Month: December 2024

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.