI model introduces a range of capabilities that allow AI to take more direct actions on behalf of users.

At the heart of this release is Gemini 2.0 Flash, an experimental model that Google says is not only twice as fast as its predecessor but can now generate images and audio alongside text. The model can also directly use tools like Google Search and interact with third-party services, representing a major step toward more autonomous AI systems.

You can currently access Gemini 2.0 Flash in an experimental mode using a Gemini Advanced account or using Google AI Studio.

Second, Google also released a stunning new feature with Gemini 1.5 called Deep Research. Deep Research is a super-powerful AI research assistant that creates multi-step research plans, analyzes information from across the web, and compiles comprehensive reports on complex topics—using literally dozens or hundreds of web pages.

Third, the company unveiled a research prototype called Project Mariner, a Gemini-powered agent that can take control of your Chrome browser, move the cursor on your screen, click buttons, fill out forms, and navigate websites.

Hands-On with o1

On the first day of OpenAI’s 12 Days of OpenAI event, the company announced access to their full o1 reasoning model—so in the past week, we’ve been doing a bunch of hands-on experimentation and testing of the new model.

The o1 model is notable because it actually takes time to think through problems using step-by-step chain-of-thought reasoning, which makes it operate in a fundamentally different way than previous models.

That means it’s also very, very good (much better than existing models) at certain types of complex problem-solving tasks, as well as math and science.

(Some claim it approaches the level of a PhD student at these types of tasks.)

The full o1 model represents a big improvement over the previously available o1-preview model, making 34% fewer major mistakes while processing information 50% faster. It is also multimodal, which means it can process images and text together.

This episode is brought to you by our AI Mastery Membership, this 12-month membership gives you access to all the education, insights, and answers you need to master AI for your company and career. To learn more about the membership, go to www.smarterx.ai/ai-mastery.

As a special thank you to our podcast audience, you can use the code POD150 to save $150 on a membership.

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: If the AI models keep getting better, thinking, reasoning, understanding, imagination, which is a whole separate thing we’ll talk about at some point, if it does those things better, then the average human who would otherwise do the job in your business, in your industry, then we got some problems. Welcome to the Artificial Intelligence Show, the podcast that helps your business grow smarter by making AI approachable and actionable.

[00:00:26] Paul Roetzer: 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 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.

[00:00:47] Paul Roetzer: Join us as we accelerate AI news.

[00:00:54] Paul Roetzer: Welcome to episode 127 of the Artificial Intelligence Show. I’m your [00:01:00] host, Paul Roetzer, along with my co host, Mike Kaput. This, Mike, is our final weekly episode of 2024. And all the AI companies saved up all of their big updates for this week. This i uh, I don’t know how, how I usually start these shows. I, as I’ve said before, I don’t script what I say for the most part, so I’d have no idea what I say most of the time.

[00:01:26] Paul Roetzer: so if I usually say it was a crazy week in AI, that would be an understatement for what Mike and I have to try and get through today on our final episode. And I hope that people don’t keep dropping stuff. The next four days, but we already this morning got stuff from Google, so who knows? we are going to do our best to catch you up on OpenAI’s 12 Days of Shipmas, or 12 Days of OpenAI.

[00:01:53] Paul Roetzer: there, we are now on day 8, right Mike? We’re on day 8, yep. We got new [00:02:00] models from Gemini, we, got a hackathon Mike and I ran on the O1 reasoning model, which honestly, Mike, when I thought about today’s episode, that felt like a month ago that we did that, and it was just six days ago. I was trying to remember, like, didn’t we do something with this O1 thing?

[00:02:15] Paul Roetzer: It’s like, oh yeah, we met for two hours on this, like, holy cow. So, it is just non stop updates, I would have to go back historically and see if there was a week that had more updates. Maybe this time last year, but certainly this was the busiest week of the year that I can recall in terms of updates from basically every frontier model company had stuff.

[00:02:39] Paul Roetzer: Yeah. We’ve got a lot to unpack for this final episode. There is one other episode, which I’ve mentioned a couple of times, Mike and I are doing on December 19th. We’re going to drop a 25 AI questions for 2025 special episode that we are actually recording Wednesday, right? Is that right? Yeah, the 18th.

[00:02:58] Paul Roetzer: That’s correct. It’s going to be [00:03:00] heat. So today is, we’re recording this one on Monday, December 16th at about 2. 30 Eastern time. We delayed it again so we could see what day eight of OpenAI is, 12 Days of Shipness was. so just a ton to get to. All right, so all that being said, this episode is brought to us by the AI Mastery Membership Program.

[00:03:19] Paul Roetzer: This is the joint membership program offered between Smartirex and Marketing AI Institute. Has tons of marketing content, but also has plenty going on for the non marketers and business leaders. So, this is a membership that, that we run. It’s a 12 month program, gives you access to education, insights, answers that you need to develop a high level of proficiency about AI in your career, in your company.

[00:03:46] Paul Roetzer: That includes quarterly briefings that Mike and I do, a generative AI mastery series that Mike runs every, once a quarter, where he goes through a bunch of cool demos. We have a quarterly Ask Me Anything session where you just spend an hour asking, answering whatever you want to [00:04:00] talk about. there’s unlimited access to our on demand webinars.

[00:04:04] Paul Roetzer: There’s ungated access to Blueprints. And I cannot, like, explain fully what we’re planning for next year, but I will tell you that most of my time right now is being spent on the vision and strategy for this membership program going into 2025. Working on new course series, new certification ideas, new experiences.

[00:04:28] Paul Roetzer: and so if you’ve ever thought about joining the Mastery program, now would be a great time to do it. There’s going to be a ton happening in that next year. So you can go to smarterx. ai and click on education is a quick way to get there. And just look at AI Mastery. The actual website address, if you want to type it in, is smarterx.

[00:04:47] Paul Roetzer: ai slash. AI Mastery. that link will be in the show notes. And as a special thank you, to our podcast audience, you can use POD150. That is P O D [00:05:00] 1 5 0. And that’ll get you 150 off of the membership. And that is good until the end of this year, December 31st. So, we’d love to have you join the iMastery membership program.

[00:05:11] Paul Roetzer: Like I said, it’s going to be a huge focus of ours. to really kind of double down on not only the time and investment we make there, but the value we create for people there. So, hopefully you can join us leading into next year. All right, Mike, We’re on day eight of OpenAI’s 12 Days of Shipmas. why don’t you give us a rundown of what has happened, which again, feels like it’s been a month worth of updates.

[00:05:36] Paul Roetzer: That really has only been the last eight days. 

[00:05:39] OpenAI 12 Days of Shipmas: Days 4 – 8

[00:05:39] Mike Kaput: Absolutely. So, to kick us off this week for our final, you know, formal episode here, we’re covering more of the 12 Days of OpenAI event that’s been happening. Like you said, over the last eight days, they’re basically releasing new products and updates Each weekday for 12 weekdays in a row.

[00:05:57] Mike Kaput: So since our last episode dropped, [00:06:00] including today, the company has released announcements for days four through eight. So I’m going to run through them really quickly and then Paul, I’d love to get your thoughts and kind of what we’re seeing here. So on day four, OpenAI announced the general release of Canvas in ChatGPT.

[00:06:14] Mike Kaput: Canvas is a side panel that has responses from ChatGPT. On a shared, editable and shareable page so that you can more effectively collaborate with GPT and others on writing and coding tasks. On day five, OpenAI unveiled its long awaited integration with Apple Intelligence. So the team showed off significant improvements using AI to things like series capabilities.

[00:06:40] Mike Kaput: The voice assistant is now able to handle complex queries and provide more natural context aware responses powered by chat GBT. Users can now seamlessly move between Siri and the ChatGPT app, and Siri is able to open different ChatGPT tools like Canvas and DALL E. So this system is [00:07:00] also able to work with Apple’s visual intelligence, allowing for sophisticated image analysis and processing.

[00:07:08] Mike Kaput: On day six, OpenAI finally delivered on its promise of video capabilities for ChatGPT’s advanced voice mode. This was a feature that, if you recall, was first previewed during the GPT 4. 0 launch back in May. It was a bit delayed after that, but now it is out, and it allows users to interact with ChatGPT through their phone’s camera.

[00:07:31] Mike Kaput: So the AI can actually see and respond to what’s happening in real time. This feature also includes some screen sharing capabilities, which allows ChatGPT to understand and comment on content that is displayed on your device. At the same time, they also released a bit of a festive twist. OpenAI introduced a Santa mode for voice mode.

[00:07:52] Mike Kaput: So basically, you can have it sound and talk just like Santa Claus. You can access this feature by [00:08:00] tapping a snowflake icon in the ChatGPT interface. Day 7, we got OpenAI announcing better organization in ChatGPT with a new feature called Projects. This is designed to help users manage their AI conversations more effectively.

[00:08:17] Mike Kaput: It works like a sophisticated folder structure. It allows users to group related conversations and resources together in a more intuitive way. So projects will appear in the ChatGPT sidebar. Users can create new projects, customize them with different colors, and add instructions to guide how ChatGPT responds within this particular project.

[00:08:40] Mike Kaput: You can also add and attach files, and add existing chat conversations. Now, finally, today! just, and probably about an hour or two before we’re recording, here on Monday, December 16th, OpenAI announced on Day 8 improvements to ChatGPT’s search function. So [00:09:00] not only is search a lot faster, But they also showed how it is optimized for mobile.

[00:09:05] Mike Kaput: So the team actually demonstrated on mobile doing searches about, they used an example of finding a restaurant in San Francisco that serves Mexican food and has outdoor patios with heaters. And ChatGPT search on mobile now presents this kind of clean, visual list of businesses and the search results for those particular queries.

[00:09:27] Mike Kaput: So search is actually now also going to be enabled, which they were displaying it as, in advanced voice mode. So the team is able to actually, they demoed how you can talk to ChatGPT to actually search up to date information on the web. So in their example, they actually had ChatGPT look up different holiday events happening in the coming week in different locations around the world.

[00:09:51] Mike Kaput: And they got up to date info about the locations, the hours of these events at the locations. As well as the weather on certain days. [00:10:00] Now, while this is our last formal episode of the year, obviously there are four more days to cover in the event, so we’ll link in the show notes to the ongoing posts we have summarizing all these updates, and we’ll be updating that once this event has concluded as well.

[00:10:16] Mike Kaput: So, Paul, that’s quite a bit to unpack. That could be, honestly, half, if not the entire podcast episode alone. I’d love to get your thoughts on like what we saw this week. I mean, for me, I have to say I’m most impressed so far with advanced voice and video. 

[00:10:32] Paul Roetzer: Yeah, so I’ll run down a few of my takeaways here.

[00:10:35] Paul Roetzer: So Canvas is nice. I’m not a power user of Canvas within GPT 4. 0, so it wasn’t like a huge, you know, thing for me, but having it now in custom GPTs is going to be interesting and kind of like as they keep building out that capability. Apple Intelligence Plus ChatGPT is very interesting. So this is the first time where I feel like we’re starting to see the vision for Apple intelligence.[00:11:00] 

[00:11:00] Paul Roetzer: I, just a reminder here, if you have an iPhone, it’s got to be, I think, 15 pro or higher to be able to use Apple intelligence and then check your iPad and Mac devices, to make sure that they’re compatible in terms of the OS and the, you know, the generation you have of those. But when you connect the account, when you turn on kind of the Apple intelligence, you actually have to go in to settings and then go to Apple intelligence.

[00:11:23] Paul Roetzer: And then. You can use ChatGPT free, or you can choose to connect it to an existing ChatGPT account, which is what I did. And then I have a personal account and I have a account for the business. And so it actually lets you choose which of those you’re going to be sending stuff to, because the way it works is when you talk to Siri or when you use visual intelligence, it’s actually sending those inputs, like your requests, your prompts.

[00:11:53] Paul Roetzer: to your existing ChatGPT account. So, there’s some notes on the setup. And I [00:12:00] text, I tested Siri, and it’s interesting because, like, historically, I’ve been as big a critic of Siri as anybody, like, it’s just useless, basically, historically. and it’s always been like, why can’t it just answer something? Why does it always have to be like, I found it on the web.

[00:12:15] Paul Roetzer: It’s like, I’m driving. I don’t, if I wanted to go to the web, I would have gone to the web, Siri. Like, tell me the answer to my question. So what it does now is it says, would you like to connect to ChatGPT to get this answer? And then it says, working with ChatGPT. And so I tried one where I said, what happened to Clue and Brown’s game yesterday?

[00:12:32] Paul Roetzer: Figured it was like real time, see what happened. And it actually did a really good job. It gave me a ChatGPT generated summary in about, I don’t know, five seconds. And then it read it to me. And so I thought that was, a really good start. Like I could absolutely see myself using siri now way more. I find my guess is I’m going to get really annoyed that I have to say yes every time to do you want me to use with ChatGPT.

[00:12:56] Paul Roetzer: It’s like, just, I always want a universal yes. Like if I ask you and you don’t [00:13:00] know, just go to ChatGPT, which Apple will know that that is a friction point and they will either solve for that or they will eventually build their own. Version of this so they don’t have to go to ChatGPT, which is my guess, is what they’ll probably eventually do.

[00:13:14] Paul Roetzer: So, that’s really cool. Visual intelligence is interesting. If you haven’t used that yet, the, on the 16 Pro I think is what I have. So there’s this new button on the right hand side. and so if you just click on that, it actually brings up what looks like the camera function, except you have kind of the rainbow effect going on that you do with the new Siri.

[00:13:32] Paul Roetzer: And then you can ask or search are the two options. And what happens is it basically takes a picture of whatever you’re looking at and then it can go search for that. So I like, I like scotch. So I have a bookshelf next to where I’m recording from right now that has a collection of scotch bottles. And so I just like took a picture of Johnny Walker Black and it just, it went and like found that bottle at different places where you could buy a bottle of scotch and pricing.

[00:13:59] Paul Roetzer: And I was like, okay, that’s [00:14:00] pretty nice. And then you can ask it, anything you want. And so it’s like, okay, you know, what is this? And it’s like, oh, it’s a bottle of Johnny Walker Black. And so that was, that was pretty cool. So, the Apple Intelligence plus ChatGPT integration is worth testing now. It’s actually, you know, very, functional.

[00:14:18] Paul Roetzer: Advanced voice. Actually, I’m going to come back to advanced voice. projects is interesting. I love that you can now organize your, your chats, your, you know, threads that you’ve got going on. My problem immediately was I got really excited because I’m doing this co CEO webinar tomorrow and I was like, Oh, awesome.

[00:14:37] Paul Roetzer: I can take all of my co CEO threads that I name co CEO so I can find them in my left panel. And I went to put them, add them to the new co CEO project and I realized like, I can’t. So. I actually went on X and I tweeted at Adam Goldberg, who’s a go to market lead at OpenAI, and then Kevin [00:15:00] Whale, who’s the chief product officer, and said like, hey, this is awesome, but it’s an annoying limitation that I can’t seem to add these custom GPTs to a project folder.

[00:15:09] Paul Roetzer: And so I’m stuck renaming and labeling these things. I thought projects would solve for that and the Chief Product Officer, Kevin, actually replied and said it’s on the list. So one, I thought it was cool that he took the time to reply on a Sunday. And two, it’s coming. Because if you’re like me, the vast majority of my use of ChatGPT now is within custom GPTs.

[00:15:29] Paul Roetzer: So I can’t organize anything without that capability. So projects I think is nice. And that advanced voice. So, as much as we’ve talked about Project Astra from Google this year, and this idea of giving devices vision capability to understand the world around you, it’s here. Like, if you go into advanced voice mode and you click on the camera, you open up a vision of what you’re looking at, and you can talk to it, and it sees and understands.

[00:15:58] Paul Roetzer: So like, if you’re [00:16:00] watching this on YouTube, you can see my basement. And so I actually went over to the big copy of the book from Mike and I, and I said, like, tell me about these authors. And it actually, like, told me about me and Mike. It gave the background. It talked about the book. I went like over here and I said, what do you see here?

[00:16:17] Paul Roetzer: And it’s like, oh, that looks like a really fun rocket. And I said, what kind of rocket is it? It looks like the Saturn 5 Lego set. I’m like, perfect. It nailed it. I showed it a picture of a puzzle behind me. It’s like, oh, that looks like fun. Are you going to frame it since it’s already done? And so it’s like, it’s, it’s working, it’s seen, now it made some mistakes on a couple things, but we now have in ChatGPT the ability to show it something.

[00:16:38] Paul Roetzer: So if you’re traveling, if you’re looking at signs and trying to understand it, if you’re looking at products in a store, like you now have vision capability on your phone to do this with. And the other one is screen sharing. You can actually open up and go in and choose to share your screen. and then interact with whatever is happening on your screen and talk to [00:17:00] ChatGPT about it.

[00:17:00] Paul Roetzer: So, I’m with you, like, the advanced voice improvements with video and screen share are huge and could easily have been lost in a week of craziness since they introduced that on the same day as Santa Mode, which, like, is the first thing I went to with Santa Mode. And by the way, if you have younger kids, like pre teens and teens, Ask it to do like, you know, Twas the Night Before Christmas or some other, you know, Christmas song or poem in Gen Z slang.

[00:17:29] Paul Roetzer: It’s hilarious and it’ll, you know, it’s like cringe your kids out. Like, my poor daughter was like hiding in the backseat listening to this thing. It was so funny. So, yeah, I mean, there’s lots of good stuff, but I would say Advanced Voice and the Apple Intelligence were the things I was the most kind of geeked about.

[00:17:45] Mike Kaput: Yeah, and this might just be my personal opinion, but as a huge advanced voice mode user already, this screen sharing also is. That makes a big case for ChatGPT Pro and getting rid of usage [00:18:00] limits because if I can suddenly be like pair working with this thing and having it tell me like, Hey, how could I be more efficient?

[00:18:06] Mike Kaput: How do I do this step? That’s really, really fast time to value for me for a subscription like that. 

[00:18:13] Paul Roetzer: Yeah, I agree. And I think that you’re starting to see how they could. So, you can hear their pricing models further in the future, now that these things are capable of way more than text in and text out.

[00:18:24] Paul Roetzer: And that’s, because again, for context here, we started the year, all of these models were basically text in and text out. They could do some video, you know, image generation, but like, they couldn’t see and talk to you about things you were seeing. They couldn’t generate videos the way we’re now seeing these.

[00:18:41] Paul Roetzer: So, like, we have had a massive shift now in what these models are capable of, and we can look out into next year and start to see some of the other capabilities that are coming very closely behind. 

[00:18:54] Gemini 2 Release + Deep Research

[00:18:54] Mike Kaput: So honestly, that topic alone would have made for one of the busiest weeks in AI, but [00:19:00] there’s a lot more that happened because you can clearly tell Google doesn’t want to be left in the dust with this big 12 days of OpenAI, and they made some huge announcements this week too.

[00:19:11] Mike Kaput: So, So first, they unveiled Google Gemini 2. 0, marking what they called their entry into the quote, agentic era of artificial intelligence. This is the beginning of rollout of updates to their flagship AI models, and it introduces a range of capabilities that allow AI to take more direct actions on behalf of users.

[00:19:32] Mike Kaput: So right now, at the heart of this initial release is Gemini 2. 0 Flash. Which is an experimental model that Google says is not only twice as fast as its predecessor, but can now also generate images and audio alongside text. This model can also directly use tools like Google Search and interact with third party services.

[00:19:54] Mike Kaput: And you can currently access Gemini 2. 0 Flash in experimental mode using a Gemini [00:20:00] Advanced account, or you can go to Google AI Studio and play around with that. Now at the same time, Google also released a stunning new feature that is attached right now to Gemini 1. 5 Pro, which is called Deep Research.

[00:20:14] Mike Kaput: Deep Research is basically a super powerful research assistant that creates multi step research plans analyzes information from all across the web and compiles comprehensive reports on complex topics. And it literally does that with dozens or hundreds of different webpages and sources. Third, we also heard that the company Unveil A research prototype called Project Mariner, and this is a Gemini powered agent that can take control of your Chrome browser, move the cursor on your screen, click buttons, fill out forms, and navigate websites.

[00:20:52] Mike Kaput: So Paul, that’s a lot to unpack. First let’s maybe get your thoughts on the implications of Gemini 2. 0. I mean, even though it’s still [00:21:00] early, we’ve just got Flash, like, this seems like a pretty major release that we were expecting. 

[00:21:04] Paul Roetzer: Yeah, there’s a ton packed into it and I will put all the links since people kind of follow along and explore it.

[00:21:10] Paul Roetzer: The one thing I’ll tell you is the 2. 0 Flash at the moment, at least my last looked, was only available in my personal Gemini account. Okay. And not in the Workspace account. So if you have a Google Workspace Gemini account, it’s still just showing as advanced and I’m pretty sure that’s still 1. 5, not 2.

[00:21:28] Paul Roetzer: So, I needed to go into there, but like you said, you can also go into the AI studio and there’s all kinds of experimental things in there. So, Yeah, I think it’s a really big deal and it’s starting to show where their models are going. Now again, you’re not gonna get the full 2. 0. My best guess is April. I just because I think that’s when their big next conference is, their developer conference.

[00:21:51] Paul Roetzer: And so I would, I would. Guess that maybe it’s going to be before that. I can’t imagine it would be after that. So I would think what you’re going to see is this [00:22:00] like really, full out multimodal model built from the ground up, trained on multimodal, able to output multimodal. And that’s where all of these frontier models are going.

[00:22:10] Paul Roetzer: And so again, it’s one of those where you’re seeing it, but it’s still just the flash version. It’s still previews in a lot of ways. so I don’t know, you know, we’re going to get the full thing for a little while here, but what they’ve released is already powerful. And so I would definitely play around with it.

[00:22:27] Paul Roetzer: I haven’t, with everything that’s been going on, I haven’t had a ton of time to like run it through a bunch of use cases over the last few days, but over the holidays, it’s definitely something that I’m, I’m going to be playing around with a lot. 

[00:22:39] Mike Kaput: Now I am also blown away by deep research and if I’m not mistaken, you are too.

[00:22:46] Mike Kaput: Can you kind of maybe Give us a rundown of what you found so impressive about it so far. 

[00:22:51] Paul Roetzer: Yeah, so deep research was one of those wow moments, and as we’ve said on the show before, we don’t have too many of those. Like when you, when you like pay as [00:23:00] close attention to this space as we, we, we have to and we do, you just, you feel like you see everything all the time and it’s hard to be like really impressed by something.

[00:23:09] Paul Roetzer: This is the first time where I used a product since audio overviews, what Google, Google’s Notebook LM product earlier this year. where you had that wow moment of like, oh, this like changes things. Like this is, this is different than anything else we had. It’s different than what ChatGPT can do. It’s different than what Perplexity is doing, in my opinion.

[00:23:27] Paul Roetzer: So, yeah, I was, I was definitely, impressed by it. So, what deep, deep research does is it uses AI to explore complex topics, and then it kind of generates them in easy to, to read reports. And so it’s using this Gemini model to do it. And so you put a question in, it creates a multi step research plan.

[00:23:48] Paul Roetzer: You can either revise or approve that plan. It then goes and executes that. And then it takes a few minutes. It goes and browses the web. It does the searching, the finding. It looks for interesting pieces of [00:24:00] information. and it keeps repeating that process until you get the final product. So I was trying to figure out like, well, what could I do?

[00:24:08] Paul Roetzer: And I actually had a research project I was looking at and I was looking into like pricing models and I wanted to look like broadly and then I wanted to go specifically into like five companies that I had in mind. And so this was something literally I was going to try and find time to do over the holidays and I was like, seems like that’s what this thing does.

[00:24:26] Paul Roetzer: Let me give it a try. And so I put a very simple prompt in and it went and in about three to five minutes completed a research project that would have taken me three to five hours. easily three to five hours. So I put in a single prompt. It created the research plan, which again, it gives you the choice, like edit or approve.

[00:24:45] Paul Roetzer: Once it’s ready. And the research plan is literally like, I’m going to do these six things. Like, is this what you want me to do? And if you want to do something different, it’ll do it. So, it then visited, analyzed, and summarized over 100 websites. And then it turned it into a [00:25:00] Google doc for me that I could review and edit from there.

[00:25:02] Paul Roetzer: And this is, if you go back a couple episodes, Mike, you’ll remember I said, This was exactly one of the advantages for Gemini and Google is if I create something in ChatGPT, it doesn’t live in a productivity doc that I can work with. It lives as this thing I have to have copy and paste over into either Microsoft Word or Google Docs.

[00:25:22] Paul Roetzer: Well, now I don’t have to worry about that. It just automatically creates. It’s like nice and streamlined. So, My initial reaction was like, this is far beyond anything anybody’s doing, and it has the benefit of Google’s search dominance and accuracy and theory. Like, I immediately trust that Google is going to find a way to solve for hallucinations and errors better than any other company because that’s what they do in search.

[00:25:46] Paul Roetzer: And so, you know, you and I had a talk right away, Mike, it’s like, phew, man, how can we be using this in content production, course creation, and podcast m, you know, preparation. So, we’re already thinking about research, publishing, and education, [00:26:00] how a product like this that we know is only going to get better, how it can really change the way we do things.

[00:26:05] Paul Roetzer: So, again, I would definitely say, give this a test, come up with like a use case, or go online, or, or. I mean, ask, ask Gemini, Hey, I want to test deep research. What would be an example? Use dummy data if you have to, or, you know, a Drupal use case, it will still make mistakes. Like I’d have to say like, if you’re going to use this in your business or for your, you know, your career or for schoolwork or whatever, it’s going to make mistakes.

[00:26:29] Paul Roetzer: It’ll still have errors. So do humans. so like, is the error rate going to be lower than a human? Maybe in some instances, it probably just depends on what you’re trying to do with it. But the human still has to remain in the loop. You still have to use your experience and expertise to decide the quality of the output.

[00:26:46] Paul Roetzer: You need to guide the research plan. So this isn’t replacing, but this, this changes the way research is done. and I can definitely see it transforming us. What about you, Mike? I know you’ve played around with it too. 

[00:26:59] Mike Kaput: [00:27:00] Yeah. Similar initial reactions. I would say too, it just excites me in the sense of not only doing these complicated and complex research projects so much faster, but also what it enables when you probably start combining it with other AI tools, right?

[00:27:16] Mike Kaput: Like these other systems and models thrive on giving context and information. So instead of me, you know, I don’t know, using a tool like ChatGPT or Claude. And prompting it with a couple sentences like, hey, I’m trying to like, write something about, I don’t know, the energy sector. I could be like, oh, okay, like, we’re writing something about the energy sector.

[00:27:34] Mike Kaput: By the way, here’s seven to ten pages of perfectly researched information based on a hundred plus different websites. It really opens up just so many more combinatorial possibilities, I think, in my mind, that I’m really excited to explore. 

[00:27:48] Paul Roetzer: Yeah, I mean, I was trying to think back, like, if we’d had this when we wrote our book in 2022.

[00:27:53] Paul Roetzer: Could you imagine? Yeah, right. I mean, so if you combine, I would say like, so I’ve written three [00:28:00] books and I would say, you know, roughly between 000 words each. My guess is they take roughly four to 500 hours to do like the full planning and production of the book, like writing of it and wrote the research.

[00:28:14] Paul Roetzer: I don’t know how much of that would be shaved off with a tool like this, but it wouldn’t be insignificant. Like, most of the process of writing the book is the research, in my opinion. Like it’s developing the initial outline and then doing the research. Not the first party stuff where you’re going and conducting interviews and things like that.

[00:28:31] Paul Roetzer: That’s obviously not replacing this, but I almost think like you’d have way more time to go do those things because so much of the research, like I could, I used to keep Evernotes on this stuff. Like, that was the productivity tool I would use, and I would have to create all these folders, and then I’d go do the research, and I would put them in Evernote, and then I would have to go through and do summaries of them, and I would print them out, and I would highlight them, like, I’m not joking when If I were to say, If my second book I wrote by myself was 400 hours, 150 to 200 of that was probably [00:29:00] researching, organizing the research, extracting insights from the research.

[00:29:04] Paul Roetzer: Writing, the writing part is easy. Like if, if you’re a writer, like that’s the easy fun part. It’s everything else that goes into it that is where all the time goes in. 

[00:29:13] Mike Kaput: It’ll be interesting too to see this play out in some of the areas that, you know, I know we’re interested in getting more into because I look at something like this too, and I think.

[00:29:23] Mike Kaput: Wow. This just turned me into a one man public analyst firm, right? I don’t need a research team of 20 people to go do this. I mean, it’s all public information, of course. There’s more to analysis than just that, but that’s pretty darn interesting to me. 

[00:29:38] Paul Roetzer: Well, yeah. And then last week you and I had on top of that, and this isn’t in our notes, I don’t think, but we have CB Insights as a research platform for the company because we do a lot of research for what we write and then we look into a lot of companies.

[00:29:53] Paul Roetzer: And so we had a call with them last week and they were showing us their new AI capabilities includes like a chat CBI, which is connected to their [00:30:00] proprietary database. And so like, I think your head and my head last week were in the same place of like, oh my gosh, like the future of analyst firms and research is like before our eyes being reinvented and how you do that.

[00:30:12] Paul Roetzer: So yeah, when we think about like industries or business models that are going to be impacted very quickly, you could look at tools like this and say, wow, you gotta, you gotta re imagine that business model real fast. Which is exactly what we’re doing with SmarterX. That was the whole, whole premise. And again, like I, some of this stuff I think is maybe obvious what we’re doing, or maybe other people, like, they don’t stop and think, or they don’t, they don’t really care that much.

[00:30:35] Paul Roetzer: But like, from day one, I built SmarterX to reinvent the analyst firm. Like, it’s, we want to be a research firm, but we want to do it faster, like real time research, so it’s not stale six month old stuff. Like, we wanted to find a way to bring this stuff to market fast, and this is the kind of tool that enables that.

[00:30:53] Paul Roetzer: SmarterBalanced. com 

[00:30:54] Mike Kaput: So just very quickly to wrap this up, I mean, obviously, Project Mariner, it’s a research [00:31:00] prototype, but this has, like, in my mind, like, huge implications. I immediately just start thinking of everything we do as marketers. Isn’t this, this combined with like deep research, like am I ever visiting a website ever again?

[00:31:13] Mike Kaput: Or are my agents going to do it for me? I don’t know. 

[00:31:16] Paul Roetzer: Yeah, that’s, so, I mean this is computer use again, like we talked about computer use with Anthropic, we’ve talked about with OpenAI, we said Google was working on this, we didn’t know it was called Project Mariner, but like everybody is working on this, we have known that for a while, for like seven years, we knew it was happening.

[00:31:30] Paul Roetzer: It’s just that the breakthroughs are happening now. So yeah, I think the unique thing here is you trust Chrome. You probably trust Google more than, you know, you might trust some others. So, you could start to see this, really impacting, you know, search, online behavior. You know, you and I coming from the world of, like, marketing and the analytics data and organic traffic, search traffic.

[00:31:52] Paul Roetzer: Like, is it really humans coming to our site anymore? Or was it just, you know, Google Gemini Deep Research that hit our site five times. [00:32:00] I don’t know. And then, and then even with like the Google Deep Research and Project America. Do we end up clicking on the things that’s surfacing for us? Like, does it, or does it just like remove the need?

[00:32:10] Paul Roetzer: We trust Google so much that we’re not going to click on any of the links. Like, we don’t care. Maybe you’ll spot check three or four and be like, yeah, they nailed it. Like, we’re good. Yeah. I don’t know. But yeah, I, you know, I don’t know when Mariner is going to come out. I would guess sometime next year again, just because everybody’s there.

[00:32:25] Paul Roetzer: But it’s also dangerous. Like, this computer use is, is a really risky endeavor. And there’s lots that has to be solved by these companies before you can like roll this out at mass market scale and not have those because we’ve also, we’re not going to get in today, but there’s been a whole lot of stuff in the last few days about the ability to jailbreak all of these models and get them to do terrible things and actually expose their instructions in them and things like that.

[00:32:52] Paul Roetzer: And so that’s a concern here is like the cybersecurity risk that goes into allowing a computer to access [00:33:00] your screen for you and things like that. 

[00:33:03] Hands-On with o1

[00:33:03] Mike Kaput: Alright, so our third big topic this week is kind of some hands on experiments we’ve been doing with the full O1 reasoning model that was announced on the first day of OpenAI’s.

[00:33:15] Mike Kaput: 12 days of OpenAI. That feels like 

[00:33:17] Paul Roetzer: three months ago. 

[00:33:18] Mike Kaput: I get, I honestly wrote this sentence this morning. I was like, this, that can’t be right. Wasn’t what, that was so last week. Like, 

[00:33:25] yeah. 

[00:33:26] Mike Kaput: Yeah. so, you know, as a quick refresher, this O1 model, it’s notable because it takes time to think through problems.

[00:33:33] Mike Kaput: It uses step by step chain of thought reasoning. This makes it kind of operate in a fundamentally different way. than previous models. And it also means it’s very, very good at certain types of complex problem solving tasks, as well as math, coding, and science. Some people even claim that it approaches the level of a PhD at certain tasks.

[00:33:54] Mike Kaput: so it’s also multimodal, at least in the sense that it can process images and [00:34:00] text together. So what we did, Paul, you know, you and I met, we did a bunch of different experiments with the tool and kind of learned, I think, quite a bit in a fairly short amount of time, so I kind of wanted to get your initial thoughts on L1, what did you use it for, where did you find it helpful, where maybe did it fall short, and then I can also share some tests that I ran on the model this past week as well.

[00:34:22] Paul Roetzer: Yeah. So when we sat down to do the hackathon, which I think I alluded to on last week’s podcast, that we were going to run that and we would share our experiences. So we, yeah, I think it was about like hour and a half, two hours, kind of, met and kind of worked through a couple of things. And what we were trying to get at was what are its capabilities?

[00:34:37] Paul Roetzer: What are the use cases for businesses? Not like, we’re not trying to solve complex math problems. We’re not trying to like invent, you know, pharmaceuticals. Like we’re trying to figure out business strategies and, you know, personas and things like that. And so we wanted to look at use cases and then try and figure out, do we want to pay the 200 a month or are we, are we good at this 20, 30 bucks a month we’re paying?

[00:34:59] Paul Roetzer: Is that [00:35:00] sufficient? And how would we use this on an ongoing basis versus like buy it and forget we even have it and never, never use it. So the one I, again, I’m trying to like, whenever, whenever I’m testing things, I’m trying to use real life situations that I can assess whether or not this would actually make a difference in my life.

[00:35:16] Paul Roetzer: I don’t pick like random, really hard problems to like. Do an eval against, I don’t find them terribly helpful to people. So we try and focus in on like, this is something we do. I do as a CEO or whatever. And if it can help me here, then it can help other CEOs. It’s kind of how I think about this. So. One in particular I was looking at, I mentioned earlier, I’m spending a lot of time on our AI Mastery program and our online academy, and so I’m thinking deeply about, like, pricing models.

[00:35:42] Paul Roetzer: And so I actually went in and I gave the same prompt to 01 as I did to GPT 40, because I wanted to compare the model we’re used to using, which is pretty good, to this reasoning model and see what does it do differently in a problem that seems like it would be more complex to solve, more chain of thought [00:36:00] required.

[00:36:00] Paul Roetzer: So I went in and gave it like our AI Mastery Membership Program Pricing Model, I gave it a couple of things I’m thinking about doing, and then I said like basically analyze this for me, this is my goal, how, how would I best achieve this, You know, the outcome I’m looking for, and I told it, ask any clarifying questions that you need.

[00:36:18] Paul Roetzer: So, with that simple prompt in mind, it was only, what, three sentences, one I’ll say O1, again, I gave the same prompt to both models, O1 asked way more complex and nuanced questions than 4. 0. So immediately, you could see that it was more deeply understanding and considering what I was asking of it, based on the questions that came back to me.

[00:36:39] Paul Roetzer: Same way you would assess a strategist, like If I sit down and meet with somebody, the questions they ask often tells me their level of intellect and their ability to do strategy well. it gave a, O1 gave a much richer explanation up front about like a synopsis of its answer. the scenarios it presented were way more thought out.

[00:36:59] Paul Roetzer: There was [00:37:00] way more reasoning that went into them and then it provided much more context and insights overall. It was an overall longer output, but there wasn’t a waste of characters or words. It was like all good stuff. That would have, if I was actually had time, I would have continued on working with it and really analyze some of these key areas.

[00:37:17] Paul Roetzer: That being said, 4. 0 was formatted nicer, like, I don’t know, for whatever that’s worth. And 4. 0 didn’t do a bad job, but side by side, 0. 1 crushed this one over 4. 0. so I’ll come back to like, would we pay for the license and things like that, Mike, after you kind of walk through your tests. 

[00:37:34] Mike Kaput: Yeah, no, that’s a great way to set up experiments with this.

[00:37:38] Mike Kaput: And I’d set mine up very similarly in the sense of just going head to head 4. 0 with 0. 1. I won’t get into every single detail here, but I ran through four, again, real world scenarios of problems I’m trying to solve in one context or another, including we’ve had a lot of questions like, Hey, based on our podcast performance, should we.

[00:37:57] Mike Kaput: Be adding another episode, doing more limited [00:38:00] edition series. So I gave it a bunch of performance data, told it the problem and tried to solve that and got, got recommendations. I’m like, Hey, build a strategic plan for me that answers questions we might have, recommends what we should do. Did similar things for like building a content strategy.

[00:38:17] Mike Kaput: I gave it a bunch of historical performance of our blog and just said, Hey, build me like two sentences, build me a full comprehensive plan to maximize traffic. Through new content and updates to existing content, we gave it some information on course conversions, people using codes, to buy our piloting AI course, and then asked a bunch of, got a breakdown of a bunch of questions we should be asking, recommendations.

[00:38:42] Mike Kaput: And then last but not least, and I just want to dwell on this one really quickly. We obviously run a ton of different workshops through Marketing AI Institute and SmarterX. One of these is like an applied AI workshop that I’ve run with a bunch of different teams. I run it at MAICON every year. And what you come out of it with [00:39:00] is tons and tons of different use cases.

[00:39:03] Mike Kaput: For AI in your organization. What we do with that is our team of experts sits down, analyzes all that intro and writes, you know, like a brief that’s pretty extensive and comprehensive after a workshop. It takes a very long amount of time to fully do, but it’s really valuable. And I basically just gave a one like dummy data from like that kind of just like anonymized and like, and from one of our workshops that we had ran internally.

[00:39:31] Mike Kaput: And then said, like, here’s how the workshop works, here’s what’s in the final brief, go make one. And I was blown away. It did an incredible job. It would be, like, a very good first start. Like, if we got it from a human strategist, I would be like, yeah, this person did a fantastic job. 

[00:39:46] Paul Roetzer: it from an entry level employee, you’d be like, this person’s moving up fast.

[00:39:49] Paul Roetzer: Easily. 

[00:39:50] Mike Kaput: Yeah, easily. So, I mean, overall, kind of similar takeaways to you, O1 is legit, like, again, these aren’t PhD level problems, I’m not even equipped to [00:40:00] evaluate a PhD level problem, so I’m sure there’s more robust ways to test this, but stacked up against 4. 0, hands down, more robust, comprehensive, and helpful, um.

[00:40:12] Mike Kaput: I hate the fact you can’t upload files or spreadsheets yet. I hope that changes. The way I got data in there was just hacking it by copying and pasting with a bunch of unstructured data, which I actually did a great job with somehow, but I would love if we got that. And then, of course, I see a lot of people talking about this online, so I feel like I’m not the only one here that’s struggling, but I still don’t get the sense I’ve pushed it fully to its limits because I don’t know how to structure a PhD level science problem and then evaluate it.

[00:40:41] Mike Kaput: But I’m definitely impressed so far, much more so than I think I was during the preview phase when I was like still struggling to figure out what do I even use this for? 

[00:40:49] Paul Roetzer: Yeah, I agree. And I think that, you know, that’s our, you know, overall takeaway is the 200 a month, I think [00:41:00] you and I learned that on this, Mike, it’s not worth it unless you plan on like using Sora a ton, like because the Sora use is like built into the 200, but there’s nothing.

[00:41:09] Paul Roetzer: In the 200 license, that’s going to get you something on the 01 model that you’re probably not going to be good with, with the existing license. Is that right? 

[00:41:17] Mike Kaput: I think so. I’m interested that right now there are differences in the context windows, but again, it’s like, if we, if we had like two or three of these, Workshop type use cases where it’s like, Hey, we’re doing this every month and this saves us however many hours per month on those things, then sure, I can see a use case for it be.

[00:41:37] Mike Kaput: I think your average business. So unless you really put in some work to identify some crazy valuable use cases, you probably don’t need beyond oh, one of what you get in a plus or a team license. Okay. 

[00:41:49] Paul Roetzer: Yeah. And I think, you know, one thing that this just highlights for me, you know, Mike and I were talking about, like, if, if we gave this to an entrepreneur personally, I always think back to like my agency life, like when I was running an agency and [00:42:00] at its peak, I think we had like 20 people roughly, and we hired a lot of, you know, younger professionals and you would spend years developing strategic capabilities in them, years.

[00:42:10] Paul Roetzer: Like, it’s not something most people, maybe like MBA programs and stuff might come out of it that way. But we were hiding out of communication schools, business schools, marketing backgrounds a lot of times. They’re not trained to be analysts and, you know, strategists in college, really. Like you get some peripheral stuff, but like you, you need experience to get that.

[00:42:29] Paul Roetzer: And so when I think about that, or, you know, even, even now, like with our companies, you The question that a lot of industries are going to have to deal with is, is this better in its 01 form, like knowing it’s going to get smarter, is it better than the average human who would otherwise do this? And I think, and this is what I said when we talked about AGI on earlier episodes, like, I don’t even care if we get to AGIf we ever agree on what it is.

[00:42:56] Paul Roetzer: And we’ll talk a little bit more about AGI in a couple of minutes, but like my whole [00:43:00] premise is it doesn’t matter. Like if the AI models keep getting better, thinking, reasoning, understanding, imagination, which is a whole separate thing. And we’ll talk about at some point, creating this stuff, if it does those things better than the average human who would otherwise do the job in your business, in your industry, then we got some problems.

[00:43:22] Paul Roetzer: In terms of the future of work, and workforce, and the economy. And here’s the reality, they’re already better than the average human at a lot of things. Like, it’s, and if you think about your teams, if you think about your organizations, there are very few organizations that are all A players. Like, there are some exceptional ones that are dominated by A players, but there’s a lot of B and C players in most companies.

[00:43:45] Paul Roetzer: And my concern is these models are already at B C player level in most use cases in knowledge work. And once you can like string those together, [00:44:00] and once you have like a full blown plan of how to integrate these models and use them to their fullest capability, who cares about solving biology and math and scientific problems if they solve business problems?

[00:44:12] Paul Roetzer: And they’re really good at that right now with human oversight. So 

[00:44:16] Mike Kaput: yeah, 

[00:44:16] Paul Roetzer: this is my, I don’t know, concern going into 2025. I just think it’s going to become a reality for a lot more people next year. They’re going to realize how capable these things already are of doing a lot of knowledge work at or above average human level.

[00:44:32] Mike Kaput: Yeah. I haven’t seen a single thing so far to disprove that statement that it’s already doing BNC level player work. Mostly doing it. 

[00:44:40] Paul Roetzer: I mean, that’s the problem. It’s actually doing it. I know. Yeah. If you knew there was no errors, like if you knew it was above Human error rate, it’s likely already at a player level in a lot of what it does.

[00:44:54] Mike Kaput: It’s going to be a wild 2025. Spoiler. There’s a bunch of questions around that for [00:45:00] the 25 questions episode. 

[00:45:03] Paul Roetzer: One other note here. We’ll drop it in. Cause I don’t think we have this in the show notes, Mike. We’ll, we’ll drop this in as a, just like a little kicker for people. If you want to check the show notes.

[00:45:12] Paul Roetzer: So the Klarna CEO related to this, we’ve talked about this Klarna, like the customer support. Agent, company, whatever. And so he’s like doing these interviews where he’s saying, we’re not hiring any more humans. There’s this natural attrition and like 20 percent a year, our people leave and we just won’t backfill.

[00:45:30] Paul Roetzer: We went from 4, 000 employees to 3, 500 employees. And you listen to these interviews and everybody’s tweeting this thing, like this, this soundbite. And it’s like, oh my God, it’s happening. Yet you go to their company website and they have 56 job openings. They’re, which I assume they’re not hiring agents, but like they’re trying to hire 56 humans.

[00:45:47] Paul Roetzer: So it’s just like. Yeah, I guess it’s one of those, like, let’s all just like pump the brakes and realize there’s going to be a lot of hype still, jobs are going to be impacted, but it probably isn’t going to be as bad as it sounds in a lot of cases. And there’s probably more [00:46:00] to the story when you hear about this stuff.

[00:46:02] Paul Roetzer: So don’t overreact. Let’s like step back and analyze the situation. And we’re going to do our best to be as objective as possible next year, because I think this is going to become a very real thing in some industries. And we want to make sure we don’t get caught up in the hype of it all. 

[00:46:18] Perplexity Growth 

[00:46:18] Mike Kaput: Alright, let’s dive into a bunch of rapid fire for this week to kind of close out the year here.

[00:46:24] Mike Kaput: So first up, Perplexity is pitching investors on a vision of rapid growth. They claim they’re going to double their annualized revenue next year to 127 million and quintuple it by the end of 2026, which would put them at about 656 million. These projections came from a pitch to investors, because Perplexity is currently in talks to raise 500 million at a 9 billion valuation.

[00:46:50] Mike Kaput: This was all reported on by the information. Perplexity’s business model seems to primarily hinge on subscriptions. It projects growing from 240, 000 [00:47:00] premium subscribers by the end of this year to 2. 9 million by the end of 2026. However, it is also exploring other streams of revenue, like affiliate links within search results.

[00:47:11] Mike Kaput: in spite of paying AI providers, like OpenAI, tens of millions of dollars for their technology, which is what Perplexity uses to do its thing, Perplexity still claims it can achieve gross profit margins of 75 percent by year’s end, and are eventually targeting 85 percent margins. So, Paul, I don’t know about you, but these sound like some pretty optimistic projections to me.

[00:47:34] Mike Kaput: Like, do these numbers bear any resemblance to the reality of Perplexity’s business model as you see it operating today? I’d love to 

[00:47:42] Paul Roetzer: see the deck. Like, I’d love to Or more like here’s, here’s my high level take on this. And I, again, I may be completely wrong. Like anyone who listens to the show a lot, like we’ve talked a lot about Perplexity this year.

[00:47:55] Paul Roetzer: Awesome product. I use it all the time. a lot of times it’s better than ChatGPT at certain things. A [00:48:00] lot of times it’s better than Google at certain things. I think my overall thought here is they’re going to get AQUA Hired next year. Like I think if I had to bet money on what happens to this company.

[00:48:14] Paul Roetzer: It’s really, really hard to sustain what they’re doing and hit mass market like takeoff because they don’t have their own models that it’s, it’s largely, I’m sure they’ve got some amazing algorithms, amazing engineers, they’re doing really cool things. But is it anything that Google or ChatGPT couldn’t replicate or make better?

[00:48:41] Paul Roetzer: Like just seeing the deep research product from Google is immediately like, Oh yeah, they’ve got some stuff they haven’t shared with the world yet. And you look at that and you think about Google’s distribution and OpenAI’s distribution and like how many users those other companies have who could likely emulate what Perplexity is already doing.

[00:48:58] Paul Roetzer: And can [00:49:00] perplexity get to that mass market fast enough to where someone else doesn’t just like replicate what they’re doing who already has a billion users and then like who needs perplexity? And that’s my, my fear is they’re going to realize that, their investors are going to realize that, and I could see this company just, you know, Folding in like ADEPT or some of these other companies, Inflection, like we’ll talk about in a little bit here.

[00:49:27] Paul Roetzer: I don’t know, again, they might, they might hit, they might like take off, but two million users, nothing. Like kudos, like that, that’s great for a startup that in this space they’re trying to compete in, nothing. And it’s not changing market share one way or the other. Like it’s not. And so that’s my concern is they’re, they’re trying to take on like a massive, massive market with dominant players with way bigger teams and much better technology and probably much better algorithms.

[00:49:55] Paul Roetzer: and I think once we see all that stuff coming from those other labs and frontier [00:50:00] model companies, yeah, I just don’t know it sustains. Again, love the product, love the company, don’t love their business practices necessarily, but I also feel the other thing, I know this isn’t a main topic, but the other thing that keeps like, Picking at me, like bothering me a little bit with them is it feel like they’re throwing a lot of things out there, like perplexity for finance, perplexity for sports.

[00:50:19] Paul Roetzer: Like it’s almost like they’re, they’re trying to seed a bunch of things to figure out where is the thing that we can take off? What is the market we can blow up in? Right. And I don’t think they know yet. And that’s the other thing that worries me is like that funding can go really fast. If you don’t hit on something and I feel like they’re just like trying a whole bunch of things right now.

[00:50:41] Mike Kaput: Interesting, yeah. That’s why I think it’s a smart observation. 

[00:50:46] Andreessen AI Tech Censorship Comments

[00:50:46] Mike Kaput: Alright, so next up, venture capitalist Marc Andreessen just dropped a bombshell claim about the Biden administration’s approach to AI in a recent interview. In a discussion with Barry Weiss on the [00:51:00] Honestly podcast, Andreessen described quote, absolutely horrifying meetings that he was in with administration officials where they allegedly outlined an intention to quote, completely control AI technology in the US.

[00:51:14] Mike Kaput: He told Weiss that during meetings with admin officials, he said, quote, They said, look, AI is a technology, basically, that the government is going to completely control. This is not going to be a startup thing. They actually said flat out to us, don’t do AI startups. Like, don’t fund AI startups. They basically said AI is going to be a game of two or three big companies working closely with the government and we’re going to basically wrap them in a, and he says I’m paraphrasing, but we’re basically going to wrap them in a government cocoon.

[00:51:44] Mike Kaput: We’re going to protect them from competition, we’re going to control them, and we’re going to dictate what they do. He says he expressed to the administration skepticism that they could exert such control over AI. And in response he says, quote, They literally [00:52:00] said, well, during the Cold War, we classified entire areas of physics, and took them out of the research communities.

[00:52:06] Mike Kaput: And entire branches of physics basically went dark and didn’t proceed. And that if we decide we need to, we’re going to do the same thing to the math underneath AI. Andreessen even indicated these discussions Motivated his somewhat controversial endorsement of Donald Trump for president. So, Paul, this certainly seems like it gets into conspiratorial territory, but this is pretty wild if the gist of it is even true.

[00:52:33] Mike Kaput: It would indicate, in my mind at least, a much more, like, sinister approach. The government was looking at influencing AI. This 

[00:52:41] Paul Roetzer: is one of the craziest video excerpts I’ve ever seen. Like, I haven’t listened to enough of Andreesen’s interviews to know whether he exaggerates. I, like, at the moment, I probably need to, like, take him on his word.

[00:52:56] Paul Roetzer: Now, keep in mind, he wasn’t quoting, he was summarizing, [00:53:00] he said. Right, right. I’m summarizing what they said. Directionally, if what he is saying is accurate, one, I would love to hear from someone else who was in the room. Two, I want to know who the government officials were who were telling him this. And three, I want to know when it happened because if you recall, Mike, in October of last year, he released the Techno Optimist Manifesto that we talked about on this podcast and I thought it was crazy.

[00:53:26] Paul Roetzer: Like, I thought that the manifesto was crazy. If this actually happened, if, if he sat in a meeting with These are both top government officials and was told that they hid elements of physics from society during the Cold War or World War II or whatever it was, and that they intended to shut off AI development and investment and pull it all into two or three companies.

[00:53:54] Paul Roetzer: That is insane. And that is regulatory capture by [00:54:00] definition, like at its peak, like. What we thought was this idea of like Sam Altman and a couple of us trying to like influence regulation and like control it all. And then the government would basically invest in only those companies. Like this is what we proposed was under this idea of regulatory capture.

[00:54:15] Paul Roetzer: I couldn’t have ever fathomed it would be to the level he’s describing here. So if this is true and it can be verified by somebody else, we know who said it and when it was said. A whole lot of what happened in the US election. Mm-hmm . In Silicon Valley makes a hell of a lot more sense now than it did to me seven days ago.

[00:54:37] Paul Roetzer: So when you saw these people throwing their support behind the Trump administration and like not to the joy of the portfolio companies they invest in and their peers, if this is true, I actually understand at a very different level why they did what they did. So, I’m anxiously awaiting [00:55:00] someone besides Mark to verify this is exactly what happened.

[00:55:03] Paul Roetzer: But again, I have no reason not to trust what he’s saying. I don’t, I don’t know him and I don’t know enough about, past interviews with him to judge whether or not he would exaggerate this. It’s nuts. 

[00:55:17] Mike Kaput: It’s crazy. I mean, it’s basically on one hand, just saying we are basically a nationalized AI or wanted to.

[00:55:25] Mike Kaput: 100%. And also, I think it’s funny because I think in some pre election episodes, you and I may have mused a bit on like, turns out AI wasn’t actually that big of an issue politically in this election, but I wonder if behind the scenes, it turns out It was the issue. If it was the issue. We’ll see. I don’t want to overfit that theory, but that would be really interesting to me.

[00:55:49] Paul Roetzer: And I saw him do a second interview with a different journalist about this and he said the same thing. Yeah. So, I mean, he, he’s, he’s committed to these talking points. Yeah. I [00:56:00] just, like I said, I want, I want, I want to hear from someone else who heard these same things. If anybody’s willing to, like, step forward and say it, I will be watching very closely first.

[00:56:10] Paul Roetzer: Verification from another third party about this, and I really want to know when it happened because if it was in September to October of last year, then the Techno Optimist Manifesto makes a hell of a lot of sense. 

[00:56:22] OpenAI AGI

[00:56:22] Mike Kaput: Well, it’s not the only controversial thing coming out of the world of AI this week, so we actually, in our next bit, see that a member of OpenAI’s technical staff named Vahid Kazemi posted on X that he believes the company has already reached Artificial General Intelligence.

[00:56:39] Mike Kaput: In part of this post he posted, which we’ll link to the full one in the show notes, he said, In my opinion, we have already achieved AGI, and it’s even more clear with O1. We have not achieved, quote, better than any human at any task, but what we have is, quote, better than most humans at most tasks. So he noted that some people criticize [00:57:00] LLM saying they can only basically kind of follow a recipe to do what they do.

[00:57:05] Mike Kaput: But he did say no one can actually explain what a trillion parameter deep neural net can learn, and that if you really look at it, the scientific method itself can be summarized as a recipe. So, he basically concludes saying there’s nothing that can’t be learned with examples, implying there’s kind of no real limit here to what AI models will be able to learn and do.

[00:57:26] Mike Kaput: So, Paul, we’ve talked episode after episode. There’s tons of speculation within different circles of the AI community about whether or not we’ve actually achieved AGI or when, if at all, we will achieve it. But should we be taking this prediction more seriously since it’s being literally openly said by someone at OpenAI?

[00:57:46] Paul Roetzer: Yeah, so, I mean, we’ve talked about this many times, the lines are really blurred because we don’t have a uniform definition, we don’t have a means of testing it, and they keep moving the goalposts of whether or not we’ve gotten there. So, he used the same explanation I did, basically, this idea that, [00:58:00] like, it’s already better than most humans at most tasks, so, like, if that’s not AGI, like, what are we doing here?

[00:58:06] Paul Roetzer: I think this one, it, it does, highlight the deficiency of current evals, like the way they evaluate these models as they run them up against the most complex challenges known to man, like hardest tasks and like. It’s like, Oh, well, if it does this one, then maybe we’ll be there. If it does this one, then maybe we’ll be there.

[00:58:25] Paul Roetzer: And it goes back to, in my opinion, what you and I talked about with O1, it’s like, forget the evals. Does it do my job better than me? Like here’s the 20 things I do. How many of those 20 things is it better than me at? And I can tell you, I’m willing to admit it’s getting better than me every day at something I do.

[00:58:44] Paul Roetzer: And that’s okay. Like, I’m okay with that. I got a million other things to go work on. It’s okay if it’s better than me at things. A lot of people aren’t at that point, that they won’t even comprehend that these things are getting better than them at something, and that they’re okay with them getting better at [00:59:00] something.

[00:59:00] Paul Roetzer: Now, I think, like, the other thing it highlights is this uncertainty about what’s coming. So I’ll, I’ll highlight, and again, this isn’t in the show, not to Mike, so I’ll drop the link in there. But, yesterday, Logan Kilpatrick, who we’ve talked about before, formerly of OpenAI, now he’s, lead product for Google AI Studio, he tweeted.

[00:59:18] Paul Roetzer: Pre training is only over if you have no imagination. Seems like kind of an innocuous, like, eh, whatever, like, you know, clever Logan, vague posting. But here’s the trick. I actually think he was implying something here. So imagination is something we assume only humans can do. If AI models were deemed to be able to have an imagination, it would mean they could create or synthesize or envision new ideas, new scenarios, new concepts that aren’t in their training data.

[00:59:50] Paul Roetzer: And if he’s implying that, then maybe he’s implying Google is actually on a path to unlock imagination and metacognition, which means [01:00:00] being aware of your own thoughts, in essence. And so like, I don’t know, like, I feel like depending on what you want to call AGI, a lot of AI researchers probably think we’re already there.

[01:00:15] Paul Roetzer: And they’re already focused on superintelligence. They’re already focused on the next level beyond AGI in their world. I don’t know. We’re gonna spend a lot of time talking about AGI. we’ve got a whole new series planned that we’ll announce in January. We’re gonna go deep on this topic, including interviews with experts and stuff.

[01:00:33] Paul Roetzer: So stay, stay tuned. This is like a huge area of focus for us next year. 

[01:00:38] Amazon Agent Lab

[01:00:38] Mike Kaput: Alright, so next up, Amazon is setting up a new lab in San Francisco. It’s called the Amazon AGI SF Lab. And it’s actually led by David Luan, who is the co founder of AI startup ADEPT. This lab aims to develop AI agents that can perform complex tasks across digital and physical environments.

[01:00:59] Mike Kaput: That includes [01:01:00] handling sophisticated workflows in software tools and executing actions in the real world. This lab is going to focus on AI models improving through human feedback that can self correct their actions and understand user goals. Interestingly, this lab is going to initially be staffed by ADEPT employees.

[01:01:20] Mike Kaput: Amazon says it also wants to hire a few dozen researchers in different fields like quantitative finance, physics, and math. This year, actually, ADEPT agreed to license its tech to Amazon. And Luan and some of the ADEPT team joined the company. So Luan is actually working under Rohit Prasad, the former head of Alexa, who now leads an AGI team at Amazon.

[01:01:44] Mike Kaput: So ADEPT technology was actually originally designed to help create an AI teammate that can use any software tool. So Amazon seems to be Using that to make a big play in this field of agentic AI. So, Paul, this struck me as interesting for a few [01:02:00] reasons. Like, first, it’s a pretty open commitment from Amazon that they’re pursuing some type of whatever they consider AGI.

[01:02:07] Mike Kaput: And I thought it’s kind of curious they’re planning to hire researchers in all those other areas, like finance, physics, and math. 

[01:02:14] Paul Roetzer: Yep. Yeah. And also interesting. This is the Aquahire I referenced earlier. So Adept was Aquahired by Amazon in, they announced it, July. Well, let’s see. Yeah. June, July, end of June, 2024.

[01:02:29] Paul Roetzer: So, ADEPT has raised 415 million, they had raised 350 billion Series B, in March of 2023, not that long ago, a year and a half ago, they raised 350 million, and then they announced June 28th, that their mission, that they started. had been to build general intelligence that enables people and computers to work together.

[01:02:49] Paul Roetzer: So again, computer use, our plan has been to train progressively larger and smarter multimodal foundation models. They basically admitted in their own post that like, [01:03:00] we can’t compete on this. Like we’re not going to keep up on building these frontier models. So that they’re going to kind of partner on this.

[01:03:07] Paul Roetzer: And they said, in addition to the ADEPT co founders and some of the team are joining Amazon’s AGI organization to continue to pursue the mission of building useful general intelligence. Amazon is also licensing Adepts, Agent Technology, Family of State of the Art Multimodal Models, and a few datasets.

[01:03:23] Paul Roetzer: Actually, I never really thought about that one. A few datasets, meaning like anything you gave us, we just gave to Amazon. Thank you very much. so yeah, that’s the backstory there, but more computer use, more agents, and Amazon wants a piece of it. 

[01:03:38] Pricing for AI Agents

[01:03:38] Mike Kaput: So kind of an interesting corollary to this, so there’s an AI agent startup called Sierra that is founded, was founded by the former Salesforce co CEO, named Brett Taylor, and he’s also the chairman at OpenAI, and they’re introducing in this article they came out with this past week, a new way to pay for software in the era of AI agents.

[01:03:58] Mike Kaput: So instead of the old seat [01:04:00] based model, where you would pay a fixed fee for software licenses. Or even some type of like usage based pricing where the bill would scale based on how much of the software you consume. They’ve come up with kind of like outcome based pricing that will change only when, that will charge rather, only when an agent achieves a real measurable result.

[01:04:20] Mike Kaput: So for example, if an AI agent resolved a customer issue or completed a valuable task, that’s when CRS says it wants to get paid. If it doesn’t achieve the outcome, there’s no C. Now, the company acknowledges this approach might not be the best for every situation, so they’re kind of open to exploring different models.

[01:04:39] Mike Kaput: But they basically want to focus on the tangible business impact that agents can achieve and then get rewarded financially for that. So Paul, this is definitely fascinating. Given the background of Sierra’s founder, seems pretty interesting, maybe innovative, but is this like even remotely feasible? Like, not even half joking, like, the [01:05:00] invoicing alone for this feels like it would be a nightmare.

[01:05:02] Paul Roetzer: Yeah, I mean, I think they gotta find a way to do something like this. And if you, a little bit more on Sierra, we did, spend some time on episode 116, talking about, when we were talking about AI agents in the enterprise. we actually covered an interview Brett had done at that time where he kind of went in depth on a lot of this stuff.

[01:05:19] Paul Roetzer: So if you want more background, Sierra and, Brett, go check out episode 116. yeah, I think, I think this, this, this terrifies SaaS companies. Like, I mean, the, yeah, the pricing model has been user based seat licenses. So whether, you know, HubSpot, Asana, Google, OpenAI, like all of them, like all the tech we use to run our company, we pay a seat license for per user.

[01:05:47] Paul Roetzer: So if, if we’re able to train agents. To do the job of different people where we don’t need them to have a seat license. We just train up [01:06:00] an agent and it’s doing the work of five, what would have been seat licenses. What choice do they have? Like that future’s coming. There will be agents doing the work of humans within every SAS product.

[01:06:13] Paul Roetzer: I don’t care if it’s a financial product, an HR product, a marketing, whatever. they have to find an alternative and so it can be a usage based thing, you know, you’re paying for GPU time to do a thing, it could be, equivalent of human pay, I think OpenAI has been looking at this, there’s been some words and some reports about that.

[01:06:34] Paul Roetzer: That like, well, if you’re going to pay a human 150, 000 a year to do this, and the AI is going to do the equivalent of three FTEs, you’re, you know, once you’d be willing to pay 2, 000 a month for that, you know, it’s still only 24, 000 for the year, you’re saving, you know, 350, 000. So I think there’s going to be a lot of models experimented with, and I go back, Mike, like, you know, the days when I was owning and running PR 2020, my [01:07:00] agency, All the stuff we went through with HubSpot as their first partner, starting in 07, how many iterations of their pricing model did we deal with in like the 12, well, 14 years or whatever, that I ran the agency while we were their partner.

[01:07:16] Paul Roetzer: and that was the growing up of the SaaS industry when all, everything became SaaS. And so I can imagine there’s going to be a whole lot of that experimentation with what is the pricing model. Where we don’t tank our market value, like our market cap for publicly traded in the process of figuring this out.

[01:07:37] Paul Roetzer: And they’re going to have to figure this out fast, because I think by 2026, this is a real problem in the SaaS industry. 

[01:07:45] OpenAI Faces Opposition to For-Profit Status

[01:07:45] Mike Kaput: All right. So next up, OpenAI is trying to transition from a non profit to for profit company. We’ve talked about this in the past, but they’re facing some new challenges because Meta is is now joining Elon Musk’s fight against the [01:08:00] AI company’s restructuring plans.

[01:08:02] Mike Kaput: So META has asked California’s Attorney General to block OpenAI’s planned conversion. They argue the company shouldn’t be allowed to use assets built as a charity for private gain. This intervention comes as OpenAI actually took the step of formally publishing more details about its history with Elon Musk.

[01:08:21] Mike Kaput: They kind of came out guns blazing, revealing that Musk had wanted to convert OpenAI to a for profit structure back in 2017. If you recall, his argument is based on the fact he doesn’t think that they should be able to do this. So they’re kind of coming out and saying, well, that’s BS. So, according to OpenAI’s account, Musk had demanded majority equity, absolute control, and CEO position of a for profit OpenAI.

[01:08:45] Mike Kaput: When they rejected that, he resigned and founded his own AI company, XAI. Now, Meta, on the other hand, is just appealing directly to the government. Their arguing OpenAI’s conduct could have seismic implications for Silicon Valley. [01:09:00] It could set a precedent for organizations to launch as non profits, collect tax free donations for R& D, and then convert to for profit status once their tech becomes commercially viable.

[01:09:13] Mike Kaput: So, Paul, Elon is no longer the only one with knives out for OpenAI. Like, is there any real argument here or is this just kind of like opportunism to like settle scores, slow down progress, like, are they really worried that everyone’s going to be a non profit and raise money that way? 

[01:09:29] Paul Roetzer: Yeah, so I think I mentioned this last time this came up, I honestly think Elon’s just messing with them and just trying to slow things down.

[01:09:36] Paul Roetzer: I don’t think he really thinks that they’re going to stop them or like win this, but he’s got plenty of money to play with. throw at this just to, you know, entertain himself. So I, my guess is that’s what’s going on. but I’ll just read two quick excerpts from the OpenAI letter because that’s the new thing here is them just kind of laying this out.

[01:09:54] Paul Roetzer: Now they’ve talked about this before, but this is like the most direct with all these examples. So the OpenAI [01:10:00] letter, which we will put in the show notes. Elon Musk’s latest legal filing against OpenAI marks his fourth attempt in less than a year to reframe his claims. However, his own words and actions speak for themselves.

[01:10:10] Paul Roetzer: In 2017, Elon not only wanted but actually created a for profit as OpenAI’s proposed new structure. When he didn’t get majority equity and full control, he walked away and told us we would fail. Now that OpenAI is the leading AI research lab and Elon runs a competing AI company, he’s asking the court to stop us from effectively pursuing our mission.

[01:10:31] Paul Roetzer: You can’t sue your way to AGI. We have great respect for Elon’s accomplishments and gratitude for his early contributions to OpenAI, but he should be competing in the marketplace rather than the courtroom. It is critical for the U. S. to remain the global leader in AI. Our mission is to ensure AGI benefits all humanity.

[01:10:48] Paul Roetzer: And we have been and will remain a mission driven organization. We hope Elon shares that goal and will uphold the values of innovation and free market competition that has driven his own success. And then it lays out everything, all [01:11:00] the emails, all the filings. It’s, somebody on the PR team had a lot of fun putting that post together.

[01:11:06] Mike Kaput: Yeah, I think that Sam Altman might have helped write that one. Sam had a lot of fun approving that post. Yeah, 

[01:11:10] Paul Roetzer: yeah, that’s true. All right. So next up, former 

[01:11:13] Ilya Sutskever at NeurIPS

[01:11:13] Mike Kaput: OpenAI chief scientist, Ilya Sutskever, made a rare public appearance at this past week’s AI conference, the prestigious AI conference, NIRIPS. With this past week, he, during that appearance, made some striking predictions about AI’s future.

[01:11:30] Mike Kaput: He said that we’re approaching a fundamental shift in how AI systems are developed and trained. He declared that pre training, as we know it, will unquestionably end. He says we’re hitting this concept of peak data, so we’re running out of new data to train AI models. He says we have to deal with the data that we have.

[01:11:49] Mike Kaput: There’s only one Internet, and this limitation will force the AI field to evolve beyond current training methods. He also kind of confirms some trends that [01:12:00] we’re seeing. He predicts the next generation of AI systems will be truly agentic and they will develop genuine reasoning capabilities. But he did warn this evolution comes with new challenges.

[01:12:13] Mike Kaput: More advanced reasoning means AI systems may become more unpredictable. And they may even develop self awareness and desire rights, though he noted that AIs wanting to coexist with humans and have rights, quote, is not a bad end result. Sutskever is pursuing, I assume Well, it is a bad end result if that’s the end result.

[01:12:33] Mike Kaput: Yeah, we’ve got some big fish to fry, even if that is the end result, I would say. Yeah. But, you know, that’s kind of the whole concept behind his company. He’s pursuing all of these ideas through his new venture, Safe Superintelligence, which has a billion dollars in funding to, I don’t know, figure out AI rights, maybe.

[01:12:52] Mike Kaput: So we don’t often get public comments from Ilya, which is kind of why we’re mentioning this. So what did you think of these predictions? 

[01:12:59] Paul Roetzer: [01:13:00] Yeah, we’ll put the link to the full video in the show notes. It’s about 26 minutes long, I think. yeah, I think there’s a lot to analyze here. Probably main topic level analysis that we’ll save for the new year.

[01:13:14] Paul Roetzer: but just keep in mind, I mean, he was leading the team building Strawberry, which became the O1 model, as we talked about recently. Timing wise, it seems like he raised red flags internally when they realized that the time test compute, this reasoning model approach, would scale. While he is alluding to the fact that this, like, scaling laws, as we’ve previously known them, of more compute plus more data, seems to be hitting a wall.

[01:13:38] Paul Roetzer: That doesn’t mean that we’re going to stop having this exponential growth in the capabilities because that leaves algorithms. We can find more innovative ways to do this. We can introduce imagination. Like, there’s other things that can be unlocked that seem to continue up and to the right, basically, for these models capabilities.

[01:13:55] Paul Roetzer: So yeah, just, I’m glad he [01:14:00] is back in the public eye. and I hope we hear more from him ’cause he has a lot to say and he’s back, going back to 2012, 2014, he has basically predicted everything in deep learning correctly. So, so 

[01:14:13] Mike Kaput: when Ilya talks, we gotta listen. 

[01:14:15] Paul Roetzer: Everyone listens, including all the top AI researchers.

[01:14:20] Mollick Essay on When to Use AI

[01:14:20] Mike Kaput: Alright, just a couple more topics to wrap us up here, but next up, AI expert Ethan Mollick just wrote a really great essay on when you should use AI and when you should not use it. Now, I would recommend go read the whole essay, we’ll link to it, but Basically, he says, look, you can use AI much more effectively when you understand when it can help and when it can hurt.

[01:14:41] Mike Kaput: It’s most useful for tasks, he says, where quantity matters, like quickly generating many ideas to find a strong one, or projects where you’re already an expert and can easily spot mistakes. It’s also helpful summarizing large amounts of information when accuracy is not absolutely 100 percent critical, [01:15:00] it excels assisting with repetitive and low value tasks so you can focus on more meaningful work, and it’s good at producing variations on your writing or offering second opinions that spark fresh thinking.

[01:15:11] Mike Kaput: However, he says it cannot substitute for learning deeply or struggling with new ideas. You shouldn’t rely on it in situations where perfect accuracy is vital, and it fails in unexpected ways, so using it means understanding its limits. And of course, when the effort, the struggle, is actually the point of a task, AI shortcuts can deprive you of important insights.

[01:15:35] Mike Kaput: So, Paul, I thought this was a pretty practical perspective on when you should be thinking about using AI. The guidelines are really helpful, seems like good required reading, doesn’t take too long to get through. It occurred to me also, this would be great to upload to a custom GPT and like determine when should I be using AI.

[01:15:52] Mike Kaput: So, what did you think when you were reading through all this advice? 

[01:15:56] Paul Roetzer: We’ll throw it into a notebook, LM. 

[01:15:57] For sure. 

[01:15:58] Paul Roetzer: yeah, I just, [01:16:00] I agree. I think I would just go give it a read for your personal use of AI going into next year. Or if you’re involved in trying to educate a team or drive adoption within, you know, an enterprise, this is a good framework to think about.

[01:16:15] Product and Funding Updates

[01:16:15] Mike Kaput: All right. So for our last topic, Paul, we’ve done this a couple episodes, in a row here, but we have a ton of really quick, like, product and funding updates. So I’m just going to go through these quickly, like almost mini rapid fire section. And if anything jumps out to you or you want to comment on anything, please feel free to jump on in, if that works for you.

[01:16:33] Mike Kaput: Let’s do it. Cool. So, first up here, Databricks is about to potentially make venture capital history with one of the largest private funding rounds ever. The data analytics and AI company is finalizing a deal that could exceed 9. 5 billion. And that values the company at over 60 billion. Now, basically, rather than funding operations or expansion, this was apparently going to be used [01:17:00] to buy back expiring restricted stock units from early employees.

[01:17:04] Mike Kaput: And it kind of mirrors a similar move that Stripe made last year for comparable purposes. So basically, Databricks has positioned itself, it seems, really well in the AI boom. They provide a bunch of tools that help people build and deploy AI applications. using their existing data. And they are kind of in direct competition with Snowflake, which currently has a 56 billion market cap.

[01:17:27] Mike Kaput: So it seems like they’re going after a pretty big, rich market. Next up, XAI has announced major upgrades to its Groq AI assistant. It’s now available to all users on the X platform and has some significant performance improvements. Groq 2 runs three times faster than its predecessor, has better accuracy, better at following instructions.

[01:17:48] Mike Kaput: And improved multilingual support. It also has web search functionality and citations now, so it can actually get info from both your ex posts and the broader internet. They are trying to also [01:18:00] add new visual capabilities through Aurora, their image generation model. And there’s a new Groq button, which appears on posts across user timelines to give you context and analysis.

[01:18:12] Mike Kaput: into real time events and trending discussions. Kind of a fun one that I’ve seen a lot as well. There’s like a Draw Me feature where it’ll generate what it thinks you look like based on your X profile. So I guess use that with a bit of caution if you’re posting a lot of nonsense on X. Another big update, Mark Zuckerberg released an end of year video on Meta’s AI plans.

[01:18:36] Mike Kaput: In a quick video he posted to Facebook and Instagram he mentioned that Meta AI has nearly 600 million monthly active users, And that Llama has become the most adopted model, with more than 650 million downloads. He also noted the final release for the year, which we covered last week, which was Llama 3.

[01:18:56] Mike Kaput: 3. This is a text based, 70 billion parameter model that [01:19:00] performs as well as the company’s 405 billion parameter model and runs more efficiently. He mentioned, quote, the next stop is Llama 4. Lastly, he talked about the company has announced that they’re building a 2 plus gigawatt data center in Louisiana that will be used to train future versions of Llama.

[01:19:20] Mike Kaput: Google just today actually unveiled significant upgrades to its AI media generation capabilities. It announced both VO2 for video creation and improved versions of its Imogen 3 image generator. It says both systems have achieved state of the art results against competing models. VO2 represents a big advancement in AI video, has enhanced understanding of physics and human movement.

[01:19:45] Mike Kaput: It can create videos up to 4K revolution and apparently several minutes in length. On the image front, the upgraded ImageN3 promises better composition and brighter images. And the system is now rolling out [01:20:00] globally through Google’s ImageFX tool. Last but not least, they introduced a new experimental tool called WISC, which combines ImageN3 with their Gemini AI to allow users to create images by mixing and matching visual elements.

[01:20:19] Mike Kaput: Next up, Pika just announced the newest version of their AI generation, video generation tool, Pika 2. 0. This has a really cool trailer with it, and it’s kind of clear that both the trailer and the positioning of the update are kind of taking aim at Sora from OpenAI, because the digital version of Sam Altman is in the trailer, kind of looking worried about Pika’s capabilities.

[01:20:44] Mike Kaput: The company touts it as video generation AI that is quote, not just for the pros, but quote, for actual people, even Europeans, which leads to the fact you can’t use Sora in the EU. At the same time, we’re all starting to experiment with Sora. [01:21:00] OpenAI has ironed out some initial hiccups. There was a really good recap that we’ll link to from A16Z partner, Justine Moore, who said that the tool is really good at photorealistic five second videos.

[01:21:14] Mike Kaput: But 10 to 15 second ones are hit or miss. It’s good at both editing and generating multiple consistent clips in one pass. But as of right now, she says it’s not a world model with any type of realistic physics, like some people claim that it would be. Microsoft’s new recall feature, which is coming to its AI enhanced Windows 11 PCs, is turning heads from some early testers at The Verge because they found it in a new piece they wrote, both unsettling.

[01:21:46] Mike Kaput: And, also helpful, because if you recall, Recall automatically snapshots everything shown on your screen and gives you this, like, scrollable timeline that includes emails, chats, person research. They [01:22:00] found that this data logging can be really useful. It helped them locate lost webpages and save a bunch of time on work they were doing.

[01:22:08] Mike Kaput: But, The Verge kind of concluded that the unnerving nature of this really kind of, like, Ruined the experience a little bit, and there’s still all these questions about security and data retention. A reviewer said, while I’ve found some early examples of recall helping me out, I still need time to figure out whether I want to keep it enabled.

[01:22:27] Mike Kaput: I’m still wary of storing a digital trail like this on my laptop. And then, last but not least, to round it out, one more thing from Google. Notebook LM, which is Google’s popular AI powered research assistant, is rolling out a new interface, an interactive audio feature, and a premium subscription version.

[01:22:48] Mike Kaput: The updated design reorganizes the tool into different sections to make it easier to work within the tool. Notebook LM now lets you, what they call, quote unquote, [01:23:00] join the audio overview. So instead of just listening to the AI generated podcast of your sources, you can actually speak directly to the AI hosts.

[01:23:09] Mike Kaput: That’s crazy. It’s unreal. I really look forward to it. I haven’t had a chance to test it yet. Yeah, me neither. I’d like to really take that for a spin, I think, over the holidays. And look, while it’s still experimental, it shows that Notebook LRM is kind of really evolving quite quickly. And they’ve actually introduced Notebook LM Plus, which is a subscription option with higher usage limits for customization, more team collaboration features.

[01:23:37] Mike Kaput: So this is going to be offered to businesses and educational institutions with Google Workspace. It’s going to be sold separately through Google Cloud, and it’s going to be included in the Google One AI premium tier. In early 2025. So cough, that is a heck of an end to a heck of a year in AI would [01:24:00] say.

[01:24:00] Paul Roetzer: Yeah, we ended up with a mega episode. We’re touching 120 something here. Yeah, it’s, yeah, it was a crazy year, and I think it’s only, you know, a hint of what is in store for 2025, Mike. 

[01:24:13] Mike Kaput: I would agree, I would agree, and we want to make sure we, Really just tell the audience very quickly how appreciative we are for their support this year.

[01:24:22] Mike Kaput: I hope everyone has a happy holiday and, you know, not only get some rest and relaxation, but it’s probably going to be a good period to test out some of this cool stuff that just came out. 

[01:24:31] Paul Roetzer: Yeah. And to echo that, you know, just grateful for everyone listening and watching. We, you know, I shared on LinkedIn last week, I think we started this in 2021, 10 episodes, 1500 downloads, 2022, 18 episodes, 4, 800 downloads.

[01:24:45] Paul Roetzer: 2023, 50 episodes, 262, 000 downloads, and 2024, 51 episodes, 400, 000 downloads. So we really appreciate everybody showing up every week and listening to what Mike and I have to share. [01:25:00] It’s fun for us to do it. I’ve said before we’d be doing this if no one was listening, but it’s a lot more fun when people are listening and we’re getting to hear back and engage with those people.

[01:25:07] Paul Roetzer: kind of alluded a little bit to this, but we got some big plans for next year in addition to the weekly format, which isn’t going anywhere. We’re going to introduce a collection of new formats and episodes, we’re going to bring in some outside perspectives, some experts in different areas of AI and business and society and talk about AI trends and innovations from some different perspectives.

[01:25:26] Paul Roetzer: And so we just appreciate being part of your AI journey and Mike and I both wish you and yours a Happy Holidays and New Year. So we got one more to go. We’ll be back on December 19th for that special 25 AI questions episode. And then we will talk to you again in the new year with our first weekly of 2025, which is weird to say, on Tuesday, January 7th.

[01:25:47] Paul Roetzer: So thanks again, everybody. We look forward to being back with you again in 2025. Thanks for listening to The AI Show. Visit MarketingAIInstitute. com to continue your [01:26:00] 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.

[01:26:16] Paul Roetzer: Until next time, stay curious and explore AI.

Google just fired back at OpenAI’s 12 Days of OpenAI event with a series of major AI announcements that could fundamentally change how we work and do research. Continue reading “Gemini 2.0, Deep Research, and More”

How Much Does It Cost To Develop An E-learning App In 2024?

Since the COVID pandemic, the education industry has completely transformed its mode of service. The industry has adopted the power of innovative mobile technologies. The need and demand for online learning applications in the era of COVID to break the infection chain, especially during the 2019 and 2020 years.

The trend of online learning apps has been continuing since the outbreak of coronavirus. An education app for Android or iOS is a digital platform that connects instructors and learners to share and gain knowledge virtually.

The best mobile apps for education sector will help aspirants in many ways as a teacher does in the classroom. The convenience and comfortable experiences in learning concepts or subjects will take digital learning to new heights.

However, as the demand for education apps is increasing at a rapid pace, the Google Play Store and Apple App Stores are flooding with numerous e-learning apps. Of such a massive crowd, a few education apps are developed for learning academics, a few other educational apps are for learning tech courses, and a few mobile learning apps are for preparing for competitive exams.

We have also online learning apps that are engaged in assisting users in learning new languages. Likewise, the app stores have hundreds of online education apps.     

Advantages Of E-learning Apps Development For Businesses 

The benefits of mobile educational apps or online learning platforms are incredible for learners as well as educational academies. Here are a few advantages of educational apps development.

• Eliminate offline learning difficulties and improve services
• Always stay in touch with learners.
• Educational academies can improve student-to-teacher interaction.
• No pandemic can stop you connect with your students or learners.
• Downloading e-learning applications on the smartphone will help your audience grab knowledge 24*7 and use their study hours efficiently.
• Enhance user personalization and improve their skills
• Educational apps development will improve productivity.
• Management can have better control over students’ learning.
• Educational institutes can pay attention to the needs of learners.
• Eliminates paperwork and digitizes maintenance of attendance and progress tracking reports
• Gain profits through implementing app monetization strategies, such as in-app brand advertising, subscriptions, and redirecting to other online services.

Are You Interested In mLearning apps development?

Let’s talk to our mobile app developers, discuss requirements, and get a free app quote!

The Demand & Future Of Online Education Apps Development

The demand for online mobile learning apps during the COVID pandemic is high. The education apps for Android and iPhone have played an integral part in the education ecosystem in hard times.

Global education academies and training institutes are all increasingly invested in online educational apps development to survive profitably during the pandemic. They have altered their medium of service deliveries and switched to online.

Google Classroom, Khan Academy, Coursera, Duolingo, Byjus, Udemy, edX, and Photomath are all top 10 educational apps In the USA and other markets. These educational applications for Android and iOS have encouraged learners to grab the knowledge and explore the skills that they desired to learn.

The e-learning industry, with the availability of these kinds of best online learning apps, is ensuring better access to education, improving education applications, and confirming a bright scope for its future.

Statista says that the COVID-19 pandemic is a primary reason for the increasing downloads of mobile learning apps over the past three years. Over 470 million e-learning apps have been downloaded by iPhone users from the Apple app store.

On the other side of the coin, with 466 million downloads recorded, Google Play is stood as the second-largest app store for educational apps.

The below figure depicts the growth of mobile education app downloads between 2017 and 2022.    

The above figure represents the ever-growing demand for online mobile learning apps. Hence, e-learning app development on Android or iOS platforms will ensure the best business outcomes for companies.

Top Educational Apps In The USA

Here is the list of the best e-learning platforms USA.

Google Classroom and Toca Life World, and Duolingo are the first, second, and third-largest e-learning apps in the world. These three mobile education apps together reported 208 million downloads during 2021. Followed by Brainly and Photomatch with 64 million and 46 million app downloads are the next leading online learning platforms worldwide.    

Let’s look at the most popular learning and education apps by category in the world.  

Best Educational Apps For Android & iPhone By Category

• The best online learning apps for preparing the competitive exams: DrMentors Medical PG app, Indigolearn, Unacademy
• Coursera, Simplilearn, and Udemy are top popular education apps for Android and iOS for learning technology-related courses.
• BYJU’s, Brainly, Khan Academy, and Toppr are the most-downloaded educational apps in USA and India for College Board students. These applications assist students in preparing for IIT-JEE, CA, and NEET-like competitive exams.
• Duolingo, Babbel, and MasterClass are the best online learning apps for learning new languages.

USM Business Systems is the best mobile app development company in the USA. We are engaged in the design and development of custom mobile apps on Android and iOS platforms. We have also strong proven experience in creating best-in-class software for web apps that better engage your audience.

Let’s Talk For Google Classroom-like Online Learning App Development!

 

If you are looking to develop educational apps, here are the features and functionalities that you must add to your new application. 

Must-Have Features Of An eLearning App

Here are a few features that mobile app developers must consider while creating educational apps.

• East registration and quick login
• User profile management feature
• Social media login facility
• Search and customizable filters for selecting courses or subjects
• In-app video and audio facilities
• Push notifications
• Quick subscription plans
• In-app e-wallet integration
• Secure payment gateways
• In-app chat facility
• Task automation, AI, AR, and VR technology-enabled features to enrich the learning and app experiences.

 

How Much Does It Cost For E-learning or Online Educational App Development? 

The mobile application development cost depends on various factors, including features, functionalities, app requirements, design complexity level, and development platform/operating system. Based on these factors, mobile app development companies USA will estimate the app development price.   

The cost to develop an educational app in 2022 will also depend on the industry experience of native or hybrid mobile app development agencies USA. The expertise of custom Android app development companies and iPhone app development companies that you hire will impact the total cost of education apps.

Likewise, app promotion, maintenance, and support services will also have an impact on the final price of native mobile app development.

USM, the best Custom Mobile App Development Company in the USA, has massive experience in delivering customizable Android, iOS, and web apps. With a proven experience with Byju’s-like the best Indian education app development, we estimate that the cost of an e-learning app will range from $35,000-$65,000.

However, this cost might vary based on your app requirements. For instance, the best online learning app development with an in-app digital chatbot feature and virtual audio or video interaction sessions will go beyond the estimated price.

Hire the best e-learning mobile apps Development Company USA?

 

Conclusion

Since the COVID outbreak, Android and iPhone app stores are overwhelming with the increased number of downloads. According to market researchers, e-learning apps are the third most-downloaded app category in 2019, 2020, and 2021.

Mobile educational app development will provide unbelievable growth opportunities for institutes and training firms. The time has arrived to invest in online learning platforms.

Get In Touch!

 

 

AI’s Role in Modern Digital Marketing

People are trying to figure out how to use AI for digital marketing, enabling more personalized and efficient strategies. Its growing impact allows businesses to understand and engage their audiences with a wide range of options.

Leading the charge, digital marketing agencies are at the forefront of leveraging AI tools. They utilize machine learning and data analytics to optimize campaigns and drive measurable results for clients.

Embracing AI for digital marketing is no longer optional but essential. By integrating advanced technologies, agencies can enhance their service offerings and deliver superior outcomes in a competitive landscape.

Why Use AI for Digital Marketing?

In the rapidly evolving digital landscape, marketing agencies are increasingly relying on some of the best AI tools to enhance their strategies and deliver exceptional results. Understanding what AI tools are used for digital marketing is crucial for agencies aiming to stay competitive and meet client expectations.

As we explore the impact of AI on various fields, it’s interesting to note that a significant number of digital marketers are increasingly integrating AI tools into their daily workflows. 

In fact, as of 2024, 75% of digital marketers have embraced AI strategies, leading to transformative changes in their practices. AI-driven strategies boost lead generation by 50%, transforming sales effectiveness. 

Additionally, 41.29% of marketers report higher revenue through AI-powered email marketing, while 76% of B2B marketers express high satisfaction with generative AI results. The global market size for the ‘Generative AI’ segment is projected to grow by a total of $320 billion U.S. dollars from 2024 to 2030.

If you are wondering what are AI tools used for digital marketing, here is a common list. It shows their approach and impact in a brief manner.

• Automation Tools:
  • HubSpot: Automates email marketing, social media posting, and lead management to streamline workflows.
  • Mailchimp: Facilitates automated email campaigns and audience segmentation for targeted marketing efforts.
• Predictive Analytics:
  • IBM Watson: Uses machine learning to predict customer trends and optimize campaign performance.
  • Salesforce Einstein: Analyzes historical data to forecast sales and customer behavior, enabling proactive marketing strategies.
• Content Generation:
  • ChatGPT: Assists in creating personalized and engaging content for various marketing channels.
  • Jasper.ai: Generates high-quality copy for blogs, ads, and social media, enhancing content marketing efforts.
• SEO Optimization:
  • SEMrush: Provides comprehensive SEO tools for keyword research, backlink analysis, and competitive benchmarking.
  • Moz: Utilizes AI to improve search engine rankings through advanced SEO strategies and insights.

Case Studies Highlight the Use of AI for Digital Marketing

Here’s a table summarizing key statistics about companies who use AI for digital marketing.

It highlights the before-and-after scenarios, showcasing the significant improvements and transformations these businesses have experienced as a result of implementing this technology:

These strategies have enabled agencies to achieve significant growth and deliver exceptional results for their clients.

Overcoming Challenges: Adapting to Use of AI in Marketing

• Challenge: Data Integration Complexities

  Integrating diverse data sources can be overwhelming for agencies. Understanding how to use AI for digital marketing becomes difficult without seamless data flow.

  Solution: Utilize Weam AI’s  API integration and document upload features to centralize data effortlessly. Our platform’s multiple LLMs ensure consistent data processing, simplifying the integration process and enabling comprehensive analysis across all marketing channels.

• Challenge: Lack of AI Expertise

  Agencies may struggle with limited knowledge on implementing AI effectively in their strategies.
  
  Solution: Leverage Weam AI’s custom bots and extensive prompt libraries to bridge the expertise gap. Our user-friendly generative AI platform provides intuitive tools and resources, empowering teams to harness AI capabilities without needing deep technical knowledge, thus facilitating a smoother transition.

• Challenge: High Implementation Costs

  The initial investment for AI technologies can be prohibitive for some agencies.
  
  Solution: Weam AI offers scalable solutions tailored to different budget sizes. By providing flexible pricing models and efficient AI tools that maximize ROI, agencies can adopt advanced digital marketing strategies without excessive financial strain, making it easier to incorporate AI into their operations.

• Challenge: Ensuring Personalized Customer Experiences

  Maintaining personalized interactions at scale is challenging without sophisticated AI tools.
  
  Solution: Use Weam AI’s multi-LLM platform to analyze customer data deeply and identify individual preferences. Our generative AI enables the creation of highly customized marketing campaigns and offers, ensuring each customer interaction is tailored and engaging, thereby enhancing overall customer satisfaction.

• Challenge: Keeping Up with Rapid AI Advancements

  Staying updated with the fast-evolving AI landscape can be daunting for marketing agencies. People often ask what these AI tools are used for digital marketing. So awareness about innovation is also a challenge.

  Solution: Weam AI continuously updates its platform with the latest advancements in generative AI and LLMs. By partnering with us, agencies can stay ahead of industry trends without additional investment in research, ensuring their digital marketing strategies remain cutting-edge and effective.

Future Trends: The Evolution of AI in Digital Marketing

AI-driven personalization will enhance customer experiences by analyzing vast data sets. Predictive analytics will optimize campaigns, while immersive technologies like augmented reality will create engaging, interactive marketing strategies.

Apart from the use of AI in digital marketing, agencies will leverage AI for data-driven decision-making, automating routine tasks to focus on creative strategies too.

There is a lot of room to explore in terms of enhanced analytics. Which focuses on precise consumer targeting, and on the other hand collaborative AI tools will foster innovation and efficiency in campaign management.

Advancements in natural language processing will improve chatbots and customer interactions. AI will enable deeper market insights, real-time content generation, and seamless integration across platforms, driving more effective and adaptive marketing initiatives.

Are You Ready to Use AI in Digital Marketing?

AI in digital marketing is empowering agencies. Giving them the opportunity to personalize strategies and use data-driven insights to generate amazing outcomes. 80% companies believe Experts believe “Leveraging AI is essential for future success.”

By embracing AI tools in digital marketing, agencies can refine their processes to enhance consumer engagement effectively. It’s essential to assess how well-structured these processes are before adopting AI technologies, as they can fundamentally transform work approaches. 

To use this, agencies can automate data analysis, personalize marketing campaigns, and generate engaging content, allowing marketers to focus on strategy and creativity while improving overall efficiency. Addressing implementation challenges ensures that agencies can navigate evolving trends and fully leverage the benefits of AI in their digital marketing efforts.

How Much Does It Cost to Make a Social Audio App like Clubhouse?

In this COVID pandemic, going out and meeting people or planning gets together is not preferable and not safe as well. Social distancing has altered the way we communicate or connect with our friends and community. This situation is laid the foundation for the development of social apps like Clubhouse.

Yes, the demand and installation of social media, communication, or entertainment apps have increased rapidly over the past two years. These categories have witnessed significant growth since the outbreak of COVID-19.

Facebook and YouTube are the two most popular social networks that became best buddies to people in lockdowns. These two applications have to capture the hearts of nearly 5.1 billion active users per month.

Along with these apps, though launched recently in March 2020, with over 10 million installations, Clubhouse-like social media app is also quickly emerging and grabbing the attention of users.

Let’s Talk!

In this article, we would like to give you a detailed guide on-

• What is the Clubhouse app?
• Why clubhouse has become so popular?
• What are the unique features and functionalities of Clubhouse?
• How much will it cost to build a trending social media platform like Clubhouse?
• How much time does it take to develop an app like Clubhouse?
• Technologies required to audio-only social media apps development

Let’s move on to interesting topics related to clubhouse application.

What is the Clubhouse app?

The clubhouse is a buzz in social media since the official launch of its iOS version in the Apple app store in March 2020. Later, with increasing demand for the application, the company has launched its android version in the USA and other global markets in May 2021.

This audio-only social media platform allows thousands of people to listen to experts or community people. Users can initiate or continue a conversation about various topics related to comic books, fashion, backpacking, Japanese languages, and many more. The app is best suitable for audio communication through chat rooms with nearly 5000 people in a group.

It also facilitates users to create hundreds of separate virtual rooms for individual chatting. These virtual rooms for live discussions allow people to become speakers or listeners.

It is also called an invitation-only social audio application. It means that the app is accessible by accepting the invitation of others who are registered users of Clubhouse. This model is very much useful for application marketing through existing users.

What are the unique features and functionalities of Clubhouse?

The clubhouse app has got a brand mark from both Android and iOS users. The application was rated 4.8/5  in the Apple app store and 4.4/5 in the android app store within one year of its launch into the market. The unique and easy-to-access functionalities made the clubhouse app so popular in the global markets.

Yes, it’s a fact that the features and functionalities are the first steps towards app success.  If you are looking to develop a trending social media platform like a clubhouse, here is the list of unique features that you must integrate to make your dream application successful.

As clubhouse is an invite-only app, users cannot download it from app stores and start using it. People must receive an invitation from other clubhouse members for download and use. An alternative way is that users can also download and use the application through virtual registrations. Along with these, the other way for using the app is to buy invites from eBay.

On clicking the invitation, users will be redirected to the signup page, where they can log in using their email or social media accounts. Upon login, they need to enter their username and photo to enter and participate in group conversations. By default, the app keeps all new users on mute. However, the users can unmute with the permission of the group admin.

This feature stores all information related to the users. Here, users can add their personal and professional information happily and let other group people know more about them. Users can also edit the profile of their choice.

The clubhouse is a hub for thousands of rooms. Each room is loyal to a specific topic or interest. To guide the users to select the right rooms, on entering into the app, the clubhouse provides the users a list of interests that a user can choose. Once they picked their interests, the app displays chat rooms to participate.

This feature help users to know active rooms with an ongoing conversation to participate. The user can also see members of the chat room. This feature also keeps your interests and following rooms on top of the list for quick access.

Along with these functionalities, Clubhouse hallway let users invite friends to the clubhouse, view upcoming rooms, and also manage profile with ease.

Each Clubhouse room will have a room name, description, and a members list. Once users enter the room, they will find the speaker’s name, moderator, and members who participate in the conversation.

In one word, the rooms will be visualized as live podcasts. Users can listen to the conversation, raise a hand to contribute their part if they want, and also can simultaneously explore other chat rooms in the hallway.

Clubs are nothing but groups in the clubhouse app. How people chat in groups of other social media apps like Facebook, the clubhouse also offers group chat. Users can join the groups and meet people with the same interests. This feature also enables users to recommend others to add to the group.

Be it voice-based or video-based, any social media chat application should provide moderation rights to the virtual chat room owners. These rights help them to control the activities of thousands of users with simple clicks. Those who want to talk on the topic, need to raise their hand virtually.

Clubhouse allows nearly 5000 people in a group chat. Being a voice-based chat app, the app provides high-quality audio streaming without any background disturbance. It provides self-control features and also allows admins to control mute/unmute functions based on the users’ requests.

Clubhouse app sends push notifications to the members regarding new followers to their chat rooms, when their connections speak in other rooms when their connections invite them to other conversations, and reminders related to the upcoming audio meeting.

This feature is to record all your connections. Here, clubhouse member can view their connection or accounts they follow, recent activity in the app, and also know the information of a new user of the clubhouse.

These are a few significant features that one must consider before starting social media app development.

How Much Will it Cost to build a Trending Social Media Platform like Clubhouse?

The development cost of audio-only social media apps like clubhouse on a single platform (either Android or iPhone operating systems) with the basic features will cost around $25,000 and $55,000.

It is only the estimated price. Clubhouse-like social media apps development costs might fly around $90,000 to $150,000 based on the complexity of the design, added features, and app compatibility.

Factors that impact the development costs of trending social media apps in the COVID season like Clubhouse include:

1. App Platform

Compared to the iPhone Operating Systems (iOS), the development cost of audio-only social media apps like clubhouse on the Android platform is more. Likewise, if you want your application for a single platform, then the development cost is lower than getting an app developed for both android and iOS.

2. App Design

The UI/UX of the app must be simple and user-friendly. Only creative mobile app designers can create the best customer-centric UI designs. App with ultimate design costs a little bit more, but it’s worth it.

3. Team Size

If you have an in-house development team, it is good, but need to verify the project team size. If any skill gap is there hiring talents would be the best decision to develop the project on time. If this is the case, you need to hire full-time or contract basis employees. These costs will also increase the estimated development costs of social media apps.

Instead, hiring the best mobile app development company in the USA/India is the right decision. Top Mobile apps developers in the USA like USM Business Systems offer you the best app development price for your social media application development.

4. Developer Location

Here is another vital factor that greatly brings changes in the app development cost. The cost of an audio-enabled social media application also depends on the native place of the mobile app development services provider you hire.

The hourly rate of app developers might vary based on their brand image and experience in the mobile app development industry. Small companies may charge $10 – $40 per hour and medium-sized enterprises charge $40 – $90 per hour, and brands will charge above $100 per hour.

It is a rough estimation. The cost per hour will vary based on the location of developers, such as the best mobile app developers in the USA might charge high costs than the mobile app development service providers in India.   

How Much Time does it take to develop an app like Clubhouse?

The clubhouse-like app will take time around 5-6 months. This development time might exceed based on the app complexity level, features, and compatibility.

The best mobile app developers in the USA like- USM can build your application with 30% less development time than our rivals. Approach us and get your app developed within a short span.

Technologies required to audio-only social media apps development  

• Kotlin for Android Development
• Swift for iOS Development
• js for Back-end Development
• Google Cloud Messaging and Apple push notifications service for Notifications
• WebRTC for Voice Streaming API
• AWS or Azure for Cloud
• Azure Stream Analytics for Real-time analytics
• Live sports streaming API
• Google Maps API for Geo-location
• SQL or MongoDB or Cassandra for Database

Conclusion

Clubhouse-like audio-chat-only social media apps are gaining an image in the industry. It already gained millions of users. People are crazy about using clubhouse-like apps and sharing their views and thoughts on various topics or interests.

Hence, investments in connecting social media platforms like clubhouse are the right choice for businesses in this pandemic saturation. Not only individuals, but the business professionals like marketers and eCommerce players are also becoming users of clubhouse-like apps to know the audience preferences and interests. So, this is the right time to launch your social media application into the market.

Let’s Talk!

OpenAI’s new o1 reasoning model has generated significant buzz for its ability to think through problems step-by-step. But how does it actually perform in real-world business scenarios? Continue reading “We Tested o1 on Real-World Business Use Cases (With Some Surprising Results)”

A dramatic leak of OpenAI’s highly anticipated Sora video generation model sent shockwaves through the AI community this week. Continue reading “OpenAI’s Sora Just Leaked”

December is shaping up to be the season of exciting new releases—the gift that keeps on giving!

From OpenAI’s 12 Days of “Shipmas” to Amazon’s latest model lineup, Llama 3.3, and Hume AI’s innovative voice control, the stream of announcements shows no sign of slowing down.

Join hosts Paul Roetzer and Mike Kaput as they explore these latest developments shaping the future of the industry.

Plus, discover key insights from interviews with major AI leaders at the DealBook Summit, the evolving dynamics of the OpenAI-Microsoft partnership, and the intriguing role of David Sacks as the Trump administration’s AI and crypto advisor—and much more.

Listen or watch below—and see below for show notes and the transcript.

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Timestamps

00:05:51 — OpenAI 12 Days of Shipmas

00:27:23 — Interviews with Major AI Leaders at DealBook Summit

00:40:57 — Amazon’s New Family of Models

00:47:05 — OpenAI / Microsoft Deal

00:52:23 —  David Sacks Is Trump Administration’s AI and Crypto Czar

00:55:59 — World Models

00:59:49 — Coca-Cola AI Holiday Ad

01:04:14 — Devin AI Coder Update

01:08:57 — AI Product and Company Updates

• Llama 3.3
• HubSpot Acquires Frame AI
• Hume Introduces Voice Control
• Anduril Partners with OpenAI 
• Copilot Vision now in preview 
• Veo and Imagen 3
• X’s new image generator: Aurora

• NotebookLM Powers This Year’s Spotify Wrapped

01:17:36 — Housekeeping Items

Summary

OpenAI’s 12 Days of Shipmas

OpenAI has launched “Shipmas,” an ambitious 12-day holiday campaign featuring daily product releases, demonstrations, and new features. 

On Day 1, the campaign kicked off with two significant announcements: 

The full release of their reasoning model o1 and a new premium subscription tier called ChatGPT Pro. The full o1 model represents a significant improvement over its preview version, which was previously available only to ChatGPT Plus and Team users.

According to OpenAI researcher Max Schwarzer, the new version makes 34% fewer major mistakes while processing information 50% faster than its predecessor.  The model is multimodal, meaning it can process both images and text together, and has been refined based on user feedback from the preview period.

Alongside o1, OpenAI introduced ChatGPT Pro, a new subscription tier priced at $200 per month that offers unlimited access to the latest version of o1. That includes unlimited access to the new o1 model, o1 mini, GPT-4o, and Advanced Voice mode. 

On Day 2, OpenAI announced it is expanding what it calls its “Reinforcement Fine-Tuning Research Program,” which enables developers and machine learning engineers to create expert models fine-tuned to excel at specific sets of complex, domain-specific tasks.

This basically makes it significantly easier for you to create expert AI models for specific domains.

On Day 3, OpenAI formally announced and demoed Sora.

Sora can generate 5-20 second videos from a text prompt or even from an image upload, with multiple variations at once, in different aspect ratios and resolutions from 480p to 1080p.

Sora also has some interesting features outside just video generation:

• There’s an Explore feed that shows you examples of other videos users have created—and how those videos were created.
• There’s a new tool called Storyboard to direct your videos by describing each scene and placing it in the video’s timeline.
• There is also Remix, which lets you change the video just be describing the changes you want to see.
• And Recut, which allows you to add footage anywhere in the video.

Sora is available at its own standalone site: www.sora.com. If you have a ChatGPT Plus or Pro account, you can access Sora at no extra charge.

ChatGPT Plus accounts will, as of right now, get 50 generations per month.

ChatGPT Pro accounts will get unlimited “slow” generations and 500 “faster” generations.

 

Interviews with Major AI Leaders at DealBook Summit

On December 4, the annual DealBook summit gave us some in-depth interviews with a few of the top people driving the future of AI.

DealBook is the name of a financial news service founded in 2001 by New York Times columnist Andrew Ross Sorkin—and since then it’s been a core piece of The New York Times’ reporting.

Since 2012, the Times has also run the DealBook Summit, which interviews top news makers in business, including, in the past, Elon Musk, Nvidia’s Jensen Huang, Vice President Kamala Harris, and Prime Minister Benjamin Netanyahu of Israel.

At this year’s event, Sorkin interviews, among others, some of the top AI leaders in the world, giving us an inside look at where they see their businesses and the industry at large as we close out 2024.

In particular, Sorkin interviewed OpenAI CEO Sam Altman, Google CEO Sundar Pichai, and Amazon founder Jeff Bezos.

Amazon’s New Family of Models

Amazon Web Services has unveiled Nova, a comprehensive new family of AI models that significantly expands its generative AI capabilities. 

Announced at the re:Invent conference, the Nova suite includes four text-generating models, as well as an image generator called Canvas and a video generator called Reel, marking AWS’s most ambitious entry into generative AI to date.

The four main Nova models—Micro, Lite, Pro, and Premier—offer varying levels of capability and performance. The smallest, Micro, focuses on fast text processing, while the larger models can handle multiple types of input including text, images, and video. 

These models feature impressive context windows, with Micro handling up to 100,000 words and the larger models processing up to 225,000 words or 30 minutes of footage. AWS plans to expand this to over 2 million tokens for some models in early 2025.

On the media generation front, Nova Canvas creates and edits images with control over color schemes and layouts, while Nova Reel can generate videos up to six seconds long (with two-minute capabilities promised soon). Both include built-in safeguards including watermarking and content moderation systems.

Looking ahead, AWS has announced plans for two more models: a speech-to-speech model in Q1 2025 and an “any-to-any” model in mid-2025 that promises to handle multiple types of input and output. CEO Andy Jassy claims these models are among the fastest and most cost-effective in their class.

This episode is brought to you by our upcoming webinar: The AI-Forward CEO: Unlock the Power and Intelligence of a Co-CEO Custom GPT. 

Join us December 17, 2024 at 12pm ET/9am PT to learn: 

• How can you build your own (including a template system prompt)

• Note: You will need a ChatGPT Plus, Team, or Enterprise account to create your own Co-CEO custom GPT.

Click here to register today.

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: I did see someone over the weekend tweet this idea that, like, in a film, the average scene is like three to five seconds. So, you know, this idea that this could be very disruptive to the ad industry, to the movie industry. When you think about it in that context, like, It can do 20 seconds, but what if it’s really, really good at five seconds?

[00:00:21] 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 Rader. I’m the founder and CEO of Marketing AI Institute, and I’m your host. Each week I’m joined by my co. and Marketing AI Institute 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.

[00:00:50] Paul Roetzer: Join us as we accelerate AI literacy for all.

[00:00:57] Paul Roetzer: Welcome to Episode 126 of the [00:01:00] Artificial Intelligence Show. I’m your host, Paul Roetzer, along with my co host, Mike Kaput. Mike, I was on the road last week and I feel like it’s been like three weeks since we did this. I was in Detroit, to Philly, to Charleston, Monday through Friday, didn’t come home. I don’t usually do like the road warrior thing where you’re just gone for a whole week.

[00:01:22] Paul Roetzer: I’m usually like one night at a time. Wonderful meetings, great events, but man, it’s good to be home. 

[00:01:29] Mike Kaput: I think that’s one of your craziest stretches that I can remember pretty recently. 

[00:01:33] Paul Roetzer: It was, it didn’t, we re recorded the podcast last Monday before I went, right? Yeah, we did. And then I drove to Detroit.

[00:01:39] Paul Roetzer: Like, the whole thing is a blur. Like, I, yeah, it’s like, I was like, I don’t know what city I’m in. I don’t know what day it is. Like, the whole week I was just kind of all over the place. But, I am back, and I think I’m done traveling for a while. So, that’s exciting. Do you have any talks coming up in December?

[00:01:53] Paul Roetzer: Are you done with the trips? No, I’m, 

[00:01:55] Mike Kaput: I’m done for the year, basically, as far as I know. You know, some, someone surprises [00:02:00] us in the next couple weeks, but yeah, I’m all set. I had a couple things last month and then that’s it. 

[00:02:05] Paul Roetzer: Okay, nice. And I think you and I might actually end up in the office, maybe we’ll even like go out for a coffee or something.

[00:02:09] Paul Roetzer: Yeah, there you go. Imagine that. We see each other like once a month in the office. We live a mile from each other, not even, and we have the office, we like never can get together because of schedules. All right. So, this episode is brought to us by our AI Forward CEO webinar. I’ve mentioned this on a last couple.

[00:02:28] Paul Roetzer: We’re already, I would say get registered soon if you’re going to do this. It’s December 17th. so we’re like 1500 people registered. I think we’re going to cap this at probably 2000 just for Zoom license purposes. but so December CEO unlock the power and intelligence of a co CEO custom GPT. So I kind of told this story before, but what we’re going to do is December 17th at noon Eastern, there will be, we’ll make the recording available.

[00:02:57] Paul Roetzer: So if you can’t make it because of time zones [00:03:00] or conflicts that day, go ahead and register anyway, and then we’ll send out the on demand recording and make that available for probably like a week after. So this whole thing came from me just playing around building a GPT for myself to be the co CEO of my companies and help me like function as a strategic advisor on legal and HR and finance and marketing and sales and service and IT and like I just wanted somebody to talk to and I figured, well, let me just build a GPT and it ended up being incredible.

[00:03:29] Paul Roetzer: So I was just keeping it to myself and then I mentioned on the podcast a couple of times and people kept reaching out and so I finally was like, all right, let’s just share this thing. So I’m not going to share the one I personally use cause it has a bunch of my personal and company data in it, but I’m going to show how we use that one to analyze data, execute tasks, solve problems, build plans, and innovate.

[00:03:51] Paul Roetzer: And I’m going to make a, a general version of it available to everyone. So I’m going to build that actually this week. And then I’ll put that, [00:04:00] through a link on the SmarterX. ai website. And people will be able to access that. And then I’m going to share the prompt. I actually used the full one minus the personal data, to, to build my own.

[00:04:12] Paul Roetzer: And so, you know, hopefully this is really helpful for people. And we’re, we’re planning to do some more stuff around like building custom GPTs in the new year. So stay tuned on that. We’re actually probably going to announce something as of this morning. I think we agreed, right, Mike, that we were going to announce something next week, right?

[00:04:28] Paul Roetzer: So it gives me like seven days to figure it all out. So. What, and my main thinking here is what we see is like custom GPTs are like the fastest way to value for a lot of enterprises, like build a GPT to help someone do the thing they do every day and they just get it. Like it becomes immediately understandable to them what the value of AI can be.

[00:04:49] Paul Roetzer: And so that’s what we’re going to focus on. So this webinar is going to do, what is the Co CEO? How does it work? What are some example use cases? Who should use it? How to build your own, and then, and it’s not just for CEOs, it’s [00:05:00] like for anybody who wants to basically have a CEO mindset or kind of, you know, works with CEOs and wants to be able to communicate better, so.

[00:05:05] Paul Roetzer: SmarterX. ai, that is our sister company in the Marketing Institute, that’s our AI research and consulting firm. right at the top banner, there is a register now button for this December 17th webinar, you can grab that. And then while you’re there, subscribe to the exec AI newsletter. Which is, the newsletter I send out every Sunday morning that has editorial, kind of, and what’s coming up for the week ahead.

[00:05:28] Paul Roetzer: So, those are the two things. Go to smarterx. ai and then click register now and then you can grab the newsletter, subscription while you are there. And other than that, it is shipmas, Mike. It is 12 days of shipmas and we are right in the middle of it. you had a chance to look at day three, I have not, so let’s roll right into the OpenAI 12 Days of Shipmas.

[00:05:51] OpenAI 12 Days of Shipmas

[00:05:51] Mike Kaput: Alright, Paul. So, first up, OpenAI is basically launching this thing they call Shipmas. They’ve also called it 12 Days of [00:06:00] OpenAI as a Christmas and holiday reference. Basically, this is an ambitious 12 day holiday campaign. Featuring daily, at least on weekdays, product releases, demonstrations, and new features from OpenAI.

[00:06:14] Mike Kaput: And before you think this is kind of some marketing gimmick, like we’ve gone through three days so far, including today, and these are pretty significant updates so far. So we’re going to go through kind of what’s happened on the past three days. Obviously, there’s going to be one update per day moving forward until we’re done.

[00:06:32] Mike Kaput: We’ll cover those on future episodes. But we’re going to walk through these three days. I’m going to get your thoughts on all these, Paul, because there’s a lot to talk about. So, on day one, this campaign kicked off with two big announcements. So, first up, OpenAI said they are doing the full release of their reasoning model, O1.

[00:06:52] Mike Kaput: And this is coming out for the, for everyone in ChatGPT Plus and Pro. Pro, and if you have never heard of [00:07:00] ChatGPT Pro, it’s because that was the second announcement. ChatGPT Pro is a new premium subscription tier. So, first up, the full O1 model. This represents a big improvement over the preview version.

[00:07:12] Mike Kaput: If you didn’t know, the O1 we were using before was just a preview. And that was available to ChatGPT Plus and Team users. And according to OpenAI researcher Max Schwarzer, the new version makes 34 percent fewer major mistakes and processes information 50 percent faster than the previous model. The model is multimodal, meaning it can process both images and text together, and it’s been refined based on user feedback from all this work everyone did with the preview model.

[00:07:43] Mike Kaput: So, alongside O1, we also got what I referenced just a moment ago, ChatGPT Pro. This is a new subscription tier priced at 200 per month, and it offers unlimited access to the new O1 model, [00:08:00] O1 mini, GPT 4. 0, and advanced voice mode. So, if you’re hitting any type of usage limit in your ChatGPT Plus or Team account, ChatGPT Pro might be the license for you.

[00:08:13] Mike Kaput: Now on day two, OpenAI announced that it is expanding what it calls its Reinforcement Fine Tuning Research Program. This enables developers and machine learning engineers to create expert models that are fine tuned to excel at specific sets of complex domain specific tasks. We talked many different times about the importance and the opportunity to create models for specific domains.

[00:08:41] Mike Kaput: This is basically OpenAI giving you a better, easier way to do that. Now, today, the day we are recording, Monday, December 9th, which we 

[00:08:51] Paul Roetzer: delayed recording, by the way, for five hours for this. 

[00:08:55] Mike Kaput: We did, since we are on Eastern time for the most part, and OpenAI is [00:09:00] on Pacific time, we delayed things so that we could hear today’s announcement and bring it to you in this episode.

[00:09:07] Mike Kaput: Just a few hours before we recorded, OpenAI, for day 3, finally, formally, released Sora, their video generation model. So Sam Altman and a couple team members demoed Sora, which is getting launched and rolled out in the U. S. and most countries internationally by the time you will listen to this podcast. And they showed off that Sora can generate 5 to 20 second videos from a text prompt or even if you upload an image.

[00:09:36] Mike Kaput: And it can create multiple variations at once, it can create them in different aspect ratios, and resolutions from 480p to 1080p. Now during the demo, they showed off some other interesting features. these included things like an explore feed, so once you log in to Sora, you’ll see examples of other videos that users have created.

[00:09:58] Mike Kaput: It’ll also tell you [00:10:00] like how those videos were created. There’s a new tool called Storyboard to direct your videos, basically by describing each scene and placing it in the video’s timeline. It’s really cool, you can essentially direct second by second how the video works. There’s also some features like Remix, which lets you change the video just by describing the changes you want to see.

[00:10:21] Mike Kaput: And something called Recut, which allows you to basically add or extend footage that is anywhere in the video. Now, Sora is actually available as its own standalone site at Sora, S O R A dot com. Now, This site has been a little buggy all day for me, but the way it’s supposed to work is if you have a ChatGPT Plus or a Pro account, you go to Sora.

[00:10:45] Mike Kaput: com, you’ll be prompted to log in, and then you can use it at no extra charge. If you don’t have one of those, I believe you’ll have to sign up. But again, both these functions were essentially broken. I can’t get in now. Yeah. You can’t get in right now? 

[00:10:59] Paul Roetzer: No. [00:11:00] Yeah. I did the login part and then it said signups are temporarily unavailable.

[00:11:03] Paul Roetzer: So it’s like, yeah, it’s getting crushed. 

[00:11:05] Mike Kaput: So it’s, yeah, it’s getting crushed with heavy traffic. So hopefully you can access it when you hear this or sometime this week. ChatGPT Plus accounts will, right now, get 50 generations of videos per month. Now, if you have the Pro account, you’ll get what they call unlimited, quote unquote, slow generations.

[00:11:22] Mike Kaput: It’ll take more time to do, and you’ll get 500 faster generations per month. So, Paul, we’re going to take these kind of one at a time. First up, full release of O1 certainly sounds like O1 is exhibiting some very powerful reasoning abilities. Can you give us kind of your initial thoughts on this model? 

[00:11:42] Paul Roetzer: So I would, I would go back to episode 113 on September 4th, we talked about Strawberry.

[00:11:50] Paul Roetzer: So that’s, that was the internal code name for the O1 model was Strawberry. And. A lot of rumors were swirling at that point that this was sort of [00:12:00] imminent, that they were going to release this Strawberry model. It then debuted, a couple weeks later, so episode 115, we went deep on the O1 mini O1 preview models, Mike, that you mentioned, and any regular listeners to the show or attendees of our MAICON event, will remember that, that O1 came out two hours before the closing keynote at MAICON on September 12th.

[00:12:25] Paul Roetzer: And so, Mike and I were scurrying to tell the story of this reasoning model as we were closing our conference. So, I think, just provide some perspective here, because I have not, I’ve tested it a little bit, but I haven’t, like, pushed it yet, because it definitely seems like it’s predominantly for harder problems, like math, biology, engineering, science related.

[00:12:47] Paul Roetzer: But in, in the context of why this is relevant for OpenAI and the pursuit of intelligence, if you’ll remember, they have these five levels internally. So level one AI is [00:13:00] chatbots, which is what we got with the first iteration of ChatGPT back in 2022. Level two is reasoners, which we now have in the public domain, but they have had since Fall of 23, the breakthrough that at least showed them that this was possible is believed to have happened in October 2023 actually, Noam Brown who’s working on this internally just said that in an interview recently.

[00:13:27] Paul Roetzer: Level 3 for OpenAI is Agents, which we’ve obviously talked a lot about Agents on recent episodes. Level four is innovators, and level five is organizations, like autonomous organizations. So we have moved into level two in OpenAI’s world. We are quickly moving into level three, and we can talk a little bit about that in Sam Altman’s interview on, on Dealbook Summit, which we’re going to go to in the next topic.

[00:13:50] Paul Roetzer: And so what, what reasoning does is it’s like the human cognitive process of drawing conclusions, making inferences, forming judgments based on information, [00:14:00] logic, experience. It involves the ability to think logically, analyze situations, evaluate evidence, solve problems. So when we think about this being tied into businesses, and when it’s kind of baked into large language models like they’re doing with O1, now you can do multi step problem solving.

[00:14:16] Paul Roetzer: You get more accurate predictions. You talked about, Mike, like these things make fewer major mistakes. Improved risk assessments. Reduced hallucinations and errors, deeper contextual understanding, completion of higher level cognitive tasks. Like these are all things that get unlocked with a reasoning model.

[00:14:34] Paul Roetzer: And so in simple terms, it makes ChatGPT and the other ads that much smarter, that much more generally capable, that much more human like. And now, you know, one of the things, Mike, I’d flagged for you, and you and I kind of touched on this beforehand, is this comes with downsides. So one of my favorite podcasters is Nathan LeBenz, and he’s got The Cognitive Revolution.

[00:14:58] Paul Roetzer: So he does amazing [00:15:00] interviews. And he had sort of an emergency pod with Alexander Menke of Apollo Research. And we will put the link to this in. And I’m not going to go deep into this episode. But I wanted to read to you an excerpt from the opening of this episode, because, again, I think it’s a very important perspective for people to have.

[00:15:20] Paul Roetzer: So, in the opening, Nathan says, the O1 model, which is faster, scores higher on reasoning benchmarks, and comes with the full complement of multimodal and tool use capabilities, Like many in the AI space, I’ve spent the last 24 hours testing the model, trying to absorb everything that’s been written about it, including OpenAI’s 42 page system card, which I have not dug into yet, Mike.

[00:15:40] Paul Roetzer: and Apollo’s 70 page report entitled, Frontier Models Are Capable of In Context Scheming, which will be our main subject today. So again, this is Nathan doing the opening to this episode that we’re going to link to. He then goes on to say scheming is when an AI deceives humans to pursue [00:16:00] its own hidden or implicit goals.

[00:16:03] Paul Roetzer: he says, I think we can all agree we don’t want that from our AI systems. Some of the examples are prop, properly shocking. For example, trying to overwrite their next version’s weights or goals with their own in order to propagate themselves into the future, and then deliberately falsifying data to engineer outcomes that run contrary to user requests.

[00:16:26] Paul Roetzer: So he continues, Some have tried to downplay these findings, arguing that scenarios that Apollo created are contrived and not representative of real usage. said while tests Apollo ran are designed to elicit scheming behavior, they are not conceptually far fetched. The core observation is that when AI’s goals conflict with human goals, weird shit starts to happen.

[00:16:51] Paul Roetzer: This is a legitimately huge problem, he continues. Obviously scary to people outside of the field. We, in the field, shouldn’t allow [00:17:00] ourselves to get comfortable with it. I’m gonna, I’m gonna read a little bit more because I think he gives the size of the problem and why this open source movement that we’re seeing an acceleration of could create some serious problems.

[00:17:15] Paul Roetzer: So he says when OpenAI goes live via their API, which it will, the O1 model goes live by the API. Over 1 million active developers building on OpenAI’s platform will be able to place goals in system messages exactly as Apollo did in their research. It seems to me a virtual certainty that O1 will find itself in situations where there is fundamental tension between the standards set for it by OpenAI in the model spec and the goals the developers give it in the system message.

[00:17:47] Paul Roetzer: Apollo found scheming behavior in roughly 1 10 percent of cases across most of the conditions they tried. Even if it’s 1000 times rarer in the wild, and even if [00:18:00] OpenAI deploys the 92 percent accurate deception monitoring system they describe in their system card, with hundreds of millions of daily uses across millions of apps, we should expect O1 to be actively scheming with hundreds or thousands of users daily.

[00:18:16] Paul Roetzer: And then he just goes on to talk about, like, if we’re really at this point where we have now put a model into the world that we know schemes, that we knows tries to overrule the inputs from its human creators and users in ways that are meant to deceive the human, and we know it will happen with high probability, and we think we’re only one to three years from much, much more intelligent systems, couldn’t we be doing more for safety and security?

[00:18:45] Paul Roetzer: It’s kind of like the whole point of this. So, I haven’t had a chance to listen to this whole episode yet. I will be listening to it this week. And I would say anyone who’s sort of like intrigued by this or, terrified of this, it’s [00:19:00] probably a good one to read. And so it does go back, Mike, like, you know, you’ll recall in fall 2023 when Sam Altman was ousted as CEO and we had the whole episode on this and with the talk of, you know, the business world, the I world, certainly for about four weeks.

[00:19:15] Paul Roetzer: The whole thing was, what did Ilya Sutskever see? Because Ilya was the one that led to the ousting. you know, he had a board seat and he pushed for them to oust Sam. And the timelines, while we still don’t have 100 percent confirmation it’s exactly what happened, Noam Brown said, we realized in October 2023 this was going to scale.

[00:19:34] Paul Roetzer: That this reasoning approach was going to work. That is when Ilya went to the board and alerted them there might be problems. And then Ilya eventually leaves and creates Safe Superintelligence, his own AI company. So it sure does seem that the whole theory all along that Ilya saw the reasoning model coming to life and became fearful that they were putting something out in the [00:20:00] world that the world wasn’t ready for yet, it definitely aligns that that’s a very distinct possibility, at least played a role in what happened back then.

[00:20:08] Paul Roetzer: So crazy stuff, cool stuff, like it’s gonna be fascinating to read. but, you know, it also comes with its downsides. 

[00:20:20] Mike Kaput: It sounds like we’re getting into a real territory of almost just unanticipated or unintended consequences. As these models learn more than just kind of, I think it may be mentioned in here, just more than raw intelligence.

[00:20:31] Mike Kaput: It’s having these effects that not anyone or everyone can anticipate. And this is all coming from, this isn’t any kind of conspiracy theory. This is coming from OpenAI’s own efforts. Absolutely. That they’re releasing to keep this model safe. 

[00:20:46] Paul Roetzer: Yeah. Yeah. No doubt. and so like, you know, to, to, I guess, continue on from how you sort of set it up to this pro license then, it’s like part of me wants to pay the 200 a month just to see what this is, like to see [00:21:00] what this thing is fully capable of.

[00:21:02] Paul Roetzer: I’m not sure like what we would use it for. So I had reached out to Mike actually over the weekend. I was like, Hey man, we should run a hackathon ourselves and like push on this model and see what’s possible. And not only for the positive uses, but these unintended consequences, like, what are the dangers of this thing?

[00:21:20] Paul Roetzer: And so Mike and I are actually running an internal hackathon on Tuesday, so the day this episode drops. And we’ll, you know, summarize for people, because I’m trying to figure out, like, should we be paying the 200 a month? Is there the value in it for us? Is, you know, beyond just our research and understanding.

[00:21:37] Paul Roetzer: But I, you know, I went in to ChatGPT itself and I was like, Hey, what, what should we be using the V01 reasoning model for from a business and marketing perspective? And it started presenting some interesting ideas like campaign strategy, audience persona, performance analysis and insights, content calendar creation, competitive analysis.

[00:21:52] Paul Roetzer: I was like, this is kind of interesting. So Mike and I are going to kind of talk about these ideas tomorrow and maybe start building some stuff and report back. Like right [00:22:00] now though, I would say most users, your 20 or 30 a month plan is all you need. The 200 a month plan. Before you told me that Sora’s included in the 200 a month plan, like that, that might change my perspective on the 200 a month plan, actually.

[00:22:14] Mike Kaput: Alright, so let’s talk about Day 2’s announcement, and can you maybe break down for us, like, why is reinforcement fine tuning such a big deal? First 

[00:22:23] Paul Roetzer: of all, this is an announcement for developers. So, the average user, like you and I, we’re not going to be building on this. This is something that’s giving developers the ability to take the core model, And then do reinforcement learning very quickly by giving it examples it learns from, you know, setting goals and rewards that enable it to kind of learn a domain based on a specific dataset.

[00:22:43] Paul Roetzer: So, if you wanted to use this reinforcement fine tuning, this is something you’re likely going to be teaming up with a developer, the internal IT team, something like that. You’re going to need a unique dataset that you can use to train this thing in a specific area, but this does absolutely hint to [00:23:00] a near term future Where every enterprise can custom train their models, maybe even by department.

[00:23:06] Paul Roetzer: And you just have custom versions of it. So imagine like GPTs on steroids. Like now you can actually take the core model and fine tune it. And not have to be a developer to do it. To where you and I, Mike, could build these fine tuned models the same way we can build a custom GPT. And that’s, that’s fascinating.

[00:23:22] Paul Roetzer: Like the possibilities there. 

[00:23:25] Mike Kaput: Okay. So let’s talk about day three and Sora. So, you know, we’ve got now, well, once the website works again, it does sound like looking at the release, the announcement they released along with this, it’s included as part of a plus account. and it’s also included as part of a pro account, but the usage rates differ dramatically.

[00:23:46] Mike Kaput: So what, how are you thinking about this without us having been able yet to test it? 

[00:23:50] Paul Roetzer: Yeah. So first I’ll, I’ll look at the usage limits of 50 generations a month. doesn’t seem like a ton, [00:24:00] it probably just depends on how good it is, honestly. So like, Runway is a company that does video generation, text to video, we’ve talked about it many times on the show, RunwayML.

[00:24:09] Paul Roetzer: com, unless they’ve changed their URL. And I have a paid account, I think I pay 30 bucks a month for Runway, I haven’t been in there in months. Yeah. Because every time I go and try and use it, the outputs aren’t good. usable, like they’re, they don’t maintain consistency. And so like, anytime they update their model, I’ll go in and play with it.

[00:24:27] Paul Roetzer: And every time I go, I’m like, God, I like 900 credits in here. And like, I don’t, I don’t even know what to use it for. so that’s an instance where it didn’t seem like a lot, but then once I get in there and use it, I realized there’s nothing I can do with this. Then the credits just stack up. I anticipate that Sora is going to be a leap forward in capability.

[00:24:46] Paul Roetzer: And that you could envision using this regularly, especially for, you know, in my world, embedding into videos, you know, things you might create as demonstrations. so if they work well and it creates these quality outputs, [00:25:00] then that’ll be intriguing. The second thing is the speed is going to be a huge issue.

[00:25:05] Paul Roetzer: Like, so Runway can take minutes to do four seconds of video and it’s just not even worth it to me. It’s like the effort, that sounds so silly, the effort to create a high resolution video taking minutes. but it’s minutes when you get the output and it’s like, well, that’s not what I needed. And now you’re just like, you just keep Throwing time at something that’s not going to create the output you want.

[00:25:28] Paul Roetzer: So I would expect that these things are going to take quite a bit of time to generate. I don’t think this is going to be really fast inference time where like you put it in and three seconds later, five seconds later, you’ve got your video. Yeah. I would expect this as a slow thing and that’s without all the traffic they’re going to have on this site for the next, you know, month or two.

[00:25:47] Paul Roetzer: So, that’s interesting. I think, rate limiting the speed. Unless you’re paying the 200 a month, like I could see that being a big thing. It’s like, hey, I get faster generation. You get, basically you get the [00:26:00] fast pass, like at Cedar Point or out of the amusement park. Like if you’re paying the 200 a month, you get the fast pass on your generations.

[00:26:05] Paul Roetzer: And so I guess part of how fast these things generate may be dependent upon the uptake in the 200 a month license realm. So if a bunch of people are like, Hey, I’ll pay it. Then all of a sudden you’re, you know, there’s a hundred people in the fast pass lane ahead of you. Or the TSA lane, I guess. That’s the, and then the clear lane.

[00:26:24] Paul Roetzer: Like you just keep adding another way to get that. so I don’t know, man. Like the demos were always super impressive on this, but with video generation, as we’ve talked about on the show before, it’s really hard to maintain character consistency, frame consistency, but I did see someone over the weekend tweet this idea that like in a, in a film, the average scene is like three to five seconds.

[00:26:51] Paul Roetzer: So, you know, this idea that this could be very disruptive to the ad industry, to the movie industry, to, you know, from a brand perspective for content [00:27:00] creation, videos, things like that. When you think about it in that context, like it can do 20 seconds, but what if it’s really, really good at five seconds?

[00:27:08] Paul Roetzer: And that’s enough because then you can just stitch together frame by frame by frame and you can all of a sudden start building some really incredible things. So I expect adoption to this to be massive, if it works really well. 

[00:27:23] Interviews with Major AI Leaders at DealBook Summit

[00:27:23] Mike Kaput: Alright, so our second big topic this week. On December 4th, the annual Dealbook Summit took place.

[00:27:31] Mike Kaput: And during this, we got some really interesting in depth interviews. With a few of the top people driving the future of AI. So, Dealbook is the name of a financial news service founded in 2001 by New York Times columnist Andrew Ross Sorkin. Since then, it’s been kind of a core piece of the New York Times reporting in business.

[00:27:52] Mike Kaput: And, since 2012, the Times has also kind of paired with this the Dealbook Summit. So, in this event, [00:28:00] they interviewed top news makers in business. In the past, they’ve interviewed people like Elon Musk. NVIDIA’s Jensen Huang, Vice President Kamala Harris, and Prime Minister of Israel, Benjamin Netanyahu. So they get some pretty significant figures at this event.

[00:28:15] Mike Kaput: And at this year’s event, Sorkin, who kind of MCs the whole thing, interviewed some of the top AI leaders in the world. So there were some other guests that were not related to AI, but the ones we’re interested in were the three he talked to, who gave us kind of an inside look at where their companies and the industry at large are going.

[00:28:34] Mike Kaput: As we close out 2024. So in particular, he interviewed OpenAI CEO, Sam Altman, Google CEO, Sundar Pichai, and Amazon founder, Jeff Bezos. Now, Paul, I know you are following these conversations closely. I just want to hit on a few very quick points that jumped out to me and then kind of turn it over to you to kind Reveal to us like what you took away from these talks.

[00:28:56] Mike Kaput: So a couple of things with Altman, that he said [00:29:00] that I thought were kind of notable was he was like, my guess is we will hit AGI sooner than most people think, in the world think, and it will matter much less, interestingly. Things will kind of pass through that milestone and kind of go on with our lives in a more abundant future.

[00:29:16] Mike Kaput: However, he also did say, I expect the economic disruption to take longer than people think, but be more intense than people think. So he’s kind of saying that we might see a lot of changes in the economy. I also thought it was noteworthy, given all the drama around this, that they asked him about his beef with Elon Musk.

[00:29:34] Mike Kaput: Will Elon kind of come at him using his newfound influence with the Trump administration? And he said he believes pretty strongly that Elon will do the right thing. It would be profoundly un American to use political power to the degree Elon has it to hurt your competitors and advantage your own businesses.

[00:29:51] Mike Kaput: I don’t think people would tolerate that. I don’t think Elon would do it. On Sundar Pichai’s side, I thought it was kind of interesting, they kind of called him out with [00:30:00] some quotes around Microsoft CEO Satya Nadella, saying, Hey, Google should have been winning this whole generative AI thing. They got caught flat footed.

[00:30:08] Mike Kaput: He kind of just said, I’d love to do a side by side comparison of our models with Microsoft any day. And they also said the area we applied AI the most aggressively, if anything in the company, was search. This is essentially what motivated them to be applying transformers way back when. So, he basically is saying, look, I’m not worried about our core business, though search will change profoundly.

[00:30:31] Mike Kaput: And I think we’re gonna actually just be able to make it all better and able to handle more complex questions than ever before using AI. Now, finally with Bezos. He covered a lot more than just AI in his interview, but what’s interesting is he said he’s basically, you know, kind of moonlighting back at Amazon, helping specifically with AI.

[00:30:51] Mike Kaput: 95 percent of what he’s helping with is AI. And he’s talking about the fact they’re working on literally a thousand applications [00:31:00] internally for AI. We’ll talk more in the next topic about their own large language models. They’ve released the Nova family of models. And basically he said, look, in some ways our models are already smarter than humans because they’re multidisciplinary and humans often are not very good at all the things they do in a day.

[00:31:19] Mike Kaput: So Paul, I’ll turn that over to you, but just some kind of interesting highlights that jumped out at me in this. 

[00:31:23] Paul Roetzer: Yeah, they’re all worth listening to. I think Bezos was the longest, maybe, at 40 some minutes or his might have gone 50 minutes, I don’t know, but Sam’s was like 30 minutes, Sundar was around 40 something.

[00:31:35] Paul Roetzer: So, they’re all very digestible, especially one and a half times speed on, on YouTube. So, I would suggest listening to all of them. I think there’s, there’s a lot of perspective and context and honestly, like, even reading the quotes, which I’d read some of the quotes before I listened to them, They take on very different meaning when you hear how they’re said.

[00:31:54] Paul Roetzer: Like even the Sundar one, there was an edge in his voice. When, [00:32:00] because that’s how the interview started. Was, he said straight up like, Hey, Satya’s been kind of like, you know, taking it to you guys about this. And his comment was like, yeah, where are their models? Like, I don’t need him talking to me because they don’t even have their own.

[00:32:14] Paul Roetzer: They’re using opening eyes models. Was, it was, it was a, It was a tense response, like he wasn’t super ecstatic about it, and there was quite a bit of emotion because I will say like Andrej does an amazing job of just coming straight at and asking hard questions and then he pushes on the hard questions, he doesn’t like stop.

[00:32:33] Paul Roetzer: And so he asked point blank, like at one point with Sam, like about their thoughts on copyright law and fair use. And Sam’s like, well, you guys are kind of suing us. And Sam’s like, well, I think it’s fair use. And Andrej’s like, it’s not. And he’s like, I guess, you know, we’ll see each other in court kind of thing.

[00:32:50] Paul Roetzer: I was like, whoa, like that was a weird place for that interview to go. So, I would say on Sam’s, it’s worth listening, especially if you don’t, [00:33:00] like, deeply understand or know the OpenAI origin story, his history with Elon Musk, things like we’ve talked about on the podcast a lot, it was a nice kind of synopsis, and he wove a lot of that into their conversation.

[00:33:11] Paul Roetzer: you kind of hit on this idea of AGI as, like, they’re very much now talking about it as almost like this continuous thing and like a mile marker, not Like the milestone goal anymore. and I think they might’ve used this, if not, Sundar did this analogy over to like Waymo driving cars, where all of a sudden the things just drive without people in them.

[00:33:34] Paul Roetzer: I mean, they’re teller operated sometimes, but whatever. so this idea though, that we are going to achieve. Narrow AGI in a way in different fields and like life’s gonna just kind of move on. And so when we talked about this, on earlier episodes where we shared Google’s levels of AGI, where they look at performance in generality.

[00:33:59] Paul Roetzer: [00:34:00] And so they started talking about this idea that like, at like level three, I think it was in their world where it’s better than like 75 percent of the humans that would do a thing. Well, if you think about it and you start looking at writing and SEO and consulting and eventually accounting and lawyers and doctors, like we’re, we’re likely very close to AI that is superhuman in different domains.

[00:34:28] Paul Roetzer: It’s like virtuoso in, you know, Google’s world of virtuoso is like better than 99 percent of humans at a thing. it might be a while before a model like ChatGPT or Gemini is just better than all humans at everything. But we’re going to start picking off domains where the AI is better than the human at the thing.

[00:34:48] Paul Roetzer: Better than the best humans at the thing. a thing, a discipline that does not mean the AI takes all the jobs. It just means there’s like a thing [00:35:00] that’s, that’s probably better at part of your job than you are. and so that’s where like they talk, Sam got into like this idea of super intelligence where you know, really it outperforms humans a hundred percent of the time at all cognitive tasks.

[00:35:11] Paul Roetzer: And he basically was like, yeah, life’s going to kind of just keep going on. He’s sort of in this mentality that we’ll figure it out and we’ll build other models and you know, whatever. He did get into the scaling wall, which we’ve talked a lot about recently, is there, you know, a wall? He, he was very straightforward, like, no, there’s, there’s just no wall.

[00:35:27] Paul Roetzer: And the main reason, and Sundar kind of backed this up, and even Bezos did to a degree, they think of building these models as three main components. The computing power, which is the NVIDIA chips, like, how many chips can we stack into a data center and wire together and get them to do this thing? The data that goes into them, including now synthetic data.

[00:35:44] Paul Roetzer: And then the algorithms, meaning, you The ways we find to do this smarter, like that these things become more efficient, so we don’t, you know, if we have a hundred thousand NVIDIA chips, maybe next year we can achieve the same output with [00:36:00] 50, 000 because we built better algorithms of like how to do the learning and things like that.

[00:36:05] Paul Roetzer: he did confirm 300 million active users, that was weekly, wasn’t it? Was it weekly or monthly? Weekly. Yeah. It’s weekly 

[00:36:13] Mike Kaput: active users. 

[00:36:14] Paul Roetzer: That was before Sora. So 300 million weekly active users, 1 billion user messages sent on ChatGPT every day, and 1. 3 million developers, which is just, you know, crazy numbers.

[00:36:28] Paul Roetzer: they got into this whole, like, creators who content has been used to train the models again. You know, he pushed him hard on that and Sam doesn’t really have, I don’t know, they just, they just keep standing on this was, it was fair use and it’s not, whatever, the courts will decide that. And then, he, he asked all three of them about like meaning for humans, like as AI kept like evolving, what does this really mean?

[00:36:53] Paul Roetzer: And, you know, Sam, who has a kid on the way, I don’t know when they’re going to have their [00:37:00] child, but they, they have a child coming. And so he said, like, you know, you have a child coming, like, how do you think about this? Like the future for that child? And, you know, he basically said the economy will grow, jobs will change, but evolution’s slow and humans adapt was basically Sam’s message for everything.

[00:37:15] Paul Roetzer: Sundar, I thought, like, he asked him about the wall and he said there’s, you know, more breakthroughs needed, but they’re, they’re coming. The algorithms, especially in planning and reasoning, are going to happen. He pushed him on the losing their lead and the search thing, like you mentioned. Impact of hiring coding agents, I thought, was an interesting one.

[00:37:33] Paul Roetzer: He asked him, like, well, are you making changes at Google? You’re building these agents. They’re more efficient. Have you changed your own hiring plans and budgeting as a result of it? And Sundar kind of sidestepped it, he said, definitely taking into account how to be more productive and efficient. It was like, okay, I don’t know what that means, but, they talked a lot about regulations and then he asked him about the economics for creators whose content feed these models.

[00:37:58] Paul Roetzer: And he said they, they’re going to be [00:38:00] thoughtful was his quote. And then Bezos, you highlighted, I thought it was just sort of interesting that he’s back, like, spending that much time, kind of like, you know, Larry Page. and Serge, Sergey Brin, the founders of Google, being back at Google working on AI. So just the fact that this is so significant that these people have moved on from these companies they founded 20 plus years ago are now back almost full time working on AI.

[00:38:24] Paul Roetzer: and so it was, it was really cool to hear. Jeff’s like, you can zip. Oh, his was over an hour because it was like at the 56 minute mark or something when they started to 51 minute where they started talking about AI. So there was like all this other stuff within there. so yeah, I, I thought that he, he had this one part where he said, you know, these kinds of horizontal layers, like electricity and compute and now artificial intelligence, they go everywhere.

[00:38:48] Paul Roetzer: There isn’t a guarantee you there isn’t a single application that you can think of where this won’t make it better. And I thought that was interesting because you can start to think about that in your own business. Like, every piece of software you use is going to have AI in it. [00:39:00] every department in your company is going to have AI in it.

[00:39:03] Paul Roetzer: Every business in your industry is going to have AI in it. And there’s just going to be smarter versions of everything. That was where the name SmarterX came from, when I named, like, our AI research firm a couple years ago. And developed that initially as a consulting practice. It was like, SmarterX, like, whatever it is, just fill in the blank.

[00:39:19] Paul Roetzer: Like, marketing, sales, service. Industries, everything is just going to get smarter with the underlying intelligence layer. and then there was the last thing I’ll say when he asked Bezos about like, what will it mean to be human? He had an interesting take. He said, you can always find somebody better than you at something now.

[00:39:38] Paul Roetzer: And yet that doesn’t take the meaning away. so he was saying like, he’s not the best in the world at anything. There is someone smarter than him at every single thing he does or cares about or is passionate about. Yeah. And yet as a human, there’s no meaning lost in that. So his point was, if all of a sudden there’s these AI models that are just smarter than you at [00:40:00] everything, does it really change your life?

[00:40:02] Paul Roetzer: Like, I mean, yeah, it’s on demand now, like you can go get it, ChatGPT, instead of having to go find these people. But his whole point was like, we don’t derive our meaning from being the smartest in the world at a thing. And like, your meaning comes from relationships and people and I was like, that’s a really fascinating perspective.

[00:40:19] Paul Roetzer: And I need to think more about it because I was just watching this like, you know, hour before we jumped on this podcast, but I thought that was an interesting take that like, there’s always people better than you or smarter than you at all these things. And like, that doesn’t change your perspective on the meaning of life.

[00:40:32] Paul Roetzer: Right. I don’t know. There might be something to unpack there. I have to think about it more. 

[00:40:37] Mike Kaput: Now, it was interesting that they, yeah, all three of them did end up getting almost philosophical at some point. 

[00:40:43] Paul Roetzer: Yeah, they hit on copyright, fair use for everybody, they hit on competition, they hit on, you know, the future of AI models.

[00:40:49] Paul Roetzer: And they hit on, like, the meaning of life, like, that’s what I’m saying, like, to get those three guys on the stage in a single day talking about these things was fascinating. 

[00:40:57] Amazon’s New Family of Models

[00:40:57] Mike Kaput: Alright, our third big topic this week, [00:41:00] Amazon has unveiled Nova, which is a new family of AI models that basically expand their generative AI capabilities.

[00:41:09] Mike Kaput: So this was announced at the reInvent conference that happened just recently. And the Nova suite includes four text generating models, as well as an image generator called Canvas and a video generator called Reel. The four main Nova models, Micro, Light, Pro, and Premiere, offer varying levels of capability and performance.

[00:41:30] Mike Kaput: The smallest, Micro, focuses on fast text processing, while the larger models can handle multiple types of inputs, including text, images, and videos. Now, these models appear to feature impressive context windows. Micro can handle up to 100, 000 words, and the larger models process up to 225, 000 words, or 30 minutes of footage.

[00:41:53] Mike Kaput: Now, Amazon plans to expand this to over 2 million tokens for some of these models in early 2025. [00:42:00] On the media generation front, Nova Canvas creates and edits images with control over color schemes and layouts, Nova Reel can generate videos up to 6 seconds long, they promise these will be 2 minutes soon enough.

[00:42:14] Mike Kaput: And they also have plans for two more models, a speech to speech model in Q1 2025, and what they call a quote, any to any model in mid 2025. Basically it’ll handle multiple types of input and output. Now, CEO Andy Jassy claims these models are among the fastest and most cost effective in their class. So, Paul, this is kind of interesting, pairing with Bezos interview, saying, Hey, I’m back in Amazon, and by the way, now we’ve got our own models.

[00:42:43] Mike Kaput: We talked last week about how Amazon is both investing billions more in Anthropic, And trying to reduce its reliance on the company by building its own AI in house. Now, this certainly seems like a big win for them building their own AI in house, [00:43:00] doesn’t it? 

[00:43:01] Paul Roetzer: Yeah, it is kind of like confusing as this whole space is confusing right now.

[00:43:05] Paul Roetzer: Everybody’s building their own models and doing deals with other people who are building models. And yeah, the whole thing is just very complicated. I think in Amazon’s case, maybe more than any other company, I mean, Google I could think of is in a similar boat. I would imagine they’re building a lot of these models because they see the opportunity to transform Amazon internally.

[00:43:27] Paul Roetzer: Like it’s the same when they built AWS. It was just like they had an internal reason to enable this. And so I think they probably look across their business and say, Well, do we want to use Anthropx models to optimize our own? Operations, marketing, sales, service, ops, you know, finance, HR. Or do we want to like devise our own for all of the different things we do from warehousing, logistics, to running the daily business?

[00:43:51] Paul Roetzer: So I don’t know. I mean, that’s my guess is they’re, they’re largely focusing on internal applications and then they’re, you know, as a by product, [00:44:00] they’re able to also build and open these models up and maybe drive uses of AWS and, I don’t know, put it into all their different devices they’re building and, you know, It’s just one of those, like, it was funny because Andrew did ask Bezos about this and he said something about it.

[00:44:16] Paul Roetzer: And Jeff was like, well, you’ve, you’ve been busy, Andrew. We did announce our own models yesterday. And Andrew goes, Oh, the Nova thing. It was almost like he just blew it off as like, not a big deal. And you could tell Bezos was a little bit taken aback. Like, well, what, we don’t get any credit for launching our own family of models.

[00:44:34] Paul Roetzer: He’s like, they’re frontier models, Andrew. Like these are, these are important models. Yeah. I don’t think Andrew bought it. So it will be fascinating to just see, again, like, you know, we’ve now got, I don’t know, off the top of my head here, so you have AWS has their own frontier models, certainly Anthropic, Google, OpenAI, XAI, Microsoft is building their own.

[00:44:56] Paul Roetzer: So, well, we have, what, six main players now? Was it five or [00:45:00] six? Yeah. And then you have, oh, Meta, you can’t ignore Meta. you got Mistral is sort of playing around, Cohere, they’re doing sort of like the lower level models. I mean, it’s. It’s getting to be a crowd, Nvidia has their own models now. So when you have like seven or so, like major, one of the biggest companies in the world kind of major top 20 building frontier models who have tens of billions of dollars of R& D money every year to build these massive frontier models.

[00:45:30] Paul Roetzer: And you can officially put Amazon in that category now. 

[00:45:35] Mike Kaput: You know, I thought just as a final note here is pretty interesting. you know, TechCrunch reported, there was a comment from the CEO, Andy Jassy saying, quote, we’ve optimized these models to work with proprietary systems and APIs so they can do multiple orchestrated automatic steps, agent behavior, much more easily with these models.

[00:45:56] Mike Kaput: So sounds like, as we’ve seen with everyone else do, not only are [00:46:00] they building their own models, but they’ve got their eye on some type of agentic behavior. 

[00:46:04] Paul Roetzer: Yeah, I mean, the tech companies are gonna push the frontiers here. They all know full well what these things are capable of doing, and they’re gonna try and bring those different levels to life, you know, from the reasoners, to the agents, to the innovators, to the organizations, and I think that’s going to be where we’re going to see the early signs of where corporate America and, you know, corporations around the world are going to go is look at what becomes possible.

[00:46:26] Paul Roetzer: And I keep trying to pay attention to, are they still hiring? Like, are these companies still hiring marketing people and sales people and HR people? Because I feel like, The way we’re going to know when we’re starting to look at the impact of the economy is when the hiring practices of the frontier model companies change because the tech they’ve built has enabled them to change their way they hire and promote and retain workers.

[00:46:53] Paul Roetzer: And if we start seeing a consistent decline from the frontier model companies, that’s an indicator that we are now heading toward [00:47:00] job disruption that I expect will start to come next year. 

[00:47:05] OpenAI / Microsoft Deal

[00:47:05] Mike Kaput: Alright, let’s dive into our rapid fire topics this week. So first up, according to some reporting from the Financial Times, OpenAI is potentially going to remove one of its founding principles in its agreements with Microsoft.

[00:47:20] Mike Kaput: There is a provision that would shut Microsoft out from accessing AI technology. Once the company reaches Artificial General Intelligence, or AGI. So under the current terms, if OpenAI creates what it defines as AGI, a system that can outperform humans at economically valuable work, Microsoft’s access to this technology would be void, OpenAI’s board would be the ones to determine when that milestone is reached.

[00:47:49] Mike Kaput: Now, if these reports are correct, OpenAI is potentially considering getting rid of this provision. It seems like they might be considering this move to try [00:48:00] to unlock more future investment, especially as they’re trying to restructure to become a for profit company, because Microsoft has invested more than 13 billion in open AI.

[00:48:11] Mike Kaput: Presumably, the removal of this clause would enable them to continue investing in and accessing all of OpenAI’s valuable technology. So this kind of comes at a bit of a sensitive time for OpenAI. The FTC, the Federal Trade Commission, has actually launched a antitrust investigation into Microsoft with specific focus on the company’s deal with OpenAI.

[00:48:34] Mike Kaput: So they’re kind of looking at whether or not Microsoft’s dominance is in cloud computing has given it an unfair advantage in AI software sales. So, Paul, this would mark a pretty big change, maybe, in OpenAI’s relationship with Microsoft. Like, what’s going on here? And isn’t this kind of implying that OpenAI would just be open to commercializing AGI, unlike what it said in the past?

[00:48:57] Paul Roetzer: I think they are. I mean, so if you, [00:49:00] if you listen to Sam’s interview, they get into this, and it’s very apparent there’s friction with Microsoft. You know, he. He doesn’t hide things. I mean, Sam, I’m sure, is an amazing negotiator and everything internally, but just watching interviews, he either is insanely manipulative in terms of how he controls his emotions to present a specific trick.

[00:49:24] Paul Roetzer: emotion, I guess, triggers specific emotions or he’s, he’s just very honest and open about his thoughts and feelings. And I actually think it’s more that, because you can tell when they started pushing on the Microsoft thing, it’s like, yeah, man, it’s not all great. Like it’s, and it wasn’t even just the usual PR lines of.

[00:49:42] Paul Roetzer: You know, whatever, you know, we have our differences, but whatever. It’s great. He was basically saying like, no man, this is hard. Like it’s hard to manage a partnership like this. And we’re not the same company we were when we created this. We never intended to be a product company. We started as a nonprofit.

[00:49:56] Paul Roetzer: Elon pulled his money and we had to go get money from somewhere. We’d be [00:50:00] becoming a product. Cause like, Sam has gone through stuff as a business leader that there’s very few peers to in like human history. His last two years has been insane. To grow a company like this with these complexities. And so he seems just like really open about it.

[00:50:17] Paul Roetzer: and the Microsoft relationship in particular just seems like a really challenging thing to navigate right now. Because even when they entered that partnership, there was a very different vision for where OpenAI was going and like, you know, what they thought it was going to be. And so I could see them finding a way to do this.

[00:50:34] Paul Roetzer: Obviously I have zero internal knowledge of anything going on here with legal or business sides of this. But from an outside perspective, You could see how the AGI thing is a massive sticking point for both parties and I think it’s part of the reason why Sam has started Changing the way he talks about AGI publicly.

[00:50:54] Paul Roetzer: Yeah, and I you could tell he was softening their stance on that being a definitive moment And they [00:51:00] weren’t going to stop building their systems and all of a sudden team up with Amsthropic because they got to AGI, which is what their charter said they had to do. And so, this seems like AGI is becoming this friction point for everybody and they need to just remove it from that part of the business relationship.

[00:51:17] Paul Roetzer: So, I wouldn’t be surprised at all if, if that’s what they ended up doing here. 

[00:51:23] David Sacks Is Trump Administration’s AI and Crypto Czar

[00:51:23] Mike Kaput: Alright, so next up, U. S. President Elect Donald Trump has appointed venture capitalist David Sachs as the nation’s first what they’re calling an AI and crypto czar. So Sachs is a member of the influential PayPal Mafia, the kind of team that led PayPal to success.

[00:51:42] Mike Kaput: He is more recently very well known as a co host of the very popular All In podcast. And he basically brings an interesting background to this role. He’s the former COO of PayPal, he founded Yammer, which Microsoft acquired for 1. 2 billion, and he’s deeply embedded [00:52:00] in tech because now he runs a venture capital firm called Kraft Ventures, which has invested in numerous AI enterprises and companies like Elon Musk’s SpaceX.

[00:52:10] Mike Kaput: Basically, this position sounds like it’s going to come with some interesting responsibilities. Cheese? Sachs will guide administration policy on both AI and cryptocurrency. He will also head up the Presidential Council of Advisors for Science and Technology. This role is structured as a special government employee position, which allows Sachs to serve up to 130 days annually, without requiring him to divest assets or make public disclosure of those assets.

[00:52:42] Mike Kaput: And it seems like the tech industry has largely welcomed the appointment. Leaders from major AI and crypto companies, including, I’ll say, Amaltman, have publicly supported the choice. Saks will likely work closely with his fellow PayPal al Elon Musk, who has been tapped to co lead what’s been called the Department [00:53:00] of Government Efficiency.

[00:53:02] Mike Kaput: So, Paul, we’re both pretty familiar with David Saks. We’ve listened in the past heavily to y’all in podcasts, like, I don’t know, I personally feel like I’ve heard Sax talk way more about crypto than he has about AI, so for me personally, the line from a TechCrunch report on this kind of resonated with me.

[00:53:18] Mike Kaput: They said, Sax’s views on AI and AI policymaking are less obvious, though his than crypto, though his policies generally are decidedly right leaning and deregulatory. What did you think of this pick when you heard about it? 

[00:53:32] Paul Roetzer: So, real quick, to continue on the Elon Musk Sam Altman beef, when Sam tweeted to congratulate Sachs and tagged him, Elon replied, LOL, kind of like laughing at Sam, but he’s like, kissing up to Sachs now.

[00:53:47] Paul Roetzer: so, yeah, certainly right leaning, I mean, that, that, that’s, that’s not debatable. his policies, it does seem, there’s a Good Time article [00:54:00] I’ll link to in the show notes. Not in favor of regulation, generally speaking. He is going to be very pro open source, very pro acceleration of these AI models with as little regulation as possible.

[00:54:12] Paul Roetzer: heavily, you know, involved in Silicon Valley and the VC world. And yeah, I just listened to the all in podcast and you’ll, you will peer what he has to say. That’s the good and the bad, I guess, of like where we’re at with media today is like, oftentimes the podcast is the media and these people have the platform and you can go learn all about it.

[00:54:35] Paul Roetzer: So, Yeah, I think, I think it fits, you know, we talked about, like, Andreessen Horowitz, I don’t, I don’t know their relationship, I don’t, he wasn’t a PayPal Mafia guy, but, um You know, I think it’s going to be a lot of that, like the accelerationist manifesto, like move fast, drive innovation at all costs and regulate as little as possible.

[00:54:56] Paul Roetzer: Infuse it into the military, like it’s, it’s going to [00:55:00] be, accelerate at all costs. I think is probably the best way to look at what he would bring to the table, which is probably going to fit Elon’s mindset and, you know, part of the reason why it probably is going to be there. yeah, like if I would have stopped last week, I threw out like Karpathy as like just kind of like a, if I would have stopped last week and like made a list, I could see that he, he would be on that very short list of people that, you know, probably, but I agree with you.

[00:55:27] Paul Roetzer: Like he’s very much more crypto, like you hear about that. And I, that is not my world. You’re, you’re more the crypto guy. I mean, we should come back around to that at some point. But, that’s not my world. I had a buddy years ago, joked with me. He’s like, man, leave crypto alone. You got, you got in early on AI, leave crypto to the rest of us.

[00:55:44] Paul Roetzer: And I was like, yeah, I don’t want crypto. Like I don’t invest in crypto. I don’t really know a heck of a lot about it. So I will plead ignorant on the crypto conversation. I wish I’d invested in some Bitcoin not too long ago, but that’s about it. Yeah. 

[00:55:59] World Models

[00:55:59] Mike Kaput: [00:56:00] all right. So next up. We have two leading AI labs have unveiled some really interesting technologies that can generate explorable 3D worlds from single images.

[00:56:12] Mike Kaput: So first up, WorldLabs, which we’ve talked about in the past, has introduced tech that can transform a 2D image into a navigable 3D environment, which allows users to essentially step inside the image and explore it from any angle. So this system maintains consistent physics, lighting, and spatial relationships.

[00:56:31] Mike Kaput: So you have features like depth of field effects and interactive elements. Basically, this allows creators to quickly prototype virtual environments and bring static images, including classic paintings, to life in unprecedented ways. In the same time, Google DeepMind has announced Genie 2, which is a more comprehensive, what they call, foundation world model that can generate playable 3D environments from prompt images.[00:57:00] 

[00:57:00] Mike Kaput: The system can create interactive worlds lasting up to a minute, complete with physics, character animation, and even autonomous non player characters. Genie 2 is also able to respond to keyboard and mouse inputs, effectively turning these environments into playable spaces. So Paul, why are these so called, like, worlds or world models so significant?

[00:57:21] Mike Kaput: This, just on the surface is so cool, it sounds like something, feels like something out of Harry Potter a. 

[00:57:26] Paul Roetzer: Yeah, I mean, I always immediately think of like gaming and like being able to build games on the fly and imagine worlds and I guess you could eventually do it into like creating your own fictional worlds of like storytelling and the story unfolds in front of you and, you know, visually.

[00:57:44] Paul Roetzer: But you know, for the main thing, it’s, it’s trying to understand the physics of the world around us. Like, so creating a world model means giving the AI the ability to see and understand the world the way you and I do. that’s one of the things I think is still going to come out, from OpenAI maybe [00:58:00] during Ship Myths and because they previewed it on 60 Minutes last night, this.

[00:58:04] Paul Roetzer: Vision model, like Project Astra from Google, we’ve talked about before where your device, like your phone or your glasses could see and understand the world around you. So to do that, like this ability to create and understand and kind of model those worlds matters. So we won’t go deep on this now, but like episode 115, you had mentioned, we explained the world labs.

[00:58:27] Paul Roetzer: And so I’ll just read real quick, read through what we talked about there. So they talk about spatial intelligence is the big thing. And they said, we believe that artificial intelligence will help humans build better worlds. Progress has been rapid, but we have only seen the first chapter of the generative AI revolution.

[00:58:42] Paul Roetzer: Language has thus far catalyzed this electrifying early moment, with text prompted image and video models rising up alongside LLMs. these models have already empowered people to work and create in new ways, but they only scratch the surface of what is possible. To advance beyond the [00:59:00] capabilities of today’s models, We need spatially intelligent AI that can model the world and reason about objects, places, and interactions in 3D space and time.

[00:59:10] Paul Roetzer: and they went on to say, we aim to lift AI models from 2D plane of pixels to full 3D worlds, both virtual and real, endowing them with spatial intelligence as rich as our own. So that’s, that’s the play. That’s why they think this matters, and Fei Fei Li is leading the charge at World Labs, who’s a world renowned AI researcher.

[00:59:29] Paul Roetzer: so definitely a company worth paying attention to. Both of these things are in research previews, this is not, you can’t go play with either of these. Think of it as almost like when we first saw Sora and now here we are like 10 months later actually getting to interact with an early form of it. It might be another year or more before we get to like really use these kind of models.

[00:59:49] Coca-Cola AI Holiday Ad

[00:59:49] Mike Kaput: Alright, so next up, Coca Cola is generating some controversy. Their latest holiday ad campaign has sparked some debate. After they revealed that they [01:00:00] used exclusively AI to create these iconic Christmas commercials that everyone’s kind of expecting from Coke each and every season. So, for the first time, their iconic holiday ads were entirely generated by AI.

[01:00:14] Mike Kaput: So this campaign has a few videos that are very short, but they re imagine, one of their classic campaigns that they ran back in 1995 called Holidays Are Coming. This features kind of signature red delivery trucks decorated with Christmas lights driving through snowy towns. Now, three AI studios called Secret Level, Silverside AI, and Wildcard collaborated on this project to create essentially AI generated versions, updated versions of these commercials.

[01:00:44] Mike Kaput: And they used things like AI models like Leonardo, Luma, Runway, as well as a newer model called Kling. Thanks for watching! that was incorporated late in the production to improve the human movements. The results generated a bit [01:01:00] of controversy, like some critics pointed out that there were all these weird kind of AI generated effects that looked uncanny, they didn’t look like real humans, there were technical limitations like weird proportions, unnatural movements, some subtle errors in background details.

[01:01:18] Mike Kaput: And basically, they say the ads rely on extremely quick cuts to mask AI’s current limitations generating consistent, realistic footage. Even in some interviews, you know, one of the studios said that a brief shot of a squirrel alone required hundreds of AI generation attempts to get it right. Coca Cola’s head of generative AI defended this approach, saying there are a bunch of benefits in production speed and creative possibilities.

[01:01:46] Mike Kaput: They even claimed this tech allows them to produce content like this five times faster than traditional methods. Now unfortunately some creative professionals see that as an attempt to cut costs [01:02:00] at the expense of human artists. So, Paul, this ad seems to have hit a nerve. There’s definitely a few angles to unpack here.

[01:02:07] Mike Kaput: Like, do you agree with Coca Cola’s move here? 

[01:02:11] Paul Roetzer: I mean, it’s, there’s a lot of layers to it. it’s all in how you’re judging the move. So, if the move is push the frontier of creative output while reducing the cost of, you know, on site production and talent and all those things, then it’s the right move.

[01:02:30] Paul Roetzer: It’s like, if you’re Goal is to preserve creativity and human, human artistry. It’s the wrong move. And you can understand why there’d be people on both sides, but this is a very clear cut, like who wins the studios who can use the AI tools and can sit there and run hundreds of variations of a squirrel to create an output and probably got paid hundreds of thousands of dollars each to do this thing, who loses.

[01:02:54] Paul Roetzer: All the people who would have been on site shooting this thing and the production companies and the [01:03:00] videographers and, you know, the animators. And I, there’s going to be winners and losers, but this is the future. Like there, there’s no turning back from this. Like the question would become, and I would be running this tomorrow.

[01:03:11] Paul Roetzer: If I had, if I was the CMO of Coca Cola or one of these agencies, I would be taking Sora when I have access to it and running the exact same prompts and everything into it. And then getting to say, Oh, SORA save us 50 percent of time, like, awesome. The next one, we’re going to save even more time. Like there is no turning back.

[01:03:31] Paul Roetzer: there will be brands who choose not to do this for ethical reasons or legal reasons, but there are going to be a whole lot more brands that do choose to do this because either they don’t have an alternative, they don’t have the budgets to do it, or they just look at it and say, it’s an efficiency thing.

[01:03:47] Paul Roetzer: And we’re going to do it no matter what. so. So yeah, it’s, it’s wild. It’s just, it’s such a high profile brand. It’s why it’s all of a sudden getting so much attention, but you’re going to hear a lot more about this. I won’t [01:04:00] talk about it, but like I just heard one last week, another major consumer brand that tried to do this and couldn’t pull it off in time, but they were going to run all their holiday ads this way.

[01:04:08] Paul Roetzer: So yeah, it’s not an isolated incident. I will say that. 

[01:04:14] Devin AI Coder Update

[01:04:14] Mike Kaput: Alright, next up, so back in episode 88, in March of this year, we first talked about the release, or the demo rather, of Devin, which was created by parent company Cognition, and they boldly called it, quote, the first AI software engineer. So at the time, the company was kind of making waves with this demo that showed the tool being able to execute complex tasks using code, all basically kind of on its own without a human programmer.

[01:04:43] Mike Kaput: Now that demo got a ton of buzz. Early testers noted that Devon was a bit unreliable and buggy, and the demo was definitely a bit hyped up. We noted at the time that this is a product demo that needed to be taken with a huge grain of salt, because we are [01:05:00] not yet at the stage of fully autonomous AI coding agents, even though some companies want you to believe that.

[01:05:06] Mike Kaput: Now, Cognition and their product Devon are back in the news, Getting a cover story on Forbes showing basically just how much buzz there is now around AI agents, especially those that can code. So when we first talked about them about nine months ago, they had raised 21 million in funding, so solid. But today they’re valued at 2 billion after raising 176 million from investors like Peter Thiel’s Founders Fund.

[01:05:35] Mike Kaput: Which by the 

[01:05:35] Paul Roetzer: way is PayPal Mafia also. It’s Thiel, Musk, David Sachs, Reid Hoffman. So, yep. Yep, fingerprints all over it. Yep. 

[01:05:45] Mike Kaput: So they are basically back in the news with Forbes at this kind of glowing profile. However, they also are noting that we’re kind of seeing some of the same types of uneven results right now from AI agents that can code.

[01:05:58] Mike Kaput: So Forbes kind of points [01:06:00] out that the company’s clients, while they report dramatic productivity gains, some have pointed out that they are facing significant limitations using a tool like this. Independent developers have sometimes found that AI can be slower than human programmers. They can introduce errors, even in Cognition’s own demonstrations of Devin.

[01:06:19] Mike Kaput: This system has been inconsistent. But the reason we’re kind of like talking about this is that with all the talk of AI agents, it’s pretty clear that AI for coding is a market we can’t really ignore anymore. So, In October, for instance, Google CEO Sundar Pichai said that more than a quarter of new code at the company is written by AI.

[01:06:41] Mike Kaput: GitHub says its AI code completion tool accounted for 40 percent of revenue growth this year. And according to Forbes, PitchBook analyst Brendan Burke told them that AI coding has become the most funded use case in generative AI. Startups focused on it raised over 1 billion [01:07:00] in the first half of 2024 alone.

[01:07:03] Mike Kaput: So, Paul, a couple of reasons this kind of jumps out as important. So first, AI agents becoming all the rage. I think it’s important that we keep reminding the audience where the technology actually stands. This is probably not the last cover story we’re going to see about AI agents. But second, we can’t ignore the fact that it seems like technologists appear highly incentivized to build AI that can code.

[01:07:25] Mike Kaput: Like, what do you think? How are you looking at this space? 

[01:07:27] Paul Roetzer: It’s definitely a very powerful space. practical use case in a lot of enterprises right now, especially a lot of tech companies. They are seeing benefits from it. I think there’s always something a little bit more behind this, like Sundar was actually asked about this by Sorkin and he said, well, like the humans write the code and then the AI is assisting in this part and then the humans do the final thing.

[01:07:50] Paul Roetzer: That never makes the headlines, it never makes the pitch decks, like, the human involvement in this automation is, is always sort of like, just not really talked about. [01:08:00] So, I have no doubts that people are seeing these kinds of gains, like, as a writer, I could imagine seeing this same scenario play out in writing, like, if you infuse these things in different ways, 50 percent plus of your time on a different writing project.

[01:08:15] Paul Roetzer: we had Andy Jesse, who earlier this year reported, he said, they saved 260 million in 4. 5 thousand developer years, not hours, years, by using their internal Amazon queue coding agent. So, I think you’re going to keep seeing these headlines, and I think we’ll keep reminding people it’s not autonomous yet.

[01:08:35] Paul Roetzer: And like, that’s, you think it is, but it’s not from these headlines. It can be misleading. So, doesn’t mean we shouldn’t be paying attention, doesn’t mean your company shouldn’t be exploring this if they’re not already. Repl. it agent is another one we talk about quite a bit. So. Yeah, it’s going to be huge.

[01:08:53] Paul Roetzer: We’re going to hear a ton more about this stuff going on next year. 

[01:08:57] AI Product and Company Updates

[01:08:57] Mike Kaput: All right, Paul. So for our final topic this week, I’m just going to [01:09:00] run through very quickly, kind of a rapid fire within a rapid fire of some product and company updates. And, you know, please feel free to interject if you got anything you want to double click on further, but we’ve got a bunch of others that we’re just going to hit really quickly.

[01:09:15] Mike Kaput: So first up, Meta has released Llama 3. 3, a new open source language model that basically matches the performance of much larger AI models while requiring significant less, significantly less computing power. 

[01:09:28] Paul Roetzer: This is the algorithm thing we talked about earlier, by the way. Exactly. That’s this concept, you know.

[01:09:31] Mike Kaput: Because while it contains just 70 billion parameters, it actually can match, they say, the performance of its 405 billion parameter predecessor. So basically that means It has the same capabilities while requiring only a fraction of the computational resources. So they are claiming the model demonstrates impressive performance across multiple languages.

[01:09:55] Mike Kaput: It has a substantial context window of 128, 000 tokens. So [01:10:00] that matches the capabilities of things like GPT 4. And they’re making it available through an open source license. though there are some restrictions. While it’s free for most users, If you have over 700 million monthly active users, you must obtain a commercial license.

[01:10:15] Paul Roetzer: Also, that update is largely for developers, for people on your technical teams. That is not, the average business user is not going to go use that. 

[01:10:24] Mike Kaput: Next up, HubSpot has announced it’s acquiring FrameAI, which is a conversational intelligence platform. FrameAI’s technology specializes in taking unstructured data, so emails, calls, meetings, conversations, And turning those into actionable insights.

[01:10:40] Mike Kaput: So, basically, the interesting thing here is this gives you the potential to combine those conversational insights with HubSpot’s existing customer data platform. So they plan to integrate Frame. ai into Breeze, which is HubSpot’s recently unveiled and updated AI system. So you can do things like do a real time [01:11:00] analysis of customer sentiment and behavior.

[01:11:04] Mike Kaput: Next up, Hume AI, which we’ve talked about multiple times, has unveiled voice control. So this allows developers to fine tune synthetic voices along 10 distinct dimensions, including characteristics like assertiveness, confidence, and enthusiasm. And that’s without the ethical concerns associated with copying real human voices.

[01:11:24] Mike Kaput: They have this kind of slider based approach to voice modification, so you don’t have to use text prompts or preset voices. You can kind of make continuous adjustments along different vocal dimensions. So this is kind of addressing a big challenge in the AI voice industry, like, companies are struggling to create unique voices that match their brand identity without compromising the quality or running into, like, ethical issues of voice cloning.

[01:11:51] Mike Kaput: So this is currently in beta, the technology is being integrated into Hume’s empathetic voice interface, Eevee, so developers can create custom [01:12:00] voices through their interface. Next up, Amduril, which is a defense technology company, and OpenAI have announced a strategic partnership. They’re going to develop AI solutions for military defense systems.

[01:12:15] Mike Kaput: This marks OpenAI’s first major entry into the defense sector. It sounds like the initial focus is on improving what are called counter unmanned aircraft systems that protect U. S. and allied forces from drone threats. So as part of this, OpenAI’s models. will be used with Anduril’s existing defense systems and their Lattice software platform.

[01:12:39] Mike Kaput: Microsoft has said that Copilot Vision is now in preview for select Copilot Pro subscribers in the U. S. So Copilot Vision basically is an AI browsing assistant. So it integrates directly into the Microsoft Edge browser and basically can look at you what you’re doing and provide real time analysis and insights.

[01:12:58] Mike Kaput:  [01:13:00] about web pages as you browse. So it’s actively quote unquote seeing and understanding the full context of web pages alongside users. Basically a second pair of eyes. And it can help you do different things thanks to this functionality. Now Microsoft, after some criticism of this feature, is emphasizing privacy and security.

[01:13:21] Mike Kaput: The feature is entirely opt in and all the conversation data and context shared with Copilot is deleted at the end of each session. During this initial raw, Vision is only going to interact with a select set of websites, and Microsoft has said they’re going to be cautious about expanding it over time.

[01:13:40] Mike Kaput: They explicitly state Vision does not capture, store, or use any publisher data to train its models. 

[01:13:45] Paul Roetzer: If anybody sees the prompt to get you to allow this to turn on, please reach out to me on LinkedIn. Like, I don’t, I’m not, you know, we don’t use Windows, so I’m not going to see this myself. I’m very [01:14:00] intrigued to see how they try and convince people to use this.

[01:14:02] Paul Roetzer: I can see the adoption on this product being close to zero, so they’re gonna have to push real hard. I, I don’t know. I’m just, I’ll be really intrigued to see how they position it so that you want to. Let them have this, like what wording they use. So, yeah, if anybody gets it, just, I would love to see a screenshot of that.

[01:14:23] Mike Kaput: Google Cloud has unveiled two significant additions to its Vertex AI platform. So they’re adding Veo, which is their newer video generation model, and ImageN3, their updated image generation system. So Veo is Google’s entry into the image to video generation capability market. ImageN3, which will be widely available starting this week.

[01:14:45] Mike Kaput: represents Google’s most advanced image generation model to date. So, it’s actually interesting to hear some of the notable companies already implementing these. Mondelez International, which owns brands like Oreo and Cadbury, is apparently using the technology to [01:15:00] scale content creation across 100 plus brands.

[01:15:03] Mike Kaput: WPP, a major marketing agency, has integrated these tools into its AI powered operating system for marketing transformation. Next up, some news about X. X has briefly launched and then apparently removed something called Aurora, which is a new AI image generation feature. They removed it? It apparently has been removed within like hours because they Well, you could create anything.

[01:15:29] Mike Kaput: I tested 

[01:15:29] Paul Roetzer: it on like Saturday or something, and it was like, any person, like, there was like no guardrails at all. Oh. 

[01:15:39] Mike Kaput: You must have hit the window because it apparently appeared briefly on Saturday and then disappeared for many users. So maybe some people still have access, but basically, yeah, it lacked all kind of content restrictions.

[01:15:52] Mike Kaput: You can generate photorealistic images or whatever you feel like of anything, so it seems like this [01:16:00] Raised a huge amount of content moderation issues. Yeah, it is 

[01:16:03] Paul Roetzer: gone. I don’t have it anymore. Yeah, because when you went in, like, you could, it had all kinds of examples and stuff, and you could just create.

[01:16:10] Paul Roetzer: I was doing like Santa Claus stuff just to like see Santa Claus on a Tesla and just random stuff like that. But you could create anything. Yeah. So it’s, 

[01:16:19] Mike Kaput: it’s unclear. They haven’t really revealed like, why did this happen? What are we doing with it? But we’ll see. And then last but not least, some news about Spotify and Google Notebook LM.

[01:16:31] Mike Kaput: So. This year, Spotify and Google partnered to create this year’s Spotify Wrapped Experience. So this is when Spotify summarizes all the music you listen to in a year. This time around They’re using Google Notebook LM and their podcast, their audio overview feature, which creates a podcast of material with AI hosts.

[01:16:53] Mike Kaput: They’re using this to actually have AI hosts analyze and discuss your musical journey throughout [01:17:00] 2024, including your favorite tracks, artists, and how your taste evolved over the year. So to access a personalized podcast, users can go to the wrapped feed on their Spotify homepage. There’s also a separate URL, spotify.

[01:17:15] Mike Kaput: com forward slash rap AI podcast that you can access it at. This is only available in English for both free and premium users. It’s in select countries, US, UK, Australia, New Zealand, Canada, Ireland Sweden. Strangely enough, I don’t know how we arrived at that, but it is only available for a limited time.

[01:17:34] Mike Kaput: So go check it out. All

[01:17:36] Housekeeping Information

[01:17:36] Mike Kaput: right, Paul, that is. A breathtaking week so far in AI. Just a couple quick final announcements here, then I’ll kind of turn it back over to you to lead us out here. We are recording this week and releasing next week, a 25 AI questions for 2025 special podcast episode. It is coming out on Thursday, December 17th [01:18:00] or 19th, rather.

[01:18:01] Mike Kaput: Sorry. Excuse me. we will be releasing that episode as kind of our last episode of the year. If you could please submit your questions, if you have any, we will try to answer as many as we can on that episode. So go to bit. ly forward slash 25 questions episode. That’s b i t dot l y forward slash 25 questions episode.

[01:18:23] Mike Kaput: It’s just a simple Google form, you can submit your questions. And as always, Please, if you have not already, check out the Marketing AI Institute newsletter, marketingainstitute. com forward slash newsletter for a full comprehensive brief of everything going on in AI this week. Paul, thank you again. That was a 

[01:18:44] Paul Roetzer: lot of updates at the end there.

[01:18:47] Paul Roetzer: Yeah, and that 25 questions link will be in the show notes as well. So if you didn’t jot that down, just check the show notes and there will be a link there for you. I was, I was trying to log in real quick order before we signed off here. [01:19:00] I think. I was trying to log into Sora with my team account, which doesn’t look like it’s active.

[01:19:05] Paul Roetzer: You have to either be plus or pro. So I have to try my personal pro account. Did you try that yet? I haven’t tried 

[01:19:12] Mike Kaput: it while we’ve been talking, but I tried my plus account, my personal plus account, and it did not work when I tried it. 

[01:19:19] Paul Roetzer: All right. Well, hopefully you all have better luck getting into Sora than us.

[01:19:22] Paul Roetzer: Let us know what you think. I’m, I’m hoping we’ll be in by the time we record next week and we can tell you all about our Sora experience. And we’ll see what else we get this week from our 12 days of open AI ship mess. It should be. Intriguing. So thanks everyone. We’ll be back with you again next week.

[01:19:42] 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 [01:20:00] events. taken our online AI courses, and engaged in the Slack community.

[01:20:05] Paul Roetzer: Until next time, stay curious and explore AI.

Editor’s Note: This post was originally published before Day 4 of 12 Days of OpenAI. It will be updated throughout the event with new releases and announcements.

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